3 late): .41 13.. . i. THESIS lllllllllllllilllllllilllilllllllllHIIHIIIUiIIHIUIIHHM 3 1293 01417 2500 This is to certify that the dissertation entitled REPRESENTING REGIONS: GEOGRAPHY, CARTOGRAPHY, AND SPATIAL UNDERSTANDING. presented by Charles Peirce Rader has been accepted towards fulfillment of the requirements for Ph ° D ° degree in Geography Major pro essor Date April 12, 1225 MS U is an Affirmative Action/Equal Opportunity Institution 0-12771 LHBRARY Michigan State University PLACE N RETURN BOXto remove thle checkout from your record. TO AVOID FINES return on or before one due. DATE DUE DATE DUE DATE DUE MSU le An Afflnnetlve ActIaVEquel Opportunity Inetltution Wane-e 1 ——-—_—_. ABSTRACT REPRESENTING REGIONS: GEOGRAPHY, CARTOGRAPHY, AND SPATIAL UNDERSTANDING. by Charles Peirce Rader Regions are commonly used in geographic research to identify areas that possess one or more unifying characteristics. Although verbal descriptions and tabular data may present the same information, maps are one of the most effective methods for representing and communicating regional information. This research was undertaken to determine the influence that five methods of representing regions (nominal, choropleth, isarithmic, continuous tone, and dot) have on peoples' understanding (cognition) of regional patterns. The research questions, resulting from a literature review and preliminary interviews with twelve academic geographers, asked how well each of the five different representations conveyed concepts of area extent, core and domain, transitional boundaries, internal variation, and comparison of different distributions. An experimental project was designed in which sixty-seven subjects performed a series of map reading tasks presented on a Macintosh computer using Aldus SuperCard. Response accuracies, reaction times, and confidence estimates were collected to assess the impact of map type on regional understanding. The results showed that no one map was most suitable for portraying all types of regional information; for four of the five map use tasks, subjects performed significantly better using some map types than others. Area extent estimation was performed best using nominal maps; isarithmic maps were most suitable for identifying internal variation, locating core areas, and comparing maps; continuous tone maps best represented transitional boundaries; and choropleth maps were slightly better at showing domain areas. Significant differences in reaction times also revealed that some map types were easier to use than others. The results from this study should provide a better view of the role of cartographic representation in the understanding of regional information and provide a more certain basis for the selection of appropriate mapping methods for representing this type of information. Copyright by Charles Peirce Rader 1995 In memory of Ian Matley, who taught me more than he realized iv ACKNOWLEDGMENTS A dissertation serves at once as partial fulfillment and the culmination of the graduate experience. In my getting there, many people have contributed both directly and indirectly, and without each of them, the experience just would not have been the same. First and foremost, I would like to thank the members of my supervisory committee: My sincere thanks to my advisor Dick Groop, whose often—not —too subtle suggestions to "get going", ability to keep me focused on the task-at-hand, and assistance in developing the topic were vital. To Judy Olson, whose contributions included providing funding, numerous suggestions that improved the test design and statistical analysis, and some needed editorial advice. To David Campbell, who provided assistance in the development of the background material, a number of insightful comments concerning the geography of this piece, and funding on the Rwanda project. To David Lusch, who provided funding through several projects at the Center for Remote Sensing and always provided interesting insights on my projects. Finally, but not least, my thanks to Patrick Dickson from the Department of Counseling, Educational Psychology, and Special Education who provided suggestions for improving the analysis and critical insights from someone outside the discipline. Thank you. A number of people in the geography department made life more interesting and easier to take. Thanks to Marilyn Bria, Sharon Ruggles, Judy Slate, and Harriet Ashby for keeping the department afloat, assisted, and advised. Many thanks to my office mates: Adam Burnett and Bill Blewett, who could always justify a trip to the Peanut Barrel (and Bill, if I knew where your field notebooks were, I probably wouldn't tell you just on principle). Cindy Brewer, Joan Kendall, Cathleen MacAnneny, Johan Liebens, Claudia Grunrebun Walter, Tarek Joseph, and Mark Guizlo, who picked up where Adam and Bill left off. Jay and Theresa Harman, Dan and Carmela Brown, Julie Winkler, Harold Winters, Ellen White, and Mike Lipsey all provided a good mix of company and guidance. Finally, a special thanks to Jenny Olson, Christof den Biggelaar, Linda Beck, and Mark Pires who provided countless diversions from this project and who will continue to provide many more. I owe a special thanks to my colleagues at the University of Wisconsin — River Falls who put up with me in the midst of completing this dissertation and teaching at the same time. Mike Albert, Don Petzold, and Dean Neal Prochnow all provided encouragement on the project. A special thanks is due to Carol Gibbs Barrett who acted as a sounding board for many of my half-baked ideas and carefully reviewed the test instrument and manuscript. I thank the 67 anonymous subjects who volunteered to participate in the study. Without my family, this whole exercise would have been pointless. To my grandfather, Harold G. Rader, and my parents, Carol and Richard Rader, who all provided love, encouragement, financial assistance at times of need, and some more not- so-subtle hints. To Velcro, who will never quite be able to appreciate what he walked into when he came to live with us and taught me that life does not revolve around a dissertation. Finally, and most important of all, I don't even know where to start to thank Nancy, my partner in all things, for her love, sense of humor, and all that she did to make this possible. vi TABLE OF CONTENTS List of Tables List of Figures II. III. Introduction: Regions and Cartography Definition of Region Statement of Problem Overview of Work Review of Literature The Concept of the Region Cartographic Representation of Regions Nominal representations Choropleth and dasymetric representations Unit grid representations Isarithmic representations Continuous tone representations Dot representations Perception of Form Synthesis: The Regional Concept and Cartography Research Design and Methods Interviews vii 10 IO 12 14 IS 17 18 20 22 26 26 Determination of Map Use Tasks Research Questions and Hypotheses Experimental Design Test maps Test questions Test sets Procedure Subjects IV. Results and Discussion Overall Results of the Experimental Project Preliminary analysis of test versions Variations in task questions and distributions Results and Discussion of Specific Map Reading Tasks Area extent task Core task Domain task Transitional boundary task Internal variation tasks Map comparison tasks Maps, Representations of Regional Information, and Spatial Understanding _ V. Summary and Conclusions Significance of Results Reflection on Methods and Procedures viii 30 31 33 33 35 41 41 43 44 44 45 46 49 50 52 55 57 59 61 69 69 71 Suggestions for Future Research Appendix A: Experimental Procedures Appendix B: Experimental Map Displays Appendix C: Experimental Data Bibliography 72 74 80 103 171 LIST OF TABLES Table 3.1 Summary of interviews defining uses of maps in presenting regional information 27 4.1 Percent correct responses by question by map type 46 4.2 Mean reaction time in seconds by question by map type 47 4.3 Mean standardized reaction times in z—scores by question by map type _ 47 4.4 Percent correct responses by question by distribution 48 4.5 Percent correct responses by question by certainty estimate 48 4.6 Results from area estimation task by map type 51 4.7 Results from core task by map type 53 4.8 Results from domain task by map type 56 4.9 Results from transitional boundary task by map type 58 4.10 Results from internal variation task by map type 60 4.11 Results from map comparison task by map type 62 4.12 Summary of research findings 65 B.1 Correct answers by test version 81 B.2 Colors used on map displays 82 Cl Test results by subject 104 LIST OF FIGURES Figure 2.1 Nominal representation 2.2 Choropleth representation 2.3 Unit grid representation 2.4 Isarithmic representation 2.5 Continuous tone representation 2.6 Dot representation 3.1 Sample question for estimation of relative extent task 3.2a Sample question for core task 3.2b Sample question for domain task 3.3 Sample question for transitional boundary task 3.4 Sample question for internal variation task 3.5 Sample question for map comparison task 3.6 Sample question for certainty rating B.l Choropleth map test displays of Michigan B.2 Nominal map test displays of Michigan B.3 Isarithmic map test displays of Michigan B.4 Continuous—tone map test displays of Michigan B.5 Dot map test displays of Michigan B.6 Choropleth map test displays of Africa B.7 Nominal map test displays of Africa 11 13 14 16 17 19 36 37 37 38 39 40 42 83 84 85 86 87 88 89 B.8 Isarithmic map test displays of Africa B.9 Continuous—tone map test displays of Africa B.10 B.11 B.12 8.13 8.14 B.15 B.16 3.17 B.18 B.19 B.20 Dot map test displays of Africa Choropleth map test displays of Georgia Nominal map test displays of Georgia Isarithmic map test displays of Georgia Continuous—tone map test displays of Georgia Dot map test displays of Georgia Choropleth map test displays of Rwanda Nominal map test displays of Rwanda Isarithmic map test displays of Rwanda Continuous—tone map test displays of Rwanda Dot map test displays of Rwanda xii 90 91 92 93 94 95 96 97 98 99 100 101 102 Geography is represented in the world of knowledge primarily by its technique of map use... Richard Hartshorne 77» Nature of Geography (I939, 464) xiii CHAPTER I INTRODUCTION: REGIONS AND CARTOGRAPHY The region is a central concept in geographic research and education. "A region is any tract of the earth's surface with characteristics, either natural or of human origin, which make it different from the areas that surround it." (Haggett 1983, 262) Thematic maps are an important source of regional information, and while people often have mental maps of regions, electronic and paper maps are often the most practical way of presenting this information. A variety of map types, such as choroplethic, dasymetric, nominal, dot, continuous tone, unit grid, and isoplethic maps, are used to represent regions. The role of maps in regional portrayal is to provide an idea of the geographic location, extent, and nature (homogeneity or variability) of the region. Furthermore, maps provide a means for describing, comparing, and analyzing intraregional and interregional distributions. A major problem for the cartographer and geographer is choosing the best map type to represent a given region. The relationship between map symbolization method and subject matter is a critical component of cartographic communication. Usually, this relationship is conceptualized as matching the data level to the appropriate symbolization method (Dobson 1975; Chang 1976; Hsu 1979). Hsu (1979, 117) further suggests that the symbolization method should reflect the essential spatial attributes of the selected phenomena. Little comparative work, however, has examined the role of symbolization method in communicating spatial attributes. Regions have underlying spatial structures that are based on the concepts, assumptions, and data that are used in their definitions. In addition, regional information is often imparted through qualitative (nominal) symbolization, and, possibly because of their deceptively simple design, these maps have received relatively little attention. Cartographers have assumed that properly chosen representations of regions reveal the underlying spatial structures of regions. This assumption may or may not be true. Any one single map may not communicate all the concepts required to develop an adequate understanding of the spatial structure of a given region. In addition, "one map solutions" may be highly nus—informative about the essential geographic distribution being mapped (Monmonier 1991). Recent developments 1 2 in the areas of computer cartography and geographic information systems (GIS) have expanded grcatly the potential for mapping regional distributions and have made it possible to generate several different representations of the same data quickly and with reasonable ease. Several map solutions to the same mapping problem are a reality. The uses of maps as tools for visualization have recently generated much interest and their potential uses as aids for problem solving at a variety of levels have been documented (T ufte 1990, DiBiase 1990, and MacEachren 1994). The problem, however, still remains of how to determine appropriate mapping methods to represent certain spatial concepts. Definition of Region A broad definition of the term region as 'an area on the surface of the earth that is defined by a similar characteristic or characteristics that differentiate the area from surrounding areas' is used throughout this work. This broad definition is employed since it includes simple single factor regions, those made up from the distribution of a single phenomenon, and complex multiple factor regions, those that are made up of two or more related distributions. Regions are often considered to be infinitely more complex than distributions since they often involve many subtle nuances of fact, fiction, education, perception, and understanding. The intent here is to view regions inclusively with all their many and varied conceptual connotations and spatial expressions. Statement of Problem The goals of this study are: 1) to determine the influence that five common cartographic methods of representing regions (nominal, choropleth, isopleth, continuous tone, and dot density) have on the spatial understanding (cognition) of regional patterns, and 2) to determine if the spatial structure of regions are adequately expressed by these different methods of representation. Within this context the research question is: When a region is represented by a particular cartographic method, what influence does that method have on a map user's acquisition and understanding of regional information? "Acquisition" deals with the internalization of regional images and "understanding" refers to differences in spatial knowledge acquired from different representation methods. 3 Specifically, I will examine the correspondence between the geographer's concept of the region and the cartographer's representation of the region to determine if key regional concepts are communicated differently by different map types. I will focus on the understanding of five sets of regional concepts (location and extent, core and domain, internal structure, transitional boundaries, and map comparisons). These key concepts were distilled from the literature on the concept of the region, the literature on cartographic representations of regions, and interviews with geographers on what they try to communicate with maps of regions. Five specific research questions were developed to examine the impact that cartographic methods have on the understanding of spatial concepts. 1) Which representations facilitate the estimation of area relationships? 2) Which representations better communicate concepts of core and domain? 3) Which representations better communicate concepts of transitional boundaries? 4) Which representations better communicate concepts of variable internal structure? 5) Which representations facilitate comparison with related regional distributions? An experimental project was developed to examine five methods of representing simple single variable regions (nominal, choropleth, isopleth, continuous tone, and dot density techniques). These are the most common mapping methods used in representing regional information and will serve to develop baseline data on the impacts of mapping methods on representing regions. Overview of Work The remainder of this dissertation is organized into three major chapters that deal with the development, results, and discussion of the experimental project. Chapter II is a literature review that develops more fully the concepts of the region and cartographic representation. In this chapter, the background for the experimental project is developed. Chapter III begins with the results from interviews with twelve academic geographers. The interviews were conducted to identify important regional concepts in research and education that these academics tried to facilitate through the use of maps. From these concepts, a set of map task questions are developed to determine the effect of map type on the understanding of regional information, and then these are further developed into a set 4 of five research hypotheses. A discussion of the development of the test maps and test instrument and a description of the test procedure and subjects conclude this chapter. Chapter IV presents a report of the results and a discussion of the five research questions. Accuracy, response times, certainty estimates, and consistency between subjects are the primary measures used to evaluate the effectiveness of the different map types in communicating regional information. The final chapter discusses the relevance of the research, potential application of the results, and directions for future research. Three appendices follow the text. Appendix A contains a description of the experimental procedure. Appendix B includes reduced black and white versions of the original color test displays for all map types, distributions, and questions. Appendix C provides a table of test data by subject. A bibliography concludes the dissertation. The results from this project should provide a more complete understanding of the role that maps play in the understanding of regions as well as a more certain basis for selecting symbols to represent regions in cartographic design. The results should have impacts that extend into the use of maps in education and research by expanding our knowledge about how people understand regional distributions as presented by different map types and thus the potential use of different map types in presentation, visualization, and geographic analysis. CHAPTER II REVIEW OF LITERATURE The mapping of regions encompasses a number of conceptual, methodological, and practical problems that range from defining 'a region' geographically to representing it cartographically. The relationship between geographic and cartographic issues centers, ultimately, on the understanding of a spatial distribution or the spatial attributes of the distribution. This relationship, which is often not made explicit in cartographic research, suggests that understanding involves not merely communicating information, but also communicating meaning, or significance (Guelke 1977, 130). A basic question is: do certain types of maps enhance a map user's understanding of certain regional concepts? Three areas of literature provide the background to the research problem. The eone¢t of the region is drawn from geography, the representation of the region from cartography, and the issues concerning perception are drawn from psychology. The Concept of the Region The concept of the region has played an important role in geographic discourse in this century. It has been viewed both as the core of geographic enquiry (James 1952) and as an anachronism ill-suited for geographic enquiry because of an emphasis on uniqueness rather than the nomothetic (Kimble 1951). Definitions of the term "region" oversimplify many aspects of the nature of regions and their use in geographic education and research. Two distinct operational definitions of the region exist, further revealing the complexity within the concept. James and Martin (1981, 371-2) note the plurality in their definition of the concept: The regional concept is the term we use to refer to the mental image of an earth's surface differentiated by an exceedingly complex fabric of interwoven strands and produced by diverse but interrelated processes. This is not the relatively unsophisticated concept of the earth's surface as made up of a "mosaic of spaces", each forming a unit of area (Gibson 1978; Paterson 1974). 6 Both definitions have been used, often without distinction, and this has led to misunderstanding and misapplication of the term "region". The first is what geographers do and the second is what others tend to think that geographers do. The term has been applied widely to many geographic problems for different reasons and at vastly different scales, and for this reason, a concise, universally acceptable definition of the region more meaningful than 'an area on the face of the earth' does not exist. In spite of its imprecision, the region, when viewed as an area reflecting processes, remains one of the most satisfactory conceptual means for organizing, presenting, and studying many geographic phenomena. Systematic studies of regions were first approached by Mackinder (1887; 1895) and Herbertson (1905) through examinations of processes that defined "natural regions". In the United States, genetic studies of landforms created by different processes were carried out by Fenneman (1928). Extensions of the concept of the natural region by Dryer (1915) and Roxby (1925 - 1926) incorporated people—land interactions into regional studies and were deterministic pieces based on Spencer's interpretation of Darwinism. These approaches, while often associated with the mosaic of spaces approach, had an underlying concern for process. Huntington's (1911; 1924) works on climatic impacts on culture are a particularly blatant example of deterministically defined regions. The rejection of determinism turned regional studies toward chorographic studies in the United States. James' (1929) work on the Blackstone River Valley in Massachusetts exemplifies this approach. Similar works in France were carried out on the pay: under the direction of Vidal de la Blache and attempted to capture the 'nature' or 'character' of a region. James and Martin (1981, 372) note that chorology is wrongly viewed as an extension of the mosaic of spaces idea; more properly, chorology encompasses the interrelations of factors that make a region unique. The idea of geography as arcal differentiation, as represented by the works of Sauer (1925) and Hartshorne (1939), extended many of the concepts and approaches of chorology. Areal differentiation moved geographic work beyond the concept of the region as the 'object' of enquiry to a tool for geographic enquiry. Sauer (1925) viewed the landscape, or region, in morphological terms and focused on uniqueness of place and the co-occurrence of events and factors that made a region unique. In contrast, Hartshorne's use of areal differentiation stressed the interpretation of the variable character of the earth's surface (Hartshorne 1959, 21). The emphasis was more on the interpretation of those processes and features in an area that formed an arca of variable character. Hartshorne (1959) and a more recent reinterpretation by Agnew (1990) have suggested that areal 7 variation is a more appropriate term, since the goal was to view regions in relation to one another and not as discrete units. The rise of systematic approaches to geography and the concept of spatial separatism, space as the object of geographic enquiry, led to a decline in regional studies, and research in this area tended more toward objective approaches to region delimitation. The rigid interpretation of the term "region" and the regional approach as description based in large part on Atwood's approach to teaching geography also aided the decline. Zobler (1958) and Berry (1964;1968) applied quantitative techniques to the characterization and definition of regions. Grigg (1965;1967) approached the region as a problem of classification. Abler, Adams and Gould (1971, 182) characterized the delineation of regions as a problem of classification; they (1971, 72) also noted the importance of the region as an "operational definition". Others, however, such as Meinig (1965), working on the Mormon culture region, produced highly original and explanatory work on culture areas from the perspective of processes which made areas unique. Recent works have examined regions as the spatial expressions of agency and structure that define "social relations" (Gilbert 1988). These studies have developed a theoretically informed regional geography as exemplified by the works of Massey (1984) and Warf (1988) in which regions are expressed by underlying social functions. The focus on the underlying processes of spatial differentiation is the common thread that unites theory-based geographic approaches to the region and the regional concept. In the last several decades, a number of working definitions for region have been adopted to better define the type of space that it identifies. mthin the framework of these definitions, two general types of regions can be identified. The first type of region is the formal, or uniform, region (Haggett 1983, 262). Formal regions are conceptualized as homogeneous areas that are often defined by distinct boundaries. Traditionally, formal regions have been used for administrative firnctions such as taxation, collection of census, and zoning. Usually, formal regions are easily, if somewhat arbitrarily, defined. Johnston (1983, 44.45) notes that "regions are characterized by their homogeneity on prescribed characteristics, selected for their salience in highlighting areal differences...[V]irtually every region [is] in effect a generalization, complete homogeneity being very rare." Thus, the assumption of spatial structure implicit in the formal region is internal homogeneity; in other words, the defining phenomena are distributed continuously within the defined boundary, as is the case with real estate taxes, or the distinguishing feature is present (and dominant) as is often the case with cultural features, such as German barn types. 8 The second type of region is the functional, or nodal, region. Functional regions are conceptualized as areas related by similar function or organization, for example, newspaper market areas and urban areas (Muehrcke 1986, 248; Haggett 1983, 262). Functional regions are often associated with the concepts core and periphery, and often are defined by an indefinite transitional boundary (Haggett 1983, 262). The implicit geographic assumption in the functional region is that the defining phenomena are concentrated in core areas and gradually disappear as one moves outward. Meinig's (1965) model of the Mormon culture region is based on ideas of core and peripheral relations expressed as a series of four formal regions: core — highest intensity; domain - dominant; sphere - zone of influence; and finally outliers - discrete significant local populations not contained within the sphere. The definition of regions for specific research problems suggests that the distinction between formal and functional regions is, perhaps, best viewed as a continuum. In Meinig's example, the linkages defining the region are functional, yet the linkages are so discrete that they can be defined almost as formal regions. Usually, the problem is not so clear cut. Symanski and Newman (1973) questioned the distinction between formal and functional regions, stating that the formal region's internal sameness is the result of processes. Haggett (1983, 262) noted that regions may be defined by one (single-feature region) or more (multiple—feature region) features. In addition, the number of fcatures used to define a region relates to how precisely a given region may be defined. Knox (1987) has also pointed out that regions have different constitutions depending on the enquiry. The issue of regional delimitation is often problematic, particularly when trying to define a region that meets both functional and administrative purposes. For example, Rader (1989) discussed the numerous problems of defining lateral boundaries for river conservation arcas, since the boundary must be clearly defined for case of administration, i.e., it must bound a formal region, and yet also include multiple features of the human and physical landscapes that are functionally linked to the river, making it a functional region as well. Similar problems of regional definition exist in regional development. Many regional development projects utilize political divisions as the basis for analysis and administration. Arcas of social inequity, often drawn along the lines of an urban—rural distinction, are identified, and development schemes are designed to redress these problems (Rondinelli 1985). However, the needs of administration often dominate the problem of urban-rural linkages. Gore (1984) has criticized this approach as being fundamentally flawed. To grossly simplify his argument, the definition of regional inequity based on social indices of 9 'development' often results in an ecological fallacy - that is, the inference that average conditions apply to all individuals in an area (Gore 1984, 53-4). This inference frequently masks significant inequalities. As an example, the impoverishment of people living in urban shanty towns in underdeveloped countries is masked by the higher income levels generated by other segments of the population living within the urban area. On average, it may appear that individuals in urban regions are better off than their counterparts in rural regions. In reality, the poverty of the shanty residents may be more acute since they may not have the ability to raise their own food, and they are simply included in the urban statistics because of the location of their shanty town. A region, however defined, embodies a number of significant spatial concepts. James and Martin (1981, 373) identify a number of derivative concepts that help make further sense out of the complex interwoven fabric of the face of the earth: location, distance, direction, extent, succession over time, pattern, circulation, and accessibility. Each of these concepts lends greater explanatory power to the process of region formation. In addition, areal distributions find further spatial expression depending on whether they are continuous, discontinuous (discrete), or contingent. Continuous distributions extend over the earth's surface varying from place to place in intensity or degree; discontinuous distributions occupy discrete areas varying from place to place by kind; and contingent distributions describe variation from place to place contingent upon another measure, usually area (James and Martin 1981, 374-375). Combined, these derivative concepts refine the spatial articulation of a region. Resolution levels modify the spatial articulation of a region, since regions mapped at one scale often disappear and different ones appear each with different levels of generalization when mapped at a different scale. In addition to the formal and functional distinction, regions have different conceptual 'morphologies' depending on their data sources. These might best be described as nominal regions and quantitative regions. Nominal regions are conceptually simple and similar in conception to the formal region. These regions describe the existence and non-existence of one or more phenomena, e.g. a newspaper market area. Quantitative regions are derived distributions that numerically describe the intensity of existence of a phenomenon, e.g. number of newspaper sales by county. This concept may be extended to a number of different phenomena, e.g. newspaper sales by education level by county. A third type of region is found between nominal and quantitative regions, and might be termed derived nominal. These are nominal regions derived from classifications of quantitative data, such as an NDVI (Normalized Difference Vegetation Index) or a principle components analysis of socio-economic data, and are used to describe areas with different characteristics. 10 The region has had a wide variety of conceptual bases, definitions, and uses in research, demonstrating that the regional concept is not only central but highly adaptable to many geographic problems. While regions are often imprecise because of conceptual complexity and/or oversights and problems in defining the boundary, the concept is still one of the most satisfactory means for presenting geographic information. Furthermore, the categorization of geographic information by regions is highly consistent with the schema individuals use to cognitively organize geographic information as sets and subsets of information by area (Eastman 1985). The generalizations implicit in a region simplify the amount of information to a level that can be easily comprehended, yet frequently these generalizations mask the essential nature of spatial processes. The spatial structure or nature of a region is often implied by its definition and use. While the use of regions in geographic explanation will continue to be fraught with problems, regions still provide a powerful means for describing and analyzing the spatial aspects of many phenomena, and often a map provides the means for these tasks. Cartographic Representation of Regions Cartographers generally employ six types of cartographic representations to portray regions: nominal maps, choropleth and dasymetric maps, unit grid maps, isarithmic maps, continuous tone maps, and dot maps. These different types of cartographic representations vary in the assumptions made about the data and the way that they graphically structure the information presented to a map reader. Some, choropleth maps for example, provide an extensively manipulated view of the data in a highly structured graphic form, and others, like the dot map, provide an almost unmanipulated view of the data in a very unstructured graphic form. Each form assumes an expression of basic spatial concepts that are readily communicated to the map reader via the symbolization. Each method will be reviewed to place the current work in the context of the cartographic literature and develop an assessment of methods for representing regions. Nominal representations Nominal maps show the distribution of one or more phenomena with lines to demarcate the boundaries and, usually, some type of shading (color, gray tone, or pattern) to identify areas occupied by one or more features (Figure 2.1). Unwin (1981) calls these representations chow-chromatic. The simplest are two-phase or binary maps that show the 11 areal coverage of one phenomenon distinguishing only the existence and non—existence of the phenomenon. Often multiple regions are presented on a map, and usually the different areas are conceptualized as mutually exclusive; for example, in land use mapping each area on the map implies only one use. However, Robinson et al. (1984, 340) note that the mutual exclusivity of these regions is often questionable, and, without a great deal of generalization, some form of interdigitation, overlapping symbols, or differential symbolization is required to show areas of overlap. The data model for this type of distribution is a raised flat plane, since only existence and nonexistence of the feature are shown. Lower Peninsula Michigan Gypey Moth Detolletlon E No detollatton - Areas experiencing _ 4|; 4 Inner Figure 2.1 Nominal representation Nominal classes of data are often derived from quantitative data. For example, on a map of vegetation stress produced from remotely sensed data, an index that relates the greenness of the spectral response of the vegetation to plant health is often used to identify areas of healthy and stressed vegetation. Once mapped, the two areas will generally be represented as areas of healthy and unhealthy plants with little or no reference to the quantitative source of the classification. Textbooks are replete with examples of this type of map, and it is perhaps one of the most used methods for presenting regional information. From a geographic perspective, these maps may promote ecologically fallacious ideas 12 because of their representation of a region with a homogeneous tone or color, thereby implying existence everywhere within the mapped region. The nature of the boundaries for nominal regions, particularly at small scale, is more often than not transitional, as in the transition between climate types. Typically this transitional boundary is represented by a hard line and has the potential to mislead map readers. Most of the research on these types of maps has examined the use of color in differentiating areas on the map (Nunez de la Cuevas 1967) and associative properties of color (van der Weiden and Ormeling 1972); however, no works have examined the quality of the information derived from these representations. Choropleth and (laymen-i: representations Choropleth and dasymetric maps are closely related and are used for the representation of regions derived from quantitative data (Figure 2.2). Choroplethic and dasymetric map forms are essentially the same, differing only in aggregation unit. Dasymetric forms possess better fidelity to the actual distribution since they are based on the known (or interpreted) limits and probabilities of the distribution rather than on artificial (and usually larger) aggregation units as are Choroplethic forms. Symbolization for choropleth maps consists of areal tints, and sometimes patterns, that mimic value progressions. Sometimes hues are used to show different subgroups of the data in double—ended schemes. The goal is to provide an idea of change of magnitude, more ink meaning "more" and less ink "less". The data model for Choroplethic and dasymetric representations is a stepped surface with the height of the areal surface representing its value. These representation forms are employed with ordinal and higher levels of data classification. Classification is often problematic for choropleth maps since changes in the classification scheme and number of classes can radically alter the patterns on the map. Most research has concentrated on Choroplethic forms and has examined the role of complexity (Monmonier 1974; Lavin 1979; MacEachren 1982a, 1982b, and 1985), number of class intervals (Olson 1972; Muller 1975), and perceptibility and discriminability of shading symbols (Williams 1958; Jenks and Knos 1961; Kimerling 1985). Recently, classless choropleth maps have been investigated by Peterson (1979), Muller (1979), and Carstensen (1982), with the findings that class intervals may not be as much of an aid to the perception of map patterns as once thought. Frequently, these classless maps are referred to as continuous tone maps; however, this implies continuous tone between units of aggregation more like a smooth surface, rather than the stepped surface suggested by Choroplethic forms. Lower Peninsula Michigan Gypsy Moth Detonation Figure 2.2 Choropleth representation The ease of construction and the generalization of distributions are the two main advantages of these techniques. Both forms, however, rely on the implicit cartographic assumption that the distribution is reasonably homogeneous within the defined boundaries. Regions are formed by the visual grouping of similar values on the map. Youngmann (1972, 12) noted: Simple and compound choropleth maps utilized by geographers are an extension of the concept of regions. In light of traditional geographic methodology, choropleth maps are representations of the areal differentiation of the face of the earth. In other words, classification of observations as they appear symbolized on a map may be interpreted by the geographer as regions. In addition to the fact that Choroplethic representations assume that each area is homogeneous, these maps are also prone to promoting generalized views of the world that may not be warranted. The basic fact is that these types of representation mask all internal variation within the area (Robinson et al. 1984, 365). The dasymetric forms ameliorate some of the masking effect; however, their construction requires more data and greater knowledge of the phenomenon. l 4 Unit grid representations Unit grid maps are a variation on the choropleth form; a given area is divided into grid cells of equal area (Figure 2.3). In addition, unit grid representations form the basis for grid—based geographic information systems (GIS) and related analytical routines. Maps of this type are usually derived from the classification of satellite imagery, such as Landsat Thematic Mapper (TM), or NCAA AVHRR data; however, some countries, notably Japan, England and Sweden, have used this technique to map census data. Each grid cell is represented by a value that indicates either membership in a category, e.g., farmland, urban land, or rangeland, or intensity of one category at a location, c.g., number of persons or temperature. Grid cell resolutions vary greatly depending on data sources. Resolutions ranging from less than a meter on a side for low altitude airborne sensors to many kilometers on a side for NOAA's weather satellites are common. Symbolization for unit grid representations may include hue, pattern, and value (tone) depending on the level of generalization. Lower Peninsula Michigan Gypsy Moth Detoliatlon Figure 2.3 Unit grid representation Unit grid representations conform to both stepped and planar data models depending on the source of their data and the level of processing to which the data has been subjected. In a raw plot of values, the model is stepped, and in the derived nominal case the data l 5 model is planar. Because of the high data density, maps from these sources usually require a great deal of classification. A typical land use map derived from a satellite image results from the classification of spectral reflectances into land use classes, thereby creating a derived nominal representation of the spectral reflectances. Maps of this sort would normally be impractical without the use of a computer to process and display the data. Little cartographic research has investigated unit grid representations, although a number of studies on choropleth maps have used unit grids to control for size and shape interactions (Olson 1972 and Lavin 1979). While similar to other Choroplethic forms, the small size of the areal units over which the data are collected insure a better representation of conditions at a particular location. Tufte (1990) has observed that these types of maps operate on two levels. The user may develop a general 'region' from similar hues or combinations of hues, but may also look at greater detail within these larger user—defined regions. In contrast, other Choroplethic forms usually only allow for the development of more general regional patterns. With increases in sources of data and the ability to manipulate them with the computer, this mode of representation will continue to increase in importance. Isarithmic representations Isarithmic or isoline maps display a distribution with a set of lines that join points of equal value (Figure 2.4). The main purpose of isarithmic mapping is to provide a general impression of variations in a spatial distribution (Muehrcke 1986, 108). These maps attempt to represent regional data as surfaces that vary continuously from one place to another. However, depending on the source of the data for the maps, these representations may or may not be continuous. Robinson et al. (1984, 335) identify two forms of isarithmic maps that differ in "the form of the original data": isometric maps are based on measurements from points on a continuous surface (e.g. weather maps), and isoplethic maps are based on areal data (e.g. population density maps). The difference between the two forms is critical since different assumptions about the distributions are made, yet the two forms are represented identically. The data model for these representations can be either a continuous surface or a stepped surface if one thinks of the spaces between the isolines as flat. These forms apply to the representation of statistical surfaces and landforms at ordinal or higher levels of measurement. The most common form of symbolization for isarithmic maps is a series of lines that describe equal values on a surface. A map reader must interpret from these lines both the 16 form of the underlying data (isometric or isoplethic) and the resulting surface. The use of layer tints between the isolines is often used to improve the perception of the surface form of the distribution (Figure 2.4). Layer tinting is commonly used on weather maps (e.g. USA Today's temperature map), and hypsometric tints are often used in the representation of land elevations. It is sometimes assumed that map readers can only determine a range of elevation values for a given point from these representations and nothing of the within- range variation (Campbell 1984, 332), although all the information is present to allow interpolation of an elevation value. Lower Peninsula Michigan Gypsy Moth Detonation Figure 2.4 Isarithmic representation Work on these forms has closely paralleled studies performed with choropleth maps. The ability of subjects to discern regions on these maps was studied by Griffin and Lock (1976). Similarly, the number of class intervals was further addressed by Phillips, DeLucia, and Skelton (1975). However, unlike choropleth maps, layer shadings on isometric maps, and frequently on isoplethic maps, always proceed in an orderly arrangement from low to high or vice-versa, placing less demand on the map reader to be able to discern fine differences in the shading. A number of studies have examined the use of shading on isarithmic maps to determine the best sequences for portraying magnitude in both single- and double-ended schemes (Cuff 1972; 1973). Patton and Crawford (1977) investigated 17 problems of color associations with conventional hypsometric tints and found that hue, particularly green, produced an unintended message about the nature of the land cover in an area. Problems of both symbolization and nature of the distribution tend to make this a difficult type of representation for some map users. Continuous tone representations More recently, with the versatility that computer assisted design has brought to map making, continuous tone maps have become a viable method for representing regional information (Figure 2.5). Continuous tone techniques apply to qualitative and quantitative representations, although nominal cases must be derived from quantitative data that represent changes in intensity of the distribution. These representations are usually developed from a regular grid of known or interpolated values and shading intensity values and hues are assigned on this basis. The data model for continuous tone maps is a smooth surface. Even the nominal case, because of its indication of intensity, might best be considered a smooth surface, much like a quilt, with raised areas representing where phenomena exist and valleys where none exist. Lower Peninsda Michigan Gypey Moth Detoliatlon Figure 2.5 Continuous—tone representation l8 Symbolization for continuous tone maps relies on hue and value progressions to indicate change in both class and intensity. Changes of hue indicate changes of class, mixtures of hue indicate transition, and changes in value indicate changes in intensity. All or one of these dimensions may be employed on continuous tone maps making it a versatile method for representing continuous distributions. The method appears to have potential in multi—factor mapping where relationships of existence, nonexistence, transitional boundaries, and overlapping areas need to be represented. Continuous tone techniques provide a potential solution to the problem of transitional boundaries. Methods for producing these types of maps have been investigated by Groop and Smith (1982), Lavin (1986), and Kumler and Groop (1990). Lavin (1986) applied dot—density shading techniques to produce continuous representations of climatic data and he suggested that better thematic interpretations of intensities and transitions were possible using this method when compared to isoline representations. Kumler and Groop (1990) applied a continuous tone technique to the representation of smooth statistical surfaces. Results indicated that subjects performed significantly better in locating surface extrema, relative and exact values at specific points, and determining slope between two points with the continuous tone maps than with block diagrams or traditional isarithmic maps (Kumler 1988, 53). A similar approach to the representation of regional boundaries as exemplified by climate regions was utilized by Groop and Harman (1988). They suggested that the use of transitional boundaries with continuously variable distributions, such as climate types, is justified since it provides a "cartographic representation that is visually commensurate with the geographic phenomenon that is being illustrated." (Groop and Harman 1988, 68) No experimental work has verified the nature of the relationship between continuous tone representations and the geographic understanding of regions. Continuous tone maps have the potential to be applied at nominal and higher levels of representation since the shading can be used to indicate transitional boundaries. In addition, continuous tone maps have the ability to indicate internal variation within the distribution. Dot representations Dot maps attempt to show both the quantity and the distribution of a phenomenon over space by placing dots representing one or more individuals of the population at the location where they reside (Figure 2.6). "The dot map can show the details of the locational character of a distribution more clearly than any other type of map. Variations 19 in pattern or arrangement, such as linearity and clustering, become apparent. The dot map provides an easily understood visual impression of relative density..." (Robinson, et al. 1984, 300). Dahlberg (1967) suggested that the dot map has a number of conceptual forms, including that of a statistical diagram with various surface configurations from smooth to stepped. Muehrcke (1986, 110) notes that two forms of dot maps exist: point symbol maps that show the location of each and every member of a population by one point (which also may be classified as a location map), and dot maps that show distribution with at least several members of a population represented by one point. Lower Peninsula Michigan Gypsy Moth Detolletlon - Each dot represents 300 acres Figure 2.6 Dot representation The symbolization on dot maps is relatively straightforward and consists of a set of tiny circular or other shaped geometric figures. The problem often is placing the right number of dots in the right amount of space to achieve the desired impact of changing density from one place to another. Usually, variations in the visual quality of the symbols are not used. Jenks (1953) suggested 'pointillism' could be used as a cartographic technique to show multiple distributions on one map and described the mapping of agricultural regions for the US with this technique. Using this method, several different color dots are used to represent different characteristics of one phenomenon or different phenomena. The mix of 20 both density and hue combinations portrays both changes in composition and intensity of the mapped features. Research on dot representations has, for the most part, concentrated on the improvement of pattern perception. Dahlberg (1967) conducted the most extensive discussion of the dot map and suggested a number of means for improving it. Olson (1975a) determined that adding additional dots in high density areas aided in the perception of density in these areas. Rogers and Groop (1981) further examined Jenks' idea of multicolor dot maps and determined that subjects were able to perceive regions with the multicolor map as easily as with single monochrome maps. The spatial concepts that dot maps are able to portray are: extent, density, transition, location and variability. Overall, the dot map is perceived to be an effective form for the representation of spatial information concerning regions covering many levels of data measurement. Perception of Form All map reading starts with some perception of the map and the spatial patterns of the data distribution. These perceptions largely fall under the realm of pattern recognition, and a brief review of the psychological findings regarding pattern recognition will help clarify the research hypotheses for this experiment. Gestalt psychologists postulate that figures, or in this case patterns, are taken in as a whole and are subject to "rules of closure and continuation". Good forms conform, and poor forms do not conform to rules of closure. Zusne (1970, 150) suggests that while the correspondence between stimulus and internal representation is coherent, there is little fidelity between the two; topological relationships (relative positions) are usually preserved in the internal representation while the topographic relationships (exact positions) are often altered. Often, the arrangement of the stimulus is more important to identification than is the precise location of the object(s). Recently, work has turned to object recognition in studying how the representation of an object leads to recognition. Two theories of recognition have been proposed. Biederman (1987) has proposed that perception is largely a problem of recognition by component parts (RBC). Serial edge tracing, proposed by Ullman (1984), suggests that the contour defining the outer edge of an object is traced from start to finish to provide object definition. RBC proposes that the mental image of an object is broken down into simple geometric primitives called geons, similar to phenomes in language. Geons consist of elements such as cylinders, blocks, and cones. Similar to phenomes, the set of geometric 21 primitives is actually quite limited, perhaps fewer than 36 (Biederman 1987, 121). The parsing into component parts is performed at regions of concavity (Biederman 1987, 117). Non-accidental properties such as vertices and symmetry provide constraints that allow for the identification of the components. Biederman (1987, 133) suggests that the breaking down of an object into component parts occurs in primal access, the earliest stage of image acquisition, and it relies on the edge—based recognition of a few simple components. A number of the parts of RBC are consistent with theories of feature detection for typography suggested by Selfridge (1959) and Gibson (1965). The experimental evidence in favor of RBC makes the model somewhat more robust than serial edge tracing. Biederman, Ju, and Clapper (1985) determined that recognition accuracy and reaction times were not affected by the complexity of the object, and in some cases the more complex objects had shorter reaction times. RBC postulates that the critical units for recognition are edge—based (Biederman 1987, 131). Serial contour tracing, as proposed by Ullman (1983), suggests that more complex objects should require an increase in recognition time because of increased edge contour length. Biederman and Ju (1986) compared color photographs to line drawings of the objects; reaction times were equivalent despite the fact that some of the objects, such as a banana, had a diagnostic color distinction. Biederman (1987, 133) does acknowledge that under conditions where edge extraction is difficult, differences in color, texture, and luminance might readily facilitate object recognition. Further work by Biederman and Blickle (1985) on the perception of degraded objects suggested that objects that have been degraded by deletion of their contour in critical areas of concavity impede or make recognition impossible (unrecoverable) under conditions where contextual inference is not possible (Biederman 1987). Recognition by components therefore suggests that recognition is edge—based and that other factors such as surface characteristics are of secondary importance in the identification of objects. The perception of indefinite boundaries, as one would find on a continuous tone map, is not well understood. However, the psychological evidence suggests that this may not be a problem for map readers. For regions with simple boundaries, the lack of a definite edge may not be much of a factor; however, for regions with complex boundaries, perception may be more difficult. Therefore, it is likely that regions lacking a well-defined edge, especially complex regions, will impede the formation of a 'mental image' of the region. For work with actual maps, this may be less of a concern. Three of the map representation types discussed in the preceding section (nominal, choropleth, and shaded isoline) possess 22 definite edges, and three of the representations, depending on the nature of the distribution, lack definite edges (unit grid, continuous tone, and dot). Synthesis: The Regional Concept and Cartography The relationship between the regional concept and regional representation lies ultimately in the understanding of a spatial distribution that they engender. In a sense, concept and region are different approaches to a similar problem. Geographers have been concerned with the development of regions that express certain ideas about processes that make a place stand out from its surroundings. In addition, they have also been concerned with internal variations within these areas. The goal has been the expression of geographically meaningful patterns. To a large extent, maps have been used to aid in the identification and explanation of these patterns. Cartographers have obliged and essentially produced data-driven graphic representations of these areas based on an idealized form of the data (discrete or continuous), the appropriate enumeration units, and the appropriate cartographic technique. However, too little concern has been placed on representing certain spatial aspects of the region. Regions are often used in geography to guide our analyses and organize our ideas about space. Little has been written on what concepts we actually try to communicate with regional information. While our understanding of the region is largely conceptual, we associate different ideas with different types of regions. The relationships are, in part, due to the spatial arrangement of the phenomena being represented and, in part, due to the nature of our geographic data on the phenomena, i.e. whether we know the location of every member of the population. Key concepts that geographers attempt to communicate about the region are: location, homogeneity, variability, definite boundaries, transitional boundaries, relative and absolute area, and, more often than not, the relationship to other distributions. Implicit in our conceptualizations and representations of regions is the idea that various expressions of spatial structure exist. Regions provide an expression of theories about what a place is like. Important geographic concepts in the representation of regions center largely on how well ideas concerning the nature of the spatial distribution are expressed. The concepts of internal variability and transitional boundaries are two areas that have seemingly been neglected in the geographic and cartographic literature. To be sure, most geographers realize that most boundaries in geographic space are transitional. The problem becomes more acute in transmitting ideas about regions and their limitations to others, who may or 23 may not understand the transitional nature of boundaries. The precision with which we can map areas utilizing global positioning systems, remotely sensed data, and geographic information systems far surpasses our conceptual abilities to define different areas on the face of the earth that form meaningful regions. Traditional cartographic representations of these areas, with sharp boundary lines and homogeneous flat tones, are often not warranted by the data since they do not adequately express ideas of variability and transition. The world cannot be carved into a jigsaw puzzle of unit areas, yet our representations and conceptualizations of regions continue to foster this view. If geographic understanding is our goal, then our verbal and graphic conceptualizations of the problem need to be more explicit. In addition to information about the location and extent of these areas, are we trying to communicate an understanding of spatial aspects such as transition, abruptness, continuity, and discontinuity? The use of different cartographic representations is governed by the purpose of the map. Although the possibility exists to represent almost any data set with any of the above symbolization types, the intended message of the map must be considered and the symbol type must be selected in accordance with this message (Hsu 1979). The question as posed by Jenks (1970) is: what concepts about the distribution are important and should be made apparent through the symbolization? For example, the use of nominal techniques to represent climate zones is inappropriate, since variability in weather conditions make certain areas transitional between zones (Groop and Harman 1988); however, on a large scale land use map such techniques are appropriate, since distinct boundaries between woodlots and fields can be identified. Implicit within these forms of mapping spatial distributions are many assumptions about the spatial nature of the phenomena. Therefore, the type of symbol employed should be capable of communicating this information to the map user. Traditionally, the problem of map symbol selection has been data driven and conceived as matching the right measurement level, idealized 'surface form', class of feature, and cartographic representation. Within cartography, there are suggested conventions for the use of particular types of maps for the representation of particular phenomena. The problem of understanding, therefore, is essentially a function of symbol-referent relationships. Hsu ( 1979), Chang (1976), and Dobson (1975) addressed the conceptual issues of symbol and subject matter relationships in cartography. Dobson tested the relationship empirically through a map title and map matching exercise. He found that map readers were able to match the conventionally recommended symbol type with the subject of the map; trained readers performed better than untrained readers (1975, 64). 24 Hsu and Chang's works were conceptual and stressed the relationship between data classes and symbolization. Their reviews emphasized the correspondence of the classes of cartographic features (points, lines, areas, and volumes) and levels of measurement (nominal, ordinal, and interval/ ratio) to symbol type. While symbols are conceptually related to their referents and this has guided symbol selection, no work has examined the impact of particular symbols on geographic understanding. Little empirical evidence is available to suggest how successfully these types of maps meet the goals of communicating spatial concepts. Jenks (1973) hypothesized that two classes of information are communicated by a thematic map: one is tabular information and the other is integrative information. The firSt can be extracted from a map fact—by—fact to determine the number of a particular phenomena in an area. The second transcends the simple extraction of information "wherein symbols are merged into fields to form patterns or regions" (Jenks 1973, 27). However, the critical question asked by Jenks (1973, 27) is: on viewing a map, do we all end up with similar or different images? Working with dot maps, Jenks determined that "there is a great diversity between patterns and boundaries reported by" different individuals (Jenks 1973, 28). Peterson (1979, 32) noted: "A major purpose of thematic mapping, however, is to convey a pattern for the distribution. Pattern arises from the graphic symbolization which promotes a type of generalization over space." Peterson's (1985) work on image quality suggests that the role of map pattern in the formation of mental images is different with different graduated symbol maps. Eastman (1985) demonstrated that the definition of spatial chunks (regions) is strongly influenced by the graphic organization of the map. Therefore, one might expect that different ' representations, even of the same distribution, will yield different graphic organizations, and ultimately, different outcomes in map reading exercises. Once the map image is internalized, the question concerns the meaning of the map symbol. Do diverse perceptions yield fundamentally different understandings of the regional information portrayed by a map? The cartographic representation of regions is essentially a problem of determining which symbolization method is best for representing the information and communicating an understanding of a geographic reality. Over the past three decades, a psychophysical approach to cartographic design has emphasized symbol design. Guelke (1977) has criticized this approach based on the fact that it emphasizes perception of 'information' and ignores understanding. More recently, cartographers have approached the problem from a cognitive perspective and have stressed the impact of the map on the individual's 25 understanding of cartographic information. Gilmartin (1981, 9) suggested that both perspectives are critical and that "cartographers ought to understand not only how people react to graphic characteristics of the map symbols, but also how symbols and the map as a whole acquire meaning." The former can be thought of as the surface structure of the map, and the latter the deep structure (Head 1984). As a result of the work conducted during the past decades, we have a good understanding of the surface structure of the map, but little understanding of the deep structure of the map. MacEachren (1991, 5) has identified the question: 'do particular symbolization methods actually communicate the particular spatial characteristics that we as cartographers associate with them?' Changing conceptions of regions have also challenged our cartographic abilities. Changes in technology, representation, and conception have stimulated the need for continued development and study of cartographic techniques. Lewis (1991, 621) stated the importance of this problem from a geographer's view: It is clearly no longer tenable to conceive of the human community as divisible into simple social units, singular jigsaw pieces completely filling geographical space....In this context, they [geographers] may begin by acknowledging that human relatedness, an inescapably spatial phenomenon, must be apprehended through maps, however contingent and imperfect they might be....In responding to the challenge, geographers should seek new cartographic models. We need map making techniques that can do justice to the enormous complexity of this topic; returning to cartography must not entail the depiction of one-dimensional—jigsaw-like patterns. Gradients must be distinguished from sharp boundaries, and boundaries transcending lateral ties must be recognized. Equally important, the mapping of relatedness must depict hierarchical series, paying particularly close attention to the problematic relationship of groups defined at different scales. Little is known of the role that maps play in the presentation and subsequent understanding of regional information. The problem is, therefore, threefold: 1) what is the basic nature of the region, 2) what are the perceptual visual aspects of the cartographic methods we use to represent regions, and 3) what is the impact of these representations on a map user, and do the representations adequately communicate concepts related to the nature of the region. The key is to determine whether or not different cartographic representations of regions develop differential understandings of the spatial structure of regions. This problem focuses on whether or not certain representations lead to a greater consistency in mental images and whether or not certain representations promote a better understanding of the internal spatial structure of the region (e.g., homogeneity, heterogeneity, transitional boundaries, etc.). CHAPTER III RESEARCH DESIGN AND METHODS The experiment in this project was designed to collect information on differences in subjects' understandings of the nature of map distributions and their ability to perform 'typical' map using tasks across five different map types. In the first part of this chapter, the logic for the test design and the research hypotheses are developed. The results from a series of interviews with professional map users and a discussion of the map use tasks provide the background for the specific research hypotheses that are used to determine the effectiveness of difierent map types in representing regional information. The design of the experiment is described in the last part of the chapter and includes a discussion of the test maps, question sets, procedure, and subjects. Interviews Twelve academic geographers1 were interviewed to collect fimdamental information on the use of regions and maps of regions in the classroom and professional work. The geographers interviewed broadly represent the diversity of the discipline and include both human and physical geographers. The interviews were informal and helped to identify a diversity of approaches to the use of both maps and regions. These, in turn, helped to determine what to ask the map users in this project and to establish more cohesively the relationships between the geographic concepts and the maps used to represent these concepts. The question posed to the geographers was, "for what do you use maps of regions in your teaching and research, and what do you expect people to learn from them?" This question ultimately yielded two responses; the first was generic to map use and the second was specific to the use of regions. The responses to this question were aggregated to determine the types of tasks one expects a map user in an academic setting to perform in 1 Sharmistha Bagchi-Sen, William Blewett, Henry Castner, David Campbell, Peter Galvin, Richard Groop, Ian Marley, Mark Pires, Randall Schaetzl, Robert Thomas, Julie Winkler, and Harold Winters were interviewed. 26 27 obtaining information from cartographic representations of regions (Table 3.1). Six categories of map use task and information were identified: presentation, location, extent, pattern, covariation, and analysis. Within each of these categories there were a number of specific responses. Distribution of knowledge, location (where), ideas of membership (inclusion or exclusion), extent (relative area), pattern (continuous or discontinuous), and relationship to other distributions (spatial interactions and associations) were mentioned by almost all respondents. The basic 'four Ws of geography' (what, where, when, and why) were covered in the explanations of use of regions. Table 3.1 Summary of interviews defining uses of maps in presenting regional information General use categories Specific uses of maps of regions Presentation Distribution of (spatial) information Interest people in a geographic problem location Existence of phenomenon (what) Geographic area (where) Extent Spatial scale and 'temporal scale' (when) Absolute area and relative area Pattern Variation and intensity Continuity and discontinuity (homogeneity and heterogeneity) Abrupt changes and transition (transitional boundaries) Core and domain (periphery) Covariation Comparison to other patterns (similarity and dissimilarity) (why) Relationship to other patterns (proximity and associations) Analysis Exploration and visualization of relationships (why) Spatial interactions and explanation of "process" Hypothesis generation (models) Uses falling under the categories of presentation, location, and extent were relatively straightforward since these correspond mainly to the mechanics of presenting information 28 and generating interest in the topic. There is a certain need to define the relative geographic coordinates and the size of the subject matter being examined. In other words, is it a question of local, national, continental, or worldwide proportions and where does it occur? Maps were used as backgrounds for discussions, to reinforce the basic geography, and to provide a 'mental image' of the area. Map presentations were often supplemented with verbal descriptions of the areas and the relevant dimensions and locations. Views on the use of regions diverged on pattern, although all the concepts expressed appeared to center on the depiction of internal variation of regions, whether expressed as different densities, continuity and discontinuity, or ideas of core and domain (periphery). The idea of transitional boundaries appeared in almost all discussions, since it is the norm for geographic distributions. Winters (1991) eloquently described the problem: 'It is hard to find meaningful lines on a map. In physical geography we need to treat every line as a transition, and people must be continually sensitized to this. Where does one soil end and the Other begin?’ The importance of transition has further ramifications since it expresses the uncertainty of the location of many spatial phenomena. One geographer questioned the efiicacy of geographic information systems on this point, by asking: 'Are we giving people the wrong impression that we know exactly where everything is?’ The impact that apparently accurate presentations have on the communication of geographic information is evidently at issue. The covariation of spatial patterns was a critical part of all discussions on the uses of regions and maps. Association with and similarities to other patterns were important issues in the use of regions for this purpose. In teaching models, map comparisons were typically used to spark interest in why certain patterns were similar or very different. In addition, the proximity of certain patterns was perceived to be important, since, although the patterns between maps may not overlap, they may correspond in different ways, for example the way that the eastern coastline of South America nests with the western coastline of Africa used in illustrations of plate tectonics. The use of single-factor regions to develop multi—factor regions is another example of the type of activity that map comparison encompassed. These activities, along with pattern, define most of the basic functions involved in understanding a spatial distribution and are perhaps the tasks most affected by both verbal and cartographic expressions of regions. The final category involved the extension of the previous concepts to analysis. The initial phases of explanation in the interviews usually led to a more thorough examination of the spatial processes used to define a region. Data exploration, expressions of spatial interaction, and expressions of hypotheses about space were characteristic of responses in 29 this category. The 'why question' of geography came to the forefront, and both conceptions and maps of regions focused on the explanation of processes occurring over space. These were used in both the generation of initial ideas (pm—hypotheses) and the confirmation and further development of existing hypotheses. In many ways maps were considered to be models of hypotheses. This last category also relied more on other sources of information. A difference in the level of map use was noted between older and younger geographers, and it may well be the result of differences in training. Older geographers had a reverence for maps that points toward the primacy that training in map use once had in the discipline, and they used maps more intensely in both their research and their teaching. Emphasis was on the integration of knowledge from maps and the use of multiple maps in understanding the area being studied. In addition, the term "region" elicited responses from older geographers that emphasized it as the cohesive character of an area, and in most cases the 'region' was a multi—factor region. In contrast, the systematic nature of the training received by many younger geographers de-emphasized the role of the map. In addition, many younger geographers treated the region as a generic area on the face of the earth that was used mainly to emphasize the location of the phenomenon under discussion, in most cases a single—factor region. One senior member of the discipline commented that many younger geographers do not look at maps and they do not know the "regional geography" of the area they are studying. The responses of the younger geographers tended to corroborate this statement. One indicated use of maps mainly to show the location and extent of an area and where it was in relationship to other areas. This person expected people to know where the core areas were. Another indicated very little use of maps in teaching and only in research to identify the location of the study area and measurements within the study area. To be sure, some of these age—related views result from different stages in course development and research programs. The main difference between the use of maps of regions in research and teaching was the specificity of preparation and discussion. Teaching invited a more informal approach to the use of maps. General patterns and knowledge of the distributions were stressed over specifics. Much of the difference was attributed to the geographic sophistication of the audience and the specificity of region definitions required for research. The same tasks were mentioned for research; however, the emphasis was clearly on the specifics of the relationships to other distributions. Illustrations for articles were primarily used for location of study area or samples within the study area, with little or no reference to the 30 patterns on the map. The differences could be characterized as a teaching orientation more towards synthesis and as a research orientation more towards analysis. In summarizing the interview data, the critical question raised is this: how well do our current forms of representing regions meet these use requirements and should, and can we as geographers be more demanding of our representational forms in expressing our ideas about regions? Determination of Map Use Tasks A set of test questions was developed to address tasks involving estimation of relative extent and the understanding of core and domain, boundary forms, intraregional variation, and map comparisons. The relative area estimation task collected information on the perception of the size of an area in relation to its overall geographic setting, as, for example, the area of national forests in Michigan. Mapped expressions of core and domain, transitional boundaries, and internal structure were designed to determine if different cartographic symbols carry implicit codes for the nature of spatial distributions. Questions concerning these map use tasks address the overall issue of whether or not certain representation methods, such as those used on nominal maps, encourage the development of ecologically fallacious impressions of strict internal homogeneity. Map comparison questions deal with the correlation, or covariation, of different distributions and attempt to arrive at information on the "why" which is so important in the analysis of regional distributions and spatial interactions. The goal of the test design was to develop tasks that represent typical map use problems rather than the artificial tasks utilized in numerous cartographic studies. McCleary (1975) and Board (1978) noted that map use tasks are an important determinant of map user performance. In addition, meaningful stimulus (Reicher 1969) and tasks (Eagle and Leiter 1964) have been demonstrated to be important facrors in experimental results in psychology. The main problem, as Guelke (1977) has suggested, is to place the test information in a meaningful map context. Since we are primarily concerned with how the map functions in promoting geographic understanding, the use of realistic map use tasks is highly desirable. Response time and accuracy are the primary measures used to judge subject performance for the different methods of representing data on maps used in the test. Certainty ratings (very certain, somewhat certain, somewhat uncertain, very uncertain) were also collected for each question. The data help to answer three basic questions regarding overall performance: 1) do some representations produce significantly 31 faster or more accurate responses, 2) are some classes of map tasks more difficult for subjects, and 3) do responses for different tasks vary with representation types, in other words, do different map representation types impart a qualitatively different understanding of the nature of the same region? Generally, more difficult tasks should yield longer response times, lower accuracy, and lower certainty estimates among subjects. Therefore, the results should reveal any differences that exist between map types and should identify empirically more difficult and less difficult map use tasks. Accuracy is also used as a measure of the stability of the representations. Muehrcke (1990, 11—12) has suggested "stability" as one measure of the cartographic accuracy of a representation, since " [i]t would be undesirable if slight alterations in data inputs or mapping parameter: (italics added) would significantly alter the view of the environment gotten from a map." The choice of symbol type is a significant mapping parameter under the control of the cartographer. Therefore, it is important that we understand how these issues are involved as we make our choices concerning cartographic representation. Five research hypotheses are proposed to assess the impact of map representation method on the spatial understanding of regions. Since the nature of the boundary lines for regions appears to be important in the internalization of representations, the hypotheses are designed around this factor as a predictor of outcomes for the different tasks. The five research hypotheses presented below are consistent with both cartographers' conceptions of representation methods and geographers' regional concepts and expressions of regional distributions. Research Questions and Hypotheses The general research question that is used to examine the impact that different cartographic representations have on map users' acquisition and understanding of regional information is: Do different methods of representing regional information produce differential performances and understandings of the nature of regional distributions among map readers performing "typical" map use tasks, such as estimation of extent, interpretation of intensity, core and domain relationships, transitional boundaries, and ability to perform map comparisons? 32 In pursuing this question, the impacts that five symbolization methods for representing regions have on the map reader's acquisition of regional information are examined. Response accuracy, response time (latency), certainty ratings, and interpretation are examined in order to provide information as to whether any of the representations are better suited to providing key regional concepts than are others. Five specific research questions are addressed: Question 1: Hypothesis 1: Question 2: Hypothesis 2: Question 3: Hypothesis 3: Question 4: Hypothesis 4: Question 5: Hypothesis 5: Which representations, if any, promote consistent estimation of relative area (extent) of a region? Subjects will perform better on estimation of relative extent using representations bounded by definite edges than using representations without definite edges. Which representations, if any, consistently communicate concepts of core and domain? Subjects will perform better on core and domain assessments using representations with internal graphic variability than on those represented by a flat tone. Which representations, if any, communicate concepts of transitional boundaries to map readers more consistently than others? Subjects will perform better on transitional boundary assessments using representations with transitional boundaries, e.g. continuous tone and dot representations. Do different representations consistently communicate concepts of variable internal structure of regional distributions? Subjects will consistently interpret nominal representations as homogeneous areas and other representations as having variable distributions. Which representations, if any, facilitate map comparison tasks with other related regional distributions? Subjects will exhibit better performance on map comparisons with representations bounded by definite edges than with representations without definite edges. 33 For the purposes of this experiment, better is defined as more accurate responses, more consistent responses, and faster response times; it is important to note that response times and accuracy should both be examined since it is possible that higher accuracy may be achieved with slower response times. The associated null hypotheses for each of the research hypotheses are that there is no difference between map types in representing these different aspects of regional information. Experimental Design Subjects performed typical map use tasks and answered questions designed to test their understanding of the symbolization used for representing regional distributions. The testing sequence required the subjects to perform the tasks while working with the maps. A brief description of the test maps, the specific test questions, test sets, test procedure, and subjects follows. T art map: A set of 20 test maps was constructed for this experiment from four different geographic distributions. Five practice maps were created from a fifth distribution. An additional 44 degraded versions of the test and practice maps were created with varying correlations to the original maps for use in the map comparison tasks. The four different distributions were mapped using each of the five different cartographic representation methods under investigation. The distributions used for this experiment were: gypsy moth defoliation by county in the lower peninsula of Michigan, agricultural production by county in Georgia, banana production by commune in Rwanda, and adherents of Islam by country in Africa. The practice set was developed from a random hypothetical distribution by country for South America. The distributions represent a variety of phenomena at different scales. Locations and/ or distributions to which North Americans have likely had little exposure were chosen, so that prior knowledge should not impact the results. Titles and legends were included on the maps to make them realistic and allow the subjects to attach "meaningful" concepts, e.g., defoliation and religion, to the maps. It was thought that the use of real data, while introducing potential problems of prior knowledge, would make the tasks more meaningful for the subjects and help maintain their interest over the course of the testing. 34 The maps were produced by processing the original distributions into different representations using MapMaker (Select Micro Systems 1989) mapping software and Map II GIS (ThinkSpace 1992) software. The maps were converted to paint—format screen images, and titles and legends were added using SuperPaint (Aldus 1993). This conversion allowed the graphics to be used efficiently within SuperCard (Aldus 1991). "Blackness" between map types of the same distribution was equalized as much as possible, since the relative blackness has proven to be a factor in map comparison decisions (Lloyd and Steinke 1976; Muller 1975). Some aesthetic color was used to make the displays visually more interesting to the subjects. The use of color as a redundant code does not appear to detract from or improve on map reading performance (Patton and Slocum, 1985). The different cartographic representations of the test distributions were developed using standard cartographic data handling techniques. The original data were count data, and these were mapped using MapMaker to plot dot density maps of each data set. These data were then standardized by area, and five-class choropleth maps were plotted using MapMaker. In the case of the map of Africa showing adherents of Islam, the data were standardized by z—scores, because of the great difference in the size of areal units between countries. Class breaks were determined at :l: 0.26 and :l:0.84 standard deviations from the mean for the distribution. These breaks equalize the probability of occurrences in each class (Olson 1972). Unclassed versions of the choropleth maps were exported to Map II for processing into continuous tone, isoplethic, and nominal representations of the data. These choropleth maps were resampled to point samples by using a randomly placed sample point for each enumeration unit. The point sample locations were initialized with cell values equal to one and all other cells in the coverage with values equal to zero, then a multiplicative overlay with the choropleth map was performed to assign the value for the enumeration unit to the sample point. An interpolation mask was then created from the choropleth map to limit processing to the map area, and the point sample coverages were then interpolated using a weighted distance routine involving the two nearest neighbors in each quadrant around the sample point within 25 cells of the sample point. These values were determined experimentally to derive reasonable looking interpolations and acceptable interpolation times. The interpolations were smoothed using two passes of a low pass filter to eliminate local maxima and minima created by the sample points. The continuous tone maps were created by continuously shading the values from low to high using a 16—step gray scale. The sixteen steps were part of the Macintosh's default 256-color lookup table and yielded near continuous tone representations. The isoplethic 35 representations were developed by using the same class breaks as determined for the choropleth maps. The nominal maps were developed by classing cells above the mean as part of the region and those below as out of the region. The unit—grid representations were eliminated from consideration during the construction of the test maps because of the difficulty of resampling the data into a "believable" pattern. In addition to the set of test stimulus maps, a set of two derivative maps with varying degrees of association to the original distribution were prepared for each distribution and mapped using the five different methods. These maps were developed by degrading the original distributions to the point where the correlations between the derivative maps and the test maps yielded correlation coefficients between .84 and .92 to the original distributions. Maps with these levels of correspondence provide enough variation and similarity to make map comparisons moderately challenging for map users (Olson 1972; Peterson 1979). The same class values and processing techniques described above were applied to each of the derivative maps. These maps were used only in the map comparison questions. The original and the derivative maps were reduced to 60% of their original size so that they would all fit on the screen. Test questions Five test questions were designed for each of the 20 test maps, one question for each research hypothesis. The five questions were functionally identical for each map with only minor changes in the wording that referred to the locations. The core and domain task required two sub-questions, the first to determine core areas and the second domain areas. Therefore, a total of 120 map and question combinations were developed for the project. A secondary question had subjects rank how certain they were of their answer to each map use question. The questions were constructed into "cards" and administered in a SuperCard project that presented the map and question followed by the certainty rating. Responses, response times, and certainty ratings were recorded. The first question was designed to collect information on people's abilities to estimate the relative extent of a region on a map (Figure 3.1). The estimation of relative extent of a distribution is a basic use of regional distribution maps. This question had subjects choose an esrimate of the area described from a limited number of possible responses. 36 Lower Peninsula Michigan Gypsy Moth Ddollltion How much area is experiencing at least slight defoliation? 85 percent 60 percent 55 percent 50 percent ‘5 percent 40 percent 35 percent 30 percent 0 O O O O O O O Figure 3.1 Sample question for estimation of relative extent task (55% reduction) The second question was designed to collect information on the understanding of concepts relating to the core and domain of a region (Figures 3.2a and 3.2b). Core and domain are associated with ideas of stronger and weaker influence on the area within and near the distribution. This question was divided into two parts. In the first part (Figure 3.2a), subjects were asked to compare several different localized areas on the map and identify the area where the distribution exerted the strongest influence. In the second part of the question (Figure 3.2b), subjects were asked to compare several different broad areas on the map and identify those areas in which the distribution existed at least to some degree. Answers to these questions will help to determine if ecologically fallacious ideas are being promoted by one or more representations. For example, some representations, such as a nominal map, may indicate internal homogeneity and a distinct boundary where in fact the distribution might be quite heterogeneous and have a transitional boundary. Therefore, subjects may guess at core area locations and they may exclude domain areas. Lower Peninsula Michigan Gypsy Moth Detolletion In which area would you expect the most intense defoliation? 0 Cannot tell Figure 3.2a Sample question for core task (55% reduction) Lower Peninsula Michigan Gypsy Moth Detolietlon thh m axperlenclngetieast sliflndeloflatlon? 0 Annie O Bde O Andi: Q A,B,endc O Aonlv O Bonlv 0 Com! 0 Csl'lnotteil Figure 3.2b Sample question for domain task (55% reduction) 38 The third question was designed to collect information on the interpretation of boundaries on a map (Figure 3.3). Regions may exhibit definite or indefinite (transitional) boundaries. The interpretation of the type of boundary is important to understanding the nature of a region and the precision with which a region can be defined. In this question, subjects were asked to describe the nature of the distribution by choosing an appropriate profile from several that most closely resembled that presented by a transect across the map. Variability of subjects' answers and their certainty ratings should indicate the transitional nature of the boundary. Maps with transitional boundaries may possibly exhibit greater variation in subjects' responses and lower certainty ratings. Lower Peninsula Michigan which profile 3,.“ Gypsy Moth Detonation Who. the pattern oi gypsy moth deiollatlon from Y to Z? Figure 3.3 Sample question for transitional boundary task (55% reduction) The fourth question was designed to collect information on the understanding of the internal variability of a region (Figure 3.4). Some phenomena are relatively evenly spread over an area, while others are differentially clustered within a region of influence. This question is similar to the second question in the logic of its construction; however, this question is used to examine the interpretation of intraregional (within) distribution rather 39 than the idea of areas of influence. In this question, subjects were asked to determine if the region exhibited constant (internal homogeneity) or varying (internal heterogeneity) intensity within its boundary by comparing several intraregional locations on the map. Lower Peninsula Michigan Gypsy Moth Detonation in which area would you expect to find the most deiollatlon? Figure 3.4 Sample question for internal variation task (55% reduction) The fifth question was designed to collect information on people's abilities to compare maps of similar regional distributions (Figure 3.5). These types of tasks are often used for establishing relationships between dilfcrent phenomena to help explain other distributions. In this question, subjects were asked to compare the two derivative maps to the original map and choose the one that most closely resembled the original map. The original map was placed at the top of the page and the two derivative maps below. Reaction times and variations in response times and certainty ratings were examined to determine the interaction of the maps for comparison tasks in which maps using similar representation methods are viewed. 40 Lower Peninsula Michigan Gypsy Moth Defollation Which map is "1°“ like the top map? Figure 3.5 Sample question for map comparison task (5 5% reduction) Three types of data were collected for each task: a response to the question, reaction time, and a certainty rating. Response accuracy was used primarily to judge whether a subject understood the information presented by the map. Reaction time was used as a surrogate masure for the cognitive cfl’iciency of the task. Additionally, the use of both reaction times and accuracy responses may help to explain potential errors. For example, if accuracy rates decrease as reaction times decrease, the data may reflect an increased willingness on the part of the subject to guess (Kosslyn and Holyoak 1982, 336). Certainty ratings were used as a nominal indieator of the difficulty subjects had with the task. Certainty ratings are often used as secondary data to strengthen or elaborate an analysis (Glanzer 1982), and they have potential uses for stratifying question difficulty and in checking for guesses. In addition to information from the test questions, background information was collected from subjects. This information included age, gender, major if they were a student or profession if they were a non-student, experience with maps, and whether or not 41 they worked with maps regularly. Finally, they were asked to define the term 'region'. In addition, as a cross check on the results of the test, subjects were invited to respond orally and informally to the test and to state whether they found any maps easier or more difficult to analyze. This allowed subjects to discuss the different types of symbols and their interpretation of these symbols; it may be possible that they "understand" the meanings of the symbols but may not be able to access this understanding in answering questions. Test 3313 Two test sets of 60 questions were prepared from the 120 map and question combinations in order to minimize subject fatigue and to keep the test to approximately 30 minutes in duration. A minimum of 30 responses was collected for each of the sets. Each subject saw all questions, representations, and distributions somewhere in the test, although they only responded to half the number of possible combinations. The questions in each test set were presented in random order for each subject. Random presentation was used to insure against problems of test ordering interactions. Procedure The experiment was administered to three subjects at a time using three Macintosh II computers and Apple 13" High Resolution RGB monitors. The tesr program was written in SuperTalk and run through SuperCard. The use of the computer facilitated the collecrion of subject reaction times for viewing (reading), tasking, and responding to questions; this information is not easily obtained with paper map tests. Reaction times are useful in accessing the cognitive 'efficiency' of representations and tasks, the rationale being that 'better' representations should lead to faster reading and response times. In addition, the software allowed for a random presentation order for each subject to counterbalance the effects of presentation order and learning. The test was designed to be approximately 30 minutes in duration. Each subject worked at his or her own pace. The test times ranged from 23 minutes to 42 minutes. The complete test procedure and script for administration are presented in Appendix A. At the start of the test, a general introduction to the test and description of the experimental procedure were presented to the subjects by the researcher. If the subject agreed to participate, s/he was asked to read and sign a consent form. Specific instructions on the operation of the computer and a set of practice questions to learn the procedure 42 followed. Once the subject was comfortable with the procedure, the test began. Subjects were given the option to repeat the practice set if they felt unsure of the procedure. Only one subject exercised this option. Each question required examination of the map followed by use of the mouse to point and click on the appropriate answer. The maps, questions, and answers were always presented in the same positions on the screen. Upon answering a question, a one—second pause was programmed into the test, then the screen blanked and a field appeared that presented the secondary question (Figure 3.6) that had the subject rank how certain s/ he was of the answer selected. Again, this question was answered by pointing to and clicking on an answer. How certain are you of your answer? Q Certain O Somewhat Certain 0 Somewhat Uncertain O Uncertain Figure 3.6 Sample question for certainty rating (5 5% reduction) Subjects were given short rests after the 20th and 40th questions to allow them to relax and defocus from the computer screen for a moment. During these breaks, subjects answered one of the questions on the background questionnaire. After completing the computer portion, subjects answered the final written question on defining a region. The 43 researcher then presented a more complete explanation of the test, and asked the subjects if they had any questions or comments on the test, and which maps they found most interesting. Finally, subjects were thanked and paid $5.00 for participating. Subjects Sixty—seven subjects were recruited from the University of Wisconsin — River Falls community with posters advertising a map reading experiment and indicating that subjects would be paid $5.00 for participating in the experiment. Thirty women and 37 men were tested ranging in ages from 14 to 59 years, with an average age of 27 years. Subjects included a mix of students, faculty, and staff; 84% were students and the remaining 16% were faculty and staff. The use of subjects from these subpopulations is justified because they are most likely to be engaged in learning regional information. Of those tested, 19% stated no regular use of maps in work and/or study and 81% stated that they used maps in either work and/or study. Sixty—six percent of the subjects came from disciplines in the college of Arts and Sciences, 31% from the college of Plant and Earth Sciences, and 3% from other categories. Geographers or geography majors composed 28% of the subjects. CHAPTER IV RESULTS AND DISCUSSION The first part of this chapter presents the overall results from the experimental project. The results and discussion of this section assess the overall quality of the data and discuss map distribution variations and task variations. The second part of this chapter examines the specific research questions and presents the results, analysis, and discussion task-by—task. Assessment of the performance on these tasks relies mainly on response accuracies and reaction times. The final section of this chapter summarizes the findings of the experimental project and discusses the role of maps in representing regional information and affecting spatial understanding. Overall Results of the Emerimental Project Responses from the 67 subjects were tabulated and scored for each question. Scores of zero (0) were assigned to correctly answered questions and of scores one (1) were assigned to incorrectly answered questions. In addition, standardized reaction times (z—scores) by subject were calculated for each question. The standardized reaction times allow comparisons within a subject to be made. The area estimation task involved a forced choice from among 8 percentages varying in 5% increments. Area estimates were judged to be correct if within 5% of the "best" answer from the choices; for example, if the "best" answer was 35% then answers of 30% to 40% were accepted as correct. For the core task, subjects were asked to identify the location where the distribution was most intense. Answers for the non-nominal maps were coded correct if the appropriate location was selected, and answers for the nominal maps were coded correct if subjects chose "Cannot tell". The domain task involved having subjects identify the areas where the distribution was present at a specified level, and answers for this task were coded as correct if the appropriate areas were identified. The surface / transitional boundary task involved having subjects pick a profile that most closely resembled a transect identified on the map. The internal variation task involved having 44 45 subjects identify whether or not the region varied in intensity within the area represented on the map. Finally, the comparison task had subjects choose the lower map that most closely resembled the upper map. The lower map with the closest correlation coefficient to the upper map was coded as the correct answer. Subjects also indieated how certain they were of their answer by selecting from four choices: certain, somewhat certain, somewhat uncertain, and uncertain. Since the question stated, "How certain are you of your answer?", people's responses to this question may be ambiguous, especially in the case of answers such as "Cannot tell" for the main question. The semantics of the rating question were awkward at best in this situation and therefore may have compromised some of the responses. Preliminary analysis of test version: A preliminary analysis was performed in order to determine whether or not the two versions of the test were samples from the same population and could be grouped together for analysis. The percent correct responses and an average reaction time for each subject were calculated. These composite scores were aggregated by test version for the 67 subjects (34 subjects took test Version I and 33 subjects took test Version II). Each version of the test was checked for normality by applying a Lilliefors test (Wilkinson 1989, 359). On the initial run using the percent correct and average reaction times by test version, the reaction times were not normally distributed. The reported probabilities (p < 0.05) for b0th accuracy and reaction time indicated that these samples departed from a normal distribution. Normal probability plots confirmed this departure and indicated that two extreme outliers in Version I and one outlier in Version II might be the cause. In Version I, one subject had completed the test in half the anticipated time (15 minutes) and the other had taken almost twice the anticipated time (49 minutes). The outlier from test Version II had exceedingly long reaction times, taking 62 minutes to complete the test. Notes taken during observations of the test sessions identified these subjects as "porential problems". These subjects were eliminated from the data sets and the Lilliefors tests were re—run. The reported probabilities (p > 0.05) on both reaction times (p > 0.837 and p > 0.065 respectively) and percent correct (p > 0.458 and p > 0.119 respectively) indicated that the data could be considered normally distributed, albeit weakly for the second group. The second step in this preliminary analysis was to determine whether or not the two versions represented overall responses from the same population. Independent sample t—tests were run on percent correct and on reaction time. For the first test, the null 46 hypothesis stated that the percent correct do not differ significantly between test versions, and for the second test it stated that the average reaction times do not differ significantly from one another. Results from the first t-test indicated no significant difference between accuracy of the two groups (p = 0.660), and the results from the second indieated no difference between reaction times of the two groups (p = 0.825). The null hypothesis that differences between the versions occurred due to chance was not rejected. Therefore, the results from the two test versions were aggregated. Variations in task question: and distributions Variations in task questions and map distributions were examined using question-by— question percent correct and mean reaction times by map types (Tables 4.1, 4.2 and 4.3). These were analyzed using AN OVA to determine if any biasing resulted from map types across all questions. Before performing the AN OVA, the data were tested for normality and homoscedasticity (equal variances) to determine if the assumptions for the procedure were met. A Lilliefors test was used to test for normality, and in all cases the data could be considered normally distributed (p > 0.05). A Bartlett's test was used to compare variances (Wilkinson 1989, 466), and in all cases the variances could be considered equivalent (p > 0.05). Therefore, a standard one-way AN OVA was performed on the data to determine if any significant differences occurred between maps across all distributions. The results from these tests for percent correct (p = 0.534), reaction time (p = 0.921), and standardized reaction times (p = 0.923) indicated no significant differences between map types across all questions. Table 4.1 Percent correct responses by question by map type Question Nominal Choropleth Isarithmic Continuous Dot 1. Area 87.5 52.9 65.3 66.7 25.8 2.1 Core 56.3 83.3 96.7 77.7 91.4 2.2 Domain 55.5 62.5 51.7 46.3 54.7 3. Transitions 50.8 59.5 59.5 70.8 57.8 4. Variation 84.4 89.2 98.3 96.7 75.0 5. Comparisons 31.3 68.6 82.6 78.3 56.3 Overall 60.9 69.3 75. 7 72. 8 60.2 47 Table 4.2 Mean reaction time in seconds by question by map type Question Nominal Choropleth Isarithmic Continuous Dot 1. Area 16.48 23.09 23.52 21.32 26.18 2.1 Core 17.99 14.20 12.84 16.15 11.87 2.2 Domain 18.82 21.62 21.91 22.71 23.58 3. Transitions 20.63 25.53 27.48 24.72 22.38 4. Variation 13.15 14.08 11.73 12.88 13.54 5. Comparisons 19.61 24.53 17.94 15.60 18.35 Overall 17. 78 20. 45 19.23 18.89 19.32 Table 4.3 Mean standardized reaction times in z—scores by question by map type Question Nominal Choropleth Isarithmic Continuous Dot 1. Area -0.245 0.427 0.437 0.200 0.641 2.1 Core -0.059 -0.442 -0.637 -O.287 -0.727 2.2 Domain 0.002 0.269 0.279 0.337 0.402 3. Transitions 0.144 0.566 0.860 0.530 0.374 4. Variation -0.573 -0.482 ~0.712 -0.637 —0.561 5. Comparisons 0.000 0.491 —0.1 15 —0.355 -0. 100 Overall -0. 122 0.138 0.019 -0.035 0.005 Differences in response accuracies were examined for each question by map distribution (Table 4.4) and by certainty estimate (Table 4.5). Percent correct responses by map distribution were examined to determine if the assumptions for AN OVA held using the procedure described above. The data were considered normally distributed and variances were considered equal. Results from the AN OVA indicated that differences in responses by distribution could be attributed to chance (12 = 0.331). The response accuracies were then examined in contrast to the certainty estimates and, in most cases, displayed a decrease in accuracy with a decrease in certainty. Normality and variances were checked and permitted the applieation of the AN OVA procedure. In this case, significant differences (p = 0.000) were detected in performance when grouped by certainty estimates. The uncertain and somewhat uncertain categories were seldom used. Furthermore, these ratings were used, more often than not, only when the response to the main question was also incorrect. 48 Table 4.4 Percent correct responses by question by distribution Question Afriea (%) Georgi: (%) Michigan (%) Rwanda (%) 1. Area 54.3 50.6 68.1 65.9 2.1 Core 86.9 68.1 86.25 82.5 2.2 Domain 46.9 66.9 66.25 33.6 3. Transitions 64.3 60.0 52.5 61.6 4. Variation 91.9 93.7 96.25 69.3 5. Comparisons 75.0 62.5 83.7 25.3 Overall 69.9 66.9 75.5 56.3 Table 4.5 Percent correct responses by question by certainty estimate Question Certain (%) Somewhat Somewhat Uncertain (%) Certain (%) Uncertain (%) 1. Area 60.0 63.6 48.4 41.4 2.1 Core 85.6 67.9 68.4 42.8 2.2 Domain 60.8 46.6 38.7 10.0 3. Transitions 67.4 54.5 48.9 37.5 4. Variation 94.5 74.3 53.3 25.0 5. Comparisons 69.12 60.6 45.2 44.4 Overall 72.9 61.25 50.5 33.5 To finish the preliminary analysis, the internal consistency of individual subject's responses were checked by running cross-tabulations for fifteen randomly selected subjects. The cross—tabulations were run for right and wrong answers by question and distribution and by question and map type. The purpose of this analysis was to determine if there were any biases in subject's responses by either the distribution or the type of map. No apparent biases were found in the responses for the subjects selected. Therefore, variation in subjects' responses do not appear to have been influenced by these factors. These results of the preliminary analysis are not surprising. Certain map use tasks are more difficult than others and yield longer reaction times, lower accuracies, and less certainty. In addition, different map types (e.g. choropleth, etc.) have characteristics that may make different tasks easier (or more difficult) than other map types. And finally, different map distributions vary in their spatial characteristics, therefore differences are to 49 be expected in performing the same tasks with different distributions. On an overall level, these results indicate that there are no systematic trends in using different map types across the different questions. No one map is best for all map use tasks tested. The overall response accuracies within questions indicate differences between map types with the different tasks. These differences are analyzed in the next section. Results and Discussion of Specific Map Reading Tasks The main objective of this portion of the analysis is to determine whether or not different representation methods afiect the understanding of regional distributions over the six sets of tasks outlined: extent, core, domain, transitions, variability, and comparisons. For this series of tests, the data were grouped by question by map type by map distribution. The cell values for Tables 4.6 - 4.11 are the mean percent correct responses (Score), mean reaction times in seconds (RT), and mean standardized reaction times in z—scores (SDRT). The overall N for each table is 20. Five separate analyses are used to assess the understanding of these aspects of regional distributions. The analyses of the tasks use methods similar to the ones described in the section above. The first step is to test the data for normality using a Lilliefors test, and the second step is to test for homoscedasticity using a Bartlett's test. The outcomes of these tests determine the appropriate form of the test for differences between map types. Since the problem is essentially one of different 'treatments', analysis of variance procedures (AN OVA) are utilized; if the data do not meet the requirements for AN OVA, the equivalent distribution—free Kruskal—Wallis test (\erlkinson 1989, 360) is appropriate to test for differences in performance on each of the five task areas. No significant difference (failure to reject the null hypotheses) in the tests would indicate that different representations do not lead to differences in understanding of regional distributions as defined by these aspects. Once the nature of the relationship between the map types is determined for a task, the specific a priori research hypothesis is tested for significance. First, the map types are grouped into two categories as specified by the research hypothesis (e.g., maps with definite edges and maps without definite edges) and then the significance of this interaction is assessed. In some instances, the research hypotheses were further refined to perform post Ivor: tests to account for alternate explanations of patterns in the test results. These were tested in the same manner as the original research hypotheses. The remainder of this 50 section reports the results from these analyses on a task—by—task basis and only notes modifications to the above procedures where necessary. Area extent task For the extent task, mean correct responses, mean reaction times, and standardized reaction times (Table 4.6) were normally distributed by map type (Lilliefors probabilities of p > 0.05) and the variances were homogeneous (Bartlett probabilities of p > 0.05). The results from the AN OVA were significant in all three cases: mean percent correct responses (p = 0.000), mean reaction times (p = 0.015), and mean standardized reaction times (p = 0.021). Therefore, the null hypothesis that there are no differences due to map types is rejected. This indicates that for area estimation there is a significant difference between these variables across different map types. Examining the data in Table 4.6 indicates that subjects performed better, having higher response accuracies and lower reaction times, using the nominal map type than they did using the other map types. The standardized reaction times for the nominal maps indicate that subjects on average performed much faster and that for dot maps, subjects performed much slower on this task. It is interesting to note that the choropleth and the dot map types were the two worst for this map use task. The contrasts between map types were then examined. The research hypothesis for this task stated that subjects would perform better on maps with definite edges to the regions (nominal, choropleth, and isarithmic) than with maps that lacked definite edges (continuous tone and dot). The results from this contrast test indicated that for response accuracy, there is a significant difiErence (p = 0.034) between the maps with definite edges and those without definite edges in performing area estimations. The results, however, failed to confirm that subjects would perform more rapidly on maps with definite edges (p = 0.201). Differences between standardized reaction times also were not significantly different (p = 0.252) between bounded and non—bounded regions. Upon examining the data to explain the results for the reaction times and standardized reaction times, it was observed that subjects performed well on all the continuous tone maps other than Georgia. The Georgia map, as it turns out, was the only truly continuous tone map used in the test. The other three distributions all used a neutral base color for the map and then applied gray continuous shading over this base color. In effect, this produced a defined boundary between the area defining the region and the base map. The result was a map with the qualities both of a nominal map defining the region and of a continuous tone map within the region. "Cartifacts" such as these are quite common in 51 Table 4.6 Results from area extent estimation task by map type Nominal representations Distribution N Score (%) RT (seconds) SDRT (z-score) Michigan 32 90.6 15.17 —0.346 Africa 32 90.6 17.93 —0. 177 Georgia 32 87.5 15.86 —0.238 Rwanda 32 81.3 16.98 -0.218 Overall 128 87.5 1648 —0.245 Choropleth representations Distribution N Score (%) RT (seconds) SDRT (z—score) Michigan 32 53.1 20.64 0.136 Africa 32 46.9 22.93 0.493 Georgia 32 50.0 24.61 0.592 Rwanda 25 64.0 24.52 0.502 Overall 121 52.9 23. 09 0. 427 Isarithmic representations Distribution N Score (%) RT (seconds) SDRT (z-score) Michigan 32 71.9 18.56 -0.082 Africa 32 59.4 22.71 0.422 Georgia 32 59.4 26.44 0.757 Rwanda 25 72.0 27.17 0.712 Overall 121 65.3 23.52 0. 437 Continuous tone representations Distribution N Score (%) RT (seconds) SDRT (z—score) Michigan 32 78.1 17.57 -0.121 Africa 32 68.8 21.62 0.214 Georgia 32 43.8 23.51 0.499 Rwanda 24 79.2 23.01 0.21 1 Overall 120 66. 7 21.32 0.200 Dot representations Distribution N Score (%) RT (seconds) SDRT (z-score) Michigan 32 46.9 18.95 0.067 Africa 32 6.3 25.16 0.515 Georgia 32 12.5 32.12 1.142 Rwanda 32 37.5 28.50 0.976 Overall 128 25. 8 26. 18 0. 641 52 map design and what may seem to be only an aesthetic decision may have an unintended impact on the interpretation of the map. A post [we test was performed to determine the nature of this interaction by regrouping the three edged-continuous tone maps together with the bounded maps and re—testing. The interactions indicated that the difference between grouped map types was significant (p = 0.000), the reaction times between grouped map types was significant (p = 0.045), and the standardized reaction times were not significant (p = 0.054), although the last one approached significance. Further contrasts were not examined statistically since it was likely that inter—distribution interactions were coming into play at this level since the Michigan map is a simpler map than the other three. After looking at the overall scores from these maps and the above analyses, I suggest that more than defined edges is involved in the determination of area estimates. Certainly edges are important; however, judging from the overall trends in the analysis, internal regional variability appears to disturb area estimates. The less cohesive and less contiguous the region, the more difficult area estimation tasks appear to be. Dot maps are particularly difficult for this task, and the other map types with internal variation are less satisfactory than the nominal map. These trends are corroborated by the reaction times and the standardized reaction times being significantly less for the nominal map; this indicates that over the five map types, area estimation is a reasonably easy task with the nominal maps and a more difficult task with the other map types. If one's primary goal is to communicate an understanding of the areal extent of a region visually, a nominal map type is best. In addition, combined map types, e.g., nominal and continuous tone, may improve the interpretation of the extent of a region. Core task For the core portion of the task, mean correct responses, mean reaction times, and mean standardized reaction times (Table 4.7) were checked for normality. Reaction times and standardized reaction times were normally distributed by map type (Lilliefors probabilities of p > 0.05) and the variances were homogeneous (Bartlett probabilities of p> 0.05). Mean correct responses were not normally distributed since they were skewed toward the higher percentages. Linear transformations failed to normalize this variable. Therefore, a nonparametric Kruskal-Wallis AN OVA was used to analyze mean correct responses, and it yielded no significant differences (p = 0.076) between map types in the analysis of variance across all map types. Therefore, on the surface, subjects performed as 53 Table 4.7 Results from core task by map type Nominal representations Distribution N Score (%) RT (seconds) SDRT (z—score) Michigan 32 78.1 14.81 0.365 Africa 32 40.6 20.78 0.133 Georgia 32 75.0 19.44 0.125 Rwanda 32 31.3 16.93 -0.131 Overall 128 56.3 17.99 -0. 059 Choropleth representations Distribution N Score (%) RT (seconds) SDRT (z-score) Michigan 32 100.0 1 1.09 0.683 Africa 32 100.0 10.54 0.834 Georgia 32 40.6 21.23 0.297 Rwanda 24 100.0 13.84 -0.585 Overall 120 83.3 14.20 -0. 442 Isarithmic representations Distribution N Score (%) RT (seconds) SDRT (z-score) Michigan 32 100.0 9.63 0.872 Africa 32 100.0 10.85 0.819 Georgia 32 87.5 17.24 0.252 Rwanda 24 100.0 13.88 -0. 593 Overall 120 96. 7 12. 84 -0. 637 Continuous tone representations Distribution N Score (%) RT (seconds) SDRT (z-score) Michigan 32 53.1 18.48 -0.031 Africa 32 93.8 10.85 —0.823 Georgia 32 71.9 22.11 0.196 Rwanda 25 96.0 12.33 -0.547 Overall 121 77. 7 16 15 -0.287 Dot representations Distribution N Score (%) RT (seconds) SDRT (z—score) Michigan 32 100.0 10.38 ~0.848 Africa 32 100.0 9.80 -0.926 Georgia 32 68.8 15.95 0.360 Rwanda 32 96.9 1 1.35 -0.774 Overall 128 91.4 1 1. 87 -0. 727 54 expected by being able to identify the central core on map types that have internal variability and by recognizing that the core areas could not be identified on the nominal map. AN OVA procedures were applied to test for differences between mean reaction times and mean standardized reaction times and were insignificant in both cases: mean reaction times (p = 0.247) and mean standardized reaction times (p = 0.132). Therefore, the null hypothesis that there are no differences due to map types is accepted. This indicates that for identification of core areas there is no significant difference between these variables across different map types. The overall data in Table 4.7, however, reveal that subjects took longer and performed less accurately using the nominal map. The standardized reaction times also reveal that while the core identification task was reasonably easy when compared with other tasks (reaction times were all well below average) that it was more difficult with the nominal map. The contrasts between map types were then examined. The research hypothesis for this task stated that subjects would perform better on maps with internal variation to the regions (choropleth, isarithmic, continuous tone, and dot) than with maps with no internal variation (nominal). The results from this interaction test indicated that there is a significant difference (p = 0.010) between the maps with internal variation and those without internal variation on the accuracy of responses. Performance with the nominal map was significantly worse than with the other map types. Subjects appeared to pick a location at the center of the region as the area of greatest intensity despite the lack of any cartographic evidence; some of this could be based on a willingness to guess or from previous exposure to the distribution with a different map type earlier in the test. Results from the reaction times failed to confirm that subjects performed more rapidly on maps with internal variation (p = 0.065). The times for the nominal representation of the data were the longest but only approached significance. Differences between standardized reaction times were, in contrast, significantly shorter (p = 0.036) for the maps with internal variation. This further supports the notion that there was more ambiguity in answering the core task with the nominal distribution than with the other map types and that determination of core areas is one of the easier tasks to perform. Internal variation within a region appears to play an important role in the identification of the core area of a distribution. Overall, the isarithmic and dot maps performed better than the choropleth and continuous tone maps over this set of distributions. Problems of tonal representation on an RGB monitor for the continuous tone maps could possibly account for poorer performance with this set of maps than with isarithmic and dot maps. 55 Monitors tend to "dump" the high values and low values of the range used. Furthermore, the actual range of the data had to be scaled to fit within the display range. In the maps on the displays, 16 values of gray were used. This approximated a continuous-tone display on the RGB monitors; however, the highest three tones and the lowest three tones formed two indistinguishable groups. This made the determination of variations in the low values virtually impossible, and may have impacted subjects' abilities to determine the darker areas of the maps when two or more of the choices were reasonably close in value. The distinctions made possible by classifying the data into fewer classes, as with isarithmic maps, apparently made these distinctions more obvious. Domain task For the domain portion of the task, mean correct responses, mean reaction times, and mean standardized reaction times (Table 4.8) were normally distributed by map type (Lilliefors probabilities of p > 0.05) and the variances were homogeneous (Bartlett probabilities of p > 0.05). The results from the ANOVA were insignificant in all three cases: mean percent correct responses (p = 0.177), mean reaction times (p = 0.394), and mean standardized reaction times (p = 0.487). Therefore, the null hypothesis that there are no differences due to map types is accepted, and thus there are no significant differences between the map types in representing the domain areas of regions. No formal research hypothesis was tested due to the resounding lack of significance in the first AN OVA. Examining the data in Table 4.8 indicates that the overall performance on this task was poor for all measures. There were a variety of interpretations as to what constituted the domain of a region. This question asked subjects to choose which of three zones designated on the map were experiencing significantly the phenomena that represented the region. In all cases, two of the three zones were within the main area of influence and the third was marginal. The low rate of response accuracy across the maps indicates that areas of influence are subject to variable interpretations. The outer marginal zone was often included in peoples' interpretations of the area of influence and suggests that people consider the boundaries as transitions. The higher—than-average standardized reaction times across the map types indicate that this was a harder task for the subjects and required greater cognitive processing of the map to make a judgment. In summary, the determination of a region's domain is highly subjective and suggests that a boundary defining the domain is transitional in nature. 56 Table 4.8 Results from domain task by map type Nominal representations Distribution N Score (%) RT (seconds) SDRT (z-score) Michigan 32 65.6 19.12 0.004 Africa 32 28.1 19.36 0.101 Georgia 32 96.9 15.46 —0.328 Rwanda 32 31.3 21.34 0.241 Overall 128 55.5 18. 82 0. 002 Choropleth representations Distribution N Score (%) RT (seconds) SDRT (z-score) Michigan 32 84.4 14.92 0.369 Africa 32 43.8 19.07 0.061 Georgia 32 68.8 25.41 0.752 Rwanda 24 50.0 28.89 0.756 Overall 120 62.5 21.62 0.269 Isarithmic representations Distribution N Score (%) RT (seconds) SDRT (z—score) Michigan 32 59.4 17.57 0.100 Africa 32 43.8 18.45 -0.065 Georgia 32 56.3 22.01 0.488 Rwanda 24 45.8 32.19 0.964 Overall 120 51.7 21.91 0.279 Continuous tone representations Distribution N Score (%) RT (seconds) SDRT (z-score) Michigan 32 56.3 20.46 0.067 Africa 32 40.6 19.36 0.100 Georgia 32 53.1 25.17 0.607 Rwanda 25 32.0 26.76 0.641 Overall 121 46.3 22.71 0.337 Dot representations Distribution N Score (%) RT (seconds) SDRT (z—score) Michigan 32 65.6 16.21 0.200 Afriea 32 78.1 17.46 —0.194 Georgia 32 59.4 32.01 1.083 Rwanda 32 15.6 28.64 0.917 Overall 128 54.7 23.58 0.402 57 Transitional boundary task For the transitional boundary task (Table 4.9), mean correct responses were considered normally distributed (Lilliefors probabilities of p > 0.05) with the exception of the continuous tone map (Lilliefors probabilities of p = 0.016). Mean reaction times and standardized reaction times were considered normally distributed by map type, and the variances were considered homogeneous for all variables (Bartlett probabilities of p > 0.05). A nonparametric Kruskal-Wallis was used to test for differences in responses due to map types since the continuous tone percent correct responses were not normally distributed and resisted transformations to normality due to three nearly identieal scores. Standard AN OVA procedures were used for mean reaction times and mean standardized reaction times. The results from the AN OVA were insignificant in all three cases: mean percent correct responses (p = 0.459), mean reaction times (p = 0.225), and mean standardized reaction times (p = 0.169). Therefore, the null hypothesis that there were no differences due to map types was accepted. There were no signifieant differences between the map types in representing transitional boundaries. The research hypothesis designed for this task stated that subjects would perform better on this task using the continuous tone and the dot maps. This research hypothesis failed to be accepted (p = 0.285) which is consistent with the results described above. This task had subjects choose the profile that "best" represented a transect on the map. Subjects needed both to understand the basic idea of a profile and to understand the implicit form of the data rather than the graphic form by which it was represented on the map. In this task, most subjects chose either the smooth or the stepped profile that showed increases in intensity near the core of a region. Subjects picked the stepped representations that "best" represented the data half as often as the smooth surface form for the same transect (56% vs. 27%). This confirmed that most subjects were familiar with the concept of a profile. They were less familiar, however, with the fact that surfaces represented on choropleth maps are actually smooth. The continuous tone map was most successful in communicating the idea of a smooth continuous surface. The response accuracy for the dot map was lower than for the isarithmic and the choropleth maps, and it is possible that this map was treated as a choropleth map because of the presence of enumeration boundaries on all maps. A post boc test of the continuous tone map against the others failed (p = 0.117) to produce a significant difference between the maps. Nominal maps were the worst for this task with the smooth profiles and the stepped surfaces being chosen nearly equally. 58 Table 4.9 Results from transitional boundary task by map type Nominal representations Distribution N Score (%) RT (seconds) SDRT (z-score) Michigan 32 46.9 24.77 0.440 Africa 32 31.3 20.95 0.235 Georgia 32 56.3 18.74 0.001 Rwanda 32 68.8 18.05 -0.101 Overall 128 50.8 20.63 0. 144 Choropleth representations Distribution N Score (%) RT (seconds) SDRT (z-score) Michigan 32 71.9 26.51 0.694 Afriea 32 65.6 20.40 0.245 Georgia 32 59.4 27.42 0.710 Rwanda 25 36.0 28.42 0.630 Overall 121 59.5 25.53 0.566 Isarithmic representations Distribution N Score (%) RT (seconds) SDRT (z—score) Michigan 32 56.3 29.08 1.135 Afriea 32 56.3 35.25 1.559 Georgia 32 59.4 19.87 0.1 10 Rwanda 25 68.0 25.21 0.575 Overall 121 59.5 27.48 0.860 Continuous tone representations Distribution N Score (%) RT (seconds) SDRT (z—score) Michigan 32 65.6 21.25 0.289 Afriea 32 84.4 28.35 0.925 Georgia 32 65.6 22.09 0.340 Rwanda 24 66.7 27.99 0.578 Overall 120 70.8 24.72 0.530 Dot representations Distribution N Score (%) RT (seconds) SDRT (z—score) Michigan 32 21.9 20.02 0.055 Africa 32 84.4 21. 52 0.375 Georgia 32 59.4 27.31 0.864 Rwanda 32 65.6 20.69 0.201 Overall 128 57.8 22.38 0.374 59 The standardized reaction times from this task revealed that it was by far the hardest. Reaction times within each subject were on average much longer for this task. This is probably a much different task than most of the subjects had ever encountered in using maps, and it may indicate the need to better educate people on how to interpret these types of maps rather than relying on raw eartographic intuition. Internal variation task The internal variation task was structured to test whether subjects perceived that the regions varied in intensity within the area of influence (Table 4.10). The data were not considered normally distributed across the mean percent correct responses; the isarithmic and dot map were both skewed (Lilliefors probabilities of p < 0.05) and could not be normalized due to an extreme value on the dot map and three equal scores on the isarithmic map. Furthermore, the variances between groups were nor considered homogeneous for mean correct responses (Bartlett probabilities of p > 0.05). Mean reaction times and standardized reaction times were considered normally distributed by map type and the variances were homogeneous for all variables (Bartlett probabilities of p > 0.05). A nonparametric Kruskal-Wallis was used to test for differences in mean correct responses by map types and standard AN OVA procedures were used for mean reaction times and mean standardized reaction times. The results from the AN OVA were insignificant in all three cases: mean percent correct responses (p = 0.137), mean reaction times (p = 0.891), and mean standardized reaction times (p = 0.851). Therefore, the null hypothesis that there were no differences between the means by map type was accepted. In this case, the null hypothesis indicated that subjects performed as expected on the map types. Subjects interpreted a lack of internal variation with the nominal representations and internal variation with the other representations consistently. Therefore, messages concerning intraregional variation are communicated by representations with internal variation and intraregional homogeneity by types without internal variation. The reaction times for this task were the shortest and were not significantly different from map to map. The overall standardized reaction times were well below average. The lower mean response accuracy for the nominal maps indicates again that some subjects may have guessed in answering this question or that they used prior knowledge from having seen a different version of the map before answering this question. The extremely low response accuracy for the dot map of Rwanda resulted beeause the three areas demarcated 60 Table 4.10 Results from internal variation task by map type Nominal representations Distribution N Score (%) RT (seconds) SDRT (z—score) Michigan 32 90.6 9.95 -0.856 Africa 32 75.0 16.59 —0.323 Georgia 32 93.8 1 1.46 —0.701 Rwanda 32 78.1 14.58 —0.410 Overall 128 84.4 13. 15 -0.5 73 Choropleth representations Distribution N Score (%) RT (seconds) SDRT (z—score) Michigan 32 96.9 12.61 -0.575 Africa 32 100.0 9.35 0950 Georgia 32 84.4 13.99 -0.430 Rwanda 24 70.8 22.48 0.199 Overall 120 89.2 14. 08 —0. 482 Isarithmic representations Distribution N Score (%) RT (seconds) SDRT (z—score) Michigan 32 100.0 10.56 —0.845 Africa 32 100.0 1 1.88 -0.726 Georgia 32 93.8 1 1.79 -0.587 Rwanda 24 100.0 13.01 -0.681 Overall 120 98.3 I I. 73 —0. 712 Continuous tone representations Distribution N Score (%) RT (seconds) SDRT (z—score) Michigan 32 100.0 10.89 0.793 Africa 32 93.8 13.87 -0.490 Georgia 32 96.9 12.23 —0.735 Rwanda 25 96.0 1 5.02 -0.500 Overall 121 96. 7 12. 88 0.637 Dot representations Distribution N Score (%) RT (seconds) SDRT (z-score) Michigan 32 93.8 9.72 -0.931 Africa 32 90.6 20.69 0. 120 Georgia 32 100.0 8.80 -0.986 Rwanda 32 43.8 14.95 -0.446 Overall 128 75. 0 13. 54 -0. 561 61 on the map had virtually an equal number of dots; this may have confused the map subjects. Overall, the map types were interpreted consistently with the symbolization used, and this map task was the easiest of the set. When the dot maps for Rwanda are removed, the isarithmic, continuous—tone, and dot maps were consistently interpreted as possessing internal variation. In contrast, the choropleth and nominal maps were more ambiguous. Map comparison task The final task involved a series of map comparisons (Table 4.11). Mean correct responses were considered normally distributed (Lilliefors probabilities of p > 0.05) with the exception of the continuous tone and isarithmic maps (Lilliefors probabilities of p = 0.010 and p = 0.026 respectively). These two maps were highly skewed due to low response accuracy on the Rwanda maps. Mean reaction times and standardized reaction times were considered normally distributed by map type and the variances were homogeneous for all variables (Bartlett probabilities of p > 0.05). A nonparametric Kruskal-Wallis was used to test for differences in responses due to map types beeause the isarithmic and continuous tone percent correct responses were not normally distributed. Standard AN OVA procedures were used for mean reaction times and mean standardized reaction times. The results from the AN OVA were insignificant for mean percent correct (p = 0.251) and significant for mean reaction times (p = 0.028) and mean standardized reaction times (,2 = 0.021). Therefore, the null hypothesis that there are no differences in accuracy due to map types in response accuracy is accepted. There were, however, signifieant differences in both mean reaction times and standardized reaction times which indicated that some of the maps were easier to work with than others. The a priori research hypothesis set out for this task stated that subjects would perform better using maps with definite edges (nominal, choropleth, and isarithmic) than with maps without definite edges (continuous tone and dot). The data were tested for the contrasts between these two groups. This research hypothesis failed to be accepted (p = 0.760). In addition, the contrasts were not significant for reaction times (p = 0.058) or for standardized reaction times (p = 0.051), although they were close. An examination of the data in Table 4.11 reveals that the logic of map comparisons being edge-based is flawed. Internal graphic variability appears to play an important part in the recognition of similar patterns. This is in apparent contrast to the psychological theories reviewed earlier that stated that recognition was edge—based and that surface characteristics were of secondary importance (Biederman 1987); but this probably reflects the fact that "recognition" is a 62 Table 4.11 Results from comparison task by map type Nominal representations Distribution N Score (%) RT (seconds) SDRT (z-score) Michigan 32 68.8 19.09 0.032 Africa 32 37.5 24.30 0.385 Georgia 32 15.6 19.56 —0.079 Rwanda 32 3.1 15.47 —0.272 Overall 128 31.3 19. 61 0.000 Choropleth representations Distribution N Score (%) RT (seconds) SDRT (z—score) Michigan 32 68.8 27.68 0.874 Afriea 32 50.0 29.20 0.872 Georgia 32 84.4 16.46 —0.217 Rwanda 25 72.0 24.86 0.421 Overall 121 68. 6 24.53 0. 491 Isarithmic representations Distribution N Score (%) RT (seconds) SDRT (z-score) Michigan 32 93.8 16.85 0.134 Africa 32 96.9 17.81 0.188 Georgia 32 93.8 18.13 -0.067 Rwanda 25 36.0 19.27 -0.061 Overall 121 82.6 17.94 0.115 Continuous tone representations Distribution N Score (%) RT (seconds) SDRT (z-score) Michigan 32 90.6 14.94 —0.402 Afriea 32 100.0 15.55 -0.360 Georgia 32 96.9 13. 50 -0.565 Rwanda 24 8.3 19.35 —0.007 Overall 120 78.3 15. 60 -0.355 Dot representations Distribution N Score (%) RT (seconds) SDRT (z—score) Michigan 32 96.9 1 5.42 —0.445 Africa 32 90.6 18.32 -0.102 Georgia 32 21.9 20.31 0.137 Rwanda 32 15.6 19.34 0.010 Overall 128 56.3 18.35 -0. 100 63 complex concept and is influenced by different features depending on the type of recognition. In addition, it was noted that performance with the Rwanda map across the map types generated unreasonably low response accuracies. Map size may have affected this question. All of the comparison maps were reduced to fit in the space available and the Rwanda maps had to be reduced the most. This reduction made comparisons between the Rwanda maps exceedingly diflicult for all map types, although it is interesting to note that for the choropleth map, the responses for Rwanda were second-to—best in accuracy and reaction times. Reduction also possibly impacted the dot maps in a different way by reducing them to randomly shaded choropleth maps, especially when the enumeration districts were small as in the Rwanda and Georgia maps. Reduction made the dots indistinguishable from one another. The Rwanda map was removed from the analysis because of the problem noted above, and the AN OVA procedures were re-run as a check on this interaction. This time the result indicated a significant difference between map types on this task across the variables. The mean percent correct responses were still considered abnormally distributed and heteroscedastic, so a Kruskal-Wallis AN OVA was applied and yielded a significant difierence between map types (p = 0.00). Mean reaction times and standardized reaction times remained significant (p = 0.049 and p = 0.045 respectively). A post has hypothesis was constructed to test the validity of the observation that surface— based recognition might be of greater importance than edge-based recognition. The contrast between those representations with well-articulated surfaces vs. edge-based representations was tested. In this case, because the dot maps appeared to perform as randomly shaded choropleth maps, they were grouped with the nominal and choropleth maps to form the edge-based representations and the isarithmic and continuous tone maps were grouped to form the surface-based representations. The presence of well-defined enumeration boundaries on all of the maps may have intensified this effect. The groups were tested for normality and homoscedasticity and met the requirements for AN OVA. The results from the AN OVA indicated that a significant difference (p = 0.012) existed between all the edge-based and surface-based representations, and therefore the null hypothesis of no difference was rejected. In addition, mean reaction times (p = 0.032) and standardized reaction times (p = 0.048) were both significant. Therefore, surface form appears to play a significant role in the comparison of different maps of the same type and in the time it takes to respond to the questions. The standardized reaction times indicate that map comparisons with the nominal and choropleth maps are average-to-hard tasks and 64 map comparisons with the isarithmic, continuous tone, and dot maps are easier—than- average map use tasks. The continuous-tone and isarithmic representations were the best once the impacts of the Rwanda maps were removed. The choropleth and dot representations performed well, and the nominal representations were generally unsuitable. Maps, Representations of Regional Information, and Spatial Understanding The results from this research have determined empirically that spatial understanding differs with different map types. No one map is best for communieating all the spatial aspects of a region. Five basic tasks were used in the experimental project: area estimation, determination of core and domain, interpretation of transitional boundaries, interpretation of internal variation, and comparison of maps. The response data collected consisted of response answers and reaction times, and these were grouped together to create three variables for analysis: mean percent correct responses, mean reaction times, and mean standardized reaction times by question by distribution by map. Responses were collected from 67 subjects, and the results from 64 of these subjects were used in the analysis. The results for the specific map use tasks are summarized in Table 4.12. Rankings of the map types for particular tasks are included as a general statement of the utility of different map types for each task. These should he approached as suggestions and only in consultation with the significance tests also cited in the table. Within these guidelines, the rankings suggest which map types are most appropriate (indieated in bold text) and which are least appropriate for particular types of tasks. In addition, the difiiculty of the tasks is ranked by standardized reaction times. One must realize, however, that some maps invite greater exploration and more careful inspection than others, resulting in an increase in response accuracy, so reaction times may not be a sole measure of the difficulty of the task or the map types. Results from the area estimation task confirmed that nominal maps were most appropriate. Response accuracy and reaction times confirmed that this map type has definite advantages over the others. Area estimation is a reasonably hard task using the other map types, perhaps due to the internal variation of the other representations. The use of a nominal form of representation in conjunction with other forms, such as the continuous tone, to demarcate the extra—regional areas may improve area estimates by sharpening the boundary but at the risk of negating the impact of the symbolism. Using a double-ended shading scheme would possibly accomplish a similar function but would 65 Table 4.12 Summary of research findings Tukl Hypothesis test results Suitabilityz Internal variation Overall: No signifieant differences for any measure. Isarithmic Continuous Ha: In this case, subjects correctly identified the lack or presence of Dot internal variation on the appropriate map types. Therefore, no Choropleth signifieance confirms an understanding of the representation. Dot _ representations rated excluding Rwanda maps (see text). Nom1nal Core Overall: No significant differences for response accuracy or Isarithmic reaction times. Subjects interpreted maps consistently with their D symbolization. 0t Choropleth Ha: Maps with internal variation were signifieantly better for Continuous response accuracy and standardized reaction times, but not for Nominal reaction times. Comparison Overall: Signifieant differences existed for reaction times but not Isarithmic for accuracy. Continuous Ha: No significant differences with maps having definite edges. Choropleth Dot Ha2: Surface-based maps were signifieantly better than edge-based Nominal maps for all measures. Domain Overall: No signifieant differences for any measure. Choropleth Nominal All of the maps expressed a similar, but variable zone of influence Dot for the region. Definite boundaries appear to neither hinder nor Isarithmic promote an understanding of this concept. . Continuous Area extent Overall: Signifieant differences existed between map types for both Nominal response accuracy and reaction times. isari I 'c Ha: Maps with definite edges were signifieantly better for response Continuous accuracy but not for reaction times. (In addition to definite edges, Choropleth low internal variability appears to aid this task.) Nominal maps ID . 0t possess a clear advantage for this task. Transitional Overall: No significant differences for any measure. Continuous boun . . dary Ha: No significant difference with the continuous-tone and dot Isarithm1c maps for any measure. Continuous—tone was best overall. Choropleth Dot 56% of the subjeCts identified correct shape and form and 27% Nominal identified correct shape and wrong form. The concept of smooth change appears to be difficult for subjects to identify and no one representation excelled in promoting an understanding of it. 1 Tasks are ordered from least difficult to most difficult by overall mean standardized reaction times. 2 Map types are listed from most suitable for the task to least suitable for the task. Map types listed in bold are considered suitable and those in roman text are considered generally unsuitable for the task. 66 likely be ineffective in creating a definite edge. The area extent task was the second hardest task for map readers in this test set. Results from the core and domain task indicated that internal variation promotes identifieation of core areas, whereas internal homogeneity inhibits their identification. Isarithmic and dot representations appeared to communicate this concept better. However, the data revealed that subjects performed most poorly with the nominal map in identification of core areas. The lack of internal variation in this representation makes it difficult to identify the area of greatest intensity. While subjects overall confirmed that they could not identify the core area, the lower response accuracy for the nominal maps was likely due to learning from a previous map or guessing. Much like ancient cartographers endowing maps with beasts in terra incognita, these maps may have hidden beasts of their own. The a priori research hypothesis was confirmed when the interactions of maps with internal variation were tested against the nominal map. This task was the second easiest for subjects. Performance on the domain task for all map types was poor. Subjects failed to correctly identify domain areas across all map types. The range of responses for this task was reasonably small and definite edges versus indefinite edges to the region did not lead to more consistent interpretations of a domain area. The dot maps performed almost as well as the nominal maps. If the maps are placed on a continuum of stepped to smooth (treating the dot map as a randomly shaded choropleth map), a trend toward lower accuracy along the continuum is recognized. This lack of agreement might be interpreted as an indication of the transitional nature of the "zone of influence". This portion of the task was slightly harder than average for subjects. Information about transitional boundaries was not represented best by any one map type. Subjects performed best using the continuous—tone maps, however this difference was not significant. Generally, the correct shape of the distribution was identified most of the time; one third of the subjects who answered the question correctly interpreted the distributions as a stepped surface, and two thirds interpreted the distributions as a smooth surface. This task relied on the subject's ability to disaggregate the data form from the graphic form, and the results indicate that the stepped graphic forms may mislead some map readers as to the nature of the distribution. This task was the hardest in the test set, and is one that would likely benefit from more explicit statements on how to interpret the information presented. The internal variation task asked subjects to determine whether the region was internally homogenous or variable. This task was similar in design to the core task; 67 however, all the areas that subjects compared fell "within" the region. The lack of significance on this test confirmed that subjects understood the maps as expected. (This is one of the unusual circumstances where one wants results to be statistically insignifieant.) The nominal map was consistently interpreted as homogeneous and the others as having a variable internal structure. In contrast to the core question, guessing did not seem to have played as much of a role in this question since the response accuracies were much higher for nominal maps in this task. This task was the easiest in the test set. Finally, the map comparison question yielded perhaps some of the most interesting results in the study, most of which were counterintuitive. Overall, signifieant differences existed in reaction times, but surprisingly, not for response accuracy. The results, however, were opposite those that I had proposed in the original research hypothesis, based on edge- differentiation being a critieal component for comparing maps. The maps with well- articulated surface forms were much easier for subjects to compare than were the simpler maps. When a post koc test was performed, the results indicated significant differences in the maps. In addition, map comparison was an easier task than expected. The standardized reaction times indicated faster-than—average performance when compared to the Other tasks for all maps except the choropleth map. During the testing procedures, several interesting behaviors were noted. First, almost half of the subjects were observed pointing at the screen using either the pointer or their fingers. Often they would trace areas on the map or point between several locations. Some subjects were observed to verbalize as in reading with their lips what they were seeing. One subject, who was tested alone, talked to himself throughout the test. His comments consisted of statements like "higher here, lower there" and so forth. Post—test comments indieated that many people "hated", in the words of one subject, the continuous tone maps. This was the only map that elicited consistent comments from nearly half of the subjects. They found it to be "confusing", "too vague", "unordered", "nasty", "ugly", and "difficult". Other subjects, however, had positive comments on the continuous tone maps. They appreciated the "view of the data" it afforded. Some of these reactions can be attributed to the novelty of these maps when compared with the more traditional cartographic forms. The "define the term 'region'" question elicited responses that covered the realm of definitions discussed in the literature review. They ranged from "an area on the face of the earth" to definitions of functional and formal regions based on a collection of defining characteristics. This range of definitions is to be expected, due to both formal and informal training and different definitions used in different disciplines. Subjects coming from the 68 natural sciences and colleges of agriculture often referred to a region as a "study area", whereas the subjects in the arts and social sciences had more articulate definitions. That definitions of regions vary is not surprising; that they form a continuum from general to specific, and that almost all of the subjects' definitions fit within the continuum lends support to the use of regions as a powerful means for conceptualizing space at a variety of levels, even among those with no formal training in geography. Different map types lead to differential understandings of regional information as confirmed by the results from the experimental project undertaken in this research. The fact that different map types lead to variations in responses and reaction times across several different map use tasks indicates that no one map is best for communicating all types of regional information. Each map type has certain graphic and cartographic characteristics that allow it to communicate different information to map readers. In addition, several different map types may be necessary to communieate all the types of information that may be needed to understand a spatial distribution. Fortunately, we now find ourselves with the abilities to produce several different types of maps from the same data with automated map production techniques and to more easily produce some maps such as continuous tone and dot maps that were quite time consuming to produce with traditional cartographic methods. With these tools we are also seeing a renaissance of the map in geographic analysis which is turning to maps as primary tools for geographic visualization. Regions do not exist until they are conceptualized to help explain processes that create variation over space, and they do not become visible until they are mapped. Once established, regions take on a life of their own and are continually used in analyses of spatial patterns. Understanding not only how regions are created but also how they are represented become critieal components of what we do as geographers and cartographers and will help us to better communicate our ideas to others. Mapping still provides one of the most satisfactory methods for this communication, and in so doing, we must articulate (dare I say "carticulate") our cartographic representations. CHAPTER V SUMMARY AND CONCLUSIONS In this dissertation, I have reviewed the geographic and eartographic literature concerning the representation of regional information and developed an experimental project to test the types of information human subjects receive from regional portrayals. The primary purpose of this research project was to determine if diflerent map types produce a differential understanding of regions, using five specific map reading tasks. In this final chapter, I address the significance of the results, issues concerning the methods and procedures used, and finally suggestions for future research. Signifieance of Results MacEachren (1991) questioned whether or not particular map symbolization methods actually communicate the particular spatial characteristics that we as cartographers associate with them. In the case of representing regions, a number of ideas concerning internal variation, core, and map comparisons are communicated adequately through some of the eartographic methods that we employ. Other concepts, such as domain and transitional boundaries, are more difficult to represent. Certain maps tend to be superior for one particular purpose, for example the use of nominal maps in area estimation, and misleading in other tasks, such as the differentiation of internal variation. The validation of these concepts across several different map tasks is unusual in the cartographic literature, and in some ways this dissertation has begun to answer some of the questions about what maps actually communicate through different symbolization. Furthermore, the results from this dissertation validate that no one map is best for communicating all information about regions. Monmonier (1991) questioned the validity of single map solutions, and this research lends support to the idea that multiple views of the data are highly desirable and should be employed to further our understanding of the spatial aspects of regions. With automated mapping and geographic information systems (GIS), we no longer are tied to a single map solution when several views can be created 69 70 efficiently. The same data are capable of supporting many different map forms. The use of maps as visualization tools is becoming exceeding important when large volumes of information must be processed and understood. Our perceptual systems are geared toward creating order and can often sense underlying patterns to which statistical and numerical analysis may not be sensitive. The use of sonification in cartography (Fisher 1994) is an example of a non—visual form of communication to identify patterns that may be difl’icult to summarize with Other methods. In some areas, the images and the understanding of the maps are stable. Muehrcke (1990) has suggested that stability of cartographic images may be a highly desirable trait for maps and that similar understandings should develop from different representations of the image. The perception of domain areas appeared to develop a stability of sorts since none of the maps performed better than the others in this task. However, the variation of the answers to this question was great. Subjects could not agree on the location of the area under the influence of or dominated by the phenomenon. In a sense, this created a transition between intra- and extra-regional zones, a true transition zone between inclusion and exclusion. Since all the map types performed in a similar fashion, they could be considered stable although they did not perform well in communicating concepts of domain. In several areas, the results from the research developed in this dissertation were counter-intuitive. The psychologieal literature pointed to the primacy of edge-based theories of perception. In the map comparison task, surface—based perception appeared to be in operation since subjects performed significantly better in recognizing the most similar patterns with representations that articulated the surface form of the data. This further suggests that the years we have spent simplifying our data to its most basic forms may in essence have done map readers a disservice. Finally, in working through the analysis it beeame apparent that there are no design choices for cartographic elements that are simply aesthetic. The use of a neutral background color, chosen for aesthetic reasons, appeared to play a major role in the success with which subjects could perform the area estimation task with the continuous tone map. This produced a redundant coding that aided the perception of the areal extent of the region. Ultimately, this sort of benefit is desirable. A second case arose in the inclusion of the boundaries for the enumeration units on the map. All enumeration boundaries were represented in black for the sake of consistency between maps and were graphically prominent on all of the maps. This may have had a corralling effect on the dots in the dot maps since these were most closely aligned with the choropleth maps in performance. In 71 essence, the dot maps may have been operating more as randomly shaded choropleth maps because of the boundaries rather than as a more continuous surface. Reflection on Methods and Procedures The empirical nature of the work has developed some baseline data by which five different map types convey an understanding of regions over a limited set of tasks. The relative merits of the various map types ean be compared to determine which cartographic method might be best for a particular purpose. The inclusion of different distributions, while replicating actual diversity in maps used to represent regions, presents a confounding influence in the analysis. Some distributions were much easier for certain tasks than others. This influence was left in the data and only removed when it was obvious that there was a problem, as in the map comparison tasks with the Rwanda map; the maps were just too small to be interpreted reliably. In spite of the "noise" created by these differences, the trends appeared to be consistent with cartographers' ideas about how these different map types should communicate regional information. In research of this type, a greater diversity of map distributions might be appropriate to avoid biases of learning. Each subject saw each distribution 15 times throughout the test. Subjects saw the same representation of the distribution three times. The maps were presented randomly for each subject. Ordering may have led to some guessing and learning interactions. Again, these were seen as part of the random cartographic background noise that exists in all map using situations. Prior knowledge will play a role and is one of the hazards of doing research of this type. Using a greater selection of maps would avoid some of these interactions. In addition, a greater diversity of color schemes should probably have been used for this experiment, and these schemes should have been rotated among the distributions used in testing. A single color plan was used for each distribution. The yellow-to-orange plan used for Michigan was definitely more legible than the other color schemes and the yellow—to— green scheme used for Rwanda was the least legible. The interactions of color schemes, distributions, and tasks could not be disaggregated. These "simply aesthetic" color choices may have played a role in the lower response accuracies for the set of Rwanda maps. On several of the questions, alternate structuring of the question could be used to better advantage. For the core and domain tasks, having the subjects point to the map and "click" on the locations of highest intensity and then digitize a line to where the phenomenon is at its lowest intensity would have provided more precision in the answers 72 for these questions. This approach, however, would rely on greater manual dexterity than most uninitiated computer users currently have, but with firture generations of computer literate users this method may be feasible. The use of large-screen high—resolution monitors would also have improved the clarity of the maps in the comparison questions. The use of an overtly empirical method is justified to gather baseline data; however, more subjective approaches may yield a much richer form of information on peoples' understandings of regional information from maps. The use of interview techniques where people are asked to explain the map symbol(s) used to portray a region would yield a greater understanding of what people are actually internalizing when they view a map. I noticed numerous times during the testing that subjects would point to different areas on the maps as they looked at the displays and trace areas with the pointer or point at the screen with their fingers. Several of the subjects were verbalizing what they were seeing as they pointed to the maps, much the way people learning to read will often move their lips while reading. This information could provide a much deeper understanding of the tasks and how people decode the map's symbols into an understanding of the region. Suggestions for Future Research The findings from this study suggest several potential areas for fiiture research that extend both into questions of map design for representing regions and into questions of interpreting an understanding of regional information presented with maps. Lewis (1991) has challenged cartographers to develop new techniques for the representation of regions. Understanding our current techniques provides a foothold for such studies. The extension of this work to consider other conventional forms for presenting regional information is justified since graduated point symbol maps and unit grid maps are also used to develop ideas of regional patterns over space. The unit grid maps have a remarkable ability to represent all forms of data from discrete to continuous in a raster format, and fortunately they are easily processed by cell-based geographic information systems. Very little research has investigated the power of these representations. These map forms may provide a basis for the better understanding of cohesiveness, internal variation, and transitions, far beyond the capabilities of more conventional map types. The impact of combining map types on representations of regional information is an interesting question. As noted with the use of a neutral background on several of the continuous tone maps in this study, the background acted as a redundant nominal coding that enhanced the edge of the region thereby making the maps more effective for the area 73 estimation task. The use of dot maps with other map forms such as choropleth maps has some tradition in state atlases in the United States. The combination of these techniques needs to be inveStigated to see if the equivalent of cartographic multiplier effects could be developed by creating cartographic understanding greater than the sum of the component parts. The role of boundaries was noted as problematic, and this is an additional design area that needs to be investigated to determine the impact they have on the interpretation of different map symbols. The noted impacts on the dot map are a case in point. The role of boundaries also extends into questions of interpretation. When a boundary represents two different regions (for example a tax region vs. a culture region), are the boundaries interpreted in the same way or differently? These questions of interpretation may be context—based, and the same symbols may represent various nuances of meaning. Finally, extension of this research should examine the use of maps of regions in decision—making processes. This includes the impact that representations have on the processing of regional information both by experts and within the context of geographic information systems. The question is, do different types of maps yield differential ideas about regions and how they are defined by the people and activities that occur within their "confines"? This echoes the question raised by Lewis (1991) and Gore (1984) of whether regions should be treated as containers of human activity or expressions of human activity and relatedness. Ultimately, maps are abstractions of reality and as such cannot take on all the various interrelations that lend credence to their creation. Understanding the differences between the map used to represent the reality and the reality itself is critical. Ultimately, this project has confirmed some of what we thought and yielded several avenues for future research. The appropriate choice of map type is an important determinant in peoples' abilities to work with the information presented. Care must be exercised in symbol selection in order to represent that which we desire to communicate. APPENDICES APPENDD( A EXPERIMENTAL PROCEDURES The procedures for the experimental project are described in this appendix. I administered the test and used this script to insure that all subjects received the same introduction and instructions. Actions are indicated with roman text and spoken portions of the procedure are indicated with italic text. Room Preparation Turn on lights and computers Put chairs in waiting area Place sign "Welcome Map Readers" on door Test Set Preparation Shuffle SuperCard project to randomize presentation order Prepare clipboard and pen with forms in the following order: Consent form Background questionnaire (write subject ID number on questionnaire) Cash receipt form Introduction Welcome subject(s) Hello (subject name6s)). Thank youfbr volunteering to participate in this experiment. My name is Charlie Rader. Flip sign on door to "Experiment in Progress" Please, have a seat here. Offer subject chair near the computer Introduction to the Experiment You are participating in a map reading experiment that will improve our understanding of how people understand map symbols. You will look at a series of maps 74 75 on the computer screen and answer a question fir each map. We will start with a practice session to help you learn how to use the computer, but all you really need to do is point with the mouse and click on an answer. You will get a couple of breaks during the experiment. During these breaks, you will answer some questions about your experience with maps. The computer portion of the test takes about 30 minutes. Are you still interested in participating? Bq‘bre we start, could you please read and sign this consent firm. It is required by the university fbr ameriments involving people. Hand clipboard and pen to subject Collect consent form(s) Do you have any further questions bcfbre we begin? Setup Computer Let's set up the computer for you. Roll your chair so that you can comfirtably see the computer screen. Howls the height of the chair? You can adjust the height with the lever on the right side of the seat. Have you ever used a mouse bq‘bre? Are you [4} or right handed? Adjust mouse pad and center mouse Howls that? You can reposition the mouse on the pad by picking it up and moving it back on the pad if you run of the edge. Practice I have a series of 12 practice maps for you to try. As you work through these you can practice using the mouse and you can see the type of questions that you will be asked to answer. You can ask me any questions that you may have. Point to and press the 'Start Practice' button on the screen. Point to the different elements on the screen as you show the subject the first card Each display will look something like this one. A map will appear on the lefi‘ and a question will appear on the right. Read the question first and then look at the map. 76 Answer the question to the best of your abilities. Point and click in the circular button by the answer you want to select. A black dot will appear in the middle of the circle to show you that it is selected. Try to answer questions as accurately and quickly as you can. Afier answering that question a secondary question will appear and you will rank how certain you were of your answer. Try a few of these to get the hang of it. Let me know if you have questions. After six cards, the 'test break' card will appear In the real test, every twenty cards this break message will appear. This will give you a chance to rest your eyes, change your position, and such. You will also answer some background questions during these breaks. Fill out the first question on the firm on the clipboard now. Do you have any questions? Continue to the end of the practice Do you want more practice were you start the test? Do you have any questions? Test O.K. , click on the 'Start Test' button to begin the real test After the 20th card, the break card appears. Subject answers question on occupation. 0.16 now point and click in the break box to start the test again After the 40th card, the break card appears. Subject answers question on their experience with maps. OK now point and click in the break box to start the test again After the 60th card, the end of test card appears. Subject answers question on regions. Congratulations, you 're almost done Post—Test Interview Do you have any questions on this map reading test? Did you find any of the maps easier or more dtflicult? 77 Write subject's comments and explanations on post-test interview form. Now, let me tell you a little more about this experiment. I am using this experiment to examine the meaning of map symbols commonly used for representing regions and how people interpret the difth types of symbols. 1 am interested in determining the impact that map symbolization has on peoples 'ability to determine the relative extent of an area, and the interpretation of core or central areas, transitional boundaries, internal variability, and comparisons of maps. In this work, I am trying to explain how maps can more efiéctively communicate these diflerent ideas and ultimately better communicate our understanding of regions to map users. Do you have any comments or questions about the test? Record comments on form or provide more explanation Thank you again firr taking the time to do this test. As advertised here is the $5fir participating. I need you to sign the receipt showing that you received the money. Give subject $5 and have them sign receipt. Keep one copy of receipt for records. Thank you again. Have a good day now. Post-Test Procedures File consent form, background questions, and post-test interview. Back—up data file to floppy disk. Flip sign on door to "Welcome map reader" side and prepare for next subject. Sample Consent Form and Background Questionnaire A copy of the informed consent form and background questionnaire used in this study appear on the next two pages. Approval for the use of human subjects in research for this study were obtained from both Michigan State University (IRB# 93—310) and University of Wisconsin — River Falls (Protocol # H9495—7). 1) 2) 3) 4) 5) 6) 7) 78 Consent to Participation in Human Research Study Project Title: Representing Regions: Geography, Cartography, and Spatial Understanding Charles P. Rader, Instructor in the Geography Department at the University of Wrisconsin - River Falls is conducting this study on map reading that involves the eartographic representation of regions. I would appreciate your participation in this study, as it will help to determine how to improve map design. As part of this study, I would like you to take a map reading test that takes 25 to 35 minutes to complete. The procedure will involve having you view a series of maps on a computer screen and answer a series of questions about the maps. During the test you will be given two brief breaks to rest your eyes and I will ask you several background questions concerning your experience with maps. Sixty map readers will participate in this study. Your participation in this study will not present any risks to your physieal or mental well being. Your participation in this study does not guarantee any beneficial results other than perhaps a heightened awareness of different types of maps. You understand that you will be paid $5 if you complete the test. The method that I am using is consistent with eartographic testing for deriving data of the type needed for map reading studies and involves standard, aeademic testing methods. The information gathered from your participation will be anonymous and held in strict confidence. Your responses will be grouped with the responses from the other subjects for analysis and any reporting on the results of this experiment. Within these limits the results of your participation will be available [0 you upon request. Your participation is on a voluntary basis and you are free to discontinue participation at anytime during the testing without penalty. Payment of the $5 will be forfeited if you choose not to continue the test. The information collected from you at that point will be destroyed. A more complete explanation and the results of this study will be made available upon your request. Should you have questions please contact: Charles P. Rader Geography Department University of Wisconsin - River Falls River Falls, Wisconsin 54022 (715) 425-3264 If you have complaints about your treatment as a participant in this study, please contact: William E. Campbell, Chair Institutional Review Board for the Protection of Human Subjects University of Wisconsin - River Falls River Falls, Wisconsin 54022 (715) 425—3195 I have received an explanation of the study and agree to participate. Name Date This research project has been approved by the University of Wisconsin - River Falls Institutional Review Board for the Protection of Human Subjects. 79 Background Information Subject Number Representing Regions: Geography, Cartography, and Spatial Understanding. Age Gender (M IF) 1. What is your occupation (or your major, if a student)? Professional _ Student 2. Do you use maps in your work or studies? Yes _ No _ If so, what types of maps do you use? 3. Have you ever studied a region in a course? Yes No How would you define a region? APPENDD( B EXPERIMENTAL MAP DISPLAYS This appendix includes reduced black and white versions of the test question displays. These are arranged by distribution and map type. All six eards for each distribution and map type appear on the same page. The answers accepted as correct for each display can be found in Table B] by question number. Question numbers are found in the upper left- hand corner of each map. The corresponding button for the correct answer is listed in the table. Buttons are arranged from 1 to n from top to bottom for each question. Since the original map displays were in color, descriptions of the colors used for each distribution and map type are provided in Table B.2. All colors were selected from the default Macintosh 256 color palette. 80 81 Table B.l Correct answers by test version Version I - Question Button Number Version 11 - Question Button Number 111.000 7.000 112.000 1.000 113.000 6.000 114.000 3.000 122.000 3.000 121.000 6.000 124.000 4.000 123.000 5.000 131.000 6.000 132.000 3.000 133.000 3.000 134.000 4.000 142.000 3.000 141.000 6.000 144.000 4.000 143.000 2.000 151.000 8.000 152.000 1.000 153.000 1.000 154.000 1.000 212.100 3.000 211.100 2.000 212.200 1.000 211.200 2.000 214.100 5.000 213.100 5.000 214.200 1.000 213.200 3.000 221.100 6.000 222.100 6.000 221.200 2.000 222.200 1.000 223.100 6.000 224.100 6.000 223.200 3.000 224.200 1.000 232.100 3.000 231.100 3.000 232.200 1.000 231.200 2.000 234.100 5.000 233.100 5.000 234.200 1.000 233.200 3.000 241.100 2.000 242.100 3.000 241.200 2.000 242.200 1.000 243.100 5.000 244.100 5.000 243.200 3.000 244.200 1.000 252.100 3.000 251.100 2.000 252.200 1.000 251.200 2.000 254.100 5.000 253.100 5.000 254.200 1.000 253.200 3.000 311.000 1.000 312.000 3.000 313.000 4.000 314.000 4.000 322.000 1.000 321.000 4.000 324.000 4.000 323.000 6.000 331.000 3.000 332.000 3.000 333.000 6.000 334.000 5.000 342.000 5.000 341.000 4.000 344.000 3.000 343.000 2.000 351.000 5.000 352.000 4.000 353.000 4.000 354.000 6.000 412.000 2.000 411.000 2.000 414.000 1.000 413.000 1.000 421.000 4.000 422.000 4.000 423.000 4.000 424.000 4.000 432.000 1.000 431.000 2.000 434.000 1.000 433.000 1.000 441.000 2.000 442.000 3.000 443.000 3.000 444.000 1.000 452.000 2.000 451.000 2.000 454.000 1.000 453.000 1.000 511.000 1.000 512.000 2.000 513.000 1.000 514.000 1.000 522.000 2.000 521.000 1.000 524.000 1.000 523.000 1.000 531.000 1.000 532.000 2.000 533.000 1.000 534.000 1.000 542.000 2.000 541.000 1.000 544.000 1.000 543.000 1.000 551.000 1.000 552.000 2.000 553.000 1.000 554.000 1.000 82 Table B.2 Colors used on map displays Michigan - Gypsy Moth Defoliation None Background Symbolization Nominal pale taupe orange Dot pale rage orange Africa - Adherents of Islam Few or None Low Moderate High Very High Choropleth pale yellow green yellow olive medium green dark gr_een Isarithmic pale yellow reen yellow olive medium green dark green Continuous pale yellow “My medium gray dark gray black Background Symbolization Nominal pale yellow medium green Dot pale yellow medium green Georgia — Agricultural Production (dollars per acre) 0 - 20 21 - 35 36 - 80 81 — 135 136 - 378 Choropleth pale yellow tan light brown medium brown dark brown Isarithmic pale yellow tan [git brown medium brown dark brown Continuous white lkgh_tgray medium iray dark gray 1 black Backgound Symbolization Nominal tan medium brown Dot tan orange Rwanda - Banana Production (kilograms per hectare) 0 - 360 361- 980 981 - 1540 1541- 2150 2151 - 5840 Choropleth pale yellow pale taupe green gray ween medium jreen Isarithmic pale yellow pale taupe green gray gr_ay_green medium Keen Continuous pale taupe May mediueray dark gray black Backggund Symbolization Nominal pale taupe gray green Dot pale taupe medium green 83 Figure B.l Choropleth map test displays of Michigan (Reduced 33% - Original displays in color) 84 Figure B.2 Nominal map test displays of Michigan (Reduced 33% - Original displays in color) 85 us else lli§ llama awn: can a... R . _ sewn“ .3- none . smears Vl'a My awn .5 fi sea agen— B.3 Isarithmic map test displays of Michigan Original disp ays in color) (Reduced 33% - Figure 86 lays of Michigan in color) test disp Original) displays Figure B.4 Continuous-tone ma (Reduced 33% 87 m n M m gan lor) p test displays of Michi Original isplays in co B 5 Dot ma uced 33% ”in: 88 Figure B.6 Choropleth map test displays of Africa (Reduced 33% - Original displays in color) 89 Figure B.7 Nominal map test displays of Africa (Reduced 33% - Original displays in color) 90 Figure B.8 Isarithmic map test displays of Africa (Reduced 33% - Original displays in color) 91 lor) lays of Afriea ma test disp Original displays in co Figure B.9 Continuous-tone (Reduced 33% 92 Figure 3.10 Dot map test displays of Afriea (Reduced 33% - Original displays in color) 93 Figure 8.1 1 Choropleth map test displays of Georgia (Reduced 33% - Original displays in color) 94 ' 3% is; 499° nastier, saga assess if?” 339%? Figure B.12 Nominal map test displays of Georgia (Reduced 33% - Original displays in color) 95 Figure B.13 Isarithmic map test displays of Georgia (Reduced 33% - Original displays in color) 96 =5 fie 3% 93%“??? -.99’$2§ ‘ N & Figure B.14 Continuous—tone map test displays of Georgia (Reduced 33% - Original displays in color) 97 4 firm “3%? ’8' $31.35* 0:3“ - £291? 5; . . iris» Var-i. - a firs Tsi’iwfin «5% sw- z'ss' .4 ssfiafliw fid?§&§fi& a a “'3 aga’égéiv a; 9" 7%.. w aw? flsfiw“ a “gaff: g? Q Fi B.15 Dot map test displays of Geor ia (Reduced 33% - Original displays in c0103 98 Figure B.16 Choropleth map test displays of Rwanda (Reduced 33% — Original displays in color) s swag A 39.? so as. 9 swarms 99 B.17 Nominal map rest displays of Rwanda Original displays in color) Reduced 33% ( F' 100 Figure B.18 Isarithmic map rest displays of Rwanda (Reduced 33% - Original displays in color) 101 Figure 8.19 Continuous—tone map test displays of Rwanda (Reduced 33% - Original displays in color) 102 al displays in color) Origin B.20 Dot map test displays of Rwanda uced 33% ‘53 Q. s; . ms: SW. . .. .7 Knmfiwgfidwmy‘, . «WWW am A , APPENDIX C MERIMENT AL DATA This appendix contains listings (Table C. 1) of the experimental data by subject. The listings are sorted by question number; however, information on presentation order is included. Data for three subjects who were excluded from the analysis due to abnormally long or short response times are listed but marked with an asterisk to indicate that they were not used. These subjects are numbers 125, 1102, and 2202. In addition, the first 15 subjects received test versions with mislabeled legends on the choropleth, isarithmic, and continuous—tone maps of Rwanda. These questions were removed from the test sets. Subject numbers starting with a one took Version I and subject numbers starting with a two took Version II of the test. A description of the data collected follows: Card Resp RT Conf Order SdRT Card number identifier for task, map type, and distribution. The button number selected by the subject as the correct answer. Reaction time for question in tics (1/60 second). Calculated by SuperCard as the difference between the time a card appeared and the time an answer was selected. The certainty rating selected by the subject for the question: 1 - Certain, 2 — Somewhat certain, 3 - Somewhat uncertain, and 4 — Uncertain. Order in which the card appeared in test presentation. Button number of the correct answer for the question. Task numbers used in stratifying analysis by question: 1 - Area , 2.1 — Core, 2.2 - Domain, 3 - Transitional boundary, 4 — Internal variation, and 5 — Comparison. Map type identifier used in stratifying analysis by representation method. Distribution identifier used in stratifying analysis by distribution. Right or Wrong calculated by subtracting correct from response. Zeros indicate a correct answer; numbers less than or greater than zero indicate a wrong answer. Standardized scores (z-scores) of reaction time for the subject by question. 103 104 Table C.l Test results by subject - Subject 110 Card 1 1 1.000 1 13.000 122.000 124.000 131.000 133.000 142.000 151.000 153.000 212.100 212.200 221.100 221.200 223.100 223.200 232.100 232.200 241.100 241.200 243.100 243.200 252.100 252.200 254.100 254.200 31 1.000 313.000 322.000 324.000 331.000 333.000 342.000 351.000 353.000 412.000 421.000 423.000 432.000 441.000 443.000 452.000 454.000 511.000 513.000 522.000 524.000 531.000 533.000 542.000 551.000 553.000 Resp. 6.000 3.000 3.000 1.000 5.000 1.000 1.000 6.000 4.000 3.000 1.000 6.000 RT 1231.000 1427.000 533.000 696.000 754.000 1092.000 873.000 871.000 901.000 347.000 720.000 1007.000 544.000 797.000 738.000 427.000 715.000 1007.000 890.000 816.000 1035.000 281.000 1680.000 469.000 1009.000 1359.000 71 1.000 843.000 657.000 1934.000 650.000 1034.000 985.000 1 164.000 253.000 229.000 712.000 481.000 413.000 380.000 646.000 370.000 1 169.000 842.000 533.000 1213.000 576.000 604.000 724.000 599.000 859.000 Conf. 1.000 1.000 1.000 1.000 1.000 1.000 1.000 2.000 2.000 1.000 1.000 1.000 1.000 2.000 1.000 14.000 51.000 19.000 45.000 56.000 Cor. 7.000 6.000 3.000 4.000 6.000 3.000 8.000 Dist SdRT 1.224 1.780 —0.758 -0.295 -0.131 0.829 0.207 0.202 0.287 -1.286 —0.227 0.588 -0.727 —0.009 -0. 176 —1.059 —0.241 0.588 0.256 0.045 0.667 -1 .474 2.498 -0.940 0.593 1.587 -0.253 0.122 —0.406 3.220 -0.426 0.525 1.033 -1.553 —1.621 ~0.250 -0.906 -1.099 -1 . 192 -0.437 -1.221 1.048 0.119 0.758 1.173 -0.636 -0.556 -0.216 0.571 0.168 Table C.l (continued) — Subject 111 Card 11 1.000 1 13.000 122.000 124.000 131.000 133.000 142.000 151 .000 153.000 212. 100 212.200 221.100 221.200 223.100 223.200 232.100 232.200 241 . 100 241.200 243.100 243.200 252.100 252.200 254.100 254.200 31 1.000 313.000 322.000 324.000 331.000 333.000 342.000 351.000 353.000 412.000 421.000 423.000 432.000 441.000 443.000 452.000 454.000 51 1.000 513.000 522.000 524.000 531 .000 533.000 542.000 551.000 553.000 Resp. 5.000 5.000 2.000 5.000 6.000 3.000 2.000 7.000 5.000 3.000 4.000 2.000 7.000 5.000 3.000 3.000 4.000 2.000 4.000 5.000 3.000 3.000 4.000 5.000 6.000 1.000 4.000 1.000 4.000 RT 454.000 1618.000 552.000 969.000 663.000 657.000 621.000 447.000 866.000 417.000 1239.000 540.000 1 110.000 517.000 462.000 313.000 404.000 699.000 723.000 854.000 745.000 309.000 402.000 327.000 21 1 1.000 860.000 545.000 1332.000 1223.000 1081.000 499.000 686.000 951.000 519.000 509.000 387.000 1 163.000 433.000 341.000 535.000 41 1.000 620.000 1026.000 440.000 809.000 499.000 746.000 594.000 528.000 611.000 436.000 Conf. 2.000 2.000 2.000 2.000 Order 20.000 6.000 21.000 17.000 45.000 55.000 56.000 25.000 39.000 4.000 24.000 19.000 2.000 38.000 59.000 42.000 49.000 13.000 1 1.000 8.000 14.000 36.000 50.000 37.000 12.000 9.000 43.000 3.000 30.000 7.000 57.000 46.000 22.000 58.000 32.000 60.000 26.000 51.000 34.000 16.000 35.000 5.000 28.000 53.000 48.000 33.000 1.000 10.000 23.000 18.000 29.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 2. 100 2.200 2.100 2.200 2. 100 2.200 2. 100 2.200 2.100 SdRT -0.693 2.559 —0.419 0.746 -0. 109 -0. 126 -0.226 -0.713 0.458 -0.796 1.500 —0.453 1.140 -0.517 —0.671 -1.087 0.833 —0.008 0.059 0.425 0.120 -1.098 —0.838 —1.048 3.937 0.441 —0.439 1.760 1.456 1.059 —0.567 -0.045 0.696 —0.51 1 .0539 -0.880 1.288 .0352 —1.009 —0.467 -0.813 -0.229 0.905 -0.732 0.299 —0.567 0.123 —0.302 —0.486 —0.254 —0.743 Table C.l (continued) - Subject 112 Card 11 1.000 1 13.000 122.000 124.000 131.000 133.000 142.000 151.000 153.000 212. 100 212.200 221.100 221.200 223. 100 223.200 232. 100 232.200 241.100 241.200 243.100 243.200 252.100 252.200 254.100 254.200 31 1.000 313.000 322.000 324.000 331.000 333.000 342.000 351.000 353.000 412.000 421.000 423.000 432.000 441.000 443.000 452.000 454.000 51 1.000 513.000 522.000 524.000 531.000 533.000 542.000 551.000 553.000 Resp. 6.000 6.000 4.000 5.000 8.000 5.000 4.000 RT 1216.000 1972.000 1309.000 1040.000 1024.000 1457.000 931.000 882.000 5162.000 584.000 1 135.000 627.000 1954.000 1365.000 1830.000 866.000 1 188.000 880.000 1 136.000 1664.000 2142.000 704.000 1 178.000 702.000 2136.000 2348.000 2141.000 1880.000 729.000 636.000 1550.000 1937.000 151 1.000 1287.000 792.000 801.000 676.000 1 141.000 1022.000 1270.000 1546.000 792.000 1517.000 2220.000 1671.000 1448.000 1016.000 1430.000 1690.000 1376.000 1639.000 Conf. 3.000 2.000 3.000 2.000 2.000 3.000 3.000 3.000 3.000 1.000 Order 25.000 32.000 19.000 12.000 14.000 30.000 20.000 34.000 7.000 16.000 40.000 55.000 1.000 37.000 2.000 23.000 35.000 39.000 10.000 38.000 13.000 28.000 9.000 46.000 60.000 58.000 56.000 21.000 36.000 52.000 24.000 1 1.000 50.000 31.000 15.000 48.000 59.000 33.000 53.000 49.000 8.000 54.000 51.000 44.000 45.000 29.000 57.000 18.000 4.000 22.000 3.000 106 Qua 1.000 1.000 1.000 SdRT -0.249 0.801 -0. 120 -0.493 -0.515 0.086 -0.712 5.230 -1. 126 -O.361 -1.067 0.776 0.042 -0.735 —0.288 -0.715 -0.360 0.373 1 .037 -0.960 -0.301 -0.962 1.029 1 .323 1.036 0.673 -0.925 -1.054 0.215 0.752 0.161 -0.150 -0.837 —0.825 -0.998 -0.353 -0.518 —0. 174 0.210 -0.837 0.169 1.145 0.383 0.073 -0.526 0.048 0.409 —0.027 0.339 Table C.1 (continued) - Subject 113 Card 1 1 1.000 1 13.000 122.000 124.000 131.000 133.000 142.000 144.000 151.000 153.000 212. 100 212.200 214. 100 214.200 221.100 221.200 223.100 223.200 232.100 232.200 234.100 234.200 241.100 241.200 243. 100 243.200 252.100 252.200 254.100 254.200 31 1.000 313.000 322.000 324.000 331.000 333.000 342.000 344.000 351.000 353.000 412.000 414.000 421.000 423.000 432.000 434.000 441.000 443.000 452.000 454.000 51 1.000 513.000 522.000 524.000 531.000 533.000 542.000 544.000 551.000 553.000 Resp. 5.000 5.000 RT 787.000 1660.000 780.000 1361.000 627.000 1550.000 1450.000 1509.000 969.000 1542.000 510.000 517.000 545.000 1013.000 544.000 793.000 1367.000 959.000 354.000 577.000 422.000 1358.000 628.000 550.000 597.000 1413.000 399.000 993.000 610.000 965.000 1402.000 1348.000 477.000 1067.000 2912.000 1245.000 1900.000 1956.000 886.000 681.000 566.000 685.000 1621.000 543.000 455.000 724.000 548.000 602.000 1094.000 1057.000 1 147.000 479.000 846.000 682.000 962.000 2150.000 592.000 971.000 397.000 706.000 Conf. 2.000 2.000 Order 54.000 10.000 53.000 14.000 44.000 18.000 35.000 55.000 1 1.000 1.000 13.000 30.000 16.000 38.000 46.000 29.000 36.000 19.000 50.000 48.000 37.000 15.000 27.000 25.000 42.000 8.000 59.000 9.000 33.000 21.000 56.000 20.000 58.000 43.000 22.000 17.000 12.000 2.000 52.000 51.000 7.000 31.000 57.000 40.000 28.000 23.000 5.000 60.000 39.000 49.000 41.000 34.000 32.000 26.000 24.000 47.000 45.000 4.000 107 2.100 SdRT —0.355 1 .364 -0.369 0.775 -0.671 1.147 0.950 1 .066 0.003 1.131 -0.901 -0.887 —0.832 0.090 -0.834 -0.344 0.787 -0.017 —1.208 -0.769 -1.074 0.769 -0.669 -0.822 —0.730 0.877 -1.120 0.050 —0.704 -0.005 0.856 0.749 —0.966 0.196 3.829 0.546 1.836 1 .947 -O. 160 -0.564 «0.791 —0.556 1 .287 -0.836 -1.009 —0.480 -0.826 —0.720 0.249 0.176 0.353 —0.962 -0.239 —0.562 -0.01 1 2.329 -0.739 0.007 -l.123 -0.5 1 5 Table C.1 (continued) — Subject 114 Card 1 1 1.000 1 13.000 122.000 124.000 131.000 133.000 142.000 144.000 151.000 153.000 212.100 212.200 214.100 214.200 221.100 221.200 223.100 223.200 232.100 232.200 234.100 234.200 241.100 241.200 243. 100 243.200 252.100 252.200 254.100 254.200 31 1.000 313.000 322.000 324.000 331.000 333.000 342.000 344.000 351.000 353.000 412.000 414.000 421.000 423.000 432.000 434.000 441.000 443.000 452.000 454.000 51 1.000 513.000 522.000 524.000 531 .000 533.000 542.000 544.000 551.000 553.000 Resp. 7.000 8.000 4.000 5.000 7.000 3.000 4.000 7.000 7.000 7.000 3.000 4.000 RT 913.000 1912.000 1 103.000 1691.000 1292.000 1025.000 1203.000 1203.000 1 186.000 2343.000 744.000 644.000 965.000 1378.000 1016.000 1356.000 899.000 1247.000 999.000 1320.000 1064.000 2012.000 1649.000 1065.000 1 142.000 1 147.000 697.000 2156.000 712.000 1612.000 4039.000 2777.000 2428.000 825.000 2395.000 1831.000 1474.000 1638.000 1097.000 1846.000 61 1.000 2245.000 729.000 534.000 690.000 527.000 737.000 877.000 890.000 1 176.000 1723.000 607.000 2225.000 404.000 1457.000 863.000 905.000 676.000 1316.000 1014.000 Conf. 4.000 4.000 3.000 2.000 3.000 4.000 3.000 Order 46.000 17.000 53.000 2.000 13.000 43.000 40.000 15.000 55.000 16.000 8.000 35.000 47.000 9.000 52.000 6.000 31.000 1.000 23.000 33.000 32.000 1 1.000 24.000 54.000 14.000 10.000 12.000 48.000 49.000 108 SdRT «0.593 0.921 -0.305 0.586 —0.018 —0.423 0.1 53 -0.153 -0.179 1.575 —0.849 4.001 -0.514 0.1 12 -0.437 0.079 -0.614 —0.087 -0.463 0.024 -0.364 1.073 0.523 —0.363 «0.246 -0.238 -0.921 1.291 -0.898 0.467 4.146 2.233 1.704 —0.726 1.654 0.799 0.257 0.506 -0.314 0.821 —1.051 1.426 —0.872 -1.168 —0.931 -1 . 178 -0.860 -0.648 -0.628 -0.194 0.635 -1.057 1.396 —1.365 0.232 -0.669 -0.605 -0.952 0.018 -0.440 Table C.l (continued) — Subject 115 Card 1 1 1.000 1 13.000 122.000 124.000 131.000 133.000 142.000 144.000 151.000 153.000 212. 100 212.200 214.100 214.200 221.100 221.200 223.100 223.200 232. 100 232.200 234.100 234.200 241.100 241.200 243.100 243.200 252.100 252.200 254.100 254.200 31 1.000 313.000 322.000 324.000 331.000 333.000 342.000 344.000 351.000 353.000 412.000 414.000 421.000 423.000 432.000 434.000 441.000 443.000 452.000 454.000 51 1.000 513.000 522.000 524.000 531.000 533.000 542.000 544.000 551.000 553.000 Resp. 8.000 7.000 4.000 4.000 7.000 3.000 3.000 5.000 7.000 2.000 RT 422.000 1727.000 985.000 583.000 830.000 885.000 1001.000 1323.000 1205.000 3092.000 402.000 1285.000 474.000 1539.000 985.000 506.000 968.000 769.000 321 .000 515.000 366.000 1285.000 698.000 843.000 1676.000 1271.000 409.000 558.000 429.000 1519.000 2721.000 625.000 762.000 617.000 949.000 746.000 3298.000 1 101.000 1390.000 1590.000 445.000 531.000 322.000 261.000 534.000 403.000 375.000 732.000 341.000 1385.000 2239.000 1293.000 2201.000 1081.000 944.000 729.000 1549.000 2220.000 1 130.000 1 108.000 Order 35.000 45.000 46.000 56.000 17.000 31.000 43.000 41.000 26.000 55.000 37.000 5.000 28.000 38.000 7.000 12.000 22.000 21.000 42.000 15.000 57.000 49.000 10.000 1.000 53.000 25.000 11.000 109 SdRT -0.915 1.012 -0.084 -0.677 —0.3 12 -0.231 -0.060 0.416 0.241 3.028 -0.945 0.360 -0.838 0.735 -0.084 -0.791 .0.109 -0.403 -1.064 -0.778 -0.998 0.360 —0.507 -0.293 0.937 0.339 -0.934 —0.714 .0305 0.705 2.480 —0.61 5 —0.413 -0.627 —0. 137 -0.436 3.332 0.088 0.515 0.810 -0.881 -0.754 -1 .063 -l .153 -O.750 -0.943 -0.984 —0.457 —1.035 0.507 1.768 0.371 1.712 0.058 -0. 144 —0.462 0.749 1.740 0.131 0.098 Table C.l (continued) - Subject 116 Card 11 1.000 1 13.000 122.000 124.000 131.000 133.000 142.000 144.000 151.000 153.000 212. 100 212.200 214. 100 214.200 221.100 221.200 223. 100 223.200 232. 100 232.200 234.100 234.200 241 . 100 241.200 243. 100 243.200 252.100 252.200 254. 100 254.200 31 1.000 313.000 322.000 324.000 331.000 333.000 342.000 344.000 351.000 353.000 412.000 414.000 421.000 423.000 432.000 434.000 441.000 443.000 452.000 454.000 51 1.000 513.000 522.000 524.000 531.000 533.000 542.000 544.000 551.000 553.000 Resp. 6.000 4.000 3.000 4.000 5.000 4.000 3.000 5.000 6.000 4.000 3.000 4.000 RT 694.000 955.000 1262.000 1260.000 437.000 2206.000 1088.000 1379.000 497.000 1735.000 660.000 404.000 801.000 2421.000 1316.000 983.000 1880.000 1424.000 639.000 1086.000 1265.000 538.000 1699.000 1093.000 2078.000 588.000 576.000 1092.000 503.000 1404.000 1558.000 1 168.000 465.000 1421.000 1716.000 763.000 1 170.000 2030.000 620.000 2215.000 561.000 1568.000 595.000 1 130.000 1 164.000 933.000 418.000 504.000 1075.000 440.000 1673.000 1009.000 1044.000 493.000 1067.000 733.000 2019.000 1302.000 1237.000 1365.000 Conf. 4.000 4.000 3.000 4.000 4.000 3.000 4.000 Order 35.000 48.000 5.000 34.000 58.000 4.000 37.000 16.000 51.000 9.000 36.000 54.000 52.000 14.000 28.000 24.000 6.000 1.000 47.000 30.000 8.000 22.000 19.000 38.000 110 Dist SdRT -0.818 -0.321 0.263 0.260 —1.307 2.060 -0.068 0.486 —1 . 193 1.164 —0.883 -1 .370 -0.614 2.469 0.366 -0.268 1.440 0.572 -0.923 —0.072 0.269 -1.1 15 1.095 -0.058 1.817 -1.020 -1.042 -1.181 0.534 0.827 0.084 -1 .254 0.566 1.128 -0.686 0.088 1.725 -0.959 2.077 —1.071 0.846 -1.006 0.012 0.077 —0.363 —1.343 -1 . 179 -0.093 —1.301 1.046 -0.218 -0.152 -1.200 0.108 -0.744 1.704 0.339 0.216 0.459 Table C.1 (continued) - Subject 117 Card 11 1.000 1 13.000 122.000 124.000 131.000 133.000 142.000 144.000 151.000 153.000 212.100 212.200 214. 100 214.200 221.100 221.200 223.100 223.200 232.100 232.200 234.100 234.200 241. 100 241.200 243.100 243.200 252. 100 252.200 254. 100 254.200 31 1.000 313.000 322.000 324.000 331.000 333.000 342.000 344.000 351.000 353.000 412.000 414.000 421.000 423.000 432.000 434.000 441.000 443.000 452.000 454.000 51 1.000 513.000 522.000 524.000 531.000 533.000 542.000 544.000 551.000 553.000 Resp. 6.000 8.000 2.000 5.000 6.000 RT 867.000 1629.000 1033.000 770.000 835.000 2298.000 1 131.000 1631.000 1033.000 1226.000 465.000 934.000 748.000 4253.000 1377.000 2008.000 893.000 1481.000 942.000 1593.000 621.000 1736.000 677.000 1428.000 1057.000 2204.000 274.000 1009.000 760.000 1682.000 1437.000 641.000 1315.000 723.000 576.000 652.000 2901.000 1042.000 1999.000 848.000 405.000 1330.000 305.000 1713.000 555.000 613.000 386.000 840.000 775.000 1905.000 1673.000 1639.000 2292.000 832.000 870.000 1236.000 903.000 499.000 808.000 604.000 Order 24.000 33.000 1 1.000 55.000 35.000 22.000 5.000 25.000 46.000 59.000 26.000 48.000 19.000 8.000 13.000 21.000 57.000 10.000 6.000 58.000 18.000 3.000 28.000 60.000 40.000 29.000 39.000 12.000 30.000 52.000 51.000 47.000 56.000 43.000 49.000 45.000 1.000 44.000 32.000 9.000 53.000 31.000 50.000 15.000 42.000 54.000 38.000 23.000 7.000 2.000 4.000 27.000 37.000 34.000 14.000 16.000 17.000 36.000 20.000 41.000 111 1.000 1.000 SdRT -0.454 -0.214 —0.593 -0.500 1.608 -0.073 0.647 -0.214 0.064 -1.033 -0.357 -0.625 4.425 0.281 1.190 -0.416 0.431 0.592 —0.808 0.798 —0.727 0.355 -0.180 1 .473 —1 .308 -0.249 -0.608 0.721 0.368 —0.779 0. 192 —0.661 -0.873 -0.763 2.477 -0.202 1.177 —0.481 -1 .1 19 0.213 -1 .263 0.765 —0.903 —0.820 -1 .147 -0.493 -0.586 1 .042 0.708 0.659 1 .599 -0.504 -0.449 0.078 -0.402 -0.984 —0.539 -0.833 Table C.1 (continued) - Subject 118 Card 1 1 1.000 1 13.000 122.000 124.000 131.000 133.000 142.000 144.000 151.000 153.000 212.100 212.200 2 14. 100 214.200 221.100 221.200 223.100 223.200 232. 100 232.200 234.100 234.200 241.100 241.200 243. 100 243.200 252.100 252.200 254.100 254.200 31 1.000 313.000 322.000 324.000 331.000 333.000 342.000 344.000 351.000 353.000 412.000 414.000 421.000 423.000 432.000 434.000 441.000 443.000 452.000 454.000 51 1.000 513.000 522.000 524.000 531.000 533.000 542.000 544.000 551.000 553.000 RT 2221.000 690.000 2400.000 1046.000 1831.000 3230.000 1236.000 1527.000 910.000 1704.000 614.000 1972.000 939.000 979.000 771.000 639.000 1400.000 1371.000 743.000 714.000 872.000 4472.000 1 177.000 707.000 1858.000 997.000 606.000 513.000 853.000 2588.000 1483.000 2125.000 383.000 775.000 1378.000 783.000 2846.000 1360.000 1083.000 2683.000 529.000 1925.000 561.000 577.000 1 156.000 768.000 1023.000 1345.000 1239.000 703.000 1214.000 474.000 1034.000 634.000 758.000 1470.000 473.000 1354.000 942.000 2547.000 Conf. 2.000 2.000 2.000 2.000 2.000 1.000 3.000 1.000 2.000 2.000 1.000 2.000 1.000 Order 23.000 59.000 3.000 12.000 26.000 30.000 460% 55.000 40.000 37.000 50.000 18.000 112 SdRT 1.187 -0.758 1.415 -0.306 0.692 2.470 -0.064 0.305 -0.479 0.530 -0.855 0.871 -0.442 .0391 —0.655 -0.823 0.144 0.107 -0.691 0.728 -0.527 4.048 -0.139 —0.737 0.726 -0.368 -0.865 0.983 -0.551 1.654 0.249 1.065 -1 . 149 -0.650 0.1 16 1.982 0.093 -0.259 1.774 -0.963 0.81 1 -0.922 —0.902 —0.166 0.659 -0.335 0.074 -0.061 -0.742 —0.092 -1 .033 ~0.321 -0.830 -0.672 0.233 -1 .034 0.085 -0.438 1.602 Table C.l (continued) - Subject 119 Card 111.000 113.000 122.000 124.000 131.000 133.000 142.000 144.000 151.000 153.000 212.100 212.200 214.100 214.200 221.100 221.200 223.100 223.200 232.100 232.200 234.100 234.200 241.100 241.200 243.100 243.200 252.100 252.200 254.100 254.200 311.000 313.000 322.000 324.000 331.000 333.000 342.000 344.000 351.000 353.000 412.000 414.000 421.000 423.000 432.000 434.000 441.000 443.000 452.000 454.000 511.000 513.000 522.000 524.000 531.000 533.000 542.000 544.000 551.000 553.000 Resp. 6.000 8.000 4.000 3.000 6.000 5.000 4.000 RT 1200.000 2139.000 2027.000 1445.000 1427.000 729.000 1621.000 1531.000 761.000 1406.000 1044.000 2336.000 1994.000 1727.000 1233.000 1 127.000 1230.000 1060.000 2557.000 1871.000 2002.000 3397.000 868.000 3216.000 2273.000 2068.000 1461.000 2309.000 780.000 1520.000 2565.000 4214.000 1 174.000 1634.000 3644.000 1995.000 1857.000 2505.000 1434.000 1010.000 1485.000 1201.000 851.000 51 1.000 1544.000 1253.000 856.000 1 182.000 2232.000 1712.000 602.000 720.000 2283.000 1496.000 2518.000 661.000 974.000 1902.000 2210.000 1699.000 Order 34.000 20.000 1.000 41.000 21.000 55.000 59.000 17.000 52.000 30.000 24.000 9.000 15.000 48.000 14.000 47.000 51.000 50.000 7.000 3.000 26.000 40.000 25.000 6.000 35.000 42.000 33.000 46.000 54.000 23.000 38.000 53.000 32.000 18.000 1 1.000 8.000 58.000 10.000 57.000 45.000 31.000 22.000 43.000 28.000 39.000 16.000 56.000 13.000 19.000 4.000 36.000 37.000 49.000 29.000 5.000 27.000 12.000 113 SdRT -0.615 0.610 0.464 -0.295 -0.319 -1.230 -0.066 —0.183 -1 .188 —0.8 1 9 0.867 0.421 0.073 -0.572 —0.710 -0.576 —0.798 1.156 0.260 0.431 2.251 -1.048 2.015 0.785 0.517 -0.274 0.832 -1.163 -0.198 1.166 3.317 -0.649 —0.049 2.574 0.422 0.242 1.088 -0.310 -0.863 -0.243 -0.614 -1.070 -1.514 —0. 166 -0.546 -0.639 0.731 0.053 -1.395 —1.241 0.798 —0.229 1.105 -1.318 —0.910 0.301 0.703 0.036 Table C.l (continued) - Subject 120 Card 11 1.000 1 13.000 122.000 124.000 131.000 133.000 142.000 151.000 153.000 212. 100 212.200 221.100 221.200 223. 100 223.200 232. 100 232.200 241.100 241.200 243. 100 243.200 252. 100 252.200 254. 100 254.200 31 1.000 313.000 322.000 324.000 331.000 333.000 342.000 351.000 353.000 412.000 421.000 423.000 432.000 441.000 443.000 452.000 454.000 511.000 513.000 522.000 524.000 531.000 533.000 542.000 551.000 553.000 Resp. 6.000 5.000 3.000 4.000 RT 646.000 1254.000 543.000 695.000 775.000 1448.000 1347.000 676.000 749.000 671.000 628.000 716.000 1016.000 852.000 529.000 381.000 561.000 692.000 784.000 601.000 788.000 520.000 680.000 512.000 1 138.000 919.000 670.000 1297.000 702.000 1510.000 1032.000 1307.000 803.000 1357.000 485.000 657.000 724.000 479.000 513.000 338.000 454.000 524.000 512.000 506.000 738.000 534.000 753.000 796.000 392.000 412.000 609.000 Order 47.000 38.000 51.000 26.000 8.000 24.000 2.000 32.000 28.000 3.000 18.000 17.000 36.000 27.000 16.000 59.000 37.000 7.000 53.000 10.000 57.000 31.000 39.000 14.000 6.000 20.000 19.000 5.000 12.000 15.000 29.000 9.000 42.000 1.000 56.000 40.000 54.000 13.000 41.000 48.000 45.000 52.000 60.000 49.000 22.000 44.000 1 1.000 35.000 50.000 55.000 34.000 114 Ques 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 2.100 2.200 2.100 2.200 2.100 Dist MI GA RW MI SdRT -0.349 1.701 «0.696 —0. 184 0.086 2.355 2.015 -0.248 -0.002 -0.265 -0.410 —0.1 13 0.899 0.346 -0.744 —1.243 -0.636 -0.194 0.1 16 -0.501 0.130 —0.774 -0.234 -0.801 1.310 0.571 -0.268 1.846 -0. 160 2.564 0.953 1.880 0.180 2.048 -0.892 -0.312 —0.086 —0.912 -0.797 —1.388 —0.996 -0.760 -0.801 -0.821 —0.039 —0.727 0.012 0.157 -1.205 -1 . 138 —0.474 Table C.l (continued) - Subject 121 Card 1 1 1.000 1 13.000 122.000 124.000 131.000 133.000 142.000 151.000 153.000 212. 100 212.200 221 . 100 221.200 223. 100 223.200 232. 100 232.200 241.100 241.200 243. 100 243.200 252. 100 252.200 254. 100 254.200 31 1.000 313.000 322.000 324.000 331.000 333.000 342.000 351.000 353.000 412.000 421.000 423.000 432.000 441.000 443.000 452.000 454.000 51 1.000 513.000 522.000 524.000 531.000 533.000 542.000 551.000 553.000 Resp. 4.000 6.000 2.000 4.000 5.000 4.000 RT 906.000 1347.000 946.000 659.000 831.000 717.000 873.000 926.000 968.000 463.000 984.000 616.000 783.000 1546.000 694.000 617.000 713.000 416.000 426.000 907.000 1488.000 440.000 526.000 518.000 993.000 1084.000 746.000 712.000 663.000 2434.000 1068.000 1230.000 889.000 1342.000 698.000 483.000 711.000 506.000 430.000 465.000 767.000 800.000 1231.000 563.000 526.000 708.000 846.000 645.000 703.000 670.000 776.000 Order 17.000 45.000 16.000 50.000 3.000 18.000 1.000 58.000 5.000 47.000 7.000 55.000 15.000 14.000 51 .000 34.000 46.000 41 .000 30.000 32.000 57.000 60.000 27.000 35.000 42.000 43.000 22.000 40.000 36.000 4.000 49.000 21.000 54.000 20.000 48.000 53.000 6.000 44.000 19.000 26.000 38.000 39.000 29.000 28.000 56.000 23.000 24.000 13.000 1 1.000 8.000 52.000 115 Dist M1 GA RW MI SdRT 0.230 1.459 0.341 -0.458 0.021 —0.297 0.138 0.286 0.403 -1 .005 0.447 —0.578 —0.1 13 2.014 -0.361 —0.576 -0.308 -1.136 —1.108 0.233 1.852 -1.069 —0.829 -0.851 0.472 0.726 -0.216 —0.31 1 -0.447 4.488 0.681 1.133 0.183 1.445 —0.350 -0.949 -0.314 -0.885 -1.097 —0.999 -0.157 1.136 -0.726 -0.829 —0.322 0.063 -0.498 -0.336 -0.428 -0.132 Table C.l (continued) — Subject 122 Card 11 1.000 1 13.000 122.000 124.000 131.000 133.000 142.000 144.000 151.000 153.000 212.100 212.200 214. 100 214.200 221. 100 221.200 223. 100 223.200 232. 100 232.200 234. 100 234.200 241.100 241.200 243.100 243.200 252. 100 252.200 254.100 254.200 311.000 313.000 322.000 324.000 331.000 333.000 342.000 344.000 351.000 353.000 412.000 414.000 421.000 423.000 432.000 434.000 441.000 443.000 452.000 454.000 51 1.000 513.000 522.000 524.000 531.000 533.000 542.000 544.000 551.000 553.000 RT 1354.000 3332.000 849.000 764.000 836.000 3244.000 3624.000 1896.000 2817.000 2914.000 553.000 1 179.000 572.000 2096.000 1353.000 1 161.000 1 127.000 750.000 780.000 803.000 528.000 2178.000 996.000 2697.000 3793.000 1858.000 401.000 1427.000 1 176.000 1 179.000 2322.000 1995.000 1099.000 1362.000 671.000 1792.000 1576.000 4773.000 2354.000 3171.000 464.000 1728.000 269.000 850.000 882.000 560.000 543.000 446.000 1817.000 2454.000 2043.000 896.000 5413.000 1375.000 1 194.000 820.000 977.000 945.000 661.000 1289.000 Order 5.000 46.000 49.000 7.000 43.000 32.000 33.000 3.000 52.000 34.000 60.000 36.000 15.000 28.000 35.000 23.000 39.000 14.000 2.000 45.000 56.000 38.000 6.000 12.000 13.000 16.000 42.000 1 1.000 10.000 57.000 27.000 24.000 50.000 26.000 40.000 22.000 8.000 1.000 47.000 21.000 51 .000 55.000 41 .000 18.000 58.000 37.000 19.000 48.000 25.000 30.000 4.000 53.000 17.000 20.000 9.000 31.000 29.000 44.000 54.000 59.000 116 SdRT —0.209 1.593 —0.746 —0.680 1.513 1.859 0.285 1.124 1.212 -0.938 -0.368 —0.921 0.467 -0.209 —0.384 -0.415 -0.759 -0.731 —0.710 —0.961 0.542 -0.535 1.015 2.013 0.250 -1.077 -0. 142 —0.371 -0.368 0.673 0.375 —0.441 —0.201 —0.831 0.190 2.905 0.702 1.446 -1.019 0.132 -1.197 -0.638 —0.932 —0.947 -1.036 0.213 0.793 0.419 -0.626 3.488 -0. 189 -0.354 -0.695 —0.552 -0.581 -0.840 —0.268 Table C.1 (continued) — Subject 123 Card 1 1 1.000 1 13.000 122.000 124.000 131.000 133.000 142.000 144.000 151.000 153.000 212.100 212.200 214.100 214.200 221.100 221.200 223.100 223.200 232.100 232.200 234. 100 234.200 241. 100 241.200 243. 100 243.200 252.100 252.200 254.100 254.200 31 1.000 313.000 322.000 324.000 331.000 333.000 342.000 344.000 351.000 353.000 412.000 414.000 421.000 423.000 432.000 434.000 441.000 443.000 452.000 454.000 51 1.000 513.000 522.000 524.000 531.000 533.000 542.000 544.000 551.000 553.000 Resp. 5.000 7.000 4.000 4.000 6.000 5.000 3.000 4.000 7.000 3.000 RT 1334.000 131 1.000 1033.000 852.000 974.000 2298.000 604.000 671.000 2181.000 1535.000 627.000 1564.000 558.000 716.000 508.000 972.000 741.000 1054.000 427.000 963.000 71 1.000 1 152.000 702.000 634.000 799.000 1643.000 759.000 535.000 1099.000 1 121.000 735.000 1723.000 1708.000 898.000 2265.000 2513.000 519.000 1373.000 517.000 1373.000 419.000 1828.000 504.000 500.000 1263.000 776.000 1 1 12.000 284.000 1000.000 436.000 2060.000 1208.000 1352.000 1581.000 1330.000 1666.000 1036.000 1369.000 655.000 595.000 Order 21.000 35.000 14.000 59.000 57.000 12.000 52.000 39.000 1 1.000 30.000 58.000 19.000 41.000 45.000 56.000 32.000 16.000 9.000 26.000 53.000 13.000 48.000 22.000 43.000 4.000 1.000 17.000 24.000 46.000 33.000 31.000 5.000 28.000 8.000 2.000 7.000 60.000 34.000 37.000 36.000 49.000 38.000 23.000 25.000 18.000 20.000 6.000 42.000 50.000 55.000 3.000 15.000 51 .000 29.000 40.000 47.000 27.000 10.000 44.000 54.000 117 SdRT 0.480 0.437 -0.084 —0.424 —0.195 2.288 —0.889 -0.763 2.069 0.857 -0.846 0.912 -0.975 -0.679 -1.069 -0. 199 -0.632 —0.045 —1.221 -0.216 —0.688 0.139 -0.705 -0.833 -0.523 1.060 —0.598 -1.018 0.040 0.081 -0.643 1.210 1.182 —0.337 2.226 2.691 -1.048 0.553 -1.052 0.553 -1.236 1.407 -1.076 -1.084 0.347 -0.566 -1.489 —0.146 —1.204 1.842 0.244 0.514 0.943 0.473 1.103 -0.079 0.546 —0.793 -0.906 Table C.1 (continued) - Subject 124 Card 11 1.000 1 13.000 122.000 124.000 131.000 133.000 142.000 144.000 151.000 153.000 212. 100 212.200 214.100 214.200 221.100 221.200 223. 100 223.200 232.100 232.200 234. 100 234.200 241 . 100 241.200 243. 100 243.200 252.100 252.200 254.100 254.200 31 1.000 313.000 322.000 324.000 331.000 333.000 342.000 344.000 351.000 353.000 412.000 414.000 421.000 423.000 432.000 434.000 441.000 443.000 452.000 454.000 51 1.000 513.000 522.000 524.000 531.000 533.000 542.000 544.000 551.000 553.000 Resp. 6.000 7.000 4.000 5.000 6.000 3.000 4.000 5.000 6.000 4.000 3.000 4.000 5.000 RT 1836.000 1907.000 1964.000 1844.000 1 150.000 1567.000 151 1.000 3498.000 1918.000 3832.000 1562.000 621.000 1092.000 2450.000 1543.000 2759.000 814.000 1248.000 834.000 1904.000 1368.000 4754.000 1780.000 701.000 2345.000 3157.000 1273.000 2012.000 846.000 3081.000 1630.000 1347.000 211 1.000 878.000 3016.000 716.000 3105.000 484.000 2175.000 2218.000 414.000 1652.000 904.000 1291.000 677.000 768.000 1040.000 1209.000 933.000 1298.000 2514.000 724.000 2101.000 1614.000 1035.000 1716.000 820.000 1388.000 3781.000 1793.000 Order 46.000 33.000 17.000 52.000 24.000 42.000 31.000 15.000 25.000 26.000 19.000 20.000 35.000 21.000 10.000 1.000 57. 000 6. 000 43. 000 5.000 4. 000 27. 000 50. 000 45 000 3. 000 53. 000 12. 000 40.000 16.000 9.000 29.000 54. 000 8. 000 56. 000 18. 000 28.000 36.000 47.000 22.000 34.000 41.000 48.000 13.000 7. 000 55. 000 38. 000 32.000 59.000 58.000 2.000 1 1.000 37. 000 60. 000 23.000 39.000 14.000 30.000 44.000 51.000 49.000 118 SdRT 0.138 0.215 0.277 0.147 .0. 606 .o_ 154 -0. 215 1.941 0.227 2.304 —0.159 —1.180 -0.669 0.804 -0.180 1.139 -0. 971 -0. 500 -0. 949 0.212 —0.370 3.304 0.077 —1.093 0.690 1.571 -0.473 0.329 —0. 936 1.489 -0. 085 -0.392 0.436 -0. 901 1.418 -1.077 1.515 -1.329 0.506 0.553 —1.405 -0.062 -0.873 -0.453 -1.1 19 -1.021 -0. 726 -0.542 —0. 842 0. 874 —1.068 0.426 -0.103 -0.731 0.008 -0.964 -0.348 2.248 0.091 Table C.1 (continued) — Subject 125 * Card 1 1 1.000 1 13.000 122.000 124.000 131.000 133.000 142.000 144.000 151.000 153.000 212. 100 212.200 214. 100 214.200 221.100 221.200 223.100 223.200 232.100 232.200 234.100 234.200 241.100 241.200 243.100 243.200 252.100 252.200 254. 100 254.200 31 1.000 313.000 322.000 324.000 331.000 333.000 342.000 344.000 351.000 353.000 412.000 414.000 421.000 423.000 432.000 434.000 441.000 443.000 452.000 454.000 51 1.000 513.000 522.000 524.000 531.000 533.000 542.000 544.000 551.000 553.000 RT 786.000 634.000 378.000 642.000 519.000 471.000 514.000 314.000 413.000 469.000 323.000 893.000 375.000 216.000 527.000 490.000 512.000 577.000 304.000 372.000 237.000 696.000 533.000 196.000 520.000 489.000 310.000 728.000 365.000 475.000 360.000 228.000 473.000 512.000 531.000 370.000 450.000 947.000 576.000 531.000 669.000 677.000 420.000 579.000 251.000 608.000 518.000 332.000 790.000 327.000 282.000 444.000 394.000 417.000 476.000 388.000 626.000 363.000 697.000 602.000 Order 7.000 20.000 23.000 39.000 26.000 22.000 34.000 44.000 46.000 27.000 45.000 17.000 48.000 52.000 42.000 56.000 43.000 35.000 60.000 18.000 50.000 41.000 16.000 58.000 24.000 59.000 12.000 31 .000 30.000 25.000 19.000 49.000 36.000 1 1.000 10.000 33.000 38.000 2.000 54.000 40.000 55.000 9.000 28.000 53.000 4.000 5.000 13.000 1.000 47.000 57.000 15.000 37.000 51.000 29.000 6.000 21.000 14.000 32.000 119 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 2.100 2.200 2.100 2.200 2.100 2.200 2.100 2.200 2.100 SdRT 1.829 0.905 -0.652 0.953 0.205 -0.087 0.175 -1.042 —0.440 -0.099 -0.987 2.480 -0.671 -1.638 0.254 0.029 0.163 0.558 -1.103 —0.689 -1.510 1.282 0.290 —1.760 0.211 0.023 -1.066 1.477 -0.732 -0.062 -0.762 —1.565 -0.075 0.163 0.278 —0.701 —0.215 2.809 0.552 0.278 1.1 18 1.166 -0.397 0.570 —1 .425 0.747 0.199 -0.932 1.854 -0.963 -1.236 -0.251 -0.555 -0.415 —0.056 -0.592 0.856 -0.744 1.288 0.710 Table C. 1 (continued) - Subject 126 Card 111.000 113.000 122.000 124.000 131.000 133.000 142.000 144.000 151.000 153.000 212.100 212.200 214.100 214.200 221.100 221.200 223. 100 223.200 232.100 232.200 234. 100 234.200 241 . 100 241.200 243. 100 243.200 252.100 252.200 254.100 254.200 311.000 313.000 322.000 324.000 331.000 333.000 342.000 344.000 351.000 353.000 412.000 414.000 421.000 423.000 432.000 434.000 441.000 443.000 452.000 454.000 511.000 513.000 522.000 524.000 531.000 533.000 542.000 544.000 551.000 553.000 RT 677.000 905.000 804.000 790.000 754.000 965.000 830.000 1 1 15.000 592.000 643.000 661.000 915.000 738.000 2663.000 1099.000 1021.000 1061.000 948.000 663.000 780.000 772.000 1 148.000 1 1 18.000 864.000 1014.000 1518.000 590.000 891.000 851.000 717.000 934.000 1341.000 1427.000 813.000 1 129.000 1 147.000 1402.000 553.000 713.000 1649.000 457.000 863.000 1 178.000 436.000 633.000 658.000 473.000 493.000 850.000 1030.000 1217.000 1 173.000 1 184.000 595.000 477.000 641.000 550.000 1398.000 444.000 1791.000 Order 47.000 37.000 56.000 24.000 18.000 54.000 43.000 1 1.000 19.000 15.000 32.000 20.000 6.000 25.000 23.000 13.000 28.000 4.000 39.000 51.000 16.000 48.000 2.000 27.000 33.000 31 .000 3.000 36.000 21 .000 53.000 8.000 57.000 41.000 58.000 45.000 46.000 5.000 29.000 14.000 34.000 60.000 10.000 12.000 50.000 26.000 17.000 55.000 9.000 30.000 1.000 22.000 7.000 35.000 49.000 40.000 52.000 38.000 59.000 42.000 120 SdRT -0.654 -0.063 -0.325 -0.361 -0.455 0.093 -0.257 0.482 -0.875 -0.742 —0.696 —0.037 -0.496 4.496 0.440 0.238 0.342 0.049 —0.691 -0.387 —0.408 0.567 0.489 -0.169 0.220 1.527 -0.880 —0.099 —0.203 -0.550 0.012 1 .068 1 .291 -0.302 0.518 0.565 1 .226 -0.976 —0.561 1.866 -1 .225 -0. 172 0.645 —1.279 —0.768 -0.703 -1.183 -1.131 -0.206 0.261 0.746 0.632 0.661 -0.867 -1 .173 —0.748 —0.984 1 .216 -1 .258 2.235 Table C.1 (continued) - Subject 127 Card 11 1.000 1 13.000 122.000 124.000 131.000 133.000 142.000 144.000 151.000 153.000 212. 100 212.200 214. 100 214.200 221. 100 221.200 223. 100 223.200 232. 100 232.200 234. 100 234.200 241.100 241.200 243. 100 243.200 252. 100 252.200 254. 100 254.200 31 1.000 313.000 322.000 324.000 331.000 333.000 342.000 344.000 351.000 353.000 412.000 414.000 421.000 423.000 432.000 434.000 441.000 443.000 452.000 454.000 51 1.000 513.000 522.000 524.000 531.000 533.000 542.000 544.000 551.000 553.000 RT 767.000 606.000 442.000 438.000 829.000 943.000 844.000 735.000 808.000 677.000 583.000 816.000 647.000 593.000 1025.000 692.000 684.000 816.000 471.000 755.000 447.000 912.000 831.000 951.000 433.000 898.000 382.000 441.000 379.000 1078.000 587.000 481.000 504.000 895.000 1203.000 790.000 952.000 962.000 773.000 1285.000 619.000 600.000 304.000 403.000 424.000 369.000 813.000 500.000 844.000 528.000 1031.000 578.000 465.000 1598.000 1353.000 419.000 606.000 909.000 613.000 618.000 Conf. 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 2.000 1.000 4.000 1.000 Order 5.000 47.000 40.000 60.000 9.000 15.000 36.000 51.000 25.000 24.000 14.000 1.000 3.000 42.000 7.000 44.000 18.000 30.000 43.000 1 1.000 45.000 56.000 23.000 12.000 53.000 26.000 32.000 55.000 46.000 29.000 57.000 34.000 48.000 49.000 27.000 28.000 2.000 13.000 39.000 4.000 16.000 17.000 58.000 6.000 59.000 35.000 38.000 20.000 21 .000 50.000 52.000 37.000 54.000 41 .000 8.000 33.000 31.000 10.000 19.000 22.000 121 SdRT 0.193 —0.4 14 -1 .031 -1.046 0.426 0.856 0.483 0.072 0.347 0.146 -0.500 0.377 —0.259 -0.463 1.164 -0.090 -0. 120 0.377 -0.922 0.148 -1.012 0.739 0.434 0.886 -1 .065 0.686 -1.257 -1.035 -1.268 1.364 -0.485 -0.884 -0.798 0.675 1.835 0.279 0.889 0.927 0.215 2.144 -0.365 -0.436 —1.551 -1.178 -1.099 -1.306 0.366 -0.813 0.483 -0.707 1.187 .0519 -0.945 3.322 2.400 -1.1 18 -0.414 0.728 '0.387 -0.368 Table C.1 (continued) - Subject 128 Card 111.000 113.000 122.000 124.000 131.000 133.000 142.000 144.000 151.000 153.000 212. 100 212.200 214. 100 214.200 221.100 221.200 223.100 223.200 232.100 232.200 234.100 234.200 241.100 241.200 243.100 243.200 252.100 252.200 254.100 254.200 311.000 313.000 322.000 324.000 331.000 333.000 342.000 344.000 351.000 353.000 412.000 414.000 421.000 423.000 432.000 434.000 441.000 443.000 452.000 454.000 511.000 513.000 522.000 524.000 531.000 533.000 542.000 544.000 551.000 553.000 RT 861.000 1084.000 530.000 734.000 1062.000 1063.000 1032.000 813.000 878.000 1429.000 667.000 1172.000 606.000 1611.000 1 1 1 1.000 858.000 1312.000 738.000 488.000 826.000 705.000 1685.000 1018.000 983.000 1183.000 1 151.000 406.000 1464.000 935.000 1758.000 1121.000 826.000 736.000 1004.000 2918.000 1045.000 3126.000 450.000 1101.000 913.000 312.000 1415.000 471.000 439.000 371.000 463.000 572.000 636.000 1106.000 530.000 2444.000 1135.000 1464.000 973.000 573.000 807.000 630.000 818.000 505.000 1562.000 Conf. 3.000 2.000 2.000 2.000 3.000 2.000 3.000 Order 46.000 26.000 53.000 15.000 6.000 52.000 7.000 60.000 1 1.000 8.000 25.000 22.000 55.000 45.000 27.000 2.000 32.000 20.000 4.000 33.000 17.000 43.000 37.000 56.000 21 .000 47.000 42.000 18.000 59.000 16.000 50.000 49.000 54.000 3.000 12.000 30.000 5.000 35.000 40.000 44.000 41.000 9.000 31.000 39.000 58.000 36.000 28.000 13.000 1.000 51.000 23.000 57.000 10.000 34.000 24.000 14.000 48.000 38.000 29.000 19.000 122 Table C.1 (continued) — Subject 129 Card 1 1 1.000 1 13.000 122.000 124.000 131.000 133.000 142.000 144.000 151.000 153.000 212. 100 212.200 214. 100 214.200 221 . 100 221.200 223.100 223.200 232.100 232.200 234. 100 234.200 241.100 241.200 243.100 243.200 252.100 252.200 254.100 254.200 311.000 313.000 322.000 324.000 331.000 333.000 342.000 344.000 351.000 353.000 412.000 414.000 421.000 423.000 432.000 434.000 441.000 443.000 452.000 454.000 51 1 .000 513.000 522.000 524.000 531.000 533.000 542.000 544.000 551.000 553.000 Resp. 5.000 8.000 4.000 4.000 4.000 5.000 3.000 5.000 5.000 4.000 RT 1869.000 991.000 1667.000 1078.000 1696.000 1 145.000 1 193.000 773.000 1249.000 1225.000 666.000 1449.000 541.000 1273.000 81.000 1018.000 1007.000 728.000 661.000 1653.000 1252.000 2030.000 949.000 1590.000 2140.000 2074.000 709.000 1 100.000 596.000 3363.000 1312.000 1033.000 1376.000 2738.000 2721.000 1 189.000 2249.000 1610.000 1056.000 985.000 546.000 2285.000 755.000 1093.000 877.000 668.000 1484.000 1332.000 2088.000 856.000 3569.000 1201 .000 2494.000 702.000 592.000 781.000 1548.000 645.000 622.000 1 109.000 Order 46.000 26.000 53.000 15.000 6.000 52.000 7.000 60.000 1 1.000 8.000 25.000 22.000 55.000 45.000 27.000 2.000 32.000 20.000 4.000 33.000 17.000 43.000 37.000 56.000 21 .000 47.000 42.000 18.000 59.000 16.000 50.000 49.000 54.000 3.000 12.000 30.000 5.000 35.000 40.000 44.000 41.000 9.000 31.000 39.000 58.000 36.000 28.000 13.000 1.000 51 .000 23.000 57.000 10.000 34.000 24.000 14.000 48.000 38.000 29.000 19.000 123 SdRT 0.780 ~0.470 0.492 -0.347 0.533 —0.251 -0.183 -0.781 —0.103 0.137 -0.933 0.182 -1.1 11 —0.069 -1.766 -0.432 -0.448 -0.845 —0.940 0.472 -0.099 1.009 -0.530 0.383 1.166 1.072 -0.872 -0.315 -1.033 2.907 -0.013 '0.411 0.078 2.017 1.993 —0.188 1.321 0.41 1 —0.378 —0.479 -1.104 1.372 —0.807 -0.325 —0.633 -0.930 0.232 0.015 1.092 -0.663 3.201 —0.171 1.670 -0.882 -1.039 —0.769 0.323 -0.963 —0.996 -0.302 Table C.1 (continued) - Subject 130 Card 1 1 1.000 1 13.000 122.000 124.000 131.000 133.000 142.000 151.000 153.000 212. 100 212.200 221 . 100 221.200 223. 100 223.200 232.100 232.200 241.100 241.200 243.100 243.200 252.100 252.200 254.100 254.200 31 1.000 313.000 322.000 324.000 331.000 333.000 342.000 351.000 353.000 412.000 421.000 423.000 432.000 441.000 443.000 452.000 454.000 511.000 513.000 522.000 524.000 531.000 533.000 542.000 551.000 553.000 Resp. 6.000 6.000 3.000 RT 1374.000 1 187.000 1042.000 1320.000 378.000 1263.000 1 147.000 3028.000 1793.000 388.000 1099.000 420.000 817.000 1505.000 578.000 385.000 1 173.000 1216.000 1613.000 915.000 1673.000 305.000 725.000 565.000 1576.000 1098.000 1573.000 909.000 806.000 1629.000 1294.000 1 122.000 1884.000 2842.000 304.000 549.000 465.000 578.000 739.000 258.000 2157.000 741 .000 3213.000 824.000 954.000 654.000 794.000 542.000 1068.000 773.000 1548.000 Conf. 2.000 2.000 2.000 2.000 2.000 2.000 2.000 2.000 2.000 1.000 Order 22.000 8.000 13.000 29.000 48.000 6.000 46.000 4.000 33.000 50.000 44.000 31 .000 47.000 21 .000 58.000 20.000 26.000 9.000 24.000 59.000 42.000 40.000 7.000 52.000 41 .000 23.000 3.000 16.000 1 1.000 54.000 27.000 49.000 17.000 32.000 34.000 5.000 36.000 14.000 12.000 45.000 10.000 30.000 43.000 2.000 19.000 60.000 25.000 56.000 35.000 57.000 51.000 124 -1.000 0.000 0.000 -1.000 -1.000 -1.000 -2.000 SdRT 0.390 0.1 10 -0.108 0.309 -1.102 0.224 0.050 2.867 1.017 -1.087 -0.022 —1.039 -0.445 0.586 —0.803 —1 .092 0.089 0.153 0.748 0.298 0.838 -1.21 1 —0.582 -0.822 0.692 —0.024 0.688 -0.307 -0.461 0.772 0.270 0.012 1.154 2.589 -1.213 -0.846 -0.972 -0.803 -0.561 —1.282 1.563 0.558 3.144 -0.434 -0.239 —0.689 —0.479 —0.856 -0.069 -0.510 0.650 Table C.1 (continued) - Subject 131 Card 1 1 1.000 1 13.000 122.000 124.000 131.000 133.000 142.000 151.000 153.000 212. 100 212.200 221 . 100 221.200 223. 100 223.200 232.100 232.200 241.100 241.200 243. 100 243.200 252.100 252.200 254.100 254.200 31 1.000 313.000 322.000 324.000 331.000 333.000 342.000 351.000 353.000 412.000 421.000 423.000 432.000 441.000 443.000 452.000 454.000 51 1.000 513.000 522.000 524.000 531.000 533.000 542.000 551.000 553.000 Resp. 4.000 3.000 3.000 3.000 4.000 2.000 RT 766.000 1 171.000 642.000 885.000 797.000 1579.000 666.000 754.000 1810.000 475.000 666.000 511.000 1001.000 515.000 949.000 376.000 741.000 686.000 849.000 409.000 1674.000 322.000 884.000 530.000 1442.000 1744.000 2187.000 2047.000 884.000 861.000 1139.000 1580.000 1333.000 1295.000 458.000 451.000 619.000 650.000 817.000 374.000 1354.000 477.000 1814.000 1490.000 832.000 423.000 1368.000 1168.000 389.000 676.000 994.000 Conf. 2.000 2.000 2.000 2.000 2.000 2.000 2.000 Order 47.000 12.000 14.000 13.000 18.000 5.000 51.000 57.000 29.000 33.000 39.000 24.000 38.000 54.000 26.000 45.000 19.000 53.000 41.000 56.000 1.000 43.000 36.000 28.000 49.000 22.000 6.000 23.000 27.000 60.000 50.000 25.000 8.000 7.000 16.000 4.000 30.000 15.000 3.000 31.000 9.000 42.000 55.000 2.000 10.000 35.000 48.000 40.000 52.000 17.000 32.000 125 Dist SdRT -0.384 0.454 -0.640 —0.137 -0.320 1.298 -0.591 -0.408 1.776 -0.986 —0.591 —0.91 1 0.103 —0.903 -0.005 -1.190 -0.435 —0.549 -0.212 —1.122 1.495 -1.302 —0.140 -0.872 1.015 1.640 2.556 2.266 -0.140 -0.187 0.388 1.300 0.789 0.71 1 —1.021 -1.035 -0.688 -0.624 -0.278 —l.195 0.833 '0.982 1.784 1.1 14 -0.247 -1.093 0.862 0.448 -1.164 -0.570 0.088 Table C.1 (continued) — Subject 132 Card 1 1 1.000 1 13.000 122.000 124.000 131.000 133.000 142.000 151.000 153.000 212. 100 212.200 221.100 221.200 223. 100 223.200 232. 100 232.200 241 . 100 241.200 243.100 243.200 252. 100 252.200 254. 100 254.200 311.000 313.000 322.000 324.000 331.000 333.000 342.000 351.000 353.000 412.000 421.000 423.000 432.000 441.000 443.000 452.000 454.000 511.000 513.000 522.000 524.000 531.000 533.000 542.000 551.000 553.000 Resp. 6.000 5.000 3.000 2.000 4.000 4.000 4.000 3.000 5.000 RT 1934.000 1 124.000 570.000 598.000 350.000 1 138.000 1094.000 537.000 1874.000 410.000 1336.000 858.000 761.000 71 1.000 576.000 329.000 800.000 679.000 792.000 1035.000 1205.000 465.000 485.000 517.000 1 141.000 1224.000 730.000 465.000 408.000 874.000 527.000 1015.000 924.000 1001.000 379.000 351.000 428.000 378.000 549.000 346.000 1243.000 430.000 569.000 878.000 1233.000 1012.000 928.000 684.000 737.000 515.000 1 131.000 48.000 16.000 126 Dist MI GA RW MI SdRT 3.076 0.899 -0.591 -0.516 -1.182 0.936 0.818 -0.680 2.915 —1.021 1.469 0.183 -0.077 —0.212 -0.575 -1.239 0.028 -0.298 0.006 0.659 1.1 16 —0.873 -0.819 -0.733 0.944 1.167 -0.161 —0.873 -1.026 0.226 -0.706 0.606 0.361 0.568 -1.104 -1.180 —0.973 -1.107 -0.647 -1.193 1.219 -0.967 —0.594 0.237 1.192 0.597 0.372 -0.284 -0.142 ~0.739 0.917 Table C.1 (continued) - Subject 133 Card 11 1.000 1 13.000 122.000 124.000 131.000 133.000 142.000 144.000 151.000 153.000 212.100 212.200 214. 100 214.200 221.100 221.200 223.100 223.200 232.100 232.200 234. 100 234.200 241 . 100 241.200 243.100 243.200 252.100 252.200 254. 100 254.200 31 1.000 313.000 322.000 324.000 331.000 333.000 342.000 344.000 351.000 353.000 412.000 414.000 421.000 423.000 432.000 434.000 441.000 443.000 452.000 454.000 51 1.000 513.000 522.000 524.000 531.000 533.000 542.000 544.000 551.000 553.000 Resp. 5.000 5.000 RT 1508.000 925.000 784.000 822.000 1836.000 241 1.000 1317.000 1227.000 1336.000 2413.000 416.000 1847.000 559.000 1788.000 1237.000 1056.000 2553.000 600.000 290.000 1575.000 744.000 2699.000 1263.000 959.000 2519.000 869.000 610.000 943.000 725.000 4086.000 1289.000 1673.000 1 1 16.000 1044.000 1843.000 860.000 1756.000 2272.000 1 169.000 2485.000 526.000 1098.000 724.000 480.000 1002.000 651.000 367.000 363.000 508.000 412.000 1058.000 1079.000 2034.000 829.000 483.000 678.000 1567.000 822.000 753.000 1276.000 Order 1.000 33.000 32.000 53.000 7.000 10.000 40.000 4.000 42.000 9.000 30.000 24.000 55.000 23.000 22.000 20.000 18.000 39.000 60.000 25.000 1 1.000 37.000 3.000 26.000 36.000 31.000 13.000 45.000 57.000 14.000 58.000 51.000 48.000 29.000 41.000 21.000 16.000 6.000 28.000 56.000 2.000 15.000 12.000 17.000 43.000 46.000 34.000 54.000 47.000 50.000 27.000 35.000 19.000 52.000 59.000 38.000 44.000 8.000 5.000 49.000 127 2.100 2.200 CH0 CH0 CHO SdRT 0.369 -0.421 —0.61 1 —0.560 0.813 1.592 0.1 10 -0.012 0.136 1.594 —1 .1 10 0.828 —0.916 0.748 0.002 -0.243 1.784 .0.861 -1.280 0.460 -0.666 1.982 0.037 -0.375 1.738 —0.496 -0.847 -0.396 -0.691 3.860 0.072 0.592 -0.162 —0.259 0.823 -0.509 0.705 1.403 -0.090 1.692 —0.961 -0.186 -0.693 -1.023 —0.316 -0.792 —1.176 —1.182 -0.985 -1.1 15 -0.240 -0.212 1.081 -0.551 -1.019 -0.755 0.449 —0.560 —0.653 0.055 Table C.1 (continued) - Subject 134 Card 1 1 1.000 1 13.000 122.000 124.000 131.000 133.000 142.000 144.000 151.000 153.000 212.100 212.200 214.100 214.200 221.100 221.200 223.100 223.200 232. 100 232.200 234.100 234.200 241.100 241.200 243.100 243.200 252.100 252.200 254. 100 254.200 31 1.000 313.000 322.000 324.000 331.000 333.000 342.000 344.000 351.000 353.000 412.000 414.000 421.000 423.000 432.000 434.000 441.000 443.000 452.000 454.000 51 1.000 513.000 522.000 524.000 531.000 533.000 542.000 544.000 551.000 553.000 Resp. 5.000 5.000 5.000 5.000 4.000 RT 1081.000 2437.000 21 19.000 704.000 1 1 14.000 1904.000 1704.000 1087.000 1546.000 1681.000 1214.000 1287.000 699.000 2953.000 1334.000 1205.000 1361.000 1293.000 533.000 924.000 1 194.000 2277.000 2447.000 2019.000 1290.000 1254.000 41 1.000 1560.000 492.000 2082.000 1001 .000 2585.000 3639.000 2017.000 1686.000 964.000 2753.000 1845.000 1 137.000 3045.000 692.000 880.000 376.000 676.000 1233.000 1982.000 868.000 593.000 3331.000 1042.000 191 1.000 1 198.000 1 150.000 1087.000 1 1 14.000 2582.000 1385.000 1013.000 896.000 1 133.000 Order 25.000 29.000 13.000 42.000 34.000 20.000 27.000 41.000 48.000 12.000 33.000 22.000 45.000 1.000 55.000 32.000 37.000 56.000 21.000 1 1.000 52.000 46.000 19.000 128 6000 SdRT -0.539 1.276 0.850 -1.043 —0.495 0.563 0.295 -0.531 0.083 0.264 —0.361 -0.263 -1.050 1.966 —0.200 -0.373 -0. 164 -0.255 —1.272 -0.749 -0.388 1.062 1.289 0.716 -0.259 —0.307 -1.436 0.102 —1.327 0.801 1 .474 2.885 0.714 0.271 -0.696 1 .699 0.484 -0.464 2.090 -1.060 —0.808 -1 .482 -1 .081 -0.335 0.667 -0.824 -1 .192 2.472 —0.591 0.572 -0.382 -0.447 -0.531 —0.495 1 .470 —0.132 -0.630 -0.787 -0.469 Table C.1 (continued) - Subject 135 Card 111.000 113.000 122.000 124.000 131.000 133.000 142.000 144.000 151.000 153.000 212.100 212.200 214. 100 214.200 221.100 221.200 223. 100 223.200 232. 100 232.200 234.100 234.200 241.100 241.200 243. 100 243.200 252.100 252.200 254.100 254.200 311.000 313.000 322.000 324.000 331.000 333.000 342.000 344.000 351.000 353.000 412.000 414.000 421.000 423.000 432.000 434.000 441.000 443.000 452.000 454.000 511.000 513.000 522.000 524.000 531.000 533.000 542.000 544.000 551.000 553.000 RT 1298.000 1377.000 939.000 1597.000 2392.000 1401.000 1062.000 1793.000 993.000 4950.000 776.000 850.000 569.000 33.000 1233.000 1595.000 901.000 929.000 523.000 1461 .000 649.000 2338.000 776.000 1282.000 1530.000 1679.000 603.000 681.000 612.000 2025.000 1440.000 2187.000 947.000 1316.000 3612.000 1767.000 1819.000 839.000 1317.000 1380.000 576.000 754.000 557.000 609.000 472.000 978.000 827.000 1518.000 1291.000 1350.000 2776.000 1098.000 1310.000 479.000 1010.000 1 170.000 750.000 1243.000 1586.000 868.000 Order 60.000 18.000 9.000 41.000 7.000 51.000 13.000 3.000 43.000 45.000 34.000 37.000 57.000 59.000 1.000 53.000 12.000 8.000 39.000 36.000 56.000 10.000 58.000 20.000 15.000 55.000 17.000 40.000 22.000 54.000 32.000 21 .000 33.000 31 .000 14.000 25.000 28.000 50.000 27.000 6.000 29.000 35.000 5.000 47.000 48.000 16.000 38.000 49.000 1 1.000 4.000 46.000 2.000 52.000 24.000 44.000 23.000 26.000 19.000 30.000 42.000 129 SdRT 0.025 0.126 -0.434 0.408 1.425 0.157 —0.277 0.658 -0.365 4.697 —0.642 —0.548 -0.907 —1.593 -0.058 0.405 -0.483 -0.447 -0.966 0.234 —0.805 1.356 0.642 0.005 0.322 0.513 —0.864 —0.764 -0.852 0.955 0.207 1.162 -0.424 0.048 2.985 0.625 0.692 -0.562 0.050 0.130 -0.898 —0.671 -0.923 -0.856 -1.031 -0.384 —0.577 0.307 0.016 0.092 1.916 -0.231 0.041 —1.022 -0.343 -0.138 -0.676 -0.045 0.394 —0.525 Table C.1 (continued) - Subject 136 Card 11 1.000 1 13.000 122.000 124.000 131.000 133.000 142.000 144.000 151.000 153.000 212.100 212.200 214.100 214.200 221.100 221.200 223.100 223.200 232.100 232.200 234.100 234.200 241.100 241.200 243.100 243.200 252.100 252.200 254.100 254.200 31 1.000 313.000 322.000 324.000 331.000 333.000 342.000 344.000 351.000 353.000 412.000 414.000 421.000 423.000 432.000 434.000 441.000 443.000 452.000 454.000 51 1.000 513.000 522.000 524.000 531.000 533.000 542.000 544.000 551.000 553.000 Resp. 6.000 3.000 RT 1223.000 1923.000 840.000 690.000 1 101.000 982.000 805.000 691.000 766.000 1083.000 316.000 2401.000 373.000 1 120.000 570.000 1 171.000 1310.000 571.000 858.000 981.000 686.000 968.000 2382.000 833.000 541.000 1280.000 417.000 633.000 609.000 1 128.000 1253.000 903.000 1 140.000 389.000 336.000 793.000 701.000 1 180.000 970.000 1427.000 219.000 1421.000 998.000 660.000 575.000 583.000 437.000 401.000 618.000 838.000 1855.000 712.000 1541.000 1 1 1 1.000 733.000 1323.000 653.000 1097.000 71 1.000 3356.000 Order 36.000 18.000 45.000 21.000 42.000 41 .000 26.000 40.000 37.000 52.000 32.000 6.000 33.000 48.000 56.000 7.000 1.000 14.000 12.000 20.000 9.000 4.000 30.000 29.000 43.000 35.000 2.000 24.000 57.000 38.000 60.000 46.000 49.000 59.000 50.000 15.000 23.000 13.000 28.000 31.000 58.000 19.000 22.000 17.000 44.000 55.000 47.000 3.000 25.000 27.000 39.000 34.000 16.000 53.000 51.000 10.000 1 1.000 54.000 130 SdRT 0.460 1.730 -0.235 -0.508 0.238 0.022 -0.299 .0506 -0.370 0.205 -1 .186 2.597 —1 .083 0.273 -0.725 0.365 0.617 —0.724 —0.203 0.020 -0.515 -0.003 2.563 -0.248 —0.778 0.563 -1 .003 -0.61 1 —0.655 0.287 0.514 -0.121 0.309 -1 .054 -1 .1 50 .0321 -0.488 0.382 0.830 -1.362 0.819 0.051 -0.562 -0.716 -0.702 -0.967 -1.032 -0.638 -0.239 1.606 —0.468 1.037 0.256 -0.430 0.641 -0.575 0.231 -0.470 4.330 Table C.1 (continued) — Subject 137 Card 111.000 113.000 122.000 124.000 131.000 133.000 142.000 144.000 151.000 153.000 212.100 212.200 214. 100 214.200 221.100 221.200 223. 100 223.200 232. 100 232.200 234.100 234.200 241.100 241.200 243.100 243.200 252.100 252.200 254.100 254.200 311.000 313.000 322.000 324.000 331.000 333.000 342.000 344.000 351.000 353.000 412.000 414.000 421.000 423.000 432.000 434.000 441.000 443.000 452.000 454.000 511.000 513.000 522.000 524.000 531.000 533.000 542.000 544.000 551.000 553.000 Resp. 5.000 7.000 3.000 3.000 5.000 3.000 4.000 3.000 4.000 4.000 3.000 4.000 RT 4622.000 1057.000 1058.000 1018.000 2115.000 1675.000 1459.000 677.000 1271.000 3793.000 406.000 950.000 748.000 1480.000 861.000 1524.000 782.000 1700.000 447.000 1701.000 1063.000 972.000 3222.000 2341.000 1 150.000 1747.000 3385.000 21 16.000 1592.000 804.000 1973.000 1863.000 356.000 1095.000 343.000 530.000 410.000 561.000 375.000 281.000 1009.000 616.000 1356.000 1598.000 1040.000 794.000 918.000 913.000 779.000 1921.000 796.000 2075.000 Conf. 3.000 4.000 3.000 3.000 2.000 2.000 3.000 3.000 4.000 4.000 Order 56.000 51.000 34.000 44.000 5.000 33.000 9.000 22.000 59.000 58.000 1 1.000 29.000 27.000 24.000 21.000 19.000 6.000 43.000 131 Dist SdRT 3.909 -0.246 -0.245 -0.291 0.987 0.474 0.223 -0.689 2.943 -1 .005 -0.371 .0109 -0.133 -0.727 -0.446 0.164 -0.636 -0.853 —0.628 0.247 -0.474 0.298 -0.566 0.504 -0.957 0.505 —0.239 -0.345 2.277 1.251 -0.137 0.558 2.467 0.988 0.378 —0.541 0.822 0.693 -1.063 -0.202 —1.078 -0.860 —1.000 -0.824 -1.041 —1.150 -0.302 —0.760 0.103 0.385 -0.266 -0.5 52 -0.408 -0.414 -0.570 0.761 -0.550 0.941 Table C.1 (continued) - Subject 138 Card 1 1 1.000 1 13.000 122.000 124.000 131.000 133.000 142.000 144.000 151.000 153.000 212. 100 212.200 214.100 214.200 221.100 221.200 223.100 223.200 232.100 232.200 234. 100 234.200 241.100 241.200 243. 100 243.200 252. 100 252.200 254. 100 254.200 31 1.000 313.000 322.000 324.000 331.000 333.000 342.000 344.000 351.000 353.000 412.000 414.000 421.000 423.000 432.000 434.000 441.000 443.000 452.000 454.000 511.000 513.000 522.000 524.000 531.000 533.000 542.000 544.000 551.000 553.000 RT 861.000 921.000 1785.000 1561.000 741.000 1762.000 1757.000 1 1 16.000 916.000 1636.000 763.000 1342.000 424.000 1017.000 672.000 2519.000 759.000 974.000 617.000 2796.000 637.000 704.000 1355.000 1555.000 1343.000 1007.000 1 1 19.000 697.000 903.000 894.000 1488.000 4773.000 1033.000 807.000 922.000 1940.000 661.000 1538.000 684.000 657.000 51 1.000 855.000 735.000 432.000 568.000 579.000 598.000 1026.000 917.000 635.000 998.000 469.000 1390.000 1 158.000 936.000 1395.000 1404.000 783.000 748.000 1285.000 Conf. 2.000 2.000 2.000 2.000 1.000 2.000 2.000 2.000 2.000 2.000 1.000 2.000 1.000 Order 48.000 49.000 28.000 1.000 43.000 15.000 5.000 59.000 24.000 38.000 16.000 10.000 47.000 25.000 40.000 9.000 44.000 45.000 39.000 6.000 12.000 34.000 4.000 50.000 52.000 53.000 17.000 30.000 41.000 26.000 29.000 2.000 56.000 13.000 37.000 3.000 57.000 46.000 36.000 23.000 14.000 55.000 18.000 35.000 58.000 21 .000 32.000 33.000 54.000 19.000 22.000 27.000 42.000 51.000 20.000 1 1.000 8.000 60.000 7.000 31 .000 132 SdRT 0.378 0.289 0.980 0.651 0.554 0.946 0.939 0.003 0.297 0.761 0.522 0.329 -1.020 0.148 0.655 2.058 0.527 0.212 0.736 2.465 0.707 0.608 0.348 0.642 0.331 0.163 0.002 0.618 0.316 0.329 0.544 5.370 0.125 0.457 0.288 1.208 0.671 0.617 0.638 0.677 0.892 0.386 0.563 -1.008 0.808 0.792 0.764 0.135 0.295 0.710 0.176 0.953 0.400 0.059 0.267 0.407 0.420 0.492 0.544 0.245 Table C.1 (continued) - Subject 139 Card 11 1.000 1 13.000 122.000 124.000 131.000 133.000 142.000 144.000 151.000 153.000 212.100 212.200 214. 100 214.200 221. 100 221.200 223. 100 223.200 232. 100 232.200 234.100 234.200 241. 100 241.200 243.100 243.200 252.100 252.200 254. 100 254.200 31 1.000 313.000 322.000 324.000 331.000 333.000 342.000 344.000 351.000 353.000 412.000 414.000 421.000 423.000 432.000 434.000 441.000 443.000 452.000 454.000 51 1.000 513.000 522.000 524.000 531.000 533.000 542.000 544.000 551.000 553.000 Resp. 5.000 7.000 2.000 4.000 8.000 3.000 8.000 5.000 6.000 8.000 3.000 4.000 5.000 6.000 6.000 2.000 6.000 3.000 3.000 4.000 RT 748.000 2233.000 1370.000 1609.000 1697.000 2766.000 2943.000 2169.000 1 199.000 1442.000 547.000 830.000 2969.000 2467.000 806.000 1783.000 1091.000 818.000 576.000 934.000 560.000 1 182.000 2433.000 2143.000 1023.000 2861.000 484.000 1493.000 71 1.000 1282.000 2750.000 1 543.000 1410.000 2129.000 1478.000 1381.000 2394.000 5644.000 1717.000 2360.000 1348.000 2751.000 400.000 407.000 389.000 1098.000 725.000 1992.000 2892.000 437.000 2050.000 861 .000 988.000 1479.000 1977.000 728.000 1240.000 1408.000 1350.000 61 1 .000 Order 19.000 37.000 34.000 7.000 4.000 12.000 5.000 2.000 27.000 40.000 49.000 55.000 1.000 28.000 35.000 32.000 24.000 14.000 45.000 38.000 54.000 48.000 16.000 46.000 52.000 6.000 29.000 41.000 10.000 50.000 18.000 51.000 26.000 1 1.000 42.000 22.000 43.000 39.000 25.000 44.000 3.000 13.000 59.000 58.000 23.000 21 .000 33.000 17.000 57.000 30.000 20.000 36.000 15.000 8.000 47.000 53.000 31.000 9.000 56.000 133 SdRT 0.866 0.734 0.196 0.062 0.156 1 .308 1.499 0.380 0.1 18 -1.082 0.778 1.527 0.986 0.803 0.249 0.496 0.791 -1.051 0.666 -1.068 0.398 0.949 0.637 0.570 1.41 1 -1.150 0.063 0.291 1.291 0.009 0.153 0.622 0.079 0.184 0.907 4.409 0.178 0.871 0.220 1.292 -1.241 -1.233 -1.253 0.489 0.891 0.474 1.444 -1.201 0.537 0.744 0.607 0.078 0.458 0.887 0.336 0.155 0.217 -1.014 Table C.1 (continued) - Subject 1101 Card 1 1 1.000 1 13.000 122.000 124.000 131.000 133.000 142.000 144.000 151.000 153.000 212.100 212.200 214. 100 214.200 221 . 100 221.200 223. 100 223.200 232. 100 232.200 234.100 234.200 241.100 241.200 243.100 243.200 252. 100 252.200 254.100 254.200 31 1.000 313.000 322.000 324.000 331.000 333.000 342.000 344.000 351.000 353.000 412.000 414.000 421.000 423.000 432.000 434.000 441.000 443.000 452.000 454.000 51 1.000 513.000 522.000 524.000 531.000 533.000 542.000 544.000 551.000 553.000 RT 829.000 1001.000 729.000 667.000 1008.000 1786.000 809.000 993.000 494.000 1 1 10.000 577.000 810.000 987.000 626.000 566.000 441.000 1770.000 669.000 882.000 1414.000 452.000 1042.000 1001.000 735.000 674.000 572.000 656.000 539.000 484.000 544.000 984.000 1220.000 1 175.000 648.000 1078.000 893.000 1039.000 843.000 403.000 171 1.000 464.000 1041.000 575.000 610.000 443.000 61 1.000 370.000 684.000 925.000 814.000 1260.000 955.000 648.000 600.000 1 195.000 1452.000 486.000 997.000 342.000 459.000 Order 56.000 22.000 1.000 33.000 25.000 50.000 55.000 7.000 41.000 15.000 13.000 21 .000 3.000 40.000 43.000 54.000 4.000 47.000 2.000 12.000 42.000 37.000 17.000 51.000 30.000 60.000 1 1.000 59.000 53.000 49.000 46.000 52.000 27.000 45.000 48.000 16.000 38.000 31 .000 32.000 10.000 58.000 36.000 23.000 19.000 18.000 26.000 44.000 5.000 8.000 20.000 24.000 14.000 57.000 39.000 6.000 28.000 35.000 9.000 29.000 34.000 134 SdRT 0.003 0.500 0.294 0.475 0.520 2.791 0.061 0.476 0.980 0.818 0.738 0.058 0.459 0.595 0.770 —1.135 2.744 0.470 0.152 1.705 —1.103 0.619 0.500 0.277 0.455 0.753 0.508 0.849 -1.010 0.834 0.450 1.139 1.007 0.531 0.724 0.184 0.610 0.038 -1.246 2.572 —1.068 0.616 0.744 0.642 -1.129 0.639 -1.342 0.426 0.278 1.256 0.365 0.531 0.671 1.066 1.816 0.488 -1.424 —1.083 Table C.1 (continued) - Subject 1 102 * Card 11 1.000 1 13.000 122.000 124.000 131.000 133.000 142.000 144.000 151.000 153.000 212. 100 212.200 214. 100 214.200 221. 100 221.200 223. 100 223.200 232. 100 232.200 234. 100 234.200 241. 100 241.200 243. 100 243.200 252. 100 252.200 254.100 254.200 31 1.000 313.000 322.000 324.000 331.000 333.000 342.000 344.000 351.000 353.000 412.000 414.000 421.000 423.000 432.000 434.000 441.000 443.000 452.000 454.000 51 1.000 513.000 522.000 524.000 531.000 533.000 542.000 544.000 551.000 553.000 RT 2444.000 2830.000 2234.000 4138.000 2627.000 4022.000 1823.000 1434.000 3384.000 2970.000 3310.000 1500.000 1826.000 3350.000 1546.000 2032.000 1643.000 987.000 1370.000 598.000 1264.000 1207.000 1502.000 1439.000 3759.000 1692.000 561.000 2379.000 592.000 1719.000 4855.000 3747.000 4093.000 955.000 3256.000 3107.000 4365.000 2246.000 1543.000 1613.000 500. 000 2735. 000 617. 000 564.000 1466.000 1243.000 442.000 446. 000 3822. 000 1407. 000 5333.000 2453.000 2694.000 1849.000 844. 000 3504. 000 1253. 000 3790.000 3371.000 1260.000 Conf. 3.000 4.000 2.000 2.000 3.000 4.000 2.000 Order 32.000 3.000 43.000 50.000 1 1.000 40.000 28.000 55.000 1.000 8.000 12.000 20.000 17.000 21.000 19.000 52.000 35.000 16.000 15.000 33.000 42.000 60.000 37.000 57.000 56.000 14.000 44.000 22.000 51.000 41.000 5.000 2.000 10.000 47.000 48.000 18.000 7.000 27.000 39.000 31.000 24.000 34.000 9.000 53.000 6.000 58.000 59.000 46.000 25.000 49.000 26.000 45. 000 36. 000 4.000 54.000 38.000 30. 000 23. 000 I3. 000 29.000 135 Dist SdRT 0.202 0.513 0.033 1.567 0.350 1.473 0.298 0. 611 0.960 0.626 0.900 0.558 0.295 0.932 0.521 0.129 0.443 0.971 0.663 —1.284 0.748 0.794 0. 556 -0. 607 1.262 0.403 -1.314 0.150 -1.289 0.381 2.144 1.252 1.531 0.997 0.856 0.736 1.750 0.043 0.523 0.467 -1.363 0.437 -1.269 —1 .312 0. 585 0. 765 -1.410 -1.407 1.312 0.633 2.529 0.210 0.404 0.277 —1.086 1 .056 0. 757 1.287 0. 949 0.751 Table C.1 (continued) - Subject 1201 Card 111.000 113.000 122.000 124.000 131.000 133.000 142.000 144.000 151.000 153.000 212.100 212.200 214. 100 214.200 221.100 221.200 223.100 223.200 232. 100 232.200 234.100 234.200 241.100 241.200 243.100 243.200 252.100 252.200 254. 100 254.200 311.000 313.000 322.000 324.000 331.000 333.000 342.000 344.000 351.000 353.000 412.000 414.000 421.000 423.000 432.000 434.000 441.000 443.000 452.000 454.000 511.000 513.000 522.000 524.000 531.000 533.000 542.000 544.000 551.000 553.000 Resp. 5.000 4.000 3.000 5.000 RT 922.000 1412.000 721.000 806.000 1300.000 1777.000 1689.000 1964.000 1 140.000 2129.000 825.000 978.000 888.000 1200.000 884.000 863.000 1 102.000 820.000 726.000 834.000 864.000 2327.000 1021 .000 1 133.000 1877.000 3358.000 821.000 651.000 609.000 1802.000 1380.000 1754.000 1229.000 1661.000 1737.000 1307.000 2784.000 1379.000 1319.000 2299.000 912.000 1539.000 551.000 823.000 733.000 71 1.000 657.000 774.000 2038.000 848.000 2543.000 1678.000 1902.000 870.000 780.000 1 188.000 1076.000 1559.000 782.000 1 178.000 Conf. 2.000 3.000 2.000 2.000 1.000 Order 18.000 56.000 58.000 55.000 2.000 57.000 5.000 21.000 15.000 8.000 42.000 60.000 19.000 51.000 37.000 50.000 29.000 24.000 38.000 59.000 36.000 23.000 34.000 33.000 13.000 1.000 9.000 41.000 47.000 39.000 52.000 4.000 43.000 40.000 1 1.000 49.000 28.000 30.000 44.000 10.000 7.000 14.000 35.000 17.000 22.000 26.000 32.000 3.000 12.000 31.000 46.000 20.000 53.000 25.000 54.000 48.000 45.000 6.000 27.000 16.000 136 2.200 2. 100 SdRT 0.618 0.203 0.954 0.812 0.016 0.815 0.668 1.128 0.252 1.405 0.780 0.524 0.674 0.152 0.681 0.716 0.316 0.788 0.946 0.765 0.715 1.736 0.452 0.264 0.983 3.464 0.787 —1.072 -1.142 0.857 0.150 0.776 0.103 0.621 0.748 0.028 2.502 0.148 0.048 1.690 0.634 0.416 —1.239 0.783 0.934 0.971 -1.061 0.865 1.252 0.741 2.098 0.649 1.024 0.705 0.855 0.172 0.359 0.450 0.852 0.189 Table C.1 (continued) - Subject 1301 Card 111.000 113.000 122.000 124.000 131.000 133.000 142.000 144.000 151.000 153.000 212.100 212.200 214. 100 214.200 221.100 221.200 223.100 223.200 232. 100 232.200 234.100 234.200 241.100 241.200 243.100 243.200 252.100 252.200 254.100 254.200 311.000 313.000 322.000 324.000 331.000 333.000 342.000 344.000 351.000 353.000 412.000 414.000 421.000 423.000 432.000 434.000 441.000 443.000 452.000 454.000 511.000 513.000 522.000 524.000 531.000 533.000 542.000 544.000 551.000 553.000 Resp. 7.000 8.000 4.000 4.000 8.000 8.000 3.000 4.000 4.000 1.000 3.000 1.000 RT 2207.000 2027.000 1072.000 1614.000 2181 .000 1814.000 2049.000 1818.000 1601.000 2009.000 884.000 1700.000 583.000 4536.000 1229.000 1322.000 1869.000 1347.000 725.000 2121.000 1064.000 2684.000 883.000 2557.000 1290.000 1007.000 968.000 1572.000 909.000 5049.000 1605.000 271 1.000 1938.000 1 174.000 1314.000 1 164.000 650.000 2125.000 1274.000 1938.000 633.000 1087.000 560.000 1005.000 1 104.000 1495.000 479.000 896.000 1336.000 1439.000 1 140.000 1055.000 967.000 740.000 1041.000 2217.000 694.000 631.000 693.000 965.000 Order 20.000 34.000 60.000 1.000 31.000 2.000 22.000 15.000 28.000 6.000 30.000 53.000 37.000 18.000 49.000 51.000 38.000 25.000 58.000 39.000 12.000 21 .000 59.000 32.000 57.000 52.000 8.000 26.000 33.000 35.000 40.000 9.000 10.000 46.000 4.000 41.000 29.000 7.000 36.000 19.000 56.000 3.000 5.000 44.000 27.000 13.000 42.000 47.000 17.000 55.000 14.000 43.000 1 1.000 16.000 23.000 48.000 45.000 50.000 54.000 24.000 137 SdRT 0.866 0.651 0.485 0.160 0.835 0.398 0.678 0.403 0.145 0.630 0.708 0.262 —1.066 3.636 0.298 0.187 0.463 0.157 0.897 0.763 0.494 1.433 0.709 1.282 0.225 0.562 0.608 0.1 10 0.678 4.246 0.149 1.465 0.546 0.363 0.197 0.375 0.986 0.768 0.244 0.546 —1.007 0.467 -1.094 0.564 0.019 -1.190 0.694 0.171 0.048 0.404 0.505 0.609 0.879 0.521 0.877 0.934 —1.009 0.935 0.612 Table C.1 (continued) - Subject 210 Card 112.000 121.000 123.000 132.000 141.000 143.000 152.000 154.000 211.100 21 1.200 213.100 213.200 222.100 222.200 224.100 224.200 231.100 231.200 233.100 233.200 242.100 242.200 251 .100 251.200 253.100 253.200 312.000 321.000 323.000 332.000 341.000 343.000 352.000 354.000 41 1.000 413.000 422.000 424.000 431 .000 433.000 442.000 451 .000 453.000 512.000 521 .000 523.000 532.000 541.000 543.000 552.000 554.000 Resp. 1.000 6.000 4.000 4.000 6.000 4.000 8.000 5.000 2.000 2.000 6.000 3.000 6.000 4.000 6.000 6.000 3.000 4.000 6.000 RT 772.000 1386.000 774.000 949.000 686.000 2128.000 1361.000 1438.000 1334.000 558.000 1438.000 1532.000 1061.000 1353.000 1064.000 1395.000 792.000 960.000 1 133.000 1052.000 436.000 1405.000 646.000 1400.000 740.000 1581.000 918.000 770.000 1622.000 1507.000 915.000 865.000 1453.000 1 1 18.000 1891.000 1594.000 526.000 2030.000 828.000 838.000 1034.000 1047.000 946.000 1939.000 1052.000 1314.000 1008.000 1485.000 514.000 91 1.000 1809.000 Order 25.000 55.000 23.000 20.000 42.000 40.000 35.000 60.000 138 Dist SdRT 0.950 0.542 0.945 0.520 —1.159 2.346 0.482 0.669 0.416 -1.470 0.669 0.897 0.248 0.462 0.240 0.564 0.902 0.493 0.073 0.270 -1.767 0.588 -1.256 0.576 -1.028 1.016 0.595 0.955 1.1 16 0.836 0.603 0.724 0.705 0.109 1.770 1.048 -1.548 2.108 0.814 0.790 0.313 0.282 0.527 1.887 0.270 0.367 0.377 0.783 —1.577 0.612 1.571 Table C.1 (continued) - Subject 211 Card 1 12.000 121.000 123.000 132.000 141.000 143.000 152.000 154.000 21 1.100 21 1.200 213. 100 213.200 222.100 222.200 224.100 224.200 231.100 231.200 233.100 233.200 242.100 242.200 25 1 . 100 251.200 253.100 253.200 312.000 321.000 323.000 332.000 341.000 343.000 352.000 354.000 41 1.000 413.000 422.000 424.000 431.000 433.000 442.000 451 .000 453.000 512.000 521.000 523.000 532.000 541.000 543.000 552.000 554.000 Resp. 8.000 RT 1041.000 605.000 1 136.000 1885.000 903.000 1781.000 952.000 1750.000 532.000 719.000 1852.000 1623.000 1051.000 885.000 780.000 1247.000 769.000 550.000 496.000 1436.000 688.000 791.000 738.000 777.000 665.000 1599.000 1320.000 1088.000 1288.000 1993.000 1335.000 1047.000 1306.000 1258.000 524.000 822.000 712.000 855.000 495.000 353.000 576.000 453.000 410.000 1327.000 1072.000 502.000 948.000 989.000 590.000 680.000 1372.000 Conf. Order 46.000 38.000 14.000 19.000 23.000 26.000 45.000 57.000 27.000 139 SdRT 0.1 15 0.901 0.337 2.083 0.206 1.841 0.092 1.769 -1.071 0.635 2.006 1.473 0.139 0.248 0.493 0.596 0.519 -1.029 —1. 155 1.036 0.708 0.467 0.591 0.500 0.761 1.417 0.766 0.225 0.691 2.335 0.801 0.129 0.733 0.621 -1.090 0.395 0.652 0.318 -1.158 —1.489 0.969 -1.256 -1.356 0.782 0.188 —1 .141 0.101 0936 0.726 0.887 Table C.1 (continued) — Subject 212 Card 1 12.000 121.000 123.000 132.000 141.000 143.000 152.000 154.000 21 1 .100 211.200 213. 100 213.200 222.100 222.200 224.100 224.200 231.100 231.200 233. 100 233.200 242.100 242.200 251.100 251.200 253.100 253.200 312.000 321.000 323.000 332.000 341.000 343.000 352.000 354.000 41 1.000 413.000 422.000 424.000 431.000 433.000 442.000 451 .000 453.000 512.000 521.000 523.000 532.000 541.000 543.000 552.000 554.000 RT 899.000 687.000 914.000 1781.000 744.000 1334.000 989.000 976.000 1245.000 657.000 836.000 779.000 824.000 648.000 744.000 557.000 455.000 521.000 404.000 1 154.000 532.000 414.000 365.000 720.000 749.000 1725.000 684.000 1872.000 768.000 1919.000 2146.000 1446.000 946.000 768.000 1440.000 1045.000 594.000 880.000 374.000 453.000 617.000 349.000 446.000 1 191.000 799.000 721.000 403.000 627.000 569.000 531.000 1400.000 Conf. 2.000 1.000 2.000 2.000 4.000 2.000 3.000 3.000 1.000 Order 55.000 14.000 8.000 18.000 15.000 21 .000 43.000 16.000 2.000 38.000 17.000 45.000 25.000 35.000 39.000 46.000 47.000 59.000 57.000 20.000 22.000 53.000 31 .000 27.000 24.000 5.000 44.000 41 .000 54.000 19.000 4.000 1 1.000 49.000 56.000 1.000 10.000 28.000 7.000 48.000 29.000 51 .000 37.000 26.000 60.000 33.000 50.000 52.000 58.000 6.000 13.000 36.000 140 Dist SdRT 0.053 0.425 0.087 2.045 0.297 1.036 0.257 0.227 0.835 0.493 0.089 0.217 0.116 0.513 0.297 0.719 0.949 0.800 -1.064 0.629 0.775 -1.042 -1 . 152 0.351 0.285 1.919 0.432 2.251 0.242 2.357 2.869 1.289 0.160 0.242 1.275 0.383 0.635 0.01 1 -1.132 0.954 0.583 -1.188 0.969 0.713 0.172 0.348 -1.067 0.561 0.692 0.778 1.185 Table C.1 (continued) - Subject 213 Card 112.000 114.000 121.000 123.000 132.000 134.000 141.000 143.000 152.000 154.000 211.100 211.200 213.100 213.200 222.100 222.200 224. 100 224.200 231.100 231.200 233.100 233.200 242.100 242.200 244.100 244.200 251.100 251.200 253.100 253.200 312.000 314.000 321.000 323.000 332.000 334.000 341.000 343.000 352.000 354.000 411.000 413.000 422.000 424.000 431.000 433.000 442.000 444.000 451.000 453.000 512.000 514.000 521.000 523.000 532.000 534.000 541.000 543.000 552.000 554.000 Resp. 3.000 2.000 6.000 5.000 5.000 4.000 5.000 5.000 3.000 4.000 2.000 2.000 2.000 6.000 1.000 1.000 RT 1221.000 663.000 908.000 1 190.000 1662.000 1 108.000 1 124.000 805.000 979.000 1 153.000 561.000 416.000 1468.000 730.000 929.000 2049.000 1243.000 697.000 532.000 773.000 662.000 1616.000 400.000 1222.000 760.000 979.000 520.000 757.000 747.000 1375.000 961.000 993.000 478.000 427.000 1027.000 593.000 927.000 641.000 974.000 888.000 669.000 457.000 382.000 408.000 426.000 1444.000 714.000 643.000 552.000 580.000 1228.000 973.000 1029.000 601.000 576.000 730.000 806.000 886.000 1257.000 635.000 Conf. 1.000 2.000 2.000 1.000 2.000 2.000 Order 44.000 55.000 46.000 48.000 36.000 17.000 39.000 37.000 50.000 33.000 57.000 28.000 21.000 22.000 38.000 1.000 4.000 59.000 31 .000 56.000 40.000 45.000 58.000 3.000 9.000 13.000 16.000 1 1.000 5.000 18.000 41 .000 20.000 51.000 60.000 141 Ques 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 2.100 2.200 2.100 2.200 2.100 2.200 Dist SdRT 0.993 0.582 0.109 0.906 2.239 0.674 0.720 0.181 0.310 0.801 0.871 -1.280 1.691 0.393 0.169 3.332 1.056 0.486 0.952 0.272 0.585 2.109 -1.325 0.996 0.308 0.310 0.986 0.317 0.345 1.428 0.259 0.350 -1.105 -1.249 0.446 0.780 0.163 0.645 0.296 0.053 0.565 -1.164 -1.376 -1.303 —1.252 1.623 0.438 0.639 0.896 0.817 1.013 0.293 0.451 0.758 0.828 0.393 0.179 0.047 1.095 0.662 Table C.1 (continued) - Subject 214 Card 1 12.000 1 14.000 121.000 123.000 132.000 134.000 141.000 143.000 152.000 154.000 21 1.100 21 1.200 213.100 213.200 222. 100 222.200 224. 100 224.200 231 . 100 231.200 233.100 233.200 242. 100 242.200 244.100 244.200 251. 100 251.200 253. 100 253.200 312.000 314.000 321.000 323.000 332.000 334.000 341.000 343.000 352.000 354.000 41 1.000 413.000 422.000 424.000 431.000 433.000 442.000 444.000 451.000 453.000 512.000 514.000 521.000 523.000 532.000 534.000 541.000 543.000 552.000 554.000 Resp. 3.000 3.000 5.000 5.000 4.000 RT 1 188.000 1256.000 790.000 1 122.000 2675.000 1056.000 1697.000 1353.000 1 155.000 1382.000 651.000 586.000 2856.000 1534.000 821.000 1425.000 1334.000 1947.000 519.000 442.000 2514.000 795.000 853.000 1928.000 767.000 3250.000 1 158.000 1091.000 1566.000 2606.000 1505.000 2685.000 2144.000 1045.000 3395.000 2095.000 1873.000 2877.000 667.000 2627.000 772.000 686.000 1928.000 1712.000 842.000 582.000 1324.000 457.000 497 .000 664.000 4782.000 3343.000 862.000 3252.000 1052.000 1633.000 646.000 761.000 1 108.000 1252.000 Conf. 1.000 2.000 1.000 1.000 2.000 1.000 2.000 2.000 1.000 1.000 Order 44.000 58.000 15.000 55.000 2.000 42.000 23.000 53.000 30.000 6.000 46.000 49.000 9.000 40.000 57.000 38.000 5.000 21 .000 50.000 51 .000 18.000 54.000 27.000 4.000 19.000 26.000 1.000 7.000 34.000 29.000 14.000 22.000 28.000 56.000 45.000 48.000 142 Dist SdRT 0.361 0.288 0.790 0.432 1.241 0.503 0.187 0.183 0.397 0.152 0.940 -1.010 1.437 0.012 0.757 0.106 0.204 0.457 -1.082 -1.165 0.785 0.722 0.436 0.815 1.861 0.393 0.466 1.167 0.019 1.252 0.669 0.515 2.017 0.616 0.377 1.459 0.923 1.190 0.809 0.902 0.436 0.204 0.734 -1.014 0.215 -1.149 -1.106 0.926 3.512 1.961 0.712 1.863 0.508 0.1 18 0.945 0.821 0.447 0.292 Table C.1 (continued) - Subject 215 Card 1 12.000 1 14.000 121.000 123.000 132.000 134.000 141.000 143.000 152.000 154.000 21 1.100 21 1.200 213. 100 213.200 222. 100 222.200 224. 100 224.200 231 . 100 231.200 233. 100 233.200 242. 100 242.200 244.100 244.200 251. 100 251.200 253. 100 253.200 312.000 314.000 321.000 323.000 332.000 334.000 341.000 343.000 352.000 354.000 41 1.000 413.000 422.000 424.000 431.000 433.000 442.000 444.000 451 .000 453.000 512.000 514.000 521.000 523.000 532.000 534.000 541.000 543.000 552.000 554.000 Resp. 8.000 6.000 5000 RT 1531.000 578.000 81 1.000 787.000 1 176.000 793.000 585.000 1 167.000 1477.000 875.000 891 .000 1013.000 713.000 929.000 773.000 631.000 721.000 462.000 1044.000 926.000 572.000 1365.000 629.000 901.000 872.000 912.000 535.000 575.000 541.000 487.000 403.000 775.000 675.000 540.000 989.000 556.000 949.000 1909.000 855.000 1034.000 600.000 1599.000 523.000 716.000 435.000 1208.000 487.000 658.000 698.000 385.000 941.000 1280.000 641.000 664.000 463.000 827.000 619.000 655.000 672.000 1348.000 Conf. 2.000 2.000 2.000 2.000 Order 45.000 59.000 36.000 12.000 46.000 48.000 55.000 35.000 1.000 42.000 5.000 7.000 34.000 44.000 28.000 25.000 15.000 57.000 14.000 38.000 22.000 41 .000 2.000 10.000 1 1.000 54.000 9.000 30.000 21 .000 58.000 37.000 17.000 53.000 60.000 27.000 49.000 39.000 6.000 20.000 26.000 16.000 13.000 51.000 47.000 33.000 56.000 29.000 50.000 23.000 43.000 52.000 31 .000 32.000 24.000 40.000 4.000 19.000 18.000 SdRT 2.194 0.759 0.037 0.1 11 1.094 0.093 0.737 1.066 2.027 0.161 0.21 1 0.589 0.341 0.329 0.155 0.595 0.316 -1.1 18 0.685 0.319 0.778 1.680 0.601 0.242 0.152 0.276 0.892 0.768 0.874 -1.041 —1.301 0.149 0.458 0.877 0.515 0.827 0.391 3.366 0.099 0.654 0.691 2.405 0.929 0.331 -1.202 1.193 -1.041 0.511 0.387 -1.357 0.366 1.416 0.564 0.493 -1.1 15 0.013 0.632 0.520 0.468 1.627 Table C.1 (continued) — Subject 216 Card 1 12.000 1 14.000 121.000 123.000 132.000 134.000 141.000 143.000 152.000 154.000 21 1.100 21 1.200 213.100 213.200 222.100 222.200 224.100 224.200 231.100 231.200 233.100 233.200 242.100 242.200 244.100 244.200 251.100 251.200 253.100 253.200 312.000 314.000 321.000 323.000 332.000 334.000 341.000 343.000 352.000 354.000 41 1 .000 413.000 422.000 424.000 431.000 433.000 442.000 444.000 451 .000 453.000 512.000 514.000 521.000 523.000 532.000 534.000 541.000 543.000 552.000 554.000 Resp. 8.000 1.000 4.000 3.000 5.000 4.000 7.000 6.000 8.000 RT 2244.000 1601.000 431.000 1 121.000 931.000 1788.000 964.000 697.000 1202.000 1013.000 816.000 1 121.000 1328.000 1950.000 1499.000 1255.000 546.000 1230.000 935.000 974.000 960.000 1394.000 414.000 1376.000 890.000 910.000 550.000 2054.000 234.000 1533.000 742.000 953.000 261.000 888.000 1830.000 672.000 1332.000 1077.000 826.000 1775.000 710.000 1051.000 983.000 683.000 222.000 487.000 369.000 885.000 367.000 419.000 1001.000 716.000 1 109.000 768.000 1 154.000 455.000 448.000 695.000 767.000 1475.000 Conf. Order 8.000 22.000 40.000 18.000 45.000 30.000 35.000 39.000 47.000 15.000 4.000 56.000 1.000 29.000 32.000 6.000 46.000 41.000 16.000 25.000 43.000 44.000 53.000 10.000 13.000 20.000 50.000 48.000 58.000 12.000 57.000 28.000 60.000 23.000 24.000 49.000 51.000 9.000 19.000 14.000 2.000 21.000 3.000 17.000 59.000 34.000 54.000 1 1.000 38.000 52.000 37.000 7.000 31 .000 27.000 26.000 55.000 33.000 42.000 36.000 5.000 144 Dist SdRT 2.683 1.313 -1.180 0.290 0.114 1.712 0.613 0.463 0.060 0.359 0.290 0.731 2.057 1.096 0.576 0.935 0.523 0.106 0.023 0.053 0.872 -1.216 0.834 0.202 0.159 0.926 2.278 -1.599 1.168 0.517 0.068 -1.542 0.206 1.801 0.740 0.197 0.338 1.684 0.585 0.141 0.643 -1.625 —1.060 -1.312 0.212 —1 .316 —1.205 0.035 0.572 0.265 0.462 0.361 -1.129 ‘1 . 143 0.617 1.645 Table C.1 (continued) — Subject 217 Card 1 12.000 1 14.000 121.000 123.000 132.000 134.000 141.000 143.000 152.000 154.000 21 1.100 21 1.200 213. 100 213.200 222.100 222.200 224. 100 224.200 231 . 100 231.200 233.100 233.200 242.100 242.200 244.100 244.200 25 1 . 100 251.200 253. 100 253.200 312.000 314.000 321.000 323.000 332.000 334.000 341.000 343.000 352.000 354.000 41 1.000 413.000 422.000 424.000 431.000 433.000 442.000 444.000 451 .000 453.000 512.000 514.000 521.000 523.000 532.000 534.000 541.000 543.000 552.000 554.000 Resp. 1.000 4.000 5.000 4.000 4.000 2.000 2.000 1.000 3.000 8.000 2.000 2.000 2.000 RT 1097.000 2108.000 785.000 713.000 1069.000 933.000 909.000 1 134.000 1 175.000 1997.000 491.000 556.000 1 127.000 842.000 1267.000 1075.000 1325.000 980.000 657.000 683.000 557.000 1 188.000 495.000 649.000 1 128.000 1915.000 959.000 817.000 750.000 1539.000 1460.000 1506.000 631.000 1549.000 1568.000 735.000 1871.000 1262.000 2109.000 1 102.000 467.000 588.000 856.000 715.000 827.000 764.000 510.000 430.000 308.000 574.000 609.000 1069.000 467.000 539.000 522.000 686.000 696.000 392.000 912.000 578.000 Order 52.000 6.000 41 .000 36.000 37.000 32.000 13.000 39.000 46.000 9.000 25.000 29.000 24.000 50.000 22.000 38.000 7.000 2.000 1.000 54.000 60.000 40.000 31 .000 44.000 16.000 5.000 17.000 23.000 34.000 18.000 14.000 21.000 1 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 2. 100 2.200 2. 100 2.200 2. 100 2.200 2. 100 2.200 Dist SdRT 0.319 2.569 0.375 0.536 0.257 0.046 0.099 0.401 0.493 2.322 -1.030 0.885 0.386 0.249 0.697 0.270 0.826 0.059 0.603 0.883 0.522 -1.021 0.678 0.388 2.140 0.012 0.304 0.453 1.303 1.127 1.229 0.718 1 .325 1.367 0.487 2.042 0.686 2.572 0.330 —1.083 0.814 0.217 0.531 0.282 0.422 0.988 -1.166 -1.437 0.845 0.767 0.257 -1.083 0.923 0.961 0.596 0.574 -1.250 0.093 0.836 Table C.1 (continued) — Subject 218 Card 112.000 1 14.000 121.000 123.000 132.000 134.000 141.000 143.000 152.000 154.000 21 1.100 211.200 213. 100 213.200 222.100 222.200 224.100 224.200 231. 100 231.200 233. 100 233.200 242. 100 242.200 244.100 244.200 251. 100 251.200 253. 100 253.200 312.000 314.000 321.000 323.000 332.000 334.000 341.000 343.000 352.000 354.000 41 1.000 413.000 422.000 424.000 431.000 433.000 442.000 444.000 451.000 453.000 512.000 514.000 521.000 523.000 532.000 534.000 541.000 543.000 552.000 554.000 Resp. 7.000 3.000 5.000 4.000 5.000 5.000 6.000 8.000 8.000 RT 863.000 2097.000 493.000 700.000 1 127.000 745.000 921.000 849.000 584.000 1074.000 803.000 1282.000 899.000 1752.000 742.000 1065.000 1471.000 1557.000 832.000 751.000 1 182.000 1449.000 750.000 901.000 477.000 1524.000 752.000 923.000 623.000 1065.000 1293.000 653.000 131 1.000 1517.000 2332.000 1556.000 1239.000 649.000 1395.000 1078.000 492.000 518.000 1 168.000 461.000 890.000 1507.000 739.000 674.000 471.000 915.000 1363.000 1479.000 843.000 1 1 15.000 1465.000 1372.000 919.000 852.000 758.000 445.000 Conf. 2.000 3.000 2.000 1.000 2.000 2.000 Order 52.000 6.000 41 .000 36.000 37.000 32.000 13.000 39.000 46.000 9.000 25.000 29.000 24.000 50.000 22.000 38.000 146 2.100 SdRT 0.404 2.602 —1.305 0.800 0.239 0.691 0.262 0.438 -1.083 0.1 10 0.550 0.617 0.316 1 .761 0.698 0.088 1 .077 1 .287 0.479 0.676 0.373 1 .024 0.679 0.31 1 -1.343 1 .206 0.674 0.257 0.988 0.088 0.644 0.915 0.687 1.189 3.174 1.284 0.5 12 0.925 0.892 0.120 -1.307 -1 .244 0.339 -1 .382 0.338 1.165 0.705 0.864 -1 .3 58 0.277 0.814 1 .097 0.452 0.210 1 .062 0.836 0.267 0.430 0.659 -1.421 Table C.1 (continued) - Subject 219 Card 1 12.000 1 14.000 121.000 123.000 132.000 134.000 141.000 143.000 152.000 154.000 211.100 21 1.200 213. 100 213.200 222. 100 222.200 224.100 224.200 231. 100 231.200 233.100 233.200 242. 100 242.200 244.100 244.200 251. 100 251.200 253. 100 253.200 312.000 314.000 321.000 323.000 332.000 334.000 341.000 343.000 352.000 354.000 41 1.000 413.000 422.000 424.000 431.000 433.000 442.000 444.000 451.000 453.000 512.000 514.000 521.000 523.000 532.000 534.000 541.000 543.000 552.000 554.000 Resp. 3.000 5.000 5.000 5.000 6.000 5.000 6.000 5.000 5.000 6.000 RT 1493.000 1475.000 962.000 878.000 1002.000 900.000 687.000 1723.000 1238.000 2422.000 698.000 1621.000 2400.000 969.000 2579.000 1226.000 1039.000 2520.000 719.000 2548.000 2788.000 1506.000 365.000 1736.000 1035.000 802.000 607.000 816.000 1318.000 2078.000 1995.000 4760.000 2357.000 538.000 1827.000 603.000 1 137.000 1465.000 1432.000 667.000 773.000 869.000 1916.000 812.000 833.000 1291.000 858.000 1035.000 806.000 873.000 1203.000 1764.000 439.000 839.000 1615.000 2076.000 1019.000 553.000 2915.000 379.000 52.000 38.000 35.000 13.000 29.000 Dist SdRT 0.163 0.140 0.504 0.610 0.454 0.582 0.8 50 0.452 0.157 1 .330 0.836 0.324 1 .303 0.495 1 .527 0.173 0.407 1.453 0.810 1 .488 1 .790 0.179 -1 .254 0.468 0.413 0.705 0.950 0.688 0.057 0.898 0.794 4.268 1 .248 —1 .037 0.583 0.955 0.284 0.128 0.086 0.875 0.742 0.621 0.694 0.693 0.666 0.091 0.635 0.413 0.700 0.616 0.201 0.503 -1 .161 0.659 0.316 0.895 0.433 —1 .018 1 .950 -1 .237 Table C.1 (continued) - Subject 220 Card 1 12.000 121.000 123.000 132.000 141.000 143.000 152.000 154.000 21 1.100 211.200 213.100 213.200 222.100 222.200 224. 100 224.200 231.100 231.200 233.100 233.200 242. 100 242.200 251.100 251.200 253. 100 253.200 312.000 321.000 323.000 332.000 341.000 343.000 352.000 354.000 41 1.000 413.000 422.000 424.000 431.000 433.000 442.000 451.000 453.000 512.000 521.000 523.000 532.000 541.000 543.000 552.000 554.000 Resp. 5.000 RT 838.000 994.000 929.000 922.000 906.000 779.000 553.000 1342.000 377.000 889.000 1245.000 3337.000 777.000 931.000 615.000 624.000 450.000 488.000 970.000 1324.000 1081.000 1840.000 341.000 654.000 1395.000 863.000 1336.000 3030.000 788.000 854.000 1369.000 2836.000 1224.000 1643.000 690.000 573.000 498.000 1214.000 579.000 802.000 474.000 1003.000 298.000 1 126.000 397.000 324.000 700.000 1256.000 723.000 456.000 415.000 Conf. 2.000 2.000 2.000 2.000 2.000 2.000 3.000 2.000 1.000 1.000 2.000 1.000 1.000 Order 54.000 37.000 55.000 50.000 33.000 24.000 57.000 45.000 47.000 32.000 16.000 9.000 56.000 48.000 46.000 60.000 44.000 43.000 3.000 38.000 8.000 31.000 23.000 25.000 6.000 28.000 19.000 4.000 17.000 58.000 41 .000 39.000 27.000 13.000 34.000 7.000 36.000 1.000 49.000 12.000 42.000 5.000 14.000 18.000 26.000 21.000 51.000 2.000 40.000 30.000 15.000 148 SdRT 0.226 0.019 0.083 0.094 0.1 19 0.318 0.673 0.565 0.949 0.146 0.413 3.695 0.321 0.080 0.575 0.561 0.834 0.775 0.019 0.537 0.156 1.346 -1.005 0.514 0.648 0.186 0.556 3.213 0.304 0.201 0.607 2.909 0.380 1.037 0.458 0.641 0.759 0.364 0.632 0.282 0.797 0.033 -1.073 0.226 0.918 -1.032 0.442 0.430 0.406 0.825 0.889 Table C.1 (continued) - Subject 221 Card 1 12.000 121.000 123.000 132.000 141.000 143.000 152.000 154.000 21 1.100 21 1.200 213. 100 213.200 222. 100 222.200 224. 100 224.200 23 1 . 100 231.200 233. 100 233.200 242.100 242.200 251.100 251.200 253. 100 253.200 312.000 321.000 323.000 332.000 341.000 343.000 352.000 354.000 41 1.000 413.000 422.000 424.000 431.000 433.000 442.000 451.000 453.000 512.000 521.000 523.000 532.000 541.000 543.000 552.000 554.000 Resp. 8.000 6.000 5.000 7.000 6.000 7.000 4.000 4.000 2.000 2.000 5.000 4.000 3.000 6.000 5.000 6.000 3.000 2.000 5.000 4.000 RT 833.000 815.000 939.000 1098.000 778.000 1283.000 897.000 1480.000 446.000 639.000 750.000 1315.000 1027.000 1247.000 642.000 648.000 527.000 1421 .000 623.000 691.000 701 .000 804.000 504.000 1338.000 647.000 991.000 515.000 1524.000 1671.000 825.000 1204.000 965.000 1360.000 1 147.000 807.000 974.000 855.000 1082.000 560.000 685.000 934.000 431.000 393.000 891.000 1603.000 675.000 665.000 529.000 635.000 1315.000 1519.000 Conf. 2.000 2.000 2.000 2.000 2.000 3.000 2.000 Order 29.000 23.000 12.000 28.000 26.000 32.000 50.000 33.000 53.000 43.000 57.000 35.000 49.000 1 1.000 6.000 38.000 41.000 24.000 58.000 42.000 51.000 37.000 48.000 14.000 34.000 17.000 59.000 22.000 45.000 60.000 15.000 25.000 36.000 7.000 54.000 8.000 47.000 19.000 30.000 40:000 39.000 2.000 16.000 18.000 31.000 27.000 3.000 1.000 149 Ques 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 2.100 2.200 2.100 2.200 2.100 2.200 2.100 SdRT 0.251 0.303 0.060 0.525 0.412 1.067 0.063 -1.384 0.819 0.494 1.161 0.318 0.962 0.810 0.793 -1.147 1.471 0.866 0.667 0.637 0.336 -1.214 1.228 0.795 0.212 —1.182 1.773 2.204 0.274 0.836 0.136 1.293 0.669 0.327 0.162 0.186 0.479 -1.050 0.684 0.045 —1.428 -1.539 0.081 2.005 0.713 0.743 -1.141 0.831 1.161 1.759 Table C.1 (continued) — Subject 222 Card 1 12.000 1 14.000 121.000 123.000 132.000 134.000 141.000 143.000 152.000 154.000 21 1.100 21 1.200 213.100 213.200 222.100 222.200 224. 100 224.200 231. 100 231.200 233.100 233.200 242.100 242.200 244.100 244.200 251.100 251.200 253. 100 253.200 312.000 314.000 321.000 323.000 332.000 334.000 341.000 343.000 352.000 354.000 41 1.000 413.000 422.000 424.000 431.000 433.000 442.000 444.000 451.000 453.000 512.000 514.000 521.000 523.000 532.000 534.000 541.000 543.000 552.000 554.000 Resp. 2.000 5.000 5.000 4.000 3.000 2.000 5.000 7.000 3.000 1.000 2.000 2.000 2.000 3.000 1.000 1.000 5.000 1.000 3.000 2.000 2.000 3.000 3.000 4.000 5.000 6.000 2.000 2.000 5.000 4.000 3.000 4.000 2.000 2.000 5.000 5.000 3.000 RT 1380.000 2055.000 681 .000 784.000 1333.000 892.000 786.000 1493.000 556.000 1093.000 414.000 574.000 601.000 1775.000 470.000 1 191.000 1 172.000 1243.000 317.000 1061.000 575.000 415.000 420.000 1073.000 709.000 943.000 385.000 972.000 994.000 1966.000 1613.000 1247.000 1684.000 590.000 872.000 2121.000 908.000 1012.000 1899.000 699.000 935.000 333.000 1 1 17.000 617.000 646.000 454.000 551.000 958.000 324.000 414.000 837.000 758.000 770.000 620.000 494.000 419.000 608.000 463.000 542.000 494.000 Order 29.000 10.000 23.000 12.000 28.000 55.000 26.000 32.000 50.000 33.000 53.000 43.000 57.000 35.000 49.000 1 1.000 6.000 38.000 41 .000 24.000 58.000 42.000 18.000 150 Table C.1 (continued) — Subject 223 Card 112.000 1 14.000 121.000 123.000 132.000 134.000 141.000 143.000 152.000 154.000 21 1.100 211.200 213. 100 213.200 222.100 222.200 224. 100 224.200 231 . 100 231.200 233.100 233.200 242. 100 242.200 244. 100 244.200 251.100 251.200 253. 100 253.200 312.000 314.000 321.000 323.000 332.000 334.000 341.000 343.000 352.000 354.000 41 1.000 413.000 422.000 424.000 431.000 433.000 442.000 444.000 451.000 453.000 512.000 514.000 521.000 523.000 532.000 534.000 541.000 543.000 552.000 554.000 Resp. 1.000 1.000 4.000 5.000 3.000 3.000 RT 1232.000 1591.000 1090.000 936.000 1332.000 2864.000 1306.000 1578.000 3537.000 1425.000 533.000 1707.000 1612.000 603.000 1573.000 1298.000 1212.000 849.000 587.000 837.000 2037.000 1316.000 754.000 1302.000 562.000 1 184.000 579.000 668.000 1397.000 919.000 785.000 2847.000 365.000 2223.000 3615.000 1838.000 1565.000 816.000 960.000 2272.000 714.000 854.000 555.000 434.000 634.000 393.000 764.000 835.000 489.000 437.000 1 156.000 1 181.000 502.000 705.000 832.000 1401.000 737.000 738.000 755.000 1249.000 Conf. Order 26.000 31.000 30.000 51 .000 22.000 2.000 21 .000 41 .000 57.000 52.000 1.000 27.000 49.000 33.000 37.000 46.000 14.000 39.000 5.000 15.000 16.000 13.000 35.000 18.000 45.000 34.000 36.000 56.000 55.000 47.000 40.000 17.000 23.000 58.000 12.000 8.000 151 SdRT 0.066 0.566 0.132 0.346 0.205 2.338 0.169 0.548 3.275 0.335 0.907 0.727 0.595 0.810 0.541 0.158 0.038 0.467 0.832 0.484 1.187 0.183 0.599 0.164 0.867 0.001 0.843 0.719 0.296 0.370 0.556 2.315 -1.141 3.384 0.910 0.530 0.513 0.313 1.514 0.655 0.460 0.877 -1.045 0.767 —1.102 0.586 0.487 0.968 —1.041 -o:oos -o.9so 0.491 0.301 0.623 0.622 0.598 0.090 Table C.1 (continued) — Subject 224 Card 1 12.000 1 14.000 121.000 123.000 132.000 134.000 141.000 143.000 152.000 154.000 21 1.100 211.200 213.100 213.200 222.100 222.200 224. 100 224.200 231. 100 231.200 233. 100 233.200 242. 100 242.200 244. 100 244.200 251.100 251.200 253.100 253.200 312.000 314.000 321.000 323.000 332.000 334.000 341.000 343.000 352.000 354.000 41 1.000 413.000 422.000 424.000 431.000 433.000 442.000 444.000 451.000 453.000 512.000 514.000 521.000 523.000 532.000 534.000 541.000 543.000 552.000 554.000 Resp. 1.000 3.000 5.000 4.000 5.000 RT 2095.000 1082.000 1092.000 732.000 1087.000 1212.000 1343.000 1061.000 701.000 1 141.000 894.000 938.000 2496.000 2004.000 2278.000 1070.000 2060.000 657.000 635.000 667.000 800.000 1379.000 1089.000 1432.000 467.000 2424.000 594.000 425.000 824.000 2101 .000 899.000 739.000 1665.000 807.000 3594.000 1 196.000 943.000 657.000 710.000 1398.000 1599.000 121 1.000 664.000 815.000 1 101.000 397.000 2190.000 728.000 1237.000 446.000 2481.000 1444.000 1271.000 618.000 2501.000 794.000 799.000 1294.000 985.000 1 166.000 Order 2.000 35.000 24.000 45.000 46.000 58.000 3.000 22.000 37.000 40.000 8.000 54.000 14.000 16.000 1 1.000 15.000 12.000 29.000 21.000 38.000 31 .000 20.000 23.000 9.000 60.000 10.000 42.000 57.000 52.000 4.000 41.000 27.000 43.000 47.000 25.000 50.000 5.000 36.000 17.000 59.000 28.000 7.000 53.000 44.000 1.000 39.000 34.000 51 .000 6.000 48.000 19.000 56.000 26.000 55.000 13.000 33.000 30.000 18.000 32.000 49.000 152 SdRT 1.345 0.210 0.195 0.747 0.202 0.010 0.191 0.242 0.795 0.1 19 0.499 0.431 1.961 1.205 1.626 0.228 1.291 0.862 0.896 0.847 0.643 0.246 0.199 0.327 -1 .1 54 1.850 0.959 -1.219 0.606 1.354 0.491 0.737 0.685 0.632 0.035 0.423 0.862 0.781 0.275 0.584 0.012 0.852 0.620 0.181 -1.262 1.491 0.753 0.028 —1.186 1.938 0.346 0.080 0.922 1.968 0.652 0.644 0.1 15 0.359 0.081 Table C.1 (continued) — Subject 225 Card 1 12.000 1 14.000 121.000 123.000 132.000 134.000 141.000 143.000 152.000 154.000 21 1.100 21 1.200 213. 100 213.200 222. 100 222.200 224. 100 224.200 231. 100 231.200 233. 100 233.200 242.100 242.200 244. 100 244.200 251 . 100 251.200 253. 100 253.200 312.000 314.000 321.000 323.000 332.000 334.000 341.000 343.000 352.000 354.000 41 1.000 413.000 422.000 424.000 431.000 433.000 442.000 444.000 451.000 453.000 512.000 514.000 521.000 523.000 532.000 534.000 541.000 543.000 552.000 554.000 Resp. 2.000 5.000 5.000 RT 777.000 813.000 817.000 926.000 1 182.000 1538.000 764.000 1 125.000 956.000 1663.000 1724.000 1282.000 960.000 738.000 1072.000 719.000 896.000 2227.000 682.000 1058.000 884.000 1201.000 849.000 824.000 1625.000 1 1 1 1.000 735.000 1018.000 967.000 1498.000 1908.000 1229.000 632.000 578.000 1225.000 1472.000 2206.000 847.000 1819.000 560.000 432.000 1700.000 718.000 660.000 814.000 529.000 652.000 733.000 345.000 568.000 1252.000 816.000 1234.000 1378.000 961.000 658.000 540.000 732.000 615.000 1003.000 Conf. 1.000 1.000 1.000 1.000 1.000 1.000 1.000 Order 22.000 5.000 33.000 27.000 31.000 29.000 19.000 46.000 14.000 47.000 43.000 24.000 45.000 37.000 6.000 12.000 2.000 39.000 34.000 30.000 44.000 20.000 35.000 58.000 54.000 50.000 40.000 59.000 28.000 1 1.000 38.000 55.000 3.000 21.000 3.2.000 52.000 16.000 153 Dist Table C.1 (continued) - Subject 226 Card 1 12.000 1 14.000 121.000 123.000 132.000 134.000 141.000 143.000 152.000 154.000 21 1.100 21 1 .200 213. 100 213.200 222. 100 222.200 224. 100 224.200 231.100 231.200 233. 100 233.200 242.100 242.200 244. 100 244.200 251.100 251.200 253.100 253.200 312.000 314.000 321.000 323.000 332.000 334.000 341.000 343.000 352.000 354.000 41 1.000 413.000 422.000 424.000 431.000 433.000 442.000 444.000 451.000 453.000 512.000 514.000 521.000 523.000 532.000 534.000 541.000 543.000 552.000 554.000 Resp. 1.000 3.000 7.000 6.000 4.000 5.000 6.000 6.000 7.000 1.000 2.000 2.000 6.000 RT 1693.000 781.000 1018.000 628.000 725.000 1838.000 708.000 1249.000 1009.000 1 149.000 409.000 482.000 1320.000 1869.000 569.000 750.000 947.000 620.000 321.000 784.000 598.000 1901.000 529.000 1067.000 759.000 860.000 594.000 616.000 1203.000 1655.000 1032.000 906.000 550.000 602.000 506.000 932.000 802.000 589.000 762.000 1082.000 419.000 488.000 444.000 722.000 338.000 544.000 538.000 505.000 390.000 625.000 966.000 1704.000 763.000 656.000 601.000 672.000 402.000 528.000 552.000 373.000 Conf. Order 6.000 33.000 22.000 32.000 48.000 46.000 31 .000 44.000 26.000 41 .000 20.000 36.000 1 1.000 12.000 58.000 53.000 37.000 14.000 38.000 7.000 25.000 1.000 17.000 24.000 27.000 29.000 59.000 5.000 15.000 50.000 16.000 39.000 SdRT 2.198 0.074 0.516 0.455 0.214 2.559 0.256 1.092 0.494 0.843 -1.001 0.819 1.268 2.636 0.602 0.151 0.339 0.475 -1.220 0.067 0.530 2.716 0.702 0.638 0.129 0.123 0.540 0.485 0.977 2.103 0.551 0.237 0.649 0.520 0.759 0.302 0.022 0.552 0.121 0.676 0.976 0.804 0.913 0.221 -1.177 0.664 0.679 0.762 —1.048 0.463 0.387 2.225 0.1 19 0.385 0.522 -1:018 0.704 4:090 Table C.1 (continued) — Subject 227 Card 1 12.000 1 14.000 121.000 123.000 132.000 134.000 141.000 143.000 152.000 154.000 21 1.100 21 1.200 213. 100 213.200 222.100 222.200 224.100 224.200 231 . 100 231.200 233.100 233.200 242. 100 242.200 244.100 244.200 251.100 251.200 253. 100 253.200 312.000 314.000 321.000 323.000 332.000 334.000 341.000 343.000 352.000 354.000 41 1.000 413.000 422.000 424.000 431.000 433.000 442.000 444.000 451.000 453.000 512.000 514.000 521.000 523.000 532.000 534.000 541.000 543.000 552.000 554.000 Resp. 1.000 3.000 6.000 5.000 3.000 2.000 7.000 4.000 7.000 2.000 RT 476.000 985.000 541.000 957.000 951 .000 1455.000 920.000 836.000 878.000 1026.000 494.000 1 159.000 879.000 1430.000 1017.000 1257.000 944.000 1418.000 590.000 965.000 899.000 1381.000 379.000 930.000 702.000 1918.000 645.000 624.000 721.000 1654.000 1 195.000 1434.000 1435.000 1845.000 3741.000 1 191.000 948.000 1744.000 1472.000 514.000 624.000 585.000 575.000 514.000 835.000 412.000 836.000 680.000 723.000 372.000 2483.000 1 189.000 557.000 580.000 589.000 1435.000 739.000 650.000 493.000 1328.000 Order 51 .000 27.000 35.000 31.000 22.000 24.000 10.000 59.000 4.000 18.000 39.000 14.000 155 SdRT 0.950 0.049 0.835 0.098 0.109 0.784 0.164 0.313 0.238 0.024 0.918 0.260 0.236 0.740 0.008 0.433 0.121 0.718 0.748 0.084 0.201 0.653 -1.122 0.146 0.550 0.651 0.688 0.516 1.136 0.323 0.747 0.748 1.475 4.834 0.316 0.1 14 1.296 0.814 0.883 0.688 0.757 0.775 0.883 0.314 —1.064 0.313 0.589 0.513 -1.135 2.605 0.313 0.807 0.766 0.750 0.748 0.484 0.642 0.920 0.559 Table C.1 (continued) — Subject 228 Card 112.000 114.000 121.000 123.000 132.000 134.000 141.000 143.000 152.000 154.000 211.100 211.200 213.100 213.200 222. 100 222.200 224.100 224.200 231.100 231.200 233. 100 233.200 242.100 242.200 244. 100 244.200 251.100 251.200 253.100 253.200 312.000 314.000 321.000 323.000 332.000 334.000 341.000 343.000 352.000 354.000 41 1.000 413.000 422.000 424.000 431.000 433.000 442.000 444.000 451.000 453.000 512.000 514.000 521.000 523.000 532.000 534.000 541.000 543.000 552.000 554.000 Resp. 3.000 3.000 6.000 RT 1387.000 1477.000 1784.000 817.000 850.000 1735.000 1622.000 1083.000 1389.000 1823.000 780.000 781.000 1375.000 1649.000 845.000 934.000 1331.000 1009.000 1 155.000 1892.000 666.000 1 186.000 420.000 974.000 603.000 1218.000 513.000 1 179.000 770.000 841.000 1319.000 2840.000 1516.000 1282.000 2780.000 1824.000 952.000 1052.000 1 151.000 1443.000 460.000 494.000 577.000 691.000 639.000 1056.000 1350.000 1455.000 835.000 514.000 2318.000 971.000 924.000 568.000 748.000 2175.000 1236.000 1602.000 1310.000 807.000 Order 51.000 27.000 35.000 31 .000 22.000 24.000 10.000 59.000 4.000 18.000 39.000 14.000 12.000 42.000 26.000 13.000 23.000 17.000 2.000 8.000 37.000 41 .000 58.000 52.000 47.000 9.000 29.000 15.000 11.000 53.000 36.000 28.000 21 .000 19.000 16.000 30.000 48.000 7.000 25.000 55.000 56.000 50.000 40.000 43.000 45.000 34.000 3.000 32.000 5.000 33.000 46.000 54.000 44.000 57.000 20.000 6.000 1.000 49.000 60.000 38.000 SdRT 0.382 0.551 1.125 0.685 0.623 1.034 0.822 0.187 0.386 1.198 0.754 0.753 0.360 0.873 0.633 0.466 0.277 0.326 0.052 1.327 0.968 -1.428 41.391 -l.086 -l .254 0.007 0.773 0.640 0.255 3.102 0.624 0.185 2.990 1 .200 0.432 0.245 0.060 0.487 -1 .3 53 —1.290 -l.134 0.921 -1.018 0.238 0.313 0.509 0.651 -1.252 2.125 0.397 0.485 -1.151 0.814 1.857 0.099 0.785 0.238 0.704 Table C.1 (continued) - Subject 229 Card 1 12.000 1 14.000 121.000 123.000 132.000 134.000 141.000 143.000 152.000 154.000 21 1.100 21 1.200 213.100 213.200 222. 100 222.200 224.100 224.200 231.100 231.200 233. 100 233.200 242.100 242.200 244. 100 244.200 25 1 . 100 251.200 253.100 253.200 312.000 314.000 321.000 323.000 332.000 334.000 341.000 343.000 352.000 354.000 41 1.000 413.000 422.000 424.000 431.000 433.000 442.000 444.000 451.000 453.000 512.000 514.000 521.000 523.000 532.000 534.000 541.000 543.000 552.000 554.000 Resp. 5.000 2.000 5.000 4.000 4.000 3.000 6.000 RT 2504.000 1564.000 1615.000 991.000 1205.000 1666.000 1620.000 2136.000 1507.000 1906.000 952.000 950.000 2230.000 3222.000 2601.000 1066.000 1563.000 1954.000 757.000 1514.000 946.000 836.000 1042.000 947.000 619.000 2708.000 921.000 1400.000 1357.000 7470.000 1468.000 1446.000 2929.000 3015.000 141 1.000 2461.000 1590.000 2277.000 1096.000 2234.000 933.000 897.000 2882.000 1 164.000 894.000 734.000 1494.000 1878.000 866.000 826.000 4177.000 1804.000 2370.000 241 1.000 2516.000 1210.000 1650.000 1653.000 1354.000 2918.000 Conf. Order 5.000 58.000 12.000 45.000 41 .000 26.000 44.000 6.000 15.000 4.000 17.000 36.000 53.000 8.000 10.000 14.000 7.000 59.000 35.000 57.000 25.000 42.000 13.000 37.000 54.000 40.000 38.000 9.000 3.000 34.000 22.000 56.000 21.000 1.000 51.000 48.000 47.000 23.000 29.000 60.000 28.000 18.000 2.000 43.000 20.000 33.000 30.000 31.000 16.000 50.000 27.000 24.000 32.000 55.000 19.000 11.000 46.000 52.000 39.000 49.000 157 SdRT 0.689 0.196 0.148 0.735 0.534 0.100 0.143 0.343 0.250 0.126 0.772 0.774 0.431 1.365 0.780 0.665 0.197 0.171 0.955 0.243 0.778 0.881 0.687 —1.085 0.881 0.801 0.350 0.391 5.363 0.286 0.307 1.089 1.170 0.340 0.648 0.171 0.475 0.636 0.435 0.790 0.824 1.045 0.572 0.827 0.977 0.262 0.100 0.853 0.891 2.264 0.030 0.563 0.601 0.700 0.529 0.1 15 0.1 12 0.394 1.079 Table C.1 (continued) - Subject 230 Card 1 12.000 121.000 123.000 132.000 141.000 143.000 152.000 154.000 21 1.100 21 1.200 213.100 213.200 222.100 222.200 224. 100 224.200 231. 100 231.200 233. 100 233.200 242. 100 242.200 251.100 251.200 253.100 253.200 312.000 321.000 323.000 332.000 341.000 343.000 352.000 354.000 411.000 413.000 422.000 424.000 431.000 433.000 442.000 451.000 453.000 512.000 521.000 523.000 532.000 541.000 543.000 552.000 554.000 RT 906.000 475.000 797.000 794.000 830.000 861.000 835.000 1 141.000 650.000 1088.000 958.000 742.000 882.000 91 1.000 418.000 1362.000 456.000 909.000 770.000 1468.000 387.000 751.000 388.000 482.000 416.000 654.000 1 145.000 720.000 795.000 1654.000 449.000 1063.000 957.000 979.000 539.000 346.000 437.000 416.000 440.000 696.000 481.000 319.000 423.000 1413.000 554.000 804.000 1 158.000 379.000 406.000 366.000 463.000 Order 5000 38.000 29.000 52.000 59.000 27.000 1.000 18.000 15.000 9.000 45.000 51.000 3.000 33.000 46.000 54.000 16.000 55.000 19.000 26.000 43.000 12.000 40.000 39.000 57.000 37.000 20.000 31.000 2.000 22.000 58.000 7.000 25.000 4.000 8.000 53.000 17.000 56.000 35.000 1 1.000 23.000 34.000 24.000 6.000 32.000 50.000 47.000 36.000 28.000 13.000 44.000 158 SdRT 0.509 0.81 1 0.175 0.166 0.276 0.371 0.291 1.229 0.275 1.066 0.668 0.007 0.435 0.524 0.986 1 .905 0.869 0.518 0.092 2.230 -1.081 0.034 —1.078 0.790 0.992 0.263 1.241 0.061 0.169 2.800 0.891 0.990 0.665 0.732 0.615 —1.206 0.928 0.992 0.918 0.134 0.793 ~1.289 0.970 2.062 0.569 0.196 1.281 -1.105 -1 .022 -1.145 0.848 Table C.1 (continued) - Subject 231 Card 1 12.000 121.000 123.000 132.000 141.000 143.000 152.000 154.000 21 1.100 211.200 213.100 213.200 222. 100 222.200 224.100 224.200 231. 100 231.200 233.100 233.200 242. 100 242.200 251 . 100 251.200 253.100 253.200 312.000 321.000 323.000 332.000 341.000 343.000 352.000 354.000 41 1.000 413.000 422.000 424.000 431.000 433.000 442.000 451.000 453.000 512.000 521.000 523.000 532.000 541.000 543.000 552.000 554.000 Resp. 1.000 4.000 4.000 3.000 4.000 2.000 3.000 6.000 2.000 2.000 RT 1055.000 654.000 764.000 967.000 961.000 1066.000 983.000 1337.000 665.000 808.000 965.000 1352.000 864.000 818.000 1040.000 647.000 242.000 425.000 322.000 1409.000 572.000 1681.000 650.000 803.000 724.000 886.000 1872.000 1535.000 418.000 927.000 374.000 408.000 735.000 828.000 699.000 409.000 1298.000 1 155.000 41 1.000 1 106.000 572.000 269.000 314.000 571.000 954.000 1238.000 521.000 952.000 554.000 868.000 468.000 Order 45.000 43.000 34.000 37.000 41.000 39.000 10.000 52.000 15.000 30.000 48.000 44.000 24.000 26.000 18.000 59.000 57.000 46.000 35.000 1 1.000 33.000 1.000 8.000 12.000 54.000 36.000 6.000 47.000 32.000 60.000 51.000 25.000 22.000 13.000 49.000 17.000 50.000 14.000 159 RW MI GA MI GA M1 M1 GA RW SdRT 0.615 0.461 0.166 0.379 0.363 0.645 0.422 1.372 0.432 0.048 0.374 1.412 0.103 0.021 0.575 0.480 -1.567 -1.076 -1.352 1.565 0.681 2.296 0.472 0.061 0.273 0.162 2.808 1.904 -1.095 0.272 —1 .213 -1.122 0.244 0.006 0.340 -1.1 19 1.268 0.884 -1.1 13 0.752 0.681 -1.495 -1.374 0.684 0.344 1.106 0.818 0.339 0.730 0.1 13 0.960 Table C.1 (continued) - Subject 232 Card 112.000 1 14.000 121.000 123.000 132.000 134.000 141.000 143.000 152.000 154.000 211.100 211.200 213. 100 213.200 222.100 222.200 224. 100 224.200 231.100 231.200 233.100 233.200 242.100 242.200 244. 100 244.200 251.100 251.200 253. 100 253.200 312.000 314.000 321.000 323.000 332.000 334.000 341.000 343.000 352.000 354.000 41 1.000 413.000 422.000 424.000 431.000 433.000 442.000 444.000 451.000 453.000 512.000 514.000 521.000 523.000 532.000 534.000 541.000 543.000 552.000 554.000 Resp. 2.000 3.000 7.000 5.000 RT 824.000 2036.000 1422.000 2106.000 2080.000 3032.000 821.000 3940.000 1678.000 884.000 613.000 1223.000 1496.000 2798.000 3241.000 2846.000 1219.000 2058.000 652.000 1691.000 1440.000 1679.000 543.000 1480.000 497.000 1 1 18.000 576.000 1014.000 2109.000 61 16.000 1250.000 5289.000 3560.000 1571.000 5175.000 1 195.000 1999.000 1631.000 1729.000 1454.000 1372.000 1232.000 2031.000 1968.000 665.000 1155.000 682.000 1854.000 348.000 402.000 4288.000 2482.000 3886.000 3721.000 2498.000 2380.000 1601.000 1010.000 1680.000 2239.000 Conf. 2.000 2.000 2.000 2.000 2.000 2.000 Order 53.000 38.000 8.000 32.000 40.000 22.000 52.000 12.000 35.000 46.000 45.000 30.000 49.000 15.000 44.000 3.000 34.000 24.000 29.000 47.000 19.000 25.000 43.000 50.000 55.000 60.000 56.000 39.000 36.000 2.000 58.000 1.000 26.000 28.000 5.000 59.000 23.000 31.000 37.000 27.000 10.000 18.000 21 .000 54.000 17.000 16.000 51.000 57.000 14.000 6.000 42.000 1 1.000 13.000 41.000 48.000 33.000 20.000 160 SdRT 0.881 0.088 0.403 0.144 0.123 0.883 0.883 1.609 0.198 0.833 -1.049 0.562 0.344 0.696 1.050 0.735 0.565 0.105 -1 .018 0.188 0.389 0.198 -1.105 0.357 —1.142 -1.079 0.729 0.146 3.348 2.687 1.305 0.284 2.596 0.584 0.058 0.236 0.158 0.377 0.443 0.555 0.084 0.033 —1.008 0.616 0.994 0.058 -1.261 -1.218 1.887 0.444 1 .566 1.434 0.457 0.363 0.260 0.732 0.197 0.250 Table C.1 (continued) - Subject 233 Card 1 12.000 1 14.000 121.000 123.000 132.000 134.000 141.000 143.000 152.000 154.000 21 1.100 21 1.200 213.100 213.200 222.100 222.200 224.100 224.200 231.100 231.200 233. 100 233.200 242. 100 242.200 244.100 244.200 25 1 . 100 251.200 253.100 253.200 312.000 314.000 321.000 323.000 332.000 334.000 341.000 343.000 352.000 354.000 41 1.000 413.000 422.000 424.000 431.000 433.000 442.000 444.000 451.000 453.000 512.000 514.000 521.000 523.000 532.000 534.000 541.000 543.000 552.000 554.000 Resp. 2.000 4.000 6.000 5.000 5.000 3.000 6.000 3.000 4.000 3.000 2.000 RT 1925.000 2408.000 71 1.000 1 122.000 809.000 1303.000 1747.000 1900.000 1524.000 3282.000 314.000 571.000 797.000 1256.000 1584.000 1326.000 1 159.000 1421.000 280.000 1623.000 1512.000 1570.000 321.000 517.000 381.000 1915.000 81 1.000 1370.000 1266.000 1510.000 1391.000 691.000 1325.000 890.000 2759.000 633.000 1808.000 1335.000 723.000 1334.000 661.000 858.000 1468.000 387.000 307.000 501.000 643.000 426.000 406.000 424.000 1708.000 895.000 694.000 729.000 1609.000 685.000 971.000 1347.000 823.000 562.000 Order 28.000 24.000 7.000 18.000 60.000 21.000 2.000 40.000 49.000 36.000 57.000 43.000 26.000 50.000 35.000 37.000 32.000 27.000 59.000 161 SdRT 1 .288 2.062 0.656 0.002 0.499 0.292 1.003 1 .248 3.461 -1.291 0.880 0.518 0.217 0.742 0.329 0.062 0.481 —1.346 0.805 0.627 0.720 -1.280 0.966 -1 . 184 1.272 0.496 0.400 0.233 0.624 0.433 0.688 0.328 0.369 2.624 0.781 1.101 0.344 0.636 0.342 0.736 0.420 0.556 -1.174 -1.303 0.992 0.765 -1.1 12 -1.144 -1 .1 1 5 0.941 0.361 0.683 0.627 0.782 0.697 0.239 0.363 0.476 0.894 Table C.1 (continued) — Subject 234 Card 112.000 114.000 121.000 123.000 132.000 134.000 141.000 143.000 152.000 154.000 211.100 211.200 213.100 213.200 222. 100 222.200 224.100 224.200 231.100 231.200 233. 100 233.200 242.100 242.200 244.100 244.200 251. 100 251.200 253. 100 253.200 312.000 314.000 321.000 323.000 332.000 334.000 341.000 343.000 352.000 354.000 411.000 413.000 422.000 424.000 431.000 433.000 442.000 444.000 451.000 453.000 512.000 514.000 521.000 523.000 532.000 534.000 541.000 543.000 552.000 554.000 Resp. 2.000 2.000 5.000 4.000 2.000 5.000 6.000 RT 2412.000 867.000 1033.000 692.000 3016.000 31 1 1.000 3661.000 1 121.000 4291.000 4479.000 595.000 946.000 741.000 1249.000 1389.000 1484.000 1260.000 2280.000 628.000 1497.000 979.000 1215.000 610.000 2878.000 1017.000 1380.000 1038.000 1623.000 1330.000 2735.000 2253.000 2226.000 2855.000 1008.000 1878.000 1991.000 2225.000 1292.000 1907.000 1416.000 951 .000 1063.000 1600.000 1432.000 619.000 388.000 868.000 678.000 547.000 714.000 1728.000 2357.000 1715.000 4709.000 793.000 1321.000 1099.000 1 167.000 1406.000 2135.000 Conf. Order 13.000 37.000 57.000 46.000 1 1.000 3.000 6.000 41.000 30.000 20.000 43.000 50.000 44.000 25.000 32.000 53.000 56.000 2.000 48.000 18.000 40.000 36.000 29.000 26.000 12.000 45.000 39.000 16.000 33.000 21.000 15.000 42.000 8.000 22.000 54.000 58.000 5.000 7.000 49.000 51.000 24.000 34.000 23.000 4.000 19.000 60.000 55.000 31 .000 47.000 10.000 1.000 38.000 35.000 27.000 14.000 17.000 9.000 52.000 28.000 59.000 162 SdRT 0.791 0.775 0.607 0.952 1.403 1.499 2.056 0.517 2.694 2.885 —1.050 0.695 0.902 0.388 0.246 0.150 0.377 0.657 -1.017 0.136 0.661 0.422 -1.035 1.263 0.623 0.255 0.601 0.009 0.306 1.1 18 0.630 0.602 1.239 0.632 0.250 0.364 0.601 0.279 0.218 0.690 0.576 0.032 0.202 -1.026 -1.260 0.774 0.966 -1.099 0.930 0.098 0.735 0.084 3.1 18 0.850 0.315 0.540 0.471 0.229 0.510 Table C.1 (continued) — Subject 235 Card 1 12.000 1 14.000 121.000 123.000 132.000 134.000 141.000 143.000 152.000 154.000 21 1.100 21 1.200 213. 100 213.200 222. 100 222.200 224. 100 224.200 231. 100 231.200 233.100 233.200 242.100 242.200 244.100 244.200 251.100 251.200 253.100 253.200 312.000 314.000 321.000 323.000 332.000 334.000 341.000 343.000 352.000 354.000 41 1.000 413.000 422.000 424.000 431.000 433.000 442.000 444.000 451.000 453.000 512.000 514.000 521.000 523.000 532.000 534.000 541.000 543.000 552.000 554.000 Resp. 8.000 5.000 5.000 1.000 3.000 6.000 2.000 RT 1441.000 608.000 554.000 372.000 872.000 674.000 786.000 1051.000 820.000 1094.000 383.000 389.000 855.000 1662.000 757.000 557.000 503.000 637.000 324.000 649.000 607.000 1 151.000 418.000 1071.000 1216.000 1027.000 305.000 845.000 430.000 1407.000 908.000 626.000 345.000 612.000 1 180.000 2430.000 606.000 1601.000 696.000 639.000 636.000 365.000 510.000 586.000 334.000 408.000 1 128.000 376.000 457.000 637.000 581.000 389.000 532.000 599.000 378.000 753.000 667.000 457.000 539.000 625.000 Conf. 1.000 2.000 Order 6.000 28.000 49.000 39.000 14.000 51.000 27.000 1.000 50.000 3.000 17.000 56.000 36.000 5.000 41 .000 19.000 47.000 43.000 24.000 13.000 25.000 35.000 44.000 12.000 16.000 29.000 38.000 37.000 20.000 45.000 21 .000 54.000 31 .000 57.000 1 1.000 10.000 26.000 15.000 59.000 40.000 8.000 60.000 58.000 2.000 46.000 23.000 7.000 53.000 30.000 55.000 33.000 22.000 42.000 18.000 52.000 9.000 34.000 4.000 48.000 32.000 Dist SdRT 1.792 0.321 0.458 0.919 0.349 0.153 0.131 0.803 0.217 0.912 0.891 0.876 0.306 2.353 0.057 0.450 0.587 0.247 -1.041 0.217 0.323 1.057 0.803 0.854 1.222 0.742 -1.089 0.281 0.772 1.706 0.440 0.275 0.988 0.31 1 1.130 4.301 0.326 2.198 0.097 0.242 0.250 0.937 0.569 0.376 —1 .016 0.828 0.998 0.909 0.704 0.247 0.389 0.876 0.513 0.344 0.904 0.047 0.171 0.704 0.496 0.278 Table C.1 (continued) — Subject 236 Card 1 12.000 1 14.000 121.000 123.000 132.000 134.000 141.000 143.000 152.000 154.000 21 1.100 211.200 213. 100 213.200 222. 100 222.200 224.100 224.200 231.100 231.200 233. 100 233.200 242.100 242.200 244. 100 244.200 251.100 251.200 253.100 253.200 312.000 314.000 321.000 323.000 332.000 334.000 341.000 343.000 352.000 354.000 41 1.000 413.000 422.000 424.000 431.000 433.000 442.000 444.000 451.000 453.000 512.000 514.000 521.000 523.000 532.000 534.000 541.000 543.000 552.000 554.000 Resp. 2.000 5.000 5.000 RT 751 .000 2464.000 1256.000 665.000 1614.000 995.000 799.000 1869.000 789.000 4806.000 1 1 18.000 1639.000 763.000 1773.000 438.000 1728.000 826.000 2409.000 353.000 1446.000 1527.000 2185.000 770.000 1001.000 810.000 2856.000 675.000 761.000 540.000 3248.000 1891.000 3666.000 2124.000 919.000 1395.000 801.000 1236.000 820.000 2412.000 902.000 486.000 1515.000 997.000 1086.000 632.000 597.000 816.000 631.000 1635.000 423.000 2015.000 1492.000 1572.000 593.000 950.000 1044.000 582.000 756.000 659.000 1513.000 Order 28.000 15.000 46.000 18.000 2.000 13.000 1 1.000 59.000 50.000 38.000 14.000 58.000 45.000 35.000 60.000 24.000 20.000 41.000 17.000 31.000 48.000 12.000 37.000 30.000 52.000 23.000 39.000 36.000 47.000 54.000 10.000 44.000 29.000 43.000 7.000 57.000 22.000 21.000 6.000 53.000 33.000 3.000 34.000 55.000 27.000 40.000 9.000 19.000 25.000 26.000 8.000 1.000 32.000 42.000 51.000 56.000 16.000 4.000 5.000 49.000 164 SdRT 0.665 0.072 0.766 0.348 0.378 0.608 0.648 0.620 4.095 0.234 0.378 0.651 0.535 —1.032 0.482 0.577 1.281 —1.132 0.151 0.246 1.019 0.642 0.371 0.595 1.806 0.754 0.653 0.912 0.673 2.757 0.947 0.467 0.091 0.606 0.095 0.584 1 .285 0.487 0.976 0.232 0.376 0.271 0.804 0.845 0.588 0.805 0.373 -1.050 0.819 0.205 0.299 0.850 0.431 0.321 0.863 0.659 0.773 0.230 1 r. Table C.1 (continued) -— Subject 237 Card 112.000 114.000 121.000 123.000 132.000 134.000 141.000 143.000 152.000 154.000 211.100 21 1.200 213. 100 213.200 222. 100 222.200 224. 100 224.200 231.100 231.200 233.100 233.200 242.100 242.200 244.100 244.200 251.100 251.200 253.100 253.200 312.000 314.000 321.000 323.000 332.000 334.000 341.000 343.000 352.000 354.000 41 1.000 413.000 422.000 424.000 431.000 433.000 442.000 451.000 453.000 512.000 514.000 521.000 523.000 532.000 534.000 541.000 543.000 552.000 554.000 Resp. 3.000 3.000 5.000 5.000 5.000 4.000 5.000 2.000 3.000 2.000 2.000 2.000 6.000 3.000 6.000 RT 1870.000 1502.000 756.000 21 17.000 1235.000 3179.000 854.000 1576.000 1632.000 1440.000 387.000 370.000 1524.000 1238.000 627.000 1255.000 710.000 1451.000 310.000 627.000 568.000 696.000 840.000 703.000 41 1.000 1492.000 390.000 528.000 755.000 1313.000 1520.000 1 187.000 793.000 1262.000 2431.000 21 1 1.000 1477.000 1763.000 1258.000 865.000 613.000 545.000 575.000 1008.000 364.000 488.000 598.000 656.000 489.000 271.000 2135.000 867.000 1239.000 1647.000 711.000 978.000 1048.000 727.000 1537.000 1 1 14.000 Order 38.000 14.000 13.000 19.000 44.000 17.000 37.000 28.000 43.000 50.000 40.000 49.000 22.000 8.000 24.000 1 1.000 59.000 16.000 48.000 3.000 35.000 54.000 4.000 26.000 58.000 47.000 56.000 23.000 42.000 2.000 46.000 7.000 57.000 21.000 5.000 53.000 31.000 15.000 30.000 6.000 39.000 27.000 34.000 1.000 52.000 25.000 18.000 55.000 12.000 60.000 20.000 51.000 33.000 41.000 45.000 29.000 10.000 36.000 9.000 32.000 165 SdRT 1.336 0.716 0.541 1.753 0.266 3.543 0.376 0.841 0.935 0.611 —1.163 -1.192 0.753 0.271 0.759 0.300 0.619 0.630 -1.293 0.759 0.858 0.643 0.400 0.631 -1.123 0.699 -1.158 0.926 0.543 0.397 0.746 0.185 0.479 0.31 1 2.282 1.742 0.674 1.156 0.305 0.358 0.782 0.897 0.847 0.1 17 -1.202 0.993 0.808 0.710 0.991 -1.359 1.783 0.354 0.273 0.960 0.617 0.167 0.049 0.590 0.775 0.062 Table C.1 (continued) — Subject 238 Card 112.000 1 14.000 121.000 123.000 132.000 134.000 141.000 143.000 152.000 154.000 211.100 211.200 213.100 213.200 222.100 222.200 224.100 224.200 231 . 100 231.200 233.100 233.200 242.100 242.200 244.100 244.200 251.100 251.200 253.100 253.200 312.000 314.000 321.000 323.000 332.000 334.000 341.000 343.000 352.000 354.000 41 1.000 413.000 422.000 424.000 431.000 433.000 442.000 444.000 451.000 453.000 512.000 514.000 521.000 523.000 532.000 534.000 541.000 543.000 552.000 554.000 RT 2507.000 2546.000 793.000 885.000 2791.000 1654.000 669.000 2371.000 2046.000 3805.000 279.000 1238.000 1256.000 896.000 651.000 622.000 405.000 1347.000 279.000 916.000 1398.000 1332.000 346.000 1603.000 461.000 1602.000 562.000 733.000 1756.000 2503.000 396.000 1272.000 3263.000 1481.000 3235.000 1371 .000 2337.000 1692.000 550.000 1853.000 329.000 345.000 1034.000 466.000 483.000 315.000 1 154.000 3207.000 463.000 381.000 2238.000 2371 .000 636.000 591.000 537.000 923.000 1031.000 630.000 2355.000 1994.000 Order 38.000 14.000 13.000 39.000 2.000 37.000 50.000 59.000 57.000 17.000 55.000 7.000 32.000 51.000 21 .000 19.000 41.000 42.000 60.000 28.000 26.000 33.000 56.000 3.000 24.000 27.000 40.000 54.000 15.000 1 1.000 58.000 6.000 31.000 9.000 12.000 46.000 43.000 8.000 25.000 34.000 36.000 48.000 16.000 49.000 23.000 35.000 20.000 10.000 47.000 18.000 1.000 5.000 53.000 22.000 45.000 30.000 52.000 44.000 29.000 4.000 166 SdRT 1.31 1 1.354 0.582 0.480 1.625 0.369 0.719 1.161 0.802 2.745 —1.149 0.090 0.070 0.739 0.771 -1.010 0.030 -1.149 0.086 0.014 -1.075 0.313 0.948 0.312 0.837 0.648 0.482 1.307 —1.020 0.053 2.146 0.178 2.1 15 0.057 1.123 0.411 0.850 0.589 -1.094 -1.076 0.316 0.943 0.924 —1 .1 10 0.183 2.084 0.946 -1.037 1.014 1.161 0.755 0.805 0.864 0.438 0.319 0.762 1.143 0.745 Table C.1 (continued) - Subject 239 Card 112.000 1 14.000 121.000 123.000 132.000 134.000 141.000 143.000 152.000 154.000 21 1.100 21 1.200 213.100 213.200 222.100 222.200 224.100 224.200 231.100 231.200 233. 100 233.200 242. 100 242.200 244.100 244.200 251.100 251.200 253.100 253.200 312.000 314.000 321.000 323.000 332.000 334.000 341.000 343.000 352.000 354.000 41 1.000 413.000 422.000 424.000 431.000 433.000 442.000 444.000 451.000 453.000 512.000 514.000 521.000 523.000 532.000 534.000 541.000 543.000 552.000 554.000 Resp. 1.000 3.000 6.000 5.000 4.000 4.000 6.000 4.000 3.000 3.000 RT 1788.000 1478.000 736.000 739.000 1307.000 1730.000 873.000 1286.000 6221.000 960.000 528.000 1321.000 1080.000 1201.000 2725.000 1863.000 1 1 13.000 782.000 800.000 846.000 1602.000 2485.000 2306.000 778.000 666.000 3385.000 892.000 1880.000 1359.000 191 1.000 1499.000 1769.000 1538.000 2074.000 4362.000 2792.000 755.000 2845.000 2421.000 1554.000 992.000 1844.000 1299.000 1 126.000 1250.000 979.000 767.000 1276.000 543.000 553.000 3398.000 1890.000 2579.000 1 174.000 2915.000 1084.000 1468.000 1735.000 1446.000 2257.000 Order 20.000 31 .000 51.000 48.000 32.000 1.000 55.000 25.000 24.000 42.000 44.000 13.000 38.000 46.000 52.000 28.000 40.000 53.000 6.000 58.000 23.000 15.000 9.000 36.000 59.000 17.000 1 1.000 8.000 27.000 57.000 45.000 50.000 167 SdRT 0.141 0.169 0.909 0.339 0.083 0.773 0.360 4.565 0.686 -1.1 17 0.325 0.566 0.445 1.076 0.216 0.533 0.863 0.846 0.800 0.045 0.836 0.658 0.867 0.979 1.735 0.754 0.232 0.288 0.263 0.148 0.122 0.109 0.426 2.710 1.143 0.890 1.196 0.772 0.093 0.654 0.197 0.347 0.520 0.396 0.667 0.878 0.370 -1.102 —1.092 1.748 0.242 0.930 0.472 1.266 0.562 0.179 0.088 0.201 0.609 Table C.l (continued) - Subject 2101 Card 112.000 1 14.000 121.000 123.000 132.000 134.000 141.000 143.000 152.000 154.000 21 1.100 21 1.200 213. 100 213.200 222. 100 222.200 224. 100 224.200 231. 100 231.200 233.100 233.200 242. 100 242.200 244. 100 244.200 25 1 . 100 251.200 253. 100 253.200 312.000 314.000 321.000 323.000 332.000 334.000 341.000 343.000 352.000 354.000 41 1.000 413.000 422.000 424.000 431.000 433.000 442.000 444.000 451.000 453.000 512.000 514.000 521.000 523.000 532.000 534.000 541.000 543.000 552.000 554.000 RT 1828.000 1431.000 1412.000 893.000 2075.000 2804.000 782.000 1012.000 1426.000 1965.000 273.000 715.000 1038.000 2075.000 191 1.000 1006.000 1331.000 1 187.000 401.000 1 175.000 556.000 1 151.000 542.000 926.000 621.000 141 1.000 537.000 1516.000 61 1.000 2496.000 793.000 444.000 1044.000 630.000 2267.000 3041.000 517.000 690.000 2726.000 1506.000 41 1.000 441.000 1044.000 762.000 880.000 558.000 1096.000 459.000 412.000 770.000 1324.000 2076.000 988.000 1 121.000 989.000 1314.000 671.000 444.000 1093.000 545.000 Order 3.000 27.000 34.000 6.000 19.000 12.000 54.000 50.000 43.000 32.000 45.000 39.000 38.000 1 1.000 29.000 20.000 17.000 16.000 28.000 30.000 60.000 58.000 4.000 26.000 14.000 36.000 23.000 5.000 52.000 48.000 21 .000 41.000 47.000 51.000 55.000 7.000 57.000 49.000 22.000 53.000 35.000 24.000 15.000 1.000 44.000 31.000 8.000 56.000 10.000 2.000 46.000 33.000 25.000 13.000 59.000 18.000 37.000 40.000 9.000 42.000 168 SdRT 1 .048 0.447 0.418 0.368 1 .422 2.526 0.536 0.188 0.439 1 .255 -1.307 0.638 0.148 1.422 1.174 0.197 0.295 0.077 -1. 1 13 0.059 0.878 0.023 0.900 0.318 0.780 0.416 0.907 0.575 0.795 2.060 0.520 —1.048 0.139 0.766 1.713 2.885 0.938 0.676 2.408 0.560 -1.098 -1.053 0.139 0.566 0.388 0.875 0.061 —1.025 -1.097 0.554 0.285 1 .423 0.224 0.023 0.223 0.269 0.704 -1.048 0.065 0.895 l E.- Table C.1 (continued) - Subject 2201 Card 112.000 1 14.000 121.000 123.000 132.000 134.000 141.000 143.000 152.000 154.000 21 1.100 21 1.200 213.100 213.200 222.100 222.200 224. 100 224.200 231 . 100 231.200 233.100 233.200 242.100 242.200 244.100 244.200 251 . 100 251.200 253.100 253.200 312.000 314.000 321.000 323.000 332.000 334.000 341.000 343.000 352.000 354.000 41 1.000 413.000 422.000 424.000 431.000 433.000 442.000 444.000 451.000 453.000 512.000 514.000 521.000 523.000 532.000 534.000 541.000 543.000 552.000 554.000 Resp. 2.000 5.000 5.000 4.000 4.000 RT 1159.000 1313.000 1023.000 1331.000 1100.000 1754.000 981.000 1494.000 2975.000 1409.000 443.000 406.000 912.000 1958.000 1059.000 677.000 866.000 1544.000 499.000 2110.000 1559.000 1325.000 356.000 1262.000 444.000 1290.000 464.000 736.000 1116.000 1125.000 Order 38.000 25.000 18.000 36.000 56.000 12.000 19.000 28.000 39.000 42.000 40.000 55.000 44.000 7.000 31 .000 53.000 21 .000 3.000 16.000 4.000 51.000 5.000 32.000 34.000 43.000 14.000 24.000 45.000 17.000 59.000 13.000 54.000 48.000 27.000 1 1.000 6.000 35.000 46.000 58.000 52.000 26.000 29.000 1.000 23.000 10.000 30.000 22.000 41.000 9.000 47.000 20.000 50.000 2.000 33.000 15.000 8.000 57.000 60.000 49.000 37.000 169 SdRT 0.036 0.197 0.242 0.225 0.125 0.865 0.305 0.471 2.713 0.343 -1.1 19 -1.175 0.410 1.174 0.187 0.765 0.479 0.547 -1.035 1.404 0.570 0.215 -1.251 0.120 —1 .1 18 0.163 -1.088 0.676 0.101 0.087 0.422 -1.1 15 0.536 0.676 2.157 0.647 0.561 0.096 0.599 0.154 0.935 0.948 0.133 -1.157 0.770 -1.009 0.566 -1.169 0.898 -1.051 0.329 1.202 2.135 0.909 0.21 1 1.045 0.418 0.026 3.535 0.1 11 Table C.1 (continued) - Subject 2202 * Card 112.000 114.000 121.000 123.000 132.000 134.000 141.000 143.000 152.000 154.000 211.100 211.200 213.100 213.200 222.100 222.200 224.100 224.200 231.100 231.200 233.100 233.200 242.100 242.200 244.100 244.200 251.100 251.200 253.100 253.200 312.000 314.000 321.000 323.000 332.000 334.000 341.000 343.000 352.000 354.000 411.000 413.000 422.000 424.000 431.000 433.000 442.000 444.000 451.000 453.000 512.000 514.000 521.000 523.000 532.000 534.000 541.000 543.000 552.000 554.000 Resp. 5.000 4.000 5.000 3.000 7.000 4.000 8.000 6.000 4.000 1.000 RT 1468.000 2310.000 1313.000 1078.000 5204.000 5025.000 5569.000 12045.000 3926.000 4489.000 1338.000 31 16.000 8861.000 8483.000 6120.000 2414.000 2099.000 3578.000 604.000 888.000 1098.000 9259.000 1045.000 2179.000 552.000 3989.000 1208.000 2417.000 3044.000 9617.000 2828.000 3935.000 9586.000 2121.000 3433.000 5752.000 11795.000 3293.000 8124.000 4025.000 556.000 1913.000 4850.000 3365.000 1221.000 748.000 1387.000 1086.000 341.000 953.000 14022.000 5742.000 1420.000 2716.000 1612.000 7320.000 3450.000 1881.000 1307.000 1430.000 Conf. 1.000 2.000 1.000 1.000 2.000 2.000 1.000 2.000 2.000 2.000 1.000 1.000 1.000 2.000 1.000 Order 35.000 59.000 51.000 45.000 13.000 12.000 10.000 4.000 27.000 48.000 34.000 2.000 28.000 29.000 17.000 41.000 1.000 9.000 49.000 8.000 54.000 26.000 18.000 24.000 60.000 43.000 31.000 32.000 33.000 50.000 39.000 36.000 22.000 1 1.000 55.000 42.000 19.000 5.000 25.000 30.000 57.000 46.000 38.000 37.000 16.000 23.000 21 .000 40.000 58.000 6.000 20.000 56.000 44.000 53.000 52.000 14.000 3.000 15.000 47.000 170 Cor. 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 2.100 2.200 2.100 2.200 2.100 2.200 2.100 2.200 2.100 2.200 2.100 2.100 Dist SdRT 0.714 0.453 0.762 0.834 0.442 0.386 0.555 2.557 0.221 0.754 0.204 1.573 1.456 0.725 0.421 0.519 0.061 0.981 0.893 1.696 0.845 0.494 0.997 0.794 0.420 0.226 1.806 0.293 0.049 1.797 0.512 0.106 0.611 2.480 0.149 1.345 0.077 0.996 0.576 0.332 0.127 0.790 0.936 0.739 0.832 —1.062 0.873 3.169 0.608 0.729 0.328 0.669 1.096 0.101 0.586 0.763 0.725 BIBLIOGRAPHY BIBLIOGRAPHY Abler, R., J. 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