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Ml 4 8 1 0 6 18 BEDFO RD ROW, LONOON WC1R 4E J, ENGLAND 8101109 G h o d s, M ehdi MICHIGAN EXTENSION AGENTS’ ATTITUDES TOWARD COMPUTERS AND COMPUTERIZED EXTENSION FORWARD PLANNING AND CONSULTING PROGRAMS: THE TELPLAN SYSTEM Michigan State University University Microfilms International PH.D. 300 N. Zeeb Road, Ann Arbor, MI 48106 1979 f&r, ' PLEASE NOTE: In a ll cases th is material has been filmed 1n the best possible way from the available copy. Problems encountered with th is document have been Identified here with a check mark . 1. Glossy photographs ________ 2. Colored Illu s tra tio n s ________ 3. Photographs with dark background ________ 4. Illu stra tio n s are poor copy________ 5. °r1nt shows through as there 1s tex t on bothsides of page ________ 6. In d istin c t, broken or small p rin t onseveral pages 7. ^ Tightly bound copy with p rin t lo s t in spine _______ 8. Computer printout pages with in d istin c t p rin t 9. 10. 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A N N A R 3 0 B Ml -18106 '3131 761 -4700 __________________________________ MICHIGAN EXTENSION AG E N T S ’ ATTITUDES TOWARD COMPUTERS AND COMPUTERIZED EXTENSION FORWARD PLANNING AND CONSULTING PROGRAMS: THE TELPLAN SYSTEM By Mehdi Ghods A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Department of Administration and Higher Education 1979 ABSTRACT MICHIGAN EXTENSION AGENTS' ATTITUDES TOWARD COMPUTERS AND COMPUTERIZED EXTENSION FORWARD PLANNING AND CONSULTING PROGRAMS: THE TELPLAN SYSTEM By Mehdi Ghods A survey of the literature shows that computers and computerized decision making aids are becoming integral parts of agricultural education programs and in particular the Cooperative Extension Services. The purpose of this study was to investigate, with respect to computers and the Telplan System, the relationships between the dependent variable, attitude, and the independent variables: age, level of formal education, length of employ­ ment, position held with the Extension Service, previous experiences with computers and the Telplan, frequency of usage, and the number of programs of the Telplan System used. Two instruments were developed to gather data from 283 field Extension agents of all the counties in Michigan. usable data collected from 224 agents were subjected to cluster analysis in order to first treat and remove the The Mehdi Ghods error of measurement or unreliability and second determine and establish the attitude clusters. The cluster analysis yielded nine clusters of which three were made up of the computer items of the attitude scale and the remaining were related to the Telplan System items. Seven null hypotheses were tested to determine the relationships between the attitude clusters and the inde­ pendent variables. All the hypotheses were tested at the .001 level of significance. The pertinent findings and conclusions of the study were: 1. Of the nine attitude clusters, six accounted for nearly 90% of all the variance contributed. 2. Age, level of formal education, length of employ­ ment, position held, and experiences with computers and the Telplan did not seem to be predictors of attitudes toward computers and the Telplan System. 3. Frequency of usage of the Telplan was related significantly to the two attitude clusters, Problem-Solving and Fear/Threat. The less frequent usage of the Telplan, the more distrust’ the agents felt toward the System. The result of this distrust manifested itself as a fear/threat factor to personalized Extension work and consequently the agents feared that they might be replaced by computers. Medhi Ghods 4. The number of the Telplan programs used was not an indicator of attitudes. However, at the level of .001 Modified Problem A Computerized Management System for Extension (Harrison and Raides, 1974). suggest a given program and not vice versa. If we view the library as containing all programs on any computer, regardless of its location, then the library is already quite large. In addition, the number of readily available p r o ­ grams is growing rapidly as the staff capable of generating programs enlarges. Thus, the software may be nearly overcome and is, in any case, diminishing. Some would argue that another prerequisite for the agents' willingness to involve themselves might be the demand by farmers for educational decision making and services. Harrison and Raides indicated that this was not "necessarily true." The farmer's educational need, would be certainly an understanding of what the computer can do for them and how 47 much in the way of benefits they can expect from the computer and at what costs. The agents' "actions", thus, could demonstrate if the usage of the computer would be feasible. Is there any reluctance on the part of the agents to b e ­ come involved with computerized educational, consulting and planning programs? If there is, why? Literature referred mostly to "fear of the unknown", "fear of being replaced by a machine", and a variety of other reasons. Purdy (1975), in an attempt to study as to why some faculty of a community college used new media, including com­ puter aids, found that for most teachers having control over the learning setting was of "crucial importance." In the study, the non-computer aid user group was found to have the feeling and reason that "personal control guaranteed order and thus the self-respect necessary to function as a teacher." Norris (1977) contended that teachers "strongly resisted the acceptance" of computerized aids because of the perception of losing their jobs and being replaced by computers. Communication gap between the developers of computer p r o ­ grams for Extension and the users--the agents--has also been mentioned as one important problem in acceptance and usage of computerized programs. Harrison and Raides (1974) noted that in order for the agents to get to know the programs and sub­ sequently use them in the field, it was required to communi­ cate with the agents on what was needed. Thompson (1971) , in a study of the usage of computers by agricultural cooperatives in the state of Oregon concluded that the utilization of 48 computers was hindered by "the magnitude of the communication gap between computer people and management." The findings showed that the firms which had developed methods of involv­ ing management in the determination and applications of computers programs, were using their systems to maximum. Another factor that literature referred to as having importance on the acceptance and success of the computerized system for Extension was agent training. Harrison and Raides (1974) noted that the significance of agents training was two fold: 1) when an agent had mastery over the use and subject matter, there was a higher likelihood that the agent would be more successful with the computerized Extension aids, and 2) the effectiveness and efficiency of both specialists and agents could greatly improve as a result of the adoption of computerized aids and this could be directly related to the necessary training for the agents. Harch (1971), reported that it was essential to increase the frequency of the training sessions for the county agents in Michigan to enable them to understand and operate the p r o ­ grams of the Telplan System. The increase in the number of training sessions was based on the assumption that as the result field acceptance of the Telplan System could be helped. Other factors that were frequently referred to in the literature as having impact on the acceptance and subsequent usage of computerized systems in Extension included complexity and applicability of the programs. Harrison and Raides in 49 discussing a study of software for farm management Extension (Candler, et a l. , 1970) stressed that "clarity, speed, and reliability" of the programs of a system were important. The conclusion was that the "bottleneck" was the applicability and usefulness of computer programs. It was argued that if the agents find the programs useful and applicable for their field needs they would willingly accept and rapidly adopt the system. In a study of a college faculty attitudes toward techno­ logy in education, Purdy (1975) reached the conclusion that: Many administrators believe a teaching innovation has been introduced successfully if they set up some hardware and see a few students using it (learning resource centers frequently fall into this category.) But unless the concerned faculty perceive the innovation as a useful teaching d e ­ vice and incorporate it in their own teaching, it remains an adjunct, doomed to remain on the periphery. The complex models of the Telplan System were found to have lower utility among the agents in Michigan (Harsh, 1971). The conclusion was that those programs of the system which needed "greater amount of. input" for solving a field problem were used by a smaller number of the agents. Computer errors were also another problem for the agents, which resulted in lowering the agents' level of confidence in a model. It was found that certain programs of the System had "a very high utility among the farmers", and therefore, were heavily used by the agents. These programs were found to have high appli­ cability to real farm problems. The author concluded that it was crucial to develop computer models that were useful in 50 the field and were also free from problems before including them in the Telplan System. In 1973, Schoonaert studied the adoption of program number 31 of the Telplan System (called Least-Cost Dairy Ration) by 48 Ingham County, Michigan dairymen. He found that the adoption rate for the group of herdowners under study was statistically significant (p < .05). It was concluded that the dairymen "did adopt" the program "because of its effec­ tiveness, potential to reduce feed costs while maintaining milk production, and its practicality". Schoonaert also reported a pilot program conducted by Hutjens, et _al^. (1972) to study the utilization of the same program number 31 of the Telplan System in eight Minnesota counties. The agents in those counties were surveyed to find the future usage, time, and cost saving as the result of utilization of, and educational effectiveness of the program. The findings showed that up to 40 cents per cow per day was the amount of cost savings for a dairyman, while the increase in milk production for another dairyman was 10 pounds per cow per day. A projection by the agents in those counties indi­ cated that in the next year (1972-73), a very high number of dairymen (297) would utilize the program. In a 1971 report, Harsh noted that only 11 programs of the Telplan System (the total number of programs in the S y s ­ tem by 1971, was 30) could be considered extensively used by the agent in Michigan. The report indicated that in the first six months of 1971, these 11 programs were used (by 51 Michigan Extension agents and all other users) a total of 2119 times (89%) as compared to only 274 times (11%) for the remaining 19 program. Michigan Extension agents (field staff), as reported by the Harsh and Hathaway (1970,1971,1972,1973,1974, and 1975), utilized the Telplan System (Touch-Tone System usage) 983 times in 1970 to a maximum of 4,065 times in 1973. This maximum dropped to a lower number of usage (3,646 times) by the agent in 1974, while the total number of usage by all the users steadily climbed throughout the period of 1970 to 1975. No reasons for the decline of usage by the agents were stated in the reports. The decline occured even though the number of programs in the library of the Telplan System was increased from 30 in 1970 to 57 in 1975. One of the charcteristics of the Telplan System is the usage by touch-tone system. (the touch-tone system usage in the Telplan System operation has been an on-going program since the creation of the System in 1967). It is in fact a dial-access system which operates in connection with the libraries of the Telplan System. This characteristic has resemblance to the concept of distance education with tele­ phone and the computer as mediums. The touch-tone system (and recently a growing number of hard-copy terminals) in Michigan counties assist the agents to ’’take the computer to the farmer". One of the reasons for establishing the dial- access system has simply been the lack of possibility for the agents (or the clientele) to go to the site housing the 52 computer. Flink, 1975), indicated that the aforementioned reason was one of the bases for distance education in the discussing of a report by Park (1974), Flink stated that the continuing education needs of medical doctors, social workers and nurses in Wisconsin were being met using a system which was developed by means of telephone lines to receive telelectures. Using telephone as a medium of instruction has considerable disadvantages in distance education, however, according to Flink (1975), when compared to advantages, the disadvantages could "almost be ignored". By referring to Short (1974) and Yeomans and Lindsy (1969) , Flink noted that the advantages were": "flexibility", "low cost", and the possibility "to reach and provide remote areas with qualified instructions". The disadvantages of the telephone as a medium in dis­ tance education, as indicated in the literature, were mainly: using audio transmission as the only means for delivering information, and the emphasis that telephone instruction seemed to be "impersonal". Flink argued that "the only way to eliminate this impersonality" was to have "face-to-face instruction". The question of whether telephone instruction was effec­ tive in advancing learning was also addressed in the literature. Flink (1975), discussed a project called DIAL (Direct Instruc­ tion for Adult Learning) which started in Virginia in 1970 (Byrd, 1972). An evaluation was carried out to compare tele­ phone instruction and conventional methods of classroom 53 instruction. However, Kelly (1977), noted that a ’’significant positive correlation between the level of participation" in the Miami-Dade Community College distance learning program and "the level of performance in the course examinations" were found. This program utilized audio, video, and printed materials jointly with a computer for the distance education. The literature revealed a variety of studies and findings as related to attitudes of users and learners toward the instruction media. One such research was done by Neidt and Baldwin (1970) who studied the attitudes of two groups of p r o ­ fessional engineering students. The group which was enrolled in off-campus courses was found to have less favorable attitudes toward the courses. However, the findings showed that the use of a medium such as videotape recordings was effective in meeting the continuing education needs of the off-campus engineering group. The literature also indicated that in the study of atti­ tudes, personal characteristics of the subjects had been taken into considerations Evans 1961). (e.g. Havelock 1973, Reese 1967, and These characteristics frequently included age, past experiences, level of formal education, and a number of other variables. In a study of business faculty and staff attitudes toward computers, as an example of the literature, Reese (1967), found and concluded that age, level of manage­ ment skills, and academic rank did not seem to be the indi­ cators of attitudes. However, Evan, et al. (1961), found that, for instance, past experiences were indicative of favorable attitudes. 54 Summary The literature reveals that there has been a rapidly growing usage of the computer and computer-processed infor­ mation in agriculture. The computer has been utilized in agriculture for a variety of purposes from increasing m a n ­ agerial efficiency to complex problem solving. Major sources of computer services to farmers have been for the most part from land grant Universities, Cooperative Exten­ sion Services, and commercial organizations. Computer technology has been applied to education instructional process since the late 1950's. The applica­ tions have been mostly in the forms of CAI and CMI. The literature indicates, however, that until the early 1970's there was a limited usage of the computer in continuing education. The usage is growing with the applications mainly for administrative purposes and for instruction of adults. There are limited amounts of research and studies deal­ ing with the subject of the computer in continuing education, agricultural education, and specifically the attitudes of the users toward computers and computerized programs in contin­ uing/agricultural education. However, a portion of the literature appears to explore the reasons for using or not using the computer and computerized programs by teachers, Extension agents and other users. The literature reveals that the apparent common reason for not using computers are fear of the unknown and fear of being replaced by machinery. CHAPTER III METHODS AND PROCEDURES This chapter presents the methods and procedures for the study. Included are the development and validation of the instrument to measure the attitudes of extension agents toward computers and the Telplan System, a background ques­ tionnaire, and a description of the population. In addition the methods used for collection and statistical treatment of the data, and a summary are presented. Attitude Scale Construction A search of the literature was made to determine and select the most appropriate instrument to measure the atti­ tude of the extension agents toward computers and the Telplan System. As a result, it was decided to utilize the method of attitude measurement originated by Likert (1932). This method of summated-ratings consists of a series of opinion statements with a range of alternatives from strongly agree, agree, undecided, disagree to strongly disagree for the respondents to indicate their feelings toward some issue--in this case computers and the Telplan System. 55 56 For the development of a Likert-type attitude scale, Likert established several criteria. Namely, (1) it is desirable to prepare more statements that are likely to be used in the final scale; and phrased (2) each statement should be worded to indicate only one issue; (3) statements must indicate the feelings about an issue; (4) each state­ ment should have one interpretation, and (5) each statement should be constructed in such a way that subjects with dif­ ferent attitudes could indicate their feelings in a varying manner, so each item could create substantial variance, and statements should not be of factual nature. In addition to the above, a number of other criteria were established. This was necessitated because of the nature of statistical treatments (reliability analysis and cluster analysis) for the analysis of the data. Items were to be constructed in such a way that the whole scale could be divided into distinct subscales (or clusters). In this case, the items forming one subscale should have similar meaning and correlate significantly with each other. One important criterion was to provide "the possibility of failure" for items of subscales (Hunter and Gerbing 1979). Since it was possible that one or more items in each subscale could fail to have significant correlation with other items and this would be detected in the statistical analysis, each important idea was to be represented by three items or more. 57 Sample size and the population's characteristics prompted the following considerations. a. The large sample size (283 agents), provided for no limitations in the maximum or minimum number of items for the attitude scale. Hunter (1978) points out that "the maximum number of items needed depends on the statistical quality of the items and on the number of persons in the study". b. It was learned that with the exception of several new agents, almost all the extension agents were familiar with the Telplan System. However, a group of the agents were to be considered as users and another group as non-users of the system. Thus, it was decided to construct two separate general subscales, one representing attitudes toward com­ puters and the other attitudes toward the Telplan System. A preliminary attitude scale consisting of 74 items was constructed. sultants. It was reviewed by research faculty and con­ As a result, 14 items were deleted and the scale was revised several times. These 14 items were rejected for representing factual data or being ambiguous. Attitude Scale Validity The revised attitude scale of 60 items was submitted to four judges with experience in computers and the usages of computers in education and business. The judges were asked to estimate and rate the face validity of each item on a 58 continuum from 4 for "very high face validity" to 0 for "no apparent face validity". The final face validity for each item and the whole scale were calculated as the following: Let ■ face validity of each item estimated by judge i, (i = 1,2,3,4) Since the highest possible rating for each item by each judge could be the number 4, then a divisor (D) could be derived: 4 I D = 4 = 16 (3.1) i=l And therefore, the face validity for each item: = 3 1 i=l R^/16 1 for j - 1,2,...,60 (number of items) or (3.2) 0.000 - Fj - 4.000 Finally, the whole scale face validity F, F = 60 I F ./60 j-1 3 (3.3) The computed face validity for each item and the whole scale are recorded in Table 3-1. For each item and the whole scale a face validity of 0.750 < Fj $ 1.000 indicates high to very high face validity. 59 TABLE 3.1. Item Number 01. 02. 03. 04. 05. 06. 07. 08. 09. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. < 30. Computed Face Validities of the Attitude Scale Items and the Whole Scale Face Validity .875 .937 .875 .875 .875 .937 .937 .937 .500 .812 .687 .687 .687 .187 .625 .625 .750 .687 .625 .625 .750 .812 .875 .875 .687 .750 .937 .937 .875 .750 Item Number Face Validity 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42. 43. 44. 45. 46. 47. 48. 49. 50. 51. 52. 53. 54. 55. 56. 57. 58. 59. 60. Face Validity for the Attitude Scale — .875 .750 .937 .937 .875 .875 .812 .812 .812 .812 .750 .687 .875 .812 .750 .875 .875 .875 .937 .687 .812 .937 .812 .687 .750 .750 .750 .937 .937 .812 .797 Items 09 and 14 with the corresponding face validities of .500 and .187 were deleted from the final attitude scale. 60 A value of .501 S face validity. 5 .749 indicate a medium to high Finally, 0.000 - F^ 5 .500 are considered to be of low to very low face validity. As shown in the table all items except 9 and 14 with the corresponding face validity of .500 and .18 7 have high face validity. The computed face validity of the whole scale (F = .797) is high also. Items 9 and 14 were deleted from the scale and to each statement of the final 58 item scale a continuum of SA for strongly agree, A for agree, N for neutral or undecided, D for disagree, and SD for strongly disagree were assigned. This final scale was prepared for a pilot test among several extension agents (Appendix A ) . It was necessary to identify a group of extension agents with which to pretest the attitude instrument. Interviews with the Regional Supervisors of the Cooperative Extension Service at Michigan State University, resulted in selecting the extension agents in five counties. These five counties consisted of Clinton, Eaton, Ingham, Jackson, and Shiawassee. A total of 26 extension agents in these counties represented 9 percent of the population sample. There were both users and non-users of the computers and the Telplan System among these agents and all of the counties were equipped with computer terminals. These agents repre­ sented all different classifications and positions of the counties1 extension agents. In addition, the close distance of the counties’ offices was a decisive factor, since it was 61 decided that offices to be visited by the investigator in order to explain the instruments, purpose of the study, and also, through interviews to gather information helpful in collection of the data. attitude instruments The agents were asked to review the (and the background questionnaire), to make suggestions and comments for each statement and the whole scale, and finally to indicate their feeling on the five point continuum. A total of 17 responses were returned which indicated 65 percent of the pilot sample. An analysis of responses revealed that all items except items numbered 32 and 45 were suitable for the collection of data. Minor revisions were made in the statements of items 32 and 45. It was decided to analyze the gathered data from the pilot sample along with the data to be collected from the population sample of the study. Background Questionnaire Development In order to gather information as related to the personal data of the respondents, a background questionnaire was developed (Appendix A ) . The independent variables of interest were the agent's age; the highest level of formal education; the length of employment by the Extension Service; position held; and experience with computers and the Telplan System. Preliminary interviews with the extension specialists and agents resulted in inclusion of two other variables in 62 the background questionnaire. These were frequency and rate of usage of the Telplan System. In addition, one optional section was designed to identify the specific programs of the System that were in frequent usage by the agents. The respondent's age and level of education were categorized. The length of employment was considered as "number of months of employment" in the analysis of the data. Statements related to the experience with computer and Telplan System were developed and included in the questionnaire. As for position with the Extension Service, the agents were asked to write their official employment titles. This resulted in ten different position categories as shown in Table 3.2. It was decided to analyze and interpret the data for each of the above employment categories. The background questionnaire was reviewed by two judges and together with the attitude scale was distributed among the pilot sample. The agents in the sample were asked to answer and react to the questions and statements of the questionnaire. The responses did not result in revision of the questions and statements. Description of the Population The population of the study consisted of all the Coop­ erative Extension Agents in the state of Michigan. These were county, multi-county, area, and district extension 63 TABLE 3.2. Rates and Percentages of Responses by Position Categories * Position Category** Mailed Returned Percentages 01. County Extension Directors 79 63 80 02. Agricultural Extension, Agricultural Marketing, Field Crop, Food and Nutrition Agents 35 31 89 03. Home Economics Exten­ sion Agents 69 50 72 04. 4-H Youth Extension Agents 62 52 84 7 4 57 14 12 86 8 6 75 08. Extension Dairy Agents*** 2 2 100 09. Multi-County and Regional Agents 6 5 83 10. Extension Livestock Agents*** 1 1 100 - 4 05. Horticultural Extension Agents 06. District Farm Manage­ ment, Resource Devel­ opment, Forestry and Marine A g e n t s , and Extension Leaders 07. District Marketing, Consumer Marketing Information, Public Policy and Public Affairs Agents Returns with blank responses for position TOTAL 283 230 - 81 *The 26 agents of the pilot sample are included. **Each of the categories 2,6,7, and 9 indicate a combination of extension employment titles for two reasons: (1) Age n ts ’ responses with these specific titles, and (2) For the purpose of analysis of the data as related to the various categories. ***These two employment titles were considered as one category for the analysis of the data. 64 agents. Excluded were those agents who directly or indi­ rectly had contributed to the development of the Telplan System. Regional supervisors, extension specialists, admim- istrative and program staff were considered to be directly and indirectly involved with the development and operation of the system and therefore were excluded from the population. The total number of agents in the population sample (including the pilot sample) were 283. Counties and the number of agents for each county can be found in Appendix G. Collection of Data In an effort to insure a high number of responses and also to inform the agents about the study, letters from the administration of the Cooperative Extension Service at Michigan State University (Appendix E) were mailed to 257 agents. Following that, an attitude scale and a background questionnaire with a cover letter were mailed to the county office headquarters for each agent. A total of 213 responses (83 percent) from 257 agents were returned by the middle of September 1978. Of these 213 responses, 6 were eliminated from the analysis of the data for the following reasons: a. One response was found to be from an agent who directly contributed in the writing of two programs for the Telplan System and therefore, the response considered (by the agent and the investigator) to be biased. b. Four agents returned the blank instrument, writing back that they never used the system and were not 65 familiar with the System and the computers, c. One instrument was returned indicating that the agent was no longer associated with the related county extension office. For the analysis of the data, 224 cases, indicating 81 p e r ­ cent response rate were used. This number is the total of 207 useable returns and the 17 responses from the pilot sample. The breakdown of the return by employment categories is given in Table 3.2. The Measurement Model It was necessary to determine and exclude those items of the attitude scale which did not correlate significantly with other items and therefore were not reliable. This represents the error of measurement or unreliability, which results the measurement of theoretical variables to be imperfect. Here, theoretical variables or traits are defined as those variables that are measured by the observed variables or items. The measurement model described here was based on the idea of determining and clustering those items that measured the same underlying variable or trait. As noted in the construction of the attitude scale, items were developed in such a way that the whole scale was to be formed from multiple indicators of the underlying theoretical concepts. These multiple indicators of the traits allowed for the statistical treatment and analysis of the data by employing a multi-variate analysis 66 technique known as cluster analysis. Cluster analysis, as noted by Hunter (1977), is an "oblique multiple groups factor analysis" and is a synthesis of the theories of factor analysis and reliability.* It is a technique which is most appropriate when measurement error and construct validity are of primary considerations. Tryon (1939) describes cluster analysis as the process of measuring underlying variables or traits by constructing unidimensional clusters. A "unidimensional" cluster as noted by Hunter (1977) is a cluster which measures exactly the same theoretical vari­ ables. In other words, it is a "perfect" cluster. A unidimensional cluster is a cluster that satisfies three tests or criteria: items; (1) homogeneity of content for the (2) internal consistency, meaning the items should reasonably correlate with each other; and (3) parallelism, or external consistency for the items. The test of homogeneity of content for a cluster is the evaluation of how well the meanings of the items relate to each other. The items in a cluster should not be inter­ preted ambiguously. similar meanings. In other words, the items should have The homogeneity test, though is not a statistical one, it is indirectly related to statistics. Hunter and Gerbring (1979) argue that if the sample corre­ lations are the main basis for inclusion of items in a *"The essence of oblique multiple groups factor analysis is to extract a single factor from each group or cluster of items. The analysis is 'oblique’, since the clusters are not forced to be uncorrelated." (Hunter, 1978). 67 cluster, then the problem of sampling error (especially for studies with less than "200” subjects), would result in a different cluster in case the study were to be redone. The test for internal consistency is based on the "flatness" of the inter-cluster correlations (Hunter, 1977). In fact, this is a check for the criterion of "unit rank" for the correlation matrix set by Spearman (1904). A brief description of internal consistency and the flatness of the inter-cluster correlations will be given here. For a detailed di s ­ cussion of unidimensionality and test for internal and external consistency, the reader is referred to Hunter (1977) and Hunter and Gerbring (197 9). Let's assume that the variables in a cluster measure the same underlying trait t and let e^,e 2 >• . . denote the error of measurement for the corresponding cluster variables ,^2*•••»V^, then the causal relations can be written in the forms of equations such as: T + e - ,..., V. = T + e . Z 1 1 (3.4) where i indicates the number of variables in the cluster. These relations can be illustrated by the following path diagram: _____ T 1 el e2 68 Considering the Spearman's condition for the "unit rank" correlation matrix and the "product rule for internal consistency" of the theorems of reliability theory, the inter-item correlations for the variables in a cluster can be shown as: ’Y j V . rV . T ’rV.T (3-5) In equation 3.5 when i = j, then: rv.v. l i (3.6) rV. T l Equation 3.6 shows that the correlation between V\ and itself is not equal to 1.00 which is supposed to be. H o w­ ever, this indicates the "communality" for variable and therefore, in a Spearman matrix of unit rank the communality of a variable Hunter is in fact its reliability. (1977) states that for practical purposes, there is a simpler test than Spearman's test for a unit rank correlation matrix. If in a cluster all the Variables have the same amount of error of measurement, then the inter-item correlations of the cluster variables are "flat", i.e. rV .V • = rW (3.7) where r^y is a number which indicates the correlation between any two variables in the cluster. When communalities are used in a cluster analysis, then there is another test for internal consistency. This test 69 is based on the criterion that the correlation between a cluster and its own true score is greater than the cluster correlations with any other cluster true scores. The test for parallelism is a check for the similarity coefficient of the variables in a cluster with other vari­ ables outside that cluster. In particular, let variables in a cluster all measure the same underlying trait. In addition, if to within sampling error they all have equal quality as measures of that trait, i.e. rV xT = rV 2T = ---- = TV ±* t3 *8 ) where i is the number of items in the cluster, then the criteria for parallelism or external consistency requires that for any other variables such as X outside the cluster we should have: Tv1x “ rv 2x = — = rv Ax t3 , 9 ) to within sampling error. The external consistency test is usually applied to traits rather than to the variables, since the reliability of a variable is lower than the underlying trait.that it measures. The aforementioned three tests are the means for determining the unidimensionality of a cluster and therefore deleting the "weak" variables (or items) from that cluster before the final analysis of the data. 70 After the unidimensional clusters are formed, the reliability of the clusters sums, i.e. Cronbach's (1951) coefficient alpha, can be obtained through cluster analysis. This coefficient alpha is in fact the index of measurement error in a cluster score. The higher the value of alpha (the closer to 1.00), the more reliable the measurement of the traits. A full discussion of reliability theory and factor analysis, communalities and cluster analysis can be found in Nunnaly (1967), Gorsuch (1974), and Hunter (1977). Once the measurement model is constructed, and neces­ sary revisions are made, the fit of the data to the model can be evaluated and if the fit is satisfactory, then the parameters and estimators of the model can be interpreted. Based on the aforementioned discussion, the measure­ ment model for the cluster analysis of the data was con­ structed. Figure 3.1 shows the algorithm (in a flow chart method), which was used for the model, and its subsequent procedures. The A Priori and the A Posteriori Cluster Analysis In order to determine whether the 58 items of the attitude scale form distinct clusters and in each cluster the items inter-correlate with one another, the a priori cluster analysis was used. Two different routines were used to perform this analysis. (1) Hunter and Cohen's (1969) PACKAGE, and (2) Tryon and Bailey's (19 70) BC TRY. This latter routine was mostly used for graphical purposes 71 CONSTRUCT MODEL ESTIMATE PARAMETERS EVALUATE FIT ABANDON PROCESS? FIT SATISFACTORY? YES Figure 3.1. The Model Building Process. YES / INTERPRET r P A RA ME T E R ESTIMATES 72 of the clusters. Almost all the cluster analysis of the data was done by employing the PACKAGE. The first step for the a priori cluster analysis was to let the PACKAGE form the distinct clusters from the items. This was done through an "oblique multiple groups" factor analysis with communalities in the diagonals. The routine (in the PACKAGE) for this factor analysis breaks down when the factor loadings fall below 1.00. As a result of this analysis two clusters from the items concerning attitudes toward computers, and five clusters from the item related to the Telplan System were formed. The a priori analysis did not delete the items which had low inter-correlation in the cluster. Nor, the content and external consistency of the items of the cluster were taken into consideration. Therefore, the a priori cluster analysis did not produce a basis for the analysis of the data. Following the a priori analysis, the a posteriori analysis was undertaken. Here, as described earlier, the three criteria of homogeniety of the content for the items in a cluster, their internal and external consistency were applied for formation of the clusters. As a result of some 20 reanalysis the a posteriori clusters were formed. During this process, the items of the a priori clusters were moved from one cluster to another. Long clusters (clusters with many items) were broken into sub-clusters in order to accomodate all of the three criteria. The items that did not satisfy all three criteria were placed in residual clusters. The residual cluster for statements as related to computers contained 8 items, and 10 items from the Telplan section of the attitude scale formed another resid­ ual cluster. Thus, 40 items from the original scale by forming 9 clusters, structured the a posteriori analysis of the data and produced a great deal of evidence in testing the hypotheses related to the agents' attitudes toward computers and the Telplan System. Out of these 9 clusters, 3 were formed from the items in the general computer state­ ments, and 6 clusters from the items in the Telplan section. Factor inter-correlations and loading matrix (showing internal consistency), and similarity coefficient matrix (showing external consistency), for the 9 clusters and 2 residual clusters can be found in Appendix (B). Table 3.3 provides the distribution of the items into 11 clusters forming the a posteriori analysis. Table 3.4 represents the inter-correlations and loading matrix for the a posterior clusters. The a posteriori cluster correlations for attenuation) are shown in Table 3.5. (corrected The clusters' names with a description of each cluster are provided in Table 3.5. The descriptions are based on the content of the items forming each cluster. The numbers 501-509 are the numbers assigned to the clusters by the PACKAGE System for cluster analysis. These numbers will continue to be the same for the corresponding clusters throughout the text and the appendices. The a posteriori cluster correlations (corrected for attenuation) are shown in Table 3.6. The 74 Table 3.3. The A Posteriori 11 Clusters Formed from the A Priori Clusters and Distribution of Items in those 11 Clusters (501-511).* Computer Clusters and Residual 501 501 502 503 502 503 510 The Telplan System Clusters and Residual 504 505 506 507 508 509 511 5 3 2 7' 504 505 4 2 506 8 507 508 6 3 509 510 8 10 511 Total Computer Items (18) (5$ The Teleplan System Items (40) *The numbers 501-511 are the ''number labels" for the clusters formed from the cluster analysis using Hunter and Cohen's (1969) PACKAGE. reliability of each cluster as determined by the Crombach's (1951) Coefficient Alpha are presented in Table 3.7. Tables providing the means and standard deviations of the 58 items of the attitude scale with number of cases for each item (i.e. excluding missing data for that item), the initial 58x58 correlation matrix, factors obtained from the FACTOR I Table 3.4. 9 11 10 6 8 2 5 3 13 13 22 42 19 21 31 S3 34 40 3? 33 38 44 37 24 34 28 29 34 39 23 27 44 41 43 25 34 49 51 SO 52 S 6i 502 503 504 503 504 507 508 509 Inter-Correlations and Loading Matrix (with Communality in the Diagonal) for the 9 A Posteriori Clusters. 9 11 10 4 31 24 21 33 24 28 41 21 21 41 22 IS 19 -9 -2 -5 -4 -1 4 -7 -B -1 9 -1 3 -9 33 21 15 22 24 -1 4 5 -1 -9 -4 -1 3 -1 4 -13 -1 4 -4 -9 -7 -7 -1 3 2 S 3 13 30 - 4 19 - 2 IB - 9 24 -14 7 21 7 28 - 3 27 19 -4 -I 14 -2 5 -3 27 23 IB -3 -3 -5 -1 -4 19 IB 13 1 -1 -4 -9 -2 -3 8 13 22 42 19 21 31 33 34 40 37 33 38 44 57 24 34 28 29 36 39 23 -1 1 - 3 -IB - 1 3 - 1 3 - I S - 1 9 - I S -21 - 2 3 -1 4 - 9 -2 0 - 8 - 2 -1 S 12 1 4 -2 -1 1 - 8 -1 3 -1 5 -1 0 -1 2 - 4 - 1 5 - I S - 5 -1 1 - 3 -1 3 - 9 9 7 8 11 4 -6 4 -1 4 - 7 - 8 -1 9 -1 3 - 9 - 1 2 - 4 - 4 -1 1 - 8 - 7 -1 3 -13 2 3 1 9 9 - 4 -1 4 -4 -13 -1 4 -1 3 -1 4 - 4 - 9 - 7 - 7 - I S -1 - 4 -2 0 -1 4 -1 2 3 4 -1 - 3 . 9 - 5 -1 1 -9 -22 - 7 - 8 - 7 -1 8 -1 0 - 9 -1 1 - 1 2 - 1 8 -2 0 -1 3 -4 - 3 1 4 4 30 0 8 -4 -2 17 1 11 3 10 -1 5 -a 7 8 13 14 -1 4 10 0 -1 4 0 - 7 -1 -8 14 -1 2 3 10 0 8 8 15 2 -1 13 14 13 8 1 4 - 1 -1 7 - 8 2 -1 9 - I S -8 -3 -5 17 14 14 9 4 - 5 14 10 2 7 3 9 - 4 -2 3 -1 9 -1 5 -2 3 -1 6 - 7 - 7 8 7 1 -1 -2 -S 2 1? 39 35 ’ 9 13 14 10 17 22 .2 -1 13 7 11 -3 - 2 -1 3 -11 -12 -21 -4 - 4 -4 -1 1 -11 -1 1 17 3 14 P ? ? P ?2 ?t ? ? s }4 6 12 - I 13 12 11 - 3 -1 0 - 4 - 4 - 2 0 - 7 4 -1 0 -5 -8 -9 7 10 14 9 IS 44 41 47 28 47 29 29 25 17 17 21 28 IB 7 7 - 7 -1 - 7 - 3 6 -IB -1 3 -2 2 1 0 9 13 22 41 43 34 37 32 44 24 37 19 14 21 38 30 - 4 - 4 - 6 - 2 3 - 3 -2 - 5 -13 -1 5 - 7 11 8 4 14 21 47 34 44 40 34 25 34 31 24 33 19 22 20 12 - 8 - 3 - 4 6 18 5 -13 -10 -8 3 8 - 5 10 2 28 37 40 24 14 19 33 30 24 17 14 30 24 14 - 4 4 -9 8 7 IS -13 -12 - 7 10 13 14 17 25 47 32 34 14 24 24 20 24 27 25 IS 29 10 2 - 4 - 2 - I S -1 4 - 2 3 1 -19 -4 -12 -IB -1 3 1 10 22- 14 29 44 25 19 24 21 13 24 30 23 19 25 27 -7 3 - 5 - 2 7 -22 -20 - 3 -1 5 -1 5 - 4 -10 -B 2 2 2 4 2? 24 34 33 20 13 20 29 14 2B 7 6 IS 12 8 20 6 32 28 IB -21 -1 5 -4 -9 4 -1 7 -1 12 23 37 31 30 24 24 29 39 34 37 34 32 24 3 - 4 - 3 -1 9 3 3 0 -2 5 - 5 -11 -11 10 13 8 13 -1 17 19 24 24 27 30 14 34 34 35 30 30 18 -4 -20 -1 9 -1 7 - 2 3 - 2 5 - 5 -1 4 -11 -a -1 -12 8 14 3 7 13 17 14 33 17 25 23 28 37 35 33 27 19 a 5 - 7 - 3 - 4 - I t -11 - I -9 - 5 - 7 - 4 -1 8 13 13 7 11 12 21 21 19 14 15 19 4 34 30 V 27 17 10 - 3 -1 -1 5 4 3 1 -20 -1 3 -1 3 -20 -20 14 8 9 - 3 11 28 3B 22 30 29 23 32 32 30 19 23 47 44 0 5 1 -13 -2 - 6 -2 -B - 9 -1 3 -1 4 -1 3 -1 4 -4 - 2 - 3 IB 30 20 24 10 27 28 24 18 a 17 41 47 11 -1 -1 1 -1 0 >6 0 -2 9 2 -12 -4 0 -1 -2 3 -1 3 -10 7 -4 12 14 ■2 -7 18 3 -4 3 10 1 11 34 24 23 32 2B 23 37 -1 3 7 3 - 3 -1 4 -1 7 -1 9 -11 - 4 - 7 - 4 -8 - 4 - 4 3 7 - 4 -20 - 7 - 3 -1 3 -4 24 26 36 24 21 22 14 5 8 1 4 4 0 -8 -1 3 -12 - 4 -1 - 4 - 3 4 -2 - 5 4 - 3 -1 9 - 3 -1 -2 -11 23 36 23 26 13 IS 17 12 11 9 -1 1 -7 2 -2 3 -21 -20 - 7 -2 3 -4 - 9 -1 5 -2 7 15 - 1 9 -1 7 - 4 -1 5 - 4 -1 32 24 26 21 23 16 16 1 4 - 4 -3 4 - I -1 9 -1 4 -4 - 7 - 3 - 5 8 -1 4 -22 12 4 3 -2 3 -11 4 -2 0 28 21 13 23 20 33 IB 4 -4 -1 4 -9 0 -7 -1 5 -7 -4 4 7 -2 18 7 -2 3 -20 8 3 -2 5 -1 1 0 0 23 22 IS 16 33 20 24 3 -2 4 9 -3 8 -8 -8 -7 -4 -10 4 -5 5 IS 1 - 3 20 0 - 5 -1 1 5 - 4 37 14 17 16 18 24 19 -11 17 8 - 5 14 0 - 4 -2 -1 0 - 7 -1 4 -1 5 -2 -2 - 1 8 -11 11 2 -1 4 -1 5 - 4 4 2 14 22 28 14 14 13 19 -1 4 -1 4 -1 8 -1 7 - 4 9 -1 3 4 24 22 18 25 10 0 7 8 22 34 7 11 20 25 14 IS 7 -1 -1 23 19 14 3 - 7 -1 4 -4 O 8 -11 - 5 1 7 20 19 32 21 3 -2 11 27 S IB 14 12 17 14 -4 2 5 17 32 3 - 7 -1 4 - 1 7 - 1 3 -1 1 IS - 7 -10 - 7 - 3 24 32 29 24 1 11 13 19 - 4 1 14 14 19 23 9 10 3 25 31 1 2 -1 4 - 2 7 - 1 3 - 4 4 7 - 4 -11 7 14 10 18 18 9 IS 8 a 32 3 2 4 IS 13 - 3 1 8 2 21 13 5 - 9 -10 - 7 - 5 -2 -IB 1 -4 7 12 23 4 B 0 14 11 18 -4 17 10 IS 12 9 2 -2 -2 7 12 - 6 -10 -10 -12 -2 -B 3 - 5 10 8 4 21 22 29 27 19 24 24 33 18 13 21 23 20 1 -4 0 6 1 -1 11 -4 -11 -1 9 -1 -1 4 -1 11 27 13 20 24 IB 14 24 10 25 12 5 14 4 -2 7 -3 9 7 2 9 21 3 -10 -1 4 -1 4 - 1 4 - 7 4 -a 0 1 -8 3 10 14 22 21 12 15 21 12 22 10 - 3 12 0 15 14 4 9 11 1 - 7 -10 -2 4 -10 -10 -0 - 5 12 13 39 35 29 30 }1 7 ° 74 14 7 5 ? 20 14 2? 17 2 -J4 7 -J3 -1 9 5 - e - 7 -22 -1 4 -3 1 -2 7 -2 4 -20 -2 5 -20 - 2 3 - 2 7 -1 9 -1 8 -3 3 -2 3 - 3 LSo & At 3 0 -1 0 9 13 6 1 4 -4 3 -11 -B 0 S3 50 37 12 24 22 7 17 5 28 - 3 - 3 9 22 20 24 22 -1 -1 7 - 3 8 - 1 7 -2 1 -2 6 -2 1 -1 6 - 8 - 9 -IB -1 3 -1 0 12 4 23 41 41 20 29 31 10 34 2? 7 9 9 14 19 4 - 4 -1 9 -1 4 - 1 5 -3 4 - 9 0 -1 1 -2 5 - 2 0 -1 8 -2 0 -2 1 2 11 13 22 27 48 47 44 51 51 46 44 32 39 40 30 52 40 11 - 6 - 2 -1 8 - 3 -1 10 -3 1 -1 4 -1 3 - 1 3 -2 2 14 18 11 13 IS 35 40 47 38 41 42 33 43 58 57 52 45 29 4 -1 5 -1 1 - 2 5 - 1 2 - 1 3 - 2 -21 -1 4 - 2 0 -2 3 -2 4 9 11 4 34 30 32 40 29 39 44 42 35 20 30 67 47 3 -4 9 -1 3 -1 0 - 6 -1 0 2 14 3 -7 4 -1 1 -1 9 -3 0 -21 -1 7 - 3 - 1 8 7 8 -2 0 -2 4 26 - 4 -3 4 -1 3 - 2 - 3 -3 38 51 48 14 43 45 44 - 7 -2 1 -3 0 -1 7 -1 1 12 -1 8 - 3 3 9 37 39 40 38 10 21 27 44 7 4 7 25 28 30 28 29 1 26 32 7 -1 0 -IB -2 9 - 1 3 - 9 4 - 7 13 27 21 38 39 29 27 40 14 ■38 22 9 8 24 24 3 -8 3 • 1 2 13 11 4 23 Table 3.4. 27 9 it to 6 a 2 s 3 13 13 22 A2 19 21 31 SS 34 40 37 33 30 44 57 24 56 20 29 36 39 23 27 46 41 43 23 54 49 51 SO 52 SOI 502 503 504 505 506 507 500 309 -11 17 B -3 14 0 -6 -2 -1 0 -7 -1 4 -1 5 -2 -2 -IB -1 1 ■ 11 2 -1 6 -1 5 -6 46 41 Continued. 43 25 34 49 51 SO S2 SOI S02 S03 S04 'SOS 306 307 SO0 S09 -1 6 3 -7 2 3 -1 0 - 4 -1 0 - 7 56 - 6 - 8 -2 3 -3 1 -2 1 2 - 7 -1 0 -1 4 - 7 -1 6 -1 4 - 9 - 1 0 -11 -1 6 - 1 0 53 S - 9 -2 0 - 1 6 - 1 6 14 -2 1 -1 8 -1 0 -1 4 -1 7 -2 7 -1 0 -1 2 - 1 9 -1 6 -2 4 47 -1 1 -IB -IB - 1 3 -2 0 3 - 3 0 -2 9 - 1 7 - 4 - 1 3 -1 3 - 7 - 2 -1 -1 6 - 1 0 47 - 8 -1 3 -2 0 -1 3 -2 5 - 7 - 1 7 -1 3 -6 0 -1 1 - 6 -5 - 8 -1 - 7 -1 0 46 0 -1 0 -2 1 - 2 2 -2 4 6 -1 1 - 9 9 3 B IS 7 -2 4 4 0 -1 0 53 12 2 16 9 -1 1 12 4 -1 3 -11 - 7 - 6 - 1 8 - 3 - I -a -5 5 30 4 11 18 11 - 1 9 -1 8 - 7 0 -5 -1 0 -1 1 1 10 11 3 12 -8 37 25 13 11 3 -3 0 - 5 13 7 4 -4 a 27 10 IB -7 12 61 22 13 - 4 -2 1 3 27 1 -7 4 7 -3 7 7 6 -1 7 9 21 6 13 16 15 -2 2 24 61 27 15 26 20 26 16 12 21 20 22 36 - 1 6 22 20 68 35 34 - 3 37 38 22 19 32 10 23 22 24 21 35 -3 1 7 29 67 40 50 -1 8 39 • 39 10 32 29 IB 6 29 18 12 29 - 2 7 17 31 66 47 32 7 40 29 B 38 27 25 21 26 18 a 27 16 IS 25 -2 4 5 10 51 3B 40 0 19 26 21 36 -2 0 28 34 51 41 29 -2 0 10 40 10 3 1 1 0 -2 11 8 16 26 10 12 11 -2 5 - 3 29 46 42 39 -2 4 21 .16 22 11 13 9 u 24 25 22 30 -2 0 - 3 7 44 33 44 26 27 38 34 27 19 IS IB 33 12 10 24 -2 3 9 9 32 63 42 -4 44 22 6 9 -2 7 22 7 7 5 -6 2 -4 18 5 -3 * 39 58 35 -3 4 11 IB 1 21 17 13 16 12 20 -1 9 20 16 40 S7 20 -1 3 25 23 20 14 14 IS 10 21 8 0 14 -IB 24 19 30 52 30 - 2 28 2 6 52 45 67 - 3 30 26 4 25 12 16 8 13 23 9 15 29 -3 5 22 2 14 17 19 B 12 20 21 14 17 -2 3 -1 -4 40 29 67 - 3 28 26 16 IS 16 23 32 9 0 4 2 -3 -1 7 -1 9 11 6 3 1 9 SB 29 3 2 0 -1 4 3 -3 8 -1 4 - 6 -1 5 -1 3 51 7 -8 7 -4 6 -2 22 9 28 -1 3 9 -1 7 -1 5 - 2 -1 1 -1 0 48 2 10 2 -2 1 7 1 4 5 14 -1 5 3 4 - 2 - 6 -3 - a -1 3 13 -2 1 -3 4 -IB -2 5 - 6 46 1 -1 2 14 23 17 25 13 0 -2 6 - 9 - 5 -1 2 -1 45 26 13 7 9 4 14 1 7 9 7 -1 0 -2 1 0 -1 -1 3 0 45 32 11 15 19 32 31 13 12 -1 19 14 6 -1 6 -1 1 10 - 2 -1 5 1 -3 - 6 11 3 11 5 44 7 9 |4 7 -2 0 - 6 -16 - 3 9 - 6 -1 4 - 1 3 -1 5 3 2 0 4 38 - 6 3 9 11 22 -2 9 - 3 9 ■33 31 29 22 69 20 7 47 44 38 39 24 . 34 - 2 44 46 42 38 32 22 6 27 2B 22 19 6B IS 6 7 18 -9 -6 0 30 42 36 41 28 13 9 8 IB -2 5 - 2 - 8 35 12 26 27 60 17 - 6 39 3B 41 35 25 17 4 5 23 -2 3 - 8 9 21 23 12 16 59 16 -1 6 24 32 28 25 17 7 -1 1 -1 4 6 9 2 3 2 19 IB 20 1 41 9 10 11 19 25 -1 7 2 32 27 - 3 34 22 13 17 6 11 43 37 32 3 9 6 4 2 11 56 49 49 -1 4 10 32 36 IS 22 9 B 13 73 2 11 7 8 3 3 19 49 43 41 -2 6 0 21 32 8 21 3 16 65 ?9 35 0 22 IB IB 23 7 25 49 41 43 -24 5 ?? 1 $5 9 -2 9 -9 - 2 5 -2 3 -1 1 - 1 7 -1 4 -2 6 .-2 4 100 - 8 -2 4 -4 2 -3 8 -4 3 7 -3 5 -3 2 -6 -3 - 6 - 2 -B -1 4 6 10 0 5 -B 100 30 19 32 16 -4 3 -B 7 -1 4 9 6 -B 9 2 11 32 21 27 -2 4 30 100 41 23 9 40 2 -3 1 -1 3 33 27 35 21 19 43 36 32 51 -42 19 41 100 70 68 - 6 54 58 -1 5 31 28 12 23 IB 37 13 8 29 -3 8 32 23 70 100 55 -2 3 45 26 2 68 55 100 -4 43 38 __ 1 29 22 26 12 20 32 22 21 35 -4 3 16 3B 1 22 19 27 16 1 2 B 5 7 -4 3 -31 -6 -2 3 -4 100 27 1 7 - 6 1 49 68 60 59 41 32 13 16 34 -3 5 - a 9 54 45 43 27 100 31 3 20 15 17 16 6 27 75 65 63 -3 2 7 40 SB 26 3B 7 31 100 77 Table 3.5. The A Posteriori Clusters Names and Description of the Content for Each Cluster 501 ANSWER: Implies that computers by providing quick answers, aid the agents to solve their client's problems. 502 INFALLIBILITY: Refers to the perfection of computers and that the computers provide cor­ rect answers to most problems. 503 ACCESS: Implies that easier communications with the computers will be possible and helpful for extension work if computer terminals are provided for all agents' offices. 504 PROBLEM-SOLVING: Refers to the potential of the Telplan System for problem solving and that the System should be used by the agents more often, because it provides for the agents to be more successful in their extension work. 505 QUALITY: Implies that the Telplan System is a means of quality for agricultural continuing education and improvement of services to the extension clientele. 506 FEELINGS: Implies that the Telplan System p ro ­ vides for the agents to have more positive attitudes toward computers and the System. 50 7 LIMITATIONS: Implies that the Telplan System is limited in scope as related to the needs of the extension clientele. It further suggest that because of inap­ plicability and complexity of the programs of the System and that the System does not provide appropriate solutions in most situations, there­ fore, the agents and their clients have difficulty in using the System for problem solving. 508 FEAR/THREAT: Refers to the age nt s’ distrust of the Telplan System because the System not only limits the agents' personalized extension work with their clients, but often threatens the agents' jobs. 78 Table 3.5. Continued. 509 INFORMATION AND TRAINING: Implies that there is a need for additional information and training for the agents as related to the Telplan System, perhaps through con­ tinuing training, in order that they become more acquainted with the System and be able to work with it. Routine of the PACKAGE program, and the a priori inter­ correlation and loading matrix with the corresponding clusters can be found in Appendix B. An examination of the Table 3.6 shows that clusters (504) "problem-solving'', (505) "quality" are highly corre­ lated with one another (r = .70 and .68 respectively). This raised the question that whether these two clusters were to form one cluster in the first place. Also, some items which were designed to serve as parts of the "computer section items" and were dispatched to the "residual" (510) cluster, correlated significantly with the clusters of the Telplan items. ( r ». 50 , One of these items (#16) correlated see Appendix B) with cluster (508) "fear/threat" and another item (#12 with r = .39) correlated with cluster (504). In order to answer the above and find out the relation­ ship of the computer items to the Telplan items, it was decided to study the structure of the a posteriori clusters. To accomplish this, two second order cluster analysis were performed. One for the Telplan System items only, and 79 Table 3.6. The A Posteriori Clusters Correlations (Corrected for Attenuation). 501 502 503 504 505 506 507 508 501 502 503 504 505 506 507 508 509 Table 3.7. 100 -8 100 -24 30 1 0 0 -42 19 41 -38 32 23 -43 16 2 7 -43 -31 -35 - 8 9 -32 7 40 100 70 100 55 -23 54 45 58 26 68 -6 100 -4 43 38 100 27 7 100 31 Standard Score Coefficient Alphas for the A Posteriori Clusters. 501-509. Cluster Name Cluster No. Alpha 501 Answer .62 502 Infallibility .45 503 Access .52 504 Problem-Solving .76 505 Quality .66 506 Feelings .61 507 Limitations .69 508 Fear/Threat .72 509 Information and Training .72 80 one for a combination of both computers and Telplan System items (including residuals items). Using the FACTOR Routine of the PACKAGE program, the matrix of inter-correlations of the Telplan clusters clusters (501-511) were factor analyzed. assigned each cluster cluster loading. (504-509), and the computer-Telplan 11 The Routine (now acting as a variable) to a new (second order), according to its highest factor Again, the Routine broke down when the factor loadings fell below 1.00. The Routine then performed the inter-correlation matrix of the new clusters. In this process if the highest factor loading of a variable (old cluster) was negative, its direction was reversed by the reflecting procedure of the Routine. A few reanalyses particularly for the 11 clusters were performed to accom­ modate the established criteria previously referred to, and deleted those non-contributing original variables. As a result of the second order cluster analysis, two new clusters from the nine old clusters formed. Table 3.8 represents these clusters (501-509) were (denoted by 601 and 602), their make ups, and reliabilities alphas). The Telplan clusters any new clusters, however, (501-511) (504-509) did not generate the 11 a posteriori clusters formed new second order clusters. viding these clusters, (coefficient Tables pro­ their varimax factors, and matrix inter-correlations can be found in Appendix B. be said about these in a later chapter. More will 81 TABLE 3.8. The Second Order Cluster Formed from the Nine A Posteriori Clusters. Cluster 601 Cluster 602 504 Problem-Solving 507 Limitations* 506 Feelings 502 Infallibility 505 Quality 503 Access 508 Fear/Threat 501 Answer* 509 Information and Training Coefficient Alpha ■ .84 Coefficient Alpha = .61 •Clusters 501 and 50 7 are "reflected” for inclusion in clusters 601 and 602 respectively. Therefore their content and names should be interpreted reversibly. Reliability Analysis The data were also treated and analyzed using SPSS Subprogram RELIABILITY developed by Specht (1976). This subprogram computes the coefficients of reliability for 82 multiple-item scales, performs analysis of variance and a number of other statistics. It provides a means for assess­ ing Mhow reliable a sum or weighted sum across variables is as an estimate of a case's true score". Here, again the measurement error is of primary consideration. A brief discussion of this error of measurement was already presented in a previous section of "internal inconsistency". Specht's (1976) subprogram estimation of reliability, is based on the following assumptions given by Guttman (1945) 1. Reliability is defined as the variation over an indefinitely large number of independent repeated trials of errors of measurement over an infinite population of objects for each item being measured. 2. The observed values of an individual on an item are experimentally independent of the observed values of any other individual on that or any item. 3. The observed values of an individual on an item are experimentally independent of the observed value for that individual on any other item. 4. The variances of the observed scores on each item and the covariances of the observed scores between items exist in the population. 83 The criterion for the formation of multiple-item scales is that the items in each scale ’'logically" relate to each other. Therefore, by grouping the items according to their contents and after some 15 reanalyses, were formed. 9 scales These scales corresponded to the 9 clusters generated by the cluster analysis. Again, 18 items in all were placed in residual scales--8 items from the computer statements and 10 items from the Telplan section of the attitude scale. The "standardized item Alphas", showing the reliability of the scales were equivalent to the value of the coefficient alphas calculated by the cluster analysis procedure. The scales, corrected item total correlations, alphas, scales variances and means are presented in Appendix C. Since the Subprogram Reliability cannot compute coefficient alphas for scales with less than 3 items, a value of 99.0 is printed in place of the item's corresponding value of alpha. Zero-order correlation analysis and multiple regression analysis were used to measure and explain the relationships between the nine attitude clusters and the independent variables: (1) age, length of employment, (2) level of formal education, (3) (4) previous experiences with computers and the Telplan System, (5) frequency of usage, (6) number of programs of the Telplan used, and (7) position held with the Extension Services. contrasts tests Also, a priori and a posteriori (Schefee's post-hoc test) were used to 84 examine and explain the relationships of the specific levels of the independent variable to the attitude clusters. Chapter four includes a description of the regression model used for the analysis of the data. Summary Two instruments were constructed to measure and examine the relationships between the dependent variable attitude of the extension agents toward computers and the Telplan System and several independent variables. A Likert-type attitude scale and a background q u e s ­ tionnaire were developed. After a review by 4 judges, a total of 58 items out of 74 statements were retained in the attitude scale and its face validity was established. The instruments were then pretested among several extension agents. They were then sent to the Michigan Cooperative Extension Agents. The data collected from 224 agents were subjected to statistical treatments and prepared for analysis and interpretation. A measurement model was constructed to treat the error of measurement or unreliability. and remove The model was based on and developed within the context of a multiple indicators approach called cluster analysis. The items of the attitude scale were subjected to the a priori cluster analysis which was followed by the a p o s ­ teriori cluster analysis. This later analysis generated 9 clusters under the unidimensionality criteria of the mea- 85 surement model. The clusters were then given specific names and their reliabilities (coefficient alphas) were determined. The clusters were then treated as variables and subjected to a second order cluster analysis to examine their relationships. Two new clusters were formed. The attitude instrument and the data were also analyzed using the Reliability Analysis. This procedure formed 9 multiple-item scales which corresponded to the 9 clusters. The Reliability Analysis, also, performed analysis of variance. Zero-order correlation analysis and multiple regression analysis were also used to analyze the data and test the hypotheses as related to the relationships between the attitudes and.the demographic data. CHAPTER IV PRESENTATION AND ANALYSIS OF THE DATA In this chapter the data gathered from the responses of 224 extension agents and analysis of the data are presented. The data collected were the agents' to the two instruments developed for the study. responses These two instruments included a 58 item attitude scale and a back­ ground questionnaire. There were six research hypotheses formulated by the researcher to examine the relationships between several independent variables and the dependent variable attitude. The rejection or acceptance of these research hypotheses were dependent on whether the statistical hypothesis of each was rejected or accepted. The nine clusters formed by the cluster analysis constituted the dependent variables and the selected personal characteristics of the agents formed the independent variables. employment, Age, length of level of formal education, experience with com­ puters and the Telplan System, frequency of usage, rate of usage of the programs, and position held with the Extension Service formed the selected agents biographic data. In the development of the background questionnaire, one of the independent variables, age, was grouped according to its numerical value. Frequency of usage of the system and 86 87 rate of usage of the programs were sorted according to cate­ gorical distribution. These were in accordance with the numerical and categorical distributions described by Freund (1960). One variable, length of employment, was sorted according to its quantitative description (month of employment). Items as related to experiences with computers and the Telplan System were categorized into positive and negative responses for the analysis of the data. Tables 4-1 to 4-7 present the frequency distributions of the independent variables. As shown in Table 4-1, for the 224 extension agents, the mean age was in the 35-44 year category. Over one-half (50.8%) of the respondents were 40 years of age and over, while only 20% were under 30 years of age. Out of 224 agents, nearly 60% had earned Mas te r’s degrees. Eighty-three agents (37.1%) had Bachelor's degrees. The dis­ tribution indicates that about 97% of the respondents had at least a Bachelor's degree. Four agents had Doctoral degrees, while only one respondent had less than a four year formal college education (Table 4.2). Table 4-3 presents the distribution of the length of employment for the agents. The range of the distribution was from less than 1 month to 396 months (33 years). The mean years of employment was about 10.4. Exactly 50% of the agents had served the Extension Service for a minimum of 8 years. One-third had a minimum of 16.5 years and 10 agents had a minimum of 28 years of service with the Extension Service. 88 TABLE 4*1* Distribution of Age ba Age Categories ra a a s s « a n a B n B B n s a a u a a B a a a B B n 8 B a B 8 a s a a B n « B a B S B a B N w i* CATEQORY(Years) CODE ABSOLUTE FREQ RELATIVE FREQ (PCT) ADJUSTED FREQ (PCT) CUM FREQ * I^ I 1 i Under 25 1 * 17 7.6 7.6 26 to 29 2 . 28 12.5 12.6 20.2 30 to 34 3* 39 17.4 17.5 37.7 35 to 39 4* 26 11.6 11.7 49.3 40 to 44 5. 28 12.5 12.6 61.9 45 to 49 6 * 24 10.7 10.8 72.6 50 to 54 7. 31 13.8 13.9 86.5 55 or Over 8 * 30 13.4 13.5 100.0 BLANK 1 .4 TOTAL 224 100.0 TABLE 4*2. MISSING 100*0 Ditribution of Level of Formal Education CATEGORY CODE 1-2 Yrs of College 2. ABSOLUTE FREQ RELATIVE FREQ (PCT) ADJUSTED FREQ (PCT) CUM FREQ (PCT 1 .4 .4 .4 Bachelor's Degree 3. 83 37.1 37.1 37.5 Master's Degree 4. 134 59.8 59.8 97.3 Doctoral Degree 5. 4 1.8 1.8 99.1 Other 6 2 .9 .9 100.0 224 100.0 100.0 . TOTAL 39 TABLE 4»3» ABSOLUTE KKEO CODE Distribution of Length of Employment by Months RELATIVE ADJUSTED FREQ FREQ CUH FREO 0 2 .9 .9 .9 198. 1 .4 .5 75.2 1. 3 1.3 1.4 2.3 204. 2 .9 .9 76.1 2. 1 .4 .5 2.7 216. 5 2.2 2.3 78.4 3. 2 .9 .9 3.6 228. 3 1.3 1.4 79.7 6• 6 2.7 2.7 6.3 240. 6 2.7 2.7 82.4 7. 5 2.2 2.3 8.6 252. 4 1.8 1.8 84.2 a. 1 .4 .5 9.0 256. 1 .4 .5 84.7 12. 8 3.6 3.6 12.6 264. 10 4.5 4.5 89.2 13. 1 .4 .5 13.1 276. 6 2.7 2.7 91.9 15. 1 .4 .3 13.5 288. 3 1.3 1.4 93.2 16. 1 .4 .5 14.0 300. 3 1.3 1.4 94.6 IB. S 2.2 2.3 16.2 324. 2 .9 .9 95.5 21. 1 .4 .5 16.7 336. 4 1.8 1.8 97.3 24. 16 7.1 7.2 23.9 348. 4 1.8 1.8 99.1 30. 1 .4 .5 24.3 360. 1 .4 .5 99,5 36. 6 2.7 2.7 27.0 396. 1 .4 .5 100.0 42. 2 .9 .9 27.9 BLANK 2 .9 48. 5 2.2 2.3 30.2 TOTAL 224 100.0 32. 1 .4 .5 30.6 54. 2 .9 .9 31.5 60. 12 5.4 5.4 36.9 66. 3 1.3 1.4 38.3 72. 10 4.5 4.5 42.8 ADJUSTED FREQ (PCT) CUM FREQ (PCT) 1* 63 28*1 28.6 28.6 Agricultural Ext; Marketing* Field Crop* Food and Nutrition Agents 2. 31 13*8 14.1 42.7 Home Economics Extension Agents 3t 50 22*3 22.7 65.5 4-H Youth Extension Agents 4* 48 21*4 21.8 87.3 Horticultural Extension Agents 5* 4 1*8 1.8 89*1 District Farm Management* Res­ ource Development* Forestry ft Marine Agents* and Extension Leaders 6 » 11 4.9 5.0 94*1 2.7 2.7 96.8 District Marketing Consumer Marketing Information*Public Policy and Public Affairs Agents 7. Extension Dairy Agents 8 « *9 Multi-County ft Regional Agents 9* 1.8 1.8 .5 Extension Livestock Agents 10* 1 .4 BLANK 4 1.8 TOTAL 224 100.0 97.7 MISSING 100.0 99.5 • 100.0 92 TABLE 4.5. Ditribution of the Freeuency of Usage of the Telplan System CATEGORY C0DE ABSOLUTE FREG RELATIVE FREG (PCT) ADJUSTED FREG (PCT) CUM FREQ (PCT) Almost Daily 1* 3 1*3 1.5 1.5 1 to 3 Times/Week 2. 9 4.0 4.4 5.9 1 to 3 Times/Honth 3* 35 15.6 17.1 22*9 < 10 Times/Year 4* 122 54.5 59.5 82.4 Never g. 36 ' 16.1 17.6 100.0 BLANK 19 8.5 TOTAL 224 100.0 TABLE 4*6* MISSING 100.0 Distribution of the Number of Programs Used CI9QSSSaSSSSSSBssesfls&tssssassasas CATEGORY CODE ABSOLUTE FREG RELATIVE FREG (PCT) ADJUSTED FREG (PCT) CUM FREG (PCT) 0 39 17.4 19.6 19.6 1 Program Only 1. 37 16.5 18.6 38.2 1 to 5 Programs 2 . 91 40.6 45.7 83.9 > 5 Programs 3. 32 14.3 16.1 100.0 BLANK 25 11.2 TOTAL 224 100.0 None MISSING 100.0 93 Nearly 41% of the agents (91) used 1 to S program of the Telplan System (Table 4-6). This percentage does not show the adjusted frequency distribution of the responses for this item, since, again a high number of agents (25) did not re­ pond to this question. While 39 agents (17.4%) did not use any of the programs, over 14% used more than 5 programs, and 37 respondents (16.5%) used one of the programs only. From the optional item of the background questionnaire, it was revealed that the one program that was used more frequently and by a higher number of the agents was program number 31 of the system named Least-Cost Dairy Ration. Positive and negative responses to 8 items related to the agents previous experiences with computers and the Telplan System are presented in Table 4-7. For each item 0 and 1 indicate a negative and a positive answer respectively. Nearly three-fourths (74.1%) of all agents had never written a computer program (EXP1). Only 9 agents (4%) had extensive training with computers and computer programming (EXP3). courses Although 41 agents (18.4%) had had computer related (EXP2), a higher percentage (23.7%) had regularly read articles and books as related to computer (EXP6). Over 77% of the agents had never had access to any computer b e ­ fore they began using the Telplan System EX P4). A fairly large number of the agents indicated that their only training with the computers had been on how to use the Telplan System (EXP5). While 23 agents (10.3%) had their own personal micro- computer_or personal electronic calculators as interpreted by TABLE 4*7* 94 Distributions of Experiences with Computers and the Telplan Sastem ba Categories (EXP1 to EXP 8 ) RELATIVE FREQ (PCT) ADJUSTED FREQ (PCT) 58 25.9 25.9 25.9 1* 166 74.1 74.1 100.0 0 182 81.3 81.6 81.6 1. 41 18.3 18.4 100.0 ']BLANK 1 •4 0 215 96.0 96.0 96.0 9 4.0 4.0 100.0 173 77.2 77.6 77.6 1. 50 22.3 22.4 100.0 BLANK 1 .4 0 127 56.7 56.7 56.7 * 97 43.3 43.3 100.0 0 171 76.3 76.3 76.3 53 23.7 23.7 100.0 167 74.6 74.9 74.9 * 56 25.0 25.1 100.0 BLANK 1 •4 0 201 89.7 89.7 89.7 . 23 10.3 10.3 100.0 TOTAL 224 100.0 100.0 CATEGORY ABSOLUTE FREG CODE Experience Number ONE (EXPi > 0 Experience Number TWO (EXP2) Experience Number THREE (EXP3) Experience Number FOUR » Data of Table 3*6 CLUSTER 501 502 503 504 505 506 507 508 509 Answer Infallibility Access Problem-Solving Quality Feelings Limitations Fear/Threat Info* and Training 501 502 503 504 505 506 507 508 509 100 -8 100 -24 30 1 0 0 -42 19 41 -38 32 23 2 -43 16 7 -43 -31 -40 - 8 9 -32 7 40 100 70 100 55 -23 58 46 58 26 68 -6 100 -4 45 38 100 26 7 100 33 100 The same cluster (S04) also has a fairly high correlation with cluster (508), Fear/Threat, Information and Training, (.58). (.54), and cluster (509), In order to see the linkages between the items forming these clusters and the linkage among the clusters, the items of the attitude scale were analyzed utilizing a cluster analysis program called STRUCTR (Allard, 1978). System attitude clusters. Figure 4.1 presents the 6 Telplan The correlations computed are the absolute values of Pearson*s-r coefficients. The broken line at the point .162 to .156 divides the diagram into distinct sections each containing a collection of clusters. Including item, number 49 and dividing the diagram with a line at .096 to .091 into two distinct parts, we actually derive the second order clusters which were formed from the a posteriori cluster analysis (Chapter III). One second order cluster (601)(from the Telplan System attitude clusters) clusters (504) Problem-Solving, is a combination of (505) Quality, (506) Feelings, 97 .474 .468 .463 .457 .452 .446 .441 .435 .430 .424 .419 .414 .408 .403 .397 .392 .386 .381 .375 .370 .364 .359 .353 .348 .342 .337 *331 .326 .320 .315 .310 .304 .299 .293 .288 .282 .277 .271 .266 .260 .255 .249 .244 .238 .233 .227 .222 .474 .468 .463 .457 .452 .446 .441 .435 .430 .424 .419 .414 .408 .403 .397 .392 .386 .381 .375 .370 .364 .359 .353 .348 .342 .337 .331 .326 .320 .315 .310 .304 .299 .293 .288 .282 .277 .271 .266 .260 .255 .249 .244 .23B .233 .227 .222 .216 .211 .206 .206 .200 .200 .195 .189 .184 .178 .173 .167 .162 .156 .4 7 9 .195 .189 .184 .178 .173 .167 .162 "~TI5r .151 .145 .140 .134 .129 .123 .118 *:r sr i45 .140 .134 .129 .123 .118 .112 .112 .107 .107 .102 .102 .102 TTTT* .091 .085 .080 .074 096 .096 2 2 2 2 2 5 2 4 4 4 5 4 3 3 3 4 3 7 4 9 8 6 5 3 16 4 9 3 7 8 0 1 t t : 1 t t t : : t t S t t t 1 t t t t : t t t 1 t t t t t : t t 1 : t t t t t : S t ttts t 1 t t t t t t t i t s t : t t 1 t t t : i tits t t ttts • t t t t t t t t s t i 1 t • : t t t t t t t t t t t t t t t t s : t t s t t t t t s s s : i * : • • • t s t t s t t t t t s t t s s t s s t t t s t s t t t s 6 : ttts t t s s s t ! t • : t • • 1 : • • 1 t t t 8 t t s t t t t : t : t 1 : ttts t t ttts : t : t 1 t s : s s t t ttts t t 1 t t t s s t t t t t :— t t t t t t t t t t 1 s t : : t s ttts : t t t 1 t t T s t ttts : 1 t : t t : t t t ttts : t t : < t s s s t t t T t > t t t 7 t t t t t t t t t t t t 7 t t ttts > t t t t t 7 t t ttts t t ! t t t : 1 : : t t 7 t t ttts t S 7 t t : s t t t t s : t ttts t t 7 t t s i— T t t s t t 1 : t 7 t t t t t t t : < 7 t t ttts T t 1 : 7 7 s t ttts 7 : t 7 t t 7 s t— 7 1 1 7 T t t 7 -7 ! 1 7 7 7 s t T t : 7 T 7 t : 7 7 1 T 7 7 t t 7 t : t 7 7 t t 7 7 7 : 7 7 7 t t 7 7 t I 7 t t 7 : t 7 s 7 7 t 7 t : 7 7 t t 7 1 : 7 ui: t 7 t t 31 7 s 7 t 3 7 7§t s t ■7 73 } s 37 vT 7»S I t a7 7r■*** t t s7 t ;t 7.21 t in7 7jl m t O 7 „: t wT ~ 7 t t TC* 2? *-? To _ ! 7 •9 T* « . 7 **T V 3 6 t t : : : : : t t t i : : t t t : t t : t t t : : t — 3 9 t t t t : t ; t : : : t : : : i i 7 .-?o Tr•I* * 73 I7 7 CD • 1; °T O T «7 ~7 0 12 t t t t 8 t — 1 T t 7 t 7 t 7 t 7 t 7 t 7 t --7 7 7 7 7 7 7 „7 St c7 -? St *-7 •5 ? St c7 0 7 37 ST i-T °7 £7 T ~T o 1 W? 2 1 25 4 4 7 t t t t 8 t 1 -- t t t s s t t t t t t t t t t t 7 t t t t t 7 7 t I t t t 7 t t t t t 7 t t t t t 7 t t t t t 7 t t s t t • t t t 7 • 0 • t 7 ——— t t t t 7 7 t t t t 7 7 t t 1 t T 7 t t t I 7 7 t t t t 7 7 t t t t 7 7 t t t --7 t t t 7 7 t tin t 7 7 — •H t T 7 7 T 7 T «£! t 7 7 7 7 38 7 7 2 ! int 7 7 -I 7 7 7 7 *>} 7 7 7 7 3* 7 7 51 7 7 T 7 9 t t : t t t t i t t t O' T ? VI ? * C ~T ft9 I V• t * 59 * ?♦> «OH rino — **7 1 ft ft *ft U 7 7 7 7 7 7 7 4f * t*J4Q Db0 h un. T T 7 7 7 7 7 7S 7 7 7 7 7 a?o ?t2 0C? |I ~*» O ' Ul 07 3 l»?U ■I7A 3 UT H 7 7 7 7 7 7 7 7 7 w ft© vft« o V c ft V c ft 0 U*f 94t MU ft T 'f' T 7 7 7 7 7 7 7 7 7 .085 085 080 ,074 .069 FIGURE 4.1. Linkages Among the Telplan System Attitude Clusters (504 to 509) 98 (508) Fear/Threat, and (509) Information and Training. The other second order cluster (602) is (507), Limitations. Therefore, the relationships of one attitude cluster to the independent variables and/or the relationships of a collec­ tion of clusters to the independent variables can be examined. Figures representing the clusters formed from the computer items, the Telplan System clusters and a posteriori 11 clusters in three different methods, can be found in Appendix D. In order to determine the amount of variance in scores explained by each of the 9 clusters, was computed. a univariate F-test As shown in Table 4.9, the 9 clusters accounted for a total of 72.6 per cent of the variance. attitude clusters The computer (501-503) accounted for a small amount (13.61) of variance and neither one were significant at the level of significance of .001 which was set for the test of hypotheses. Cluster (503), Access, with 6.7% of the total of variance tended to have a significance at the .03 level. Among the 6 Telplan System attitude clusters, ProblemSolving (504) and Fear/Threat (508) accounted for well over half (37.9%) of the amount of variance for the 9 clusters. The related F-values for these two scales, 4.43 and 4.01 respectively, had a significance level of .00001. Cluster (506), Feelings, seemed to contribute fairly (7.6%) with a level of significance of nearly .01 to the total amount of variance. The remaining 3 clusters, (505) Quality, (507) Limitations, and (509) Information and Training accounted for 13.7 per cent of the total amount of variance and none had a significant F-value. Table 4.9. Univariate F-test for the Computer Attitude Clusters (501-503) and the Telplan System Attitude Clusters (504-509). Attitude Cluster Multiple R Multiple R 2 Adjusted R 2 F-value Sig. of F 501 Answer .297 .088 .025 1.283 .227 502 Infallibility .326 .106 .044 1.580 .095 503 Access .356 .127 .067 1.934 .029 504 Problem-Solving .500 .240 .198 4.428 .00001 505 Quality .298 .089 .026 1.298 .218 506 Feelings .368 .136 .076 2.089 .017 507 Limitations .347 .120 .060 1.824 .043 508 Fear/Threat .481 .232 .179 4.013 .00001 509 Information and Training .335 .112 .051 1.683 .068 D.F. = 13,173 100 Regression Model for Attitude Clusters and the Independent Variables The intercorrelations of the attitude clusters and also of the independent variables caused the difficulty and com­ plexity of explaining the relationships of the agent's attitudes and the selected personal characteristics. The difficulty arose when Pearson's-r coefficients were computed. If the correlations among the attitude clusters and among the independent variables were all zero, then the difficulty could have been avoided and therefore, it would have been possible to state without any ambiguity the proportion of variance in the attitude clusters accounted for by each of the independent variables. However, as explained in the previous discussion, linkages with high correlations existed among the attitude clusters. In addition, the independent variables in most behavioral research as Kerlinger and Pedhazur (1973) point out, are "usually correlated, sometimes substantially." These two authors by examining the two studies done by Cutright (1969) and Coleman et al. ("Equality of Educational Opportunity," 1966), discuss the effect of the intercorre­ lations of the independent variables and the subsequent dif­ ficulty of interpreting the results because of the high correlations among the independent variables. The way out of this difficulty, as Kerlinger and Pedhazur suggest, is the control of variables and the use and computation of semipartial correlation to assist achieve control and explication of the variables. The method calls 101 for simply removing the variance of each variable after the computation for that variable in the squared multiple corre­ lation formula is completed and second variable is to enter the formula for calculation of its variance. Applying the method, for instance, to the attitude cluster (504) and the three independent variables, age, frequency of usage of the System (freq), the previous experi­ ences with computers and the System (exp), for the squared multiple correlation formula, we have: R2 = r2 (504).age,freq,exp (504)age r2 (504)(freq.age) + r2 (504)(exp.age,freq) [4.1] Formula 4.1 indicates that the independent variable age is the first to enter the computation and therefore, the first ? expression r (5 0 4 )age t^ie var^ance shared by the dependent variable (504) and the independent variable age. The second 2 expression r (504)(freq.age) is the s-0rder Corr. Analysis Multiple Regression Analysis Sig. of r B .071 .147 .263 .055 .550 .583 r B t-value Sig. of t Answer 502 Infallibility -.110 .051 -.780 -.177 -1.777 .077 503 Access -.052 .222 -.255 -.080 -.818 .414 504 Problem-Solving -.224 .001 -.063 - .010 -1.100 .912 505 Quality -.021 .382 .469 .128 1.273 .205 506 Feelings -.206 .001 -.214 -.089 -.912 .363 507 Limitations -.140 .021 .192 .027 .272 .786 508 Fear/Threat -.249 .001 .214 .040 .431 .667 509 Information and Training -.139 .021 -.178 -.061 - .619 .536 119 501 120 For the remaining past experience numbers 6, 7 and 8, the hypotheses 4f, 4g and 4h were not rejected for either of the analysis (Tables 4.18, 4.19 and 4.20). Each hypothesis stated that, specifically, there was no relationship between Exp.6, Exp.7 or Exp.8 and the attitude clusters. rejecting the hypotheses, By not it was concluded that no significant relationship was found between Exp.6, Exp.7 or Exp.8 and the attitudes toward computers and the System. The only visible semipartial correlation coefficient was .16 with a t-value significant at a level approaching .03 (Exp.8 and cluster 509; Table 4.20). This suggested that having personal micro­ computer (or programmable calculators as Exp.8 was inter­ preted by many agents) assisted the agents in their needs for more training and information as related to computers and the Telplan System. Exp.6 and Exp.7 showed negative semipartial correlations with most of the attitude clusters; meaning that there was a tendency for those of the agents who read more books and articles about computers, and those who worked with computers through terminals only, to have less of a variable attitude toward computers and the System. Overall, the past experiences contributed very little to the total variance in attitude scores. The range was from zero percent for most of the clusters to a maximum of 3.1 percent for Exp.5 and cluster (502), Infallibility. Past experiences did not seem to be predictors of the agents' attitudes toward the System and computers. Table 4.18. Zero-OTder Correlation and Multiple Regression Analysis for Computer Attitude Clusters C501-503) and the Telplan System Attitude Clusters (504-509) with Previous Experience Number 6 (EXP6). Attitude Cluster Zero-Order Corr. Analysis r Sig. of r Multiple Regression Analysis B 3 t-value Sig. of t .075 .134 - .003 .011 .151 .880 Infallibility -.031 .325 - .373 -.073 -.982 .327 503 Access -.073 .139 -.152 -.042 -.565 .573 504 Problem-Solving -.091 .093 -.644 -.088 -1.292 .198 505 Quality -.154 .013 -.531 -.126 -1.665 .098 506 Feelings -.042 .270 - .186 -.067 -.913 .362 507 Limitations -.080 .123 -.776 -.094 -1.269 .206 508 Fear/Threat .016 .407 .105 .017 .243 .808 509 Information and Training -.008 .452 -.151 -.045 -.606 .545 501 Answer 502 Table 4.19. Zero-Order Correlation and Multiple Regression Analysis for Computer Attitude Clusters (501-503) and the Telplan System Attitude Clusters (504-509) with Previous Experience Number 7 (EXP7). Attitude Cluster Zero -Order C or r. Analysis r Sig. of r Multiple Regression Analysis B 8 t-value Sig. of t Answer - .057 .198 -.177 -.032 -.422 .673 502 Infallibility -.061 .182 -.006 -.001 -.015 .988 503 Access -.004 .474 -.160 -.044 -.586 .558 504 Problem-Solving .038 .292 .129 .018 .256 .798 505 Quality -.039 .284 -.257 -.061 -.797 .427 506 Feelings - .091 .094 -.277 -.101 -1.343 .181 507 Limitations .063 .182 .319 .039 .516 .607 508 Fear/Threat - .051 .228 -.568 -.092 -1.302 .195 509 Information and Training .032 .322 -.039 -.012 -.154 .898 122 501 Table 4.20. Zero-Order Correlation and Multiple Regression Analysis for Computer Attitude Clusters (501-503) and the Telplan System Attitude Clusters (504-509) with Previous Experience Number 8 (EXP8). Attitude Cluster Zero-Order Corr. Analysis .r Sig. of r Multiple Regression Analysis B 3 t-value Sig. of t 501 Answer -.063 .177 -1.012 -.134 -1.750 .082 502 Infallibility -.051 .227 -.237 -.034 -.447 .655 503 Access .052 .220 .677 .135 1.801 .073 504 Problem-Solving .037 .297 1.257 .126 1.810 .072 505 Quality -.012 .429 .352 .061 .792 .429 506 Feelings .085 .109 .576 .152 2.033 .044 50 7 Limitations -.086 .108 -.924 -.082 -1.085 .279 508 Fear/Threat -.031 .324 .106 .012 .177 .860 509 Information and Training .082 .114 .744 .162 2.140 .034 124 Hypothesis Five The hypothesis was formulated to determine the relation­ ship between frequency of usage of the Telplan System and the agents' attitudes. Specifically, it was hypothesized that there was no relationship between the frequency of usage and the attitude clusters (i.e. H:r = 0 and H:3 ■ 0). This hypothesis was rejected at the .001 level for one computer attitude cluster and four Telplan System cluster for zeroorder correlation analysis. When multiple regression anal­ ysis was used, the hypothesis was rejected for only two of the Telplan attitude clusters. Computer attitude cluster (503), Access, implied that easier communications with the computers would be possible and more helpful if computer terminals were provided for all extension offices. The correlation coefficient, r, for this attitude cluster, was (Table 4.21). .24 with .001 level of significance As far as the zero-order correlation analysis was concerned, the rejection of the hypothesis meant that as the number of computer terminals for the agents' offices increased a higher frequency of usage of the Telplan System was realized. The hypothesis was not rejected for cluster (503), Access, when multiple regression analysis was used. The semipartial correlation coefficient, 3, was with a t-value significant at nearly .01. .23, but Contribution to the total variance by Access and frequency of usage was 5.1 percent. Table 4.21. Zero-Order Correlation and Multiple Regression Analysis for Computer Attitude Clusters (501-503) and the Telplan System Attitude Clusters (504-509) with Frequency of Use. Attitude Cluster Zerc>-0rder Corr. Analysis r 501 Answer 502 Sig. of r Multiple Regression Analysis B 3 t-value Sig. of t -.084 .118 -.164 -.054 -.591 .555 Infallibility .023 .372 .149 .053 .587 .558 503 Access .238 .001 .456 .227 2.531 .012 504 Problem-Solving .410 .001 1.343 .335 4.029 .0001 505 Quality .126 .038 .331 .142 1.552 .122 506 Feelings .227 .001 .253 .166 1.863 .064 50 7 Limitations -.048 .253 - .527 -.116 -1.289 .199 508 Fear/Threat .416 .001 1.081 .316 3.752 .0002 509 Information and Training .213 .001 .370 .200 2.215 .028 126 Two of the Telplan attitude clusters showed correla­ tions significant at the .001 level for the first analysis, but did not indicate significant (at the .001 level) semi­ partial correlations with (506), Feelings, and (509), Infor­ mation and Training. The relationship of (509) to frequency of usage was significant at the .03 level when multiple r e ­ gression analysis was used. This positive relationship indicated a tendency that there was a need for more informa­ tion and training as related to the Telplan System. hypotheses for these two clusters second analysis were not rejected. The (506) and (509) in the The amount of variance which was explained by regression for both clusters was 6.7 percent. Table 4.22 represents contrasts for the levels of fre­ quency of use. trasts tests A priori contrasts and a posteriori co n ­ (Scheffe post hoc test) were used for each of the clusters, Access, Problem-Solving, Feelings, and Fear/ Threat and frequency of use. The combination of the group using the System up to three times per week and the group with up to three times per month as compared to the group that never used the System had a significance level (.001). Thus, the finding showed that those agents that never used the System had more favorable attitudes toward the System as far as access was concerned. For Problem-Solving, the significant T-values showed that the agents who used the System fewer times consistently had more favorable attitudes as compared to those who used Table 4.22. Cluster A Priori and A Posteriori Contrasts Test for Frequency of Use (Five Levels), and the Clusters, Access, Problem-Solving, Feelings, and Fear/Threat. Almost Daily 1-3 Times /Wee k 1-3 Times /Month Less Than 10/year Never T-Value T-Prob. In Favor of D.F. 4k .001 -2.62 .009 -3.84 .000 a -5.48 .000 < 10/year aa -5.78 .000 < 10/year -6.95 .000 -2.66 .009 -2.92 .004 -3.55 .000 -5.90 .000 a < 10/year Never 1 a * • 2 i 3 • H < H* 4 3 00 5 * a a a a < 10/year 1-3/month 4* • 40 Never a * a 2 * a Scheffe's Tests (a used). Cluster Access a .006 Problem-Solving .006 aa aa aa Feelings Fear/Threat .001 .001 Never < 10/year Never * indicates one level is contrasted with another level or, a combination of levels are contrasted with aa. 1 * 1 2 < 10/year - 195 • Fear/ ■ Threat 1 a * '4-193 1 1 'Feelings • (508) 1 *4 1 1 > (506) 1 1 Also: a 1 Problem-Sc -3.29 a * 2 .065 ' 1 1 W (504) s3 w -1.86 - 197 1 a * 128 the System more often. Feelings, however, This was also the indication for the significant only at the cluster, .009 and .004 levels. The groups of agents who never used the System or used the System less frequently (up to ten times per year) as compared to those who used the System from three times per month to three times per week showed the concern and fear that the System would threaten their job and/or personalized relationship with their clientele. . Hypothesis Six The research hypothesis number six was concerned with the relationship between the rate of usage of the programs of the Telplan System and the agents’ attitude. hypothesized, It was specifically, that there was no relationship between the number of programs used and the attitude clusters. The hypothesis was not rejected at the .001 level of signifi­ cance (multiple regression analysis, Table 4.23). Thus, there was no statistically significant linear relationship between the two variables at the .001 level. clusters However, (507) and (508), showed relationships with the rate of usage of the programs which were significant at the .009 level. For these two scales, the hypothesis six was rejected at the .001 level as far as zero-order correlation analysis was concerned. The correlations between Limitations and the independent variable indicated that as the number of the programs used increased, the agents felt that the System became limited in scope as it related to the needs of the extension clientele. The complexity of many of the programs Table 4.23. Zero-Order Correlation and Multiple Regression Analysis for Computer Attitude Clusters (501-503) and the Telplan System Attitude Clusters (504-509) with Number of Programs Used. Attitude Cluster Zeroi-Order Corr. Analysis r Multiple Regression Analysis Sig. of r B (S t-value Sig. of t 501 Answer -.156 .010 .0 39 .016 .154 .878 502 Infallibility -.091 .089 -.404 -.182 -1.741 .083 503 Access .102 .065 - .073 - .046 -.445 .657 504 Problem-Solving .376 .001 .594 .187 1.950 .053 505 Quality .131 .028 .291 .157 1.493 .137 506 Feelings .275 .001 .130 .107 1.044 .300 507 Limitations .265 .001 .992 .275 2.657 .009 508 Fear/Threat .436 .001 .697 .256 2.646 .009 509 Information and Training .131 .027 .135 .092 .886 .377 130 and the lack of appropriate solutions to the problems were also increasingly felt by the agent. A search of the responses revealed that many of the comments made by the agents were related to programs of the System. A brief com­ pilation and discussion of the comments can be found in the next Section. There seemed, also, to be a tendency for the agents to feel their personal communications were more threatened as the programs of the System were increasingly used. Contributions to the total variance as accounted for clusters (5 0 8 ) and (509) and the independent variable, number of programs used were 7.6% and 6.5% respectively. The other Telplan attitude clusters that showed sizable correlations with the rate of usage were: Problem-Solving with r * 37.6 significant at the .001 level; Quality, and Information and Training each with r * 13.1 significant at the .03 level. Multiple correlation analysis indicated no statistically significant relationships for these clusters at the .001 level. For one computer attitude cluster (502), the slope of the regression equations were slightly negative suggesting that as the use of the number of programs was increased, the agents felt less favorably toward computers in providing correct answers to the problems. The rate of usage of the programs contributed the second highest amount to the total variance after frequency of use. The amount of variance contributed as related to all of the attitude clusters was 25.5 percent. Table 4.24. Cluster A Priori and A Posteriori Contrasts Tests for Nunber of Programs Used (Four Levels), and the Clusters, Problem-Solving, Feelings, Limitations, and Fear/Threat. More Than 5 Progs. 1 -5 Progs. . Prog. T-Value T-Prob. In Favor of -2.59 .010 1-5 Progs. -3.44 .001 a* -5.20 .000 1 5 None * -S.S9 .000 None None D.F. Scheffe's Test a Used ■u cr H* 2 * 3 * 4 * *tl o * 1 * 2 a 1 * 2 * 3 * 4 a * ** aa a* -3.23 .001 1 6 None a -4.02 .000 None aa -3.32 .001 1 § None a -2.97 .003 None -2.73 .007 -3.35 .001 aa -5.86. .000 1 8 None a -6.68 .000 None • N O 00 p N * aa 04 1 O 0t O O M P « O a N * * aa 1 Prog. Q 3 - 209 r/Threat a 1-5 Progs. i - .001 tt (508) 2 a 1 Prog. B , ! * a i i , 1 (507) Feelings, Limitations (506) 1 * - .001 * 3 -209 em-Solv: [504) 9 W 1 132 Four of the clusters which showed significant relation­ ships (zero-order analysis) with the variable, number of programs used, were subjected to a priori contrasts and a posteriori contrasts tests (Scheffe post hoc test)(Table 4.24). The break-down and comparison of groups showed signif­ icant T-values in favor of those agents who used none or fewer number of the programs. This finding was consistent for all four clust.ers, Problem-Solving, Feelings, Limitations, and Fear/Threat. The findings particularly for Limitations indicated that the agents who used none or fewer programs of the System did so, for, mostly, the complexity or the lack of applicability of the programs to their area of service. This was especially true for the frequent users as compared to those who used none or only one program of the Telplan System. Hypothesis Seven For hypothesis seven, it was specifically stated that there were no relationships between the attitude clusters and the employment position. Since the levels of this indepen­ dent variable position were rather nominal (as opposed to other independent variables which had ordinal levels), it was subjected separately to analysis of variance. The analysis was done while the other independent variables, as well as the dependent variables, were controlled. Table 4.25 represents the various tests for employment position and the attitude clusters. 133 Table 4.25. Zero-order Correlation Analysis, Univariate F-Tests, and Multivariate Tests of Significance for Attitude Clusters and Employment Position. Attitude Cluster 501 Answer Zero -Order Co rr . Analysis Univariate F-Tests with (4 - 167) D.F. r F-Value Sig. of F Sig. of r -.171 .006 2.537 .042 502 Infallibility .056 .207 1.401 .236 503 Access .032 .321 .442 .778 504 Problem-Solving .240 .001 2.514 .043 505 Quality - .009 .451 .396 .811 506 Feelings - .215 .001 .760 .553 507 Limitations .208 .001 3.212 .014 508 Fear/Threat .310 .001 3.508 .009 509 Information and Training .126 .034 3.068 .018 Multivariate Tests of Significance Tests Name Significance of F Pillais .009 Hotellings .006 Wilks .008 134 For zero-order correlation analysis, Problem-Solving, Feelings, Limitations, and Fear/Threat indicated significant relationships position. four clusters, (at the .001) with However, multivariate test of significance for three different tests did not indicate significance levels at the .001. The subsequent univariate F-tests, thus, were not significant at the .001 level for any of the clusters and position. The only cluster which had a sizable F-value was Fear/Threat, however, significant only at the .009 level. The hypothesis, therefore, was not rejected at the .001 level of significance; meaning that there was no statistically sufficient linear relationship between the employment posi­ tion and the agents' attitudes toward computers and the Telplan System. In order to find out how the attitudes of the agents as far as their positions with the Extension Service were concerned, the position levels were subjected to a priori, a posteriori contrasts tests and the Scheffe's post hoc test. The clusters of interests were, Answer from the computer clusters and Problem-Solving, Limitations, Fear/Threat, and Information and Training from the Telplan clusters. These clusters indicated significant relationships with the posi­ tion in the zero-order correlation analysis level), and in the univariate F-tests (four at the .001 (none at the .001 level). For attitude cluster, Answer, as shown in Table 4.26, counties extension directors, agricultural agents, and other agents who used the Telplan most indicated more favorable Table 4.26. A Priori and A Posteriori Contrasts Tests for Employment Position (Five Levels), and the Clusters Answer, Problem-Solving, Limitations, Fear/Threat, and Information and Training. CED AEA, HEA, DFMA,EDA, MCRA.OLA EHE 4 -HA 1 > In 9 O (A n H < 2 ^ <• * * •* ** 3 * Cluster •d , 2 1 o* <-N t/> ° 4k w M o * 2 L 3 » , w 3 O M 2. * * 3 2 I—, H‘ ut it * B) 7 it In Favor of 3.70 .000 CED, $ AEA,HEA, DFMA.EDA.MCRA.ELA 2.75 .006 A E A ,H E A ,DFMA,EDA, MCRA,ELA 3.39 .001 * •* ** -5.75 .900 EHE 8 4 -HA * ** ** -5.84 .000 EHE $ 4 -HA ** ** -3.40 .000 EHE 3 4 -HA * -4.66 .000 4 -HA -5.31 .000 EHE * 1 T-Prob. ** 4 t" H* 3 ** T-Value ** * 3 00 s O s ** DMA DCMA * * ** ** -3.94 .000 EHE 3 4 -HA ** ** -3.59 .000 EHE 3 4 -HA 4* 1 N H H P 4* • P O w ** ** * * -3.00 .003 'EHE 3 4 -HA 3.07 .002 EHE 3 4 -HA -3.59 .000 4 -HA N O ** w * * ■ • o Continued on next page 1 * O 4* P 1 * Scheffe's Test a Used CED * 4 5 D.F. M < © H Table 4.26. Cluster l Continued CED AEA, HEA, DFMA.EDA, MCRA.ELA EHE 4 -HA * * *« • •* DMA DCMA T-Value T-Prob. In Favor of ** -5.37 .000 EHE 8 4 -HA • ft -4.36 .000 EHE 8 4 -HA -4.80 .000 4 -HA -2.62 .009 EHE D.F. Scheffc's Test a Used o p in H 2 o H 00 3* 3 W ft (B * P r+ 4 W 5 * P «-» 1 * 3 3 P Hi o 2 o i i 3 to P p ^ r* 3 3 MM* O 3 3 4 OQ 5 * * ** ** -4.61 .000 EHE 8 4 -HA * ** ** -3.4S .001 EHE 8 4 -HA * ** ** -3.49 .001 EHE 8 • 4 -HA * -2.95 .003 4 -HA -3.03 .003 EHE * Q 1 o * * • *• 2.189 .030 O u o to •ffc • K> 0 o M * ■ * « • o N EHE 8 4 -HA CED--County Extension Directors, AEA--Agricultural Extension Agents, HEA--Horticultural Extension Agents, DFMA-District Farm Management Agents, EDA--Extension Dairy Agents, MCRA--Multi-County $ Regional Agents, ELA--Bxt. Livestock Agents, EHE--Extension Home Economist, 4-HA--4-II Youth Agents. DMA--District Market Agents, DCMA--District Consumer Market Agents. ‘ indicated one level is contrasted with another level or, a combination of levels are constrated with *• or a combination of **. 137 attitudes. The a used for Scheffe's test for Answer and position was .04. For all other clusters the tests were exclusively in favor of extension home economists and 4-H agents (as applied); meaning that (with the exception of Fear/Threat) other agents had more disfavorable attitudes. The value of ot for Scheffe's tests for these clusters and position ranged from .009 for Fear/Threat to .04 for ProblemSolving. The findings showed that though employment position did not become a predictor of attitudes, at the .001 level of significance, however, at the levels of nearly .04, counties extension directors, agricultural agents, extension dairy agents, district farm, multi-counties, and extension live­ stock agents perceived and felt that: (1) the Telplan System was limited in scope as far as the applicability of the p r o ­ grams were concerned, and (2) the Telplan was not signifi­ cantly helpful in problem-solving. On the other hand, exten­ sion home economists and 4-H youth agents indicated: (1) a distrust for the Telplan and feared that the use of the System might threaten their jobs, and (2) a need for additional information about and training with the Telplan System. 138 Analysis of the Findings from Optional Items In the background questionnaire, one optional section including two items (Appendix A) was designated to gather information related to the specific programs of the Telplan System in frequent and minimum use by the Extension agents. A total of 118 respondents (521 of 224 agents) completed this section by denoting the specific programs they used either frequently and the ones that they used at least once. Also, included in this section most respondents had comments related to various aspects of the System and/or the computer and in general computerized services in Extension. The com­ ments had a variation from personal satisfaction with the whole System and the computerized Extension programs to personal frustration as related to the problems involved with the System. Table 4.27 presents a compilation of the responses for the programs which were in frequent usage by the agents. Program number 31 titled Least-Cost Dairy Ration was used more frequently than any other program of the System. It seemed that this program was highly applicable in the field and had a high popularity among the agents' clientele. This finding was in agreement with the results of studies done by Schoonaert (1973) and Hutjens et al. (1972) as related to field applicability of the program number 31. The program was used mostly by agricultural Extension agents and county Extension directors and almost all district farm management agents. 139 As shown in Table 4.2 7 the four most frequent used programs were almost exclusively utilized by the field staff extending mostly farm educational services. However, the programs related to family living (e.g. 49 and 60) were used most frequently by Extension home economist. Also, Extension home economist were mostly the agents who used the programs 60 and 68 at least once (Table 4.28). These two programs seemed to be highly applicable to family living education. Least-Cost Dairy Ration program (No. 31), again indicated a high number of first time usage. A com­ parison of Table 4.27 and 4.28 showed that a total of seven programs of the System (programs numbered 07, 15, 30, 39, 59, 62, and 71) were never used by the agents who responded to the optional items. The Tables 4.27 and 4.28 indicate that a very limited number of programs (nearly 14%) were used either frequently or at least once. Ironically, almost all those programs which were used on afrequent basis were used at least once by a number of other agents. It seemed that most programs were used by a few agents once, however, their continued usage did not materialize. The agents’ comments revealed a variety of reasons for utilizing or not using the System. Applicability of the programs seemed to be the most visible and/or significant factor. Agricultural agents (AEA) tended to be more sup­ portive of the programs in use. However, Extension home economists (EHE) and 4-H youth agents (4-HYA) indicated Table 2.27. Usage of the Telplan Programs (Frequent Use). Program Number Program Title Used Freq. by (No. of Agents) 31 Least-Cost Dairy Ration 42 A EA(18), CED(IS), DRMA(5), 0(5) 36 Financial Long-Range Whole-Farm Budgeting 16 A 0 A ( 5 ) , RqDA(5) C E D ( 3 ) , 0(3) 05 Income Tax Management Analysis 14 C E D (4), A E A (3), R5DA{3), 0(4) 03 Capital Investment Model 12 C E D (3), A E A (3) A6DFMA(3), 0(3) 49 Family Financial Analysis 10 E H E (4), 0(6) 60 Dollar fc'atch 8 EHE(6), 0(2) 44,70 * 7 ** 52,65 * 5 ** Swine Ration Formulation 4 ** 12 02,18,22,46,54 55,56,63,68 * 3 jt* 16,28,32,34,64 * 2 ** * 1 ft* 01,06,13,14,20,21 26,37,40,47,48,57 AEA--Agricultural Extension Agent, CED--County Extension Director, DFMA--District Farm Management Agent, R5DA--Regional 5 Dairy Agents, At|DFMA--Area § DFMA, EHE--Extension Home Economist, 0 - -Others. •See Appendix A for the title of these programs. ••Varying user(s) for different programs. Table 4.28. Usage of the Telplan Programs (Once Only) Program Number Program Title Used Once by (No. of Agents) User(s) 60 Dollar Watch 23 EHE (Almost Exclusively) 68 In The Bank or Up The Chimney 17 EHE (Mostly), 0 36 Financial Long-Range Whole-Farm Budgeting 14 AEA, CED, O * 12 EHE, CED, AEA, 0 03 Capital Investment Model 11 AEA, CED, O 31 Least-Cost Dairy Ration 10 AEA, CED, O 46 Michigan Dairy Farm Planner 05,63 8 DFMA, DA, AEA, CED 44,70 * 7 ** 01,02,34,52 * 6 ft* 18,32,38,47,65 * 5 ft* 25,28,49 * 4 ** 11,12,14,19,20 23,26,27,29,42 * 3 ft* 04,06,08,09,10,16, 21,27,41,53,55 * 2 ** 17,22,35,43,48,SO, 51,54,58,69,72,73 * 1 ft* 07,13,IS,30,39,40, 56,57,59,62,64,71 * 0 *" EHE--Extension Home Economist, AEA--Agricultural Extension Agent, C0D--County Extension Director, DFMA--District Farm Management Agent, DA--Dairy Agent, 0--0thers •Sec Appendix A for the title of these programs. ••Varying user(s) for different programs. 142 frustration for lack of programs and relevancy of the exist­ ing programs to their areas of services. The following are typical comments with reference to applicability of the programs: "Have reviewed the programs available and find them to be reasonable and appropriate for conduction of extension programs." (County extension director, CED) "...Some programs are not practical or useful and some are very practical and very useful." (AEA) "Agents should demand more relevant programs." (EHE) "The programs for families are not all that us e ­ ful." (EHE) "It is not used in 4-H and working with people. We need programs written on how to solve people problems not just dollar problems." (4-HYA) "Feel computers can be of great value to extend technical information that can be provided to clientele if programs are designed for audience needs." (EHE) Training, (especially in-service training) was another factor which was widely commented upon by many of the respondents. The following comments reflects the desire and/or expressed need for training in the part of some of the agents: "Would like to have in-service training in computers and use more programs in the youth area." (4-HYA) "I need more intensive training and practice in filling out input forms and also using the computer terminals." (AEA) "I have not used the computer because I feel I need more training." (EHE) "In-service training is needed." Agent) (Public Policy 143 As the above indicate, the need for training by a variety of agents, including agriculture Extension agents. A few respondents experienced difficulties and problems in accessing and working with the computer and the Telplan System: "It (the System) needs to be more responsive more quickly." (EHE) "Every CES should have a terminal to retrieve information and to aid in communication." (4-HYA) "... a very important facet of Telplan use: It takes time to get ready to run and then adjust and rerun." (District agent) "It takes a planned, concerted effort to learn how to use Telplan efficiently." (AEA) Many agents expressed an important point that might explain some of the underlying reasons for limited usage of many programs of the System. Presenting the System to, informing and involving the clientele, as well as the agents were major concerns for those respondents. The following are typical comments: "Farmers are not sold enough on the programs to come in and ask you to run them--you must seek them out." (CED) "Extension staff needs to be more aware of total programs designed for Telplan System. EHE's are beginning to be involved--have little knowledge of Telplan outside of their own area of program­ ming." (EHE) "I have used the computer programs in family spending etc. with the ELE program--I don't know if this is part of the Telplan System or not." (CED) 144 "To be most effective, Telplan must have persons assigned to promote it with agents and clients. Someone is urgently needed to update materials and be available to assist during problem times.” As indicated in the preceding comments and suggestions by the respondents, the agents were generally concerned about the applicability of programs of the System to the field problems, more training in using the System, having easy access to the computers, and promoting the System among the Extension staff as well as their clientele. Considering the comments as written by the respondents on one hand, and the attitude clusters derived from the cluster analysis of the attitude scale on the other hand, the representation of the agents1 concerns could be found in those clusters. Summary Seven null hypotheses were tested to find the relation­ ship between the Extension agents’ attitude and a number of demographic variables with respect to computers and the Telplan System. The hypotheses were stated in the following general form: There were no relationships between the attitude clusters and the independent variables age, level of formal education, length of employment, past experiences with computers and the Telplan System, frequency of usage, number of programs used, and employment position. 145 All of the hypotheses were tested at the .001 level of significance. The findings showed that: Age, level of formal education, length of employment, position, and past experiences with computers and the Telplan System did not seem to be predictors of the agents’ attitudes toward computers and the Telplan System. As far as clusters, Problem-Solving and Fear/Threat were concerned, frequency of usage of the Telplan System showed a significant relationship with the attitudes. The number of programs used tended to have a significant relationship with the two attitude clusters, Limitations and Fear/Threat. It was found further that only a limited number of the programs of the System were used by the responding agents. It was revealed that the important factors for using or not using the System were generally: usefulness of the programs in the field, additional information about the programs and training with the System. CHAPTER V SUMMARY AND CONCLUSIONS The purpose of this study was to investigate the atti­ tudes of Extension agents toward computers and computerized planning and consulting programs System). (specifically the Telplan More specifically, the study aimed to examine, with respect to computers and the Telplan System, the relationship between the dependent variable, attitude, and the independent variables: age, level of formal education, length of employment, previous experiences with computers and the Telplan, frequency of usage, number of programs used, and position held in the Extension Service. To accomplish the above objective two instruments were developed. These were an attitude scale and a background questionnaire. The face validity of the Likert-type attitude scale of 60 items was first established and then along with the back­ ground questionnaire it was pretested among 10% of the pop u­ lation of the study. The necessary revisions were made and as the result a 58 item attitude scale and the background questionnaire were then distributed to all field Extension agents in the state of Michigan. A total of 224, (81%), of the returned instruments were considered for the analysis of the data. 146 147 The attitude scale was then subjected to a priori and a posteriori cluster analysis to determine and cluster those items that measured the same underlying variable or trait and subsequently establish reliability, each cluster. (coefficient a ) , for The process aimed to construct unidimensional clusters satisfying three tests or criteria: of content for items; (1) homogeneity (2) internal consistency; and (3) par­ allelism, or external consistency for the items. The cluster analysis of the attitude scale resulted in the formation of nine clusters. Eighteen items from the original scale did not satisfy the unidimensionality crite­ rion and therefore were included in the residual clusters. The nine clusters of forty items formed the attitude clusters, three of which consisted of the items as related to attitudes toward computers, and the remaining six clusters were related to the Telplan items. The clusters were then logically named and included in the analysis of the data for hypothesis testing. Seven null hypotheses were tested in an attempt to answer questions relative to the purpose of the study. hypotheses, The in a general null form, stated that there were no relationships between the attitude clusters and the selected personal characteristics of the agents. All of the hypotheses were tested at the .001 level of significance utilizing zeroorder correlation analysis, multiple regression analysis and a number of other statistical procedures. The findings were also reported at the significance levels greater than the 148 .001 level. The a priori and a posteriori contrasts tests, and Scheffe's post hoc test were used to determine the relationships between the levels of each independent vari­ able and the related attitude cluster in the analysis. Summary of Findings The second order cluster analysis revealed that high positive correlation existed between the Telplan attitude cluster Problem-Solving and four other clusters: Quality, Feelings, Fear/Threat, and Information and Training. The Two Telplan attitude clusters Problem-Solving and Fear/Threat accounted for well over half of the amount of variance which was contributed by all of the nine clusters. The following are hypotheses and related findings: 1. Hypothesis one stated that there was no relationship between age and the attitude clusters. This hypothesis was not rejected at the .001 level of significance! Thus, age did not become a predictor of the agent's attitudes toward computers and the Telplan System. The findings indicated that there was a tendency for the younger agents to have more favorable attitudes toward computers and the Telplan. 2. Hypothesis two stated that there was no relation­ ship between the attitude clusters and the level of formal education. The hypothesis was not rejected at the .001 level of significance. There was a tendency for the agents with higher level of formal education to feel less favorably toward computers and the Telplan. Contrasts tests confirmed 149 this finding (at the .04 level) for agents having Bachelor's degrees as compared to those having Master's degrees. 3. It was hypothesized that there was no relationship between the length of employment and the attitude clusters. This hypothesis was not rejected at the .001 level. There­ fore, the years of employment with the Extension Service did not indicate it to be a predictor of attitudes toward com­ puters and the Telplan System. 4. The multivariate hypothesis four included eight univariate sub-hypotheses for eight separate independent variables as related to the previous experiences with com­ puters and the Telplan. The multivariate form stated that there were no relationships between the attitude clusters and the past experiences with computers and the Telplan System. The sub-hypotheses were not rejected at the .001 level. 5. Hypothesis five stated that there was no relation­ ship between frequency of usage of the Telplan System and the attitude clusters. This hypothesis was rejected for the Telplan clusters Problem-Solving and Fear/Threat. It was not rejected for the other attitude clusters at the .001 level. The contrasts tests indicated that the less frequent usage of the Telplan, the less the agents perceived the System to be successful for problem solving. Also the less frequent usage of the Telplan the more fear and/or threat the users felt created by the System to their Extension work and job. 150 The relationship of computer attitude cluster to the frequency of use indicated that the frequency of usage was related to whether communication with and access to the computers were easily provided for the agents. 6. It was stated that there were no relationships between the attitude clusters and the independent variable number of programs used. This hypothesis was not rejected at the .001 level of significance. However, the hypothesis was rejected for the two Telplan attitude clusters, Limitations and Fear/Threat, at the .009 level. Findings further indi­ cated that for these two clusters, Problem-Solving and Feelings, the relationship was significant and in favor of those agents that used none or fewer number of programs. 7. Hypothesis seven stated that there was no relation­ ship between employment position and the attitude clusters. The hypothesis was not rejected at the .001 level. Thus, position did not indicate it to be a predictor of attitudes toward computers and the Telplan System. The relationship for Fear/Threat was significant at the .009 level. Findings as related to different employment positions (at the greater levels of significance than the .001 level) indicated that: (1) for the computer attitude cluster Answer, county extension directors, agricultural, dairy, district farm management, horticultural, and regional agents had more favorable attitudes, (2) for the Telplan clusters Problem-Solving, Feelings, and Limitations extension home economist and 4-H youth agents had more favorable attitudes, 151 and (3) for Fear/Threat, extension home economists and 4-H youth agents had more disfavorable attitudes. The findings as related to specific programs of the Telplan and their frequency of usage indicated that only a limited number of programs (14%) were used by the agents. Those programs were found to be highly applicable to the field. The agents, also, indicated need for more training, easier access to the computer, and the promotion of the Telplan among the agents as well as the clientele. Conclusions Within the delimitations of the study, the following conclusions can be noted: 1. Of the nine clusters, one from the computer clusters, Access, and five from the Telplan clusters: Problem-Solving, Feelings, Limitations, Fear/Threat, and Information and Training accounted for nearly 90% of the variance contributed. 2. The independent variables, age, level of formal edu­ cation, length of employment, position in the Extension Ser­ vice, previous experiences with computers and the Telplan had no significant relationships to the attitudes of the agents toward computers and computerized forward planning and con­ sulting programs (The Telplan System). However, at a lower level of significance (.001 < a < .05) the following can be concluded: a. Extension agents holding a higher level of academic 152 degree (roaster's as compared to bachelor's) tended to feel that the Telplan was not useful in the field. b. Extension home economists and 4-H youth agents were in need of continuing training and showed a distrust for the Telplan and feared that the usage of the Telplan System might threaten their jobs. ♦ On the other hand, the agents involved primarily in farm services felt that most programs of the Telplan were not applicable to the agricultural problems with which the agents dealt. c. The agents with longer length of employment tended to have more training with computers. However, they had a more disfavorable attitude toward the accessi­ bility of the Telplan System. 3. Frequency of usage of the Telplan was a predictor of the agents' attitudes toward the Problem-Solving potentials of the Telplan and Fear/Threat attitude cluster. The agents who used the System more frequently had less favorable atti­ tudes toward the Telplan as a result of a lack of successful usage in Extension work. Also, the less frequent usage of the Telplan the more distrust the agents felt toward the System. The result of this distrust manifested itself as a fear/threat factor to personalized Extension work and co n ­ sequently the agents feared that they might be replaced by computers. 153 4. The number of programs used was not an indicator of either favorable or unfavorable attitudes toward computers and the Telplan. However, at the level of .001 < a < .05 this independent variable became a predictor of the agents' attitudes toward three of the Telplan clusters-- ProblemSolving, Limitations, and Fear/Threat. In particular, c o m­ plexity and lack of applicability of most of the programs were the cause for using none of the programs or fewer of the pro­ grams offered by the Telplan System. 5. The major factors for using a program were its us e ­ fulness in and its applicability to the real field problems. Program number 31, Least-Cost Dairy Ration was used more frequently than any other programs of the Telplan System. Extension home economist and 4-H youth agents found the Telplan to be greatly related to educational services in agriculture but less to 4-H and family-living Extension. Discussion and Implications of the Study The Telplan attitude clusters showing significant rela­ tionships with most of the independent variables were: Problem-Solving, Limitations, Fear/Threat and to a lesser extent Information and Training. These relationships were also confirmed by and concluded from the agents' cerns and comments. stated con­ The only computer attitude clusters which indicated a near significant relationship with some of the independent variables and which were also drawn from the agents' comments, were Answer and Access. 154 Based on this study, the Extension agents can be divided into two major categories: 1. The agents whose primary Extension functions are related to agricultural education (marketing, farm manage­ ment, dairy, livestock, etc.)* mostly of consists agricultural Extension agents, district and regional agents 2. This group and county extension directors. The agents in family living areas and 4-H Programs, Home economic Extension and 4-H Youth agents comprise the second group. The two groups share the following commonalities and differences a. (Figure 5.1): Both groups (with group two more strongly than group one) feel that most programs of the Telplan System are not related to the needs of the Extension agents and their clientle; most programs are not useful in the field; and they are complex and difficult to use b. (Limitations). While the agents in group one perceive that com­ puters by providing quick answers aid the agents in their Extension work, feel that a shortage of computer terminals touch tone) since there is (hard copy and/or in the Extension offices the computer aids have not been of satisfactory help (Access). Group two, on the other hand, differs with group one in this respect. — r~ 4 Information Answer GROUP 1 2 Limitations Less Freouent Users Training Freouent Users and GROUP Using None or Fewer Number of the Prosframs Using Greater Number of the Programs Problem-Solving Arrows Indicate the Directions and Weights of the Relationships i Dashed vs. Solid in Meanings. Figure 5.1. Extension Agents in Two Groups and Linkages to Six Attitude Clusters. 156 c. Although the agents in group two view the Telplan as a potent forward planning and consulting system in problem solving areas, they feel strongly threatened by the Telplan and fear the System might limit their personalized Extension work with their clients. However, group one, indicates opposite views and perceptions. d. Group two indicates the need for receiving addi­ tional information and continuing training as re ­ lated to the Telplan System. The general consensus among most of the Extension agents, in terms of the limitations of the Telplan, implies that: -Most programs of the Telplan System need to be revised to become less complex and more useful in the field. -The development of new programs needs to be based on their applicability to the needs of the Extension agents and their clientele. -Greater interaction is needed between the field agents and the Extension staff in developing and operating the Telplan System. The latter point is drawn from a general view among many of the agents who feel they are "left out" of the development of the Telplan, although in fact they believe they are the primary users of the System. The following is a typical comment by a county extension director: "There apparently is far greater value placed on the use of computers and specific Telplans (programs) by MSU based staff than is really practical in the day 157 to day Extension operations of an Extension office in the County! Some Telplans (programs) are very inter­ esting and practical for occasional u s e . Clientele don't call up requesting for Telplans ^programs)." Or a district farm management agent, a frequent user of the Telplan wrote: "I am concerned about your survey... . People solve problems--the computer is a tool--Telplan has we ak ­ nesses. We need programs that you did not expose them--easy to use in the field and with depth--good programs in the Telplan are: 36, 52, 65, 70, 55, program 3 could be." The feeling of being "left out" is viewed differently by the two previously established groups. For group one it meant not being included in the process of developing the programs of the Telplan and as a result they feel there are less useful programs for their needs. One district agent wrote: "Most of the programs currently available to all Extension agents are not that applicable to the clientele I deal most directly with so use only two or three that are especially designed for Food Marketing. Those, however, are frequently used for special programs and events. We need more programs in the CMI (Consumer Marketing Information) and are working on some." Group two feels they are "left out" because there are no or very few programs in the Telplan related to their area of Extension work. following is a typical The comment by a 4-H Youth Agent: "I have never used the Telplan programs with 4-H clientele. There are no programs written for my area of work, we deal more with human relations, management, supervision and organization of adults..." The lack of interaction between the agents and the special­ ists developing the Telplan System indicates urgent need for communication among the field agents and the MSU based staff, 158 and in particular, the development of more programs as related to 4-H and family-living Extension areas. This study has indicated the need for additional infor­ mation and training for the agents especially for those in group two. A comment by one county Extension director explains some of the training areas: "I feel very positive about the use of Telplan and like programs. The field staff, however, must fully understand the input forms and the computer output. Computer data must be evaluated with a personal touch with the farm and/or family situation in mind. Wrong information can be interpreted from the computer out­ put if field staff and specialists don't fully under­ stand the program. There are still hardware problems. We should computerize some of the day to day questions which effects agents schedules, i.e. herbicide resi­ dues, metric equivalent, area measurements, weights measures, moisture discounts (wt.), etc..." The study has shown also that the lack of information about the Telplan extends to the Extension clientele. This suggests a need for promotion of the programs among the agents as well as their clients. This study did not demonstrate the relationships Cif any) between the clusters Information/Training and Fear/Threat or other attitude clusters. The lack of easy access to the computer and the Telplan System implies the need for equipping the Extension offices with more computer terminals. Recommendat ions Finally, on the basis of the results, the following recommendations are made: 159 1. It is strongly recommended that path models for the attitude clusters and the independent variables be constructed to study the causal relationships among the variables. The path models should be based on the technique and the theory of path analysis. Excellent discussion of path analysis can be found in Wright (1921,1934,1954), Alvin and Hauser (1975), and Duncan (1975). 2. In revising the programs and/or developing new p r o ­ grams for the Telplan System, usefulness and applicability of the programs to the real field problems should be taken into consideration by the administrators and specialists of the Cooperative Extension Service. 3. Continuing training programs as related to com­ puters and the Telplan System should be developed for the agents, particularly for the Extension home economists and the 4-H youth agents. A path analysis may reveal the link­ age (if any) between the clusters Fear/Threat and Information and Training. This linkage (if any), especially for the aforementioned agents, demands further study. 4. Costs and benefits of using computers and the computerized forward planning and consulting programs (specifically the Telplan System) versus the traditional method of problem solving should be investigated. LIST OF REFERENCES LIST OF REFERENCES Allard, William B. STRUCTR (Programmed originally by HICLUS, Bell Laboratories), Applications Programming Group, Michigan State University. Alvin, Duane F, and Hauser, Robert M. "The Decomposition of Effects in Path Analysis." American Sociological R eview, 40: 37-47, February, 1975. Amara, Roy. Toward Understanding the Social Impact of Computers^ Menlo Park, CA: Institute for the Future, 1974. Available from Institute for the Future, 2740 Sand Hill Road, Menlo Park, CA 94025. Not available from EDRS. ED 110011. Anastasiv, Nicholas J. and Jerman, Max. "Introduction to Computer-Based Drill and Practice in Arithmatic," Handbook, L. W. Singer Co., 1968. Anderson, Ronald E. "First Progress Report on an Inventory of Research Measuring Perception of Computerization." Paper presented at the Workshop on Computer Percep­ tions, Attitudes, and Literacy; Institute for the Future, Menlo Park, CA, May, 1974. Ashenhurst, Robert L. "The Problem of Computer Literacy." Paper presented at the Workshop on Perceptions, Att i­ tudes, and Literacy; Institute for the Future, Menlo Park, CA, May, 1974. Ashley, Montagu and Snyder, Samuel S. Man and the Computer. Philadelphia, PA: Auerbach Publishers, 1972. 32 pp. Assimov, Issac. "His Own Particular Drummer." Phi Delta Kappan, Vol. 58, No. 1, pp. 99-103, September, 1976. Atkinson, Richard C. and Wilson, H. A., eds. ComputerAssisted Instruction: A Book of Readings. New York, London: Academic Press, 1969. Axinn, George H. "A System Approach to Extension," Journal of Cooperative Extension, 89-94, Summer 1969. Barre, C. E. "The Measurement of Attitudes Toward ManMachine Systems," Human Factor, 8, 1966. Berkeley, Edmund C. The Computer Revolution. Doubleday and Co., 1962. New York: Borg, Jerry W. "Computer is Farm Machinery: Farm Business Planning and Analysis," Agricultural Education Maga­ zine, 47: 66-67, September 1974. 160 161 Brown, Lauren H. and Dexter, Wilber A. "A History of the Telfarm Project." Michigan State University, Mimeo­ graphed, July 1974. Byrd, P. F. "Dial System Initiated in Virginia," Adult Leadership, 21: No. 4, 122-123, October 1972. Candler, Wilfred and others. "Computer Software for Farm Management Extension," American Journal of Agricul­ tural Economics, LII, 71-77, February 1970. Carnegie Commission on Higher Education. The Fourth Revolu­ tion: Educational Technology in Higher Education. New York: McGraw-Hill Book Co., 1973. 75 pp. Christopher, George R. The Influence of A Computer-Assisted Instruction Experience Upon the Attitudes of: School" Administrators. Unpublished Doctoral Dissertation. The Ohio State University, 1969. Church, Virginia W. Teachers are People? Being the Lyrics of Agatha Brown, Sometime Teacher in the Hilldale High School. 3d ed. Hollywood, CA: David Graham Fischer Corporation, 1925. Cole, James L. The Application of Computer Technology to the Instruction of Under-Educated Adults. Final Report. October 1971. 60 pp. ED056304. Cordell, James. Television Performance Effectiveness: A Study of Related Variables and the Effects of Inservice Training and Evaluation Feedbaclc^ Unpublished Doctoral Dissertation. The University of Wisconsin, 1968. Cronbach, Lee J. "Coefficient Alpha and the Internal Structure Tests," Psychometrika, 16: 297-334, 1951. Cunningham, William G. "The Need for Dialogue Between Educators and Technologists," Phi Delta Kappan, 58: No. 6, 450, February 1977. Darby, C. A. and others. The Computer in Secondary Schools: Its Instruction and Administrative U sa ge . New York: Praeger Publishers, 1972. Dick, Walter. "An Overview of Computer Assisted Instruction for Adult Educators." Paper presented to the National Institute for Adult Basic Education; North Carolina State University, Raleigh, NC, July 28, 1969. ED033611 Duncan, Otis D. New York: Introduction to Structural Equation Model-;. Academic Press, 1975. — ------------- 162 Doneth, John C. and Boger, Lawrence L. MA Program to Establish an Automated Farm Planning System and Con­ sulting Service: Annual Report.11 Michigan State University, Mimeographed, 1968. Evans, Richard I. and others. "The Effect of Experience in Telecourse on Attitudes Toward Instruction by Tele­ vision and Impact on a Controversial Television Program," Journal of Applied Psychology, 45: 11-15, 1961. Faure, Edgar and others. 1972. Learning to Be. Paris: UNESCO, FIink, Rune. The Telephone as an Instructional Aid in Distance Education: A Survey of the Literature. Pedagogical Reports, No. 1, Lund University, Sweden. 1975. ED112942. Ford, John D. and Slaugh, Dewey A. Development and Evalu­ ation of Computer Assisted Instruction for Navy Electronics Training. 1. Alternating Current Funda­ mentals . May 1970. Available from National Technical Information Service, Springfield, VA 22151. AD-707-728. Freund, John E. Modern Elementary Statistics. 2nd. ed. Englewood Cliffs, New Jersey: Prentice-Hal1, 1960. General Accounting Office. Report to Congress, The Adult Basic Education P r o g r a m : P r o g r e s s in Reducing Illit­ eracy and Improvements NeededT Washington, DC: U.S. Government Printing Office, 1975. Goodman, Henry J. The Effects of Team Learning and of the Counteracting of Misinformation Upon Attitudes Toward Computer Instructed Learning-! Unpublished Doctoral Dissertation. University of California, 1968. Gorsuch, Richard L. Factor Analysis. Philadelphia: W.B. Saunders, 1974. Grabowski, Stanley M. "ERIC: Role of the Computer in Adult Education," Adult Leadership, 21: No. 5, 178-179, November 19 7T! Grossman, Alvin and Howe, Robert. L. Datal Processing for Educators, Chicago: Educational Methods, Inc . , 1970. Guttman L. "A Basis for Analyzing Test-Retest Reliability," Psychometrika, 10: No. 4, 255-282, December 1945. Hadleman, Stanley D. A Comparative Study of Teacher A t t i ­ tudes Toward Teaching by Closed Circuit Television." Unpublished Doctoral Dissertation. New York University. 163 Hadsell, Carl D. and Ervin, Ronald W. "Continuing Education Unit Computer-Assisted System at West Virginia Univer­ sity," College and University, Fall 1975. 49-61 pp. Harrison, Gerald A. and Rades, Robert J. "The Computer in Extension Farm Planning," Journal of Extension, 12: No. 1, 47-53, Spring 1974. Harsh, Stephen B. "A Progress Report on Telplan Activities," Michigan State University, Mimeographed, 1977. Harsh, Stephen B. and Hatheway, Dale E. "A Program to Establish an Automated Farm Planning System and Con­ sulting Service: Annual Report," Michigan State University, Mimeographed, 1971. Hess, Robert D. and Tenezakis, Maria D. "Selected Finding from 'The Computer as a Socializing Agent: Some Socioeffective Outcomes of C A I 1," Audio Visual Communication Review, 21: No. 3, 311-325, 1973. Hotelling, H. "Analysis of a Complex Statistical Variables into Principal Components," Journal of Educational Psychology, 24,417-421,498-520, 1933. Hunter, John E., "Cluster Analysis: Reliability, Construct Validity, and the Multiple Indicators Approach to Measurement." Paper presented at the Advanced Statis­ tics Workshop; U.S. Civil Service Commission, March 21, 1977. Available from Department of Psychology, Michigan State University, East Lansing, MI 48824. Hunter, John E. "Factor Analysis." Paper presented at the Conference "Multivariate Analysis in Communication Research," Asilomar, CA. April 3-6, 1977. Available from Department of Psychology, Michigan State Univer­ sity, East Lansing, MI 48824. Hunter John E. and Cohen, Stanley H. "PACKAGE: A System of Computer Routines for the Analysis of Correlational Data." Educational and Psychological Measurement. 29: 697-700, 1969. Hunter, John E. and Gerbing, David W. Unidimensional Measure­ ment Via Confirmatory Factor Analysis. Technical paper, Michigan State University, January 1979. Hutjens, M. F. and Hasbargen, P. R. "Ration Formulation and Balancing with the Computer." Paper presented at the 67th Annual Meeting of ADSA. 1972. International Business Machines Corporation. IBM, 1975. Annual Report. 164 Janison, Dean and others. "The Effectiveness of Alternative Instructional Media: A Survey," Review of Educational Research, Winter 1975. Kelly, Terence J. and Anandam Kamala. "Instruction for Distant Learners Through Technology." Paper presented at the International Conference on Improving Univer­ sity Teaching, 3rd, New Castle-Upon-Tyne, England, June 8-11, 1977. ED139455. Kerlinger, F. N. and Pedhazer E. Multiple Regression in Behavioral Research. New York: Holt, Rinehart and Winston, 1973. Kibler, Tom R. and Campbell, Patricia B. "Reading, Writing, and Computing: Skills of the Future," Educational Technology, 16: 44-46, 1976. Krombrout and others. "Conference on Computers in Under­ graduate Science Education: A Computer-Assisted and Managed Course in Physical Sciences." Florida State University, 1970. ED046240. Lacy, John W. Attitudes of Coal Miners Toward Their Unions. Unpublished Doctoral Dissertation. West Virginia University, 1962. Lambert, William W. and Lambert, Wallace E. Social Psychology. Englewood Cliffs, New Jersey: Prentice-Hall, Inc., 1964. 50 pp. Lawler, Michael R. and others. "Selected Instructional Strategies in Computer Managed Instruction." Florida State University, 1972. ED064880. Longo, Alexander A. The Implementation of Computer Assisted Instruction in the U.S. Army Basic Electronics Training. Follow-up of a Feasibility S tu dy . September 1969. ED038021. Mavick, Dwaine. "The Impact of Computerization in Los Angeles: 1973, Some Sample Survey Differentials in Perception and Attitudes." Paper presented at the Workshop on Computer Perceptions, Attitudes, and Literacy; Institute for the Future, Menlo Park, CA. May 1974. ED110011. Miller, R. H. "The Computer as an Aid in Animal Improvement," The Science Teacher, 37: No. 3, 40-41, March 1970. Neidt, Charles 0. and Baldwin. "Use of Videotape for Teach­ ing In-Plant Graduate Engineering Courses," Adult Education Journal, 20: 154-167. Nierai, John A. "Technology and Media for Lifelong Learning," Journal of Research and Development in Education. 7: 7T-8'67"T974. ------------------Nold, Ellen W. "Fear and Trembling: The Humanist Approaches the Computer," College Composition and Communication, 26: 269-273, 1975. Norris, William C. "Via Technology to a New Era in Education, Phi Delta Kappan, 58: No. 6, 451-453, 1977. Nunnally, Jun C. Psychometric Theory. New York: McGraw-Hill, 1967. Ottinger, Antoney. Run, Computer, R u n . Cambridge, Massachu­ setts: Harvard University Press, 1969. Paeschke, Phyllis A. "Computer Models in Adult Education." Paper presented at the Adult Education Conference, Toronto, Ontario, April 9, 1976. 67 pp. ED123390. Parker, L. A. "Educational Telephone Network and Subsidiary Communication Authorization: Educational Media for Continuing Education in Wisconsin," Educational Technology, February 1974, 34-36 pp. Pillai, K.C.S. and Jayachandran, K. "Power Comparison of Tests of Two Multivariate Hypothesis Based on Four Criteria," Biometrika, 54: 195-210, 1967. Purdy, Leslie. "Community College Instructory and the Use of New Media: Why Some Do and Some Don't," Educational Technology, 15: 9-12, March 1975. Reese, Donald. Attitudes of Collegiate Business Faculty and Management Personnel Toward Computers and Computer Utilization. Unpublished Doctoral Dissertation. University of Iowa, 1967. Report of the Commission on Instructional Technology to the President and Congress of the United States, Washing­ ton, DC: U.S. Government Printing Office, 1970. Scanland, Francis W. An Investigation of the Relative E f ­ fectiveness of Two Methods of Instruction, Including Computer-Assisted Instruction, as Techniques for Changing the Parental Attitudes of Negro Adu lt s. Florida State University, Tallahassee7 FL. ComputerAssisted Instruction Center, July 1970. ED043224. Scheffe, H. A. 1959. The Analysis of Variance. New York: Wiley, Schoonaert, James H. Adoption of a Computerized Least-Cost Dairy Ration Program by Ingham County, Michigan Dairy­ m e n . Unpublished Master's thesis. Michigan State University, East Lansing, MI, 1973. 166 Schwartz, H. A. and Haskell, R. J. Jr. MA Study of Com­ puter-Assisted Instruction in Industrial Training." In Journal of Applied Psychology, 50: No. 5, 360-363, 1966. Schwartz and Oseroff. "Clinical Teacher Competencies for Special Education." Florida State University, 1972. ED082440. Sherman, Mark A. and Klare, George R. "Attitudes of Adult Basic Education Students Toward Computer-Assisted Instruction." Cambridge, Massachusetts: Harvard University, 1970. Short, J. "Teaching by Telephone: The Problems of Teaching without the Visual Channel," Teaching at a Distance, No. 1, Open University, Milton Keynes, 1974. Skinner, B. F. "Some Prior Considerations." Kappan, 58: No. 6, 456, 1977. Phi Delta Slade, Mark, Language of Change: Morning Images of M a n . Toronto! Holt, Rinehart and Winston of CanadaT, Limited, 1970. Smith, William D. and others. "Literacy." In Toward Under­ standing the Social Impact of Computers. Menlo Park, CA: Institute for the Future, 1974. EDI10011. Sofranko, Andrew J. "The Computer and the County Agent," Journal of Extension, 12: No. 4, 29-37, Winter 1974. Spearman, C. "General Intelligence, Objectively Determined and Measured," America Journal of Psychology, 15: 201-293, 1904. Specht, David A. and Hohlen, Mina. SPSS RELIABILITY: Sub­ program for Item and Scale Analysis. Northwestern University, Document No. 32 3, May 1976. Suppes, Patrick and Morningstar, Mana. "Computer-Assisted Instruction," Science, 166: No. 10, 458 pp, 1977. Suppes, Patrick and Morningstar, Mana. Computer Assisted Instruction at Stanford, 1966-1968. New York: Academic Press, 1972. Tannenbaum, Percy H. "Public-Policy Issues." In Toward Understanding the Social Impact of Computers. Menlo Park, CA: Institute for the Future, 1974. ED11011. Thelen, Herbert A. "Profit for the Private Sector." Delta Kappan, 58: No. 6, 458, 1977. Phi 167 Thompson, Stanly R. Types of Computers and Their Use by Agricultural Cooperatives. Oregon State University, Corvallis Cooperative Extension Service. May 1971. ED0S5629. Townsend, Robert. Up the Organization: How to Stop the Corporation from Stifling People and Strangling Profits. New York: Alfred A. Knopt, 1970. 202 pp. Tryon, Robert C. Cluster Analysis. Edwards Brothers, Inc., 1939. Ann Arbor, Michigan: Tryon, Robert C. and Bailey, Daniel E. Cluster Analysis. New York: McGraw-Hill Book Company, 1970. U.S. Department of Health, Education, and Welfare. U.S. Office of Education Support of Computer Activities. Washington, DC: Government Printing Office, publication OE-12044, 1969. Walker, Terry M. and Cotterman, William W. An Introduction to Computer Science and Algorithmic Processes. Boston: Allyn and Bacon, 1970. Wedemeyer, Charles A. "Trouble at ’Castle Perilus1: Ap ply­ ing Media and Technology to Instruction," Massachusetts Media and Adult Education. Englewood Cliffs, New Jersey: Educational technology Publications, 1971. White, Thomas. "Measurement." In Toward Understanding the Social Impact of Computers. Menlo Park, CA: Institute for the Future, 1974. ED110011. Winer, B. J. Statistical Principles in Experimental Design. 2nd ed. New York: McGraw-Hill, 1971. Wright, Sewell. "Correlation and Causation." Journal of Agricultural Research, 20: 557-58S, January,1921. Wright, Sewell. "The Method of Path Coefficients." Annals of Mathematical Statistics, 5: 161-215, September,1934. Wright, Sewell. "The Interpretation of Multivariate Systems'.’ In Statistics and Measurment in Biology, pp. 11-33. Edited by Kempthorne, 0., Bancroft, t T A . , Gowen, J. W. and Lush, J. L. Ames: Iowa University Press, 1954. Yeomans, G. A. and Lindsey, H. C. "Telelectures." Speech Teacher, 18: No. 1, 65-67, 1969. The APPENDICES APPENDIX A ATTITUDE SCALE AND BACKGROUND QUESTIONNAIRE A TITTUPS SCALE The statements of the attitude scalo have been prepared so that you can indicate your feelings about computers and the Telplan System* There are 58 items about conputers and the Telplan System, Ploase indicate the extent to which you agree or disagree with the statements* by making a check mark on the dots under the symbols to the right of each item* The symbols used arei SA - if you Strongly Agree With the statement* A - if you Agree with the statement but not strongly so* N - if you are undecided or Neutral about the statement. D - if you disagree with the statement but not strongly sr>, SD - if you Strongly Disagree with -the statement. Sample SA A 1* Host grapes are street* - - / . 2* Most grapes do not have seeds* t • If you have any questions please contact me either by mail or call me collect at (517) 332-**l**8 . Sincerely Yours, Mohdi Chods P.O. Box **28 ' £* Lansing, MI **8823 OR Physics Department Michigan State University East Lansing, MI **882*1 169 N D « /. SD 170 A. Attlludos Toward Cornjmtcrsi SA A M b SD 1. Computers do not generate much usoful information. ........... 2. Computers almost always give correct answers. ........... 3» Computers have tho potential to answer most of your questions. . . . . . t. Computers often mako mistakes. ........... 3. It is difficult to disagruo with solutions gonorated by computers. ........... 6 . Computors usually answer questions quickly. . . . . . 7. Computors ofton give confusing answers. ........... 8. It is difficult to obtain answers from computers. . . . . . 9. Computors have improvod tho lives of people. ........... 10. Computers should not bo usod in solving agricultural problems. . . . . . 11. Extension agents should discourage the uso of computers within their extension offices................................. ........... 12. I profor to solve my clients* problems by computors rather than by conventional methods........................................ 13. My job porformanco would improve if I had oasior access to tho computers at the University of Michigan. . . . . . lb, Computors are fascinating. ........... 13. In tho interest of hotter communication with computers, all County Extension Offices should be equippod with printing terminals............................................. ........... 16. Tho activo role of agents in problem solving for their clientele will dirur.ish as tho computer gradually takes over their duties. . . . . . 17. A basic understanding of computer hardware often helps a person to bocomo a skillod computer programmer................ ........... (computer hardwaro rofors to the physical units making up a computer system) 18. Computer softwaro developments as related to oxtonslon work have not boon as advanced and sophistciatod as*the development of computer hardwaro. (computer software rofors to all "programs'* which can bo usod on a particular computor system) . . . . . 171 B. Attl tudas Toward tho Telplan System! SA A 19. Extension aronts should uso tho Tolplan System for problom solving. • « . . . 20. The Telplan System only makos mistakes when the wrong information is fod intoIt. . . . . . 21. Problom solving with tho Tolplan System has boon succossful. ............ 22. Agents should tako tho opportunity to uso as many of tho programs of the Tolplan System as they can. . . . . . 23. Telplan System docs not give appropriate answers in all cases. N D . . . . . 2k. Tho Tolplan System docs not offer suitable programs for all of my clients'probloms. . . . . . 25. Agonts'rolos aro threatened by tho usage of the Tolplan System. • . . . . 26. Tho introduction of moro programs in the Tolplan System would require higher skill levels in many extension jobs. 27. Some of the programs that are within the Tolplan System aro too complex and too time consuming to use in exten­ sion work. ............ ............ 28. Somo of the programs in the Telplan System doal with unimportant matters. ............ 29. Tho Telplan System lacks the capability of assist­ ing the agonts with many of thoir client's needs. ............ 30. Increased usage of the Tolplan System has moant the agents and farmers keep moreaccuraterecords. 31. Researchers and extension specialists should pross hardor to lncrcaso the adoption of tho Tolplan System among tho agonts. 32. The Telplan System as it exists now is of little holp to small farmors. * 33. Using the Telplan Systom in classrooms or extonslon train­ ing will raiso tho quality of agricutural education. . . . . . . . . . . ............ ............ y*. Somo of tho ngonts and their clients do not appre­ ciate tho potentials of tho Tolplan Systom. . . . . . . 35. Tho programs in tho Tolplan System requiro moro in­ formation about pcoplo's private lives than is nocossary. . . . . . SD 172 SA A K D SD 36. Docause of tho Tolplan Systom I rarely have trouble in helping my clients solvo their probloms. • • • • • 37. lhe increased usage of the Telplan Systom has holpod to raise the farmers' standard of living• ........... 38. The Tolplan System will help improve the services available to thecommunity. • » * • • 39* lhe increased use of tho Tolplan System has provi­ ded for moro leisure timefor my clientele. • • • . • hO. The expandod usage of the Telplan Systom increases the quality of oducatlon for extension clientele in Michigan• ........... hi. Using tho Tolplan System detracts from an agent's ability to establish a porsonalizod relationship with clients. ........... h2. I am very enthusiastic about the Telplan System because I find it very useful in solving my clients' problems. • • • . • h3. Because of the Telplan System, too much information about agents and their clientele is available to outsiders. ........... hh. The Telplan System has improved my attitudes toward computors. . . . . . h5. The Telplan System does more reliable problem solving than agents. ........... h6. The Tolplan Systom will eventually put most of the agonts out of work. . . . . . h?. The Tolplan System has bocome an everyday necessity for extension work. . . . . . h8. The Tolplan System is appropriate only for crucial decision making in problom solving. ........... h9 , The Tolplan Systom assists the agents to become more compotent in their extension work. . . . . . 50. All agents should know somo tiling about tho Tolplan Systom whother or not thoy use it, . . . . . 51. Thoro should bo more training provided to agonts on tho uso of tho computor and the Telplan Systom. . . . . . 173 SA A W D SD 52. Agonts should constantly learn more about tho Telplan System in order to be able to work with it. . . . . . 53* Iho increased usage of the Tolplan System would ac­ tually increase employment in tho fiold of extension. ........... 54. Agonts must have a groat deal of training with compu­ ters in order to bo able to work with the Telplan System. . . . . . 55* Since tho County Extension Office began using the Tel­ plan System. 1 work more -efficiently. . . . . . 56. Somo of the programs of the Telplan System are not applicable to real world -problems. • 57. Hy attitude toward the Telplan System is moro favor­ able than it was bofore I began working with it. . . . . . • . . 58. In order to understand moro about the Telplan System. agents should pursue additional college course work. PLEASE COMPLETE THE NEXT QUESTIONNAIRE ALSO . . . . . . 174 BACKGROUND QUESTIONNAIRE In order to intorprot tho data as related to tho attitudo survey, the following Information would bo of direct valuo. Please respond to all of tho questions b y oither filling In the blank, or by a chuck mark in tho appropriate box. All rosponses will be treatod confidentially. 1. What is vour aro rroup? a. ( ) under 23 d. ( ) 35-39 ( b. ( ) 26-29 e. ( ) bo-hh h. ( c. ( ) 30-3* f. ( ) *5-*9 ) 50-5* ) 55 or over 2. What is the hlrhest level of formal education you have attained? a. ( ) High School d. ( ) Master’s degree b. ( ) 1-2 years of college e. ( ) Doctoral dogroe c. ( ) Bachelor’s degree f. ( ) other (ploase specify) 3. How long have you been employed by the Michigan Extension Service? U. What is your position with the County Extension Office? 5. Experience with computers and the Telplan System!(please chock all applicable statement) a. ( )I have never writton a computer program. b. ( )I have had computer programming courses. c. ( ) I have extensive training with computors and computer programming, d. ( ) I havo had access to a computer before I bog.in using the Tolplan Systom. e. ( ) My only training with computers has boon on how to use the Tolplan System. f. ( )I haveread articles and books on computers. g. ( )I havo worked with computors only through terminals, but I have novor scon a computer. h. ( ) I havo my own personal micro-computor. 175 1. I havo usod tho Tolplan Systom 1. .( ) almost daily 2. ( ) ono to throo timos wookly 3. ( ) one to threo times monthly ft. ( ) loss than tontimos a yoar j. I havo usod the programs of the Tolplan System at the rate oft 1. ( ) one program only 2. ( ) ono to five programs 3. ( ) more than five programs OPTIONAL! Please specify the program numbers of the Telplan Systom that you 1. frequently usei 2. have used (at least once): THANK YOU Your Comonts and/or suggestions would bo groatly appreciated. APPENDIX B TABLES OF CLUSTER ANALYSIS FROM PACKAGE Table B.l. Factor Inter-Correlations and Loading Matrix (Internal Consistency) for the A Posteriori __________ Cluster Analysis (2 Residual Clusters Included). COMWUM L W III THE PIMO h Ai. 11 ' II 34 31 11 30 -1 -I -I I -II -3 -II -13 - -II -13 -13 -3 1 -3 3 - -14 - -f -3 0 -8 -2 -I 3 13 1 4 -3 •II -1 4 - 1 -? - 3- 3 -10 -4 - 21 41 32 IS IB -9 -2 -3 -4 -14 -7 -a -I* -13 -9 33 21 13 22 24 -14 3 -I -? -4 -13 -i4 -13 -14 -4 2 •a -31 -4 a -4 -II -ii 0 1 1 2 -1 - -10 a • -V 4 3 I 34 3 47 -4 -II - a - -IB -| -I 3 -I 0 -B 3 1 -I 4 4-7 4 0 -10 0 4 4 1 4 II -2a 4 3-4 B 12 -3 -I -I I -2 -IB -IS -1 - 1 2 -7 4 -3 3 -3 1 -2 1 2 -7 -1 0 -3 -3 4 -2 -II -V -22 -7 -B -7 -4 -3 -V -14 7 30 37 IV -3 17 7 I II 1 10 -7 -4 -ia 13 -13 -20 -30 14 -13 -14 -13 -I 2 -12 -4 0 3 3 -3 -14 4 0 1 4 » -1 I -7 -4 -3 4 -I -14 -9 0 -7 t -3 a -a a -s 14 o -16 -17 -4 V -14 -4 0 a -17 -13 -II IS -27 -11 -4 7 -10 - -7 -14 -1 1 -1 4 -1 1 30 IV IB 34 21 7 -3 -4 -4 -7 -10 -0 -4 -7 -V 4 -II -13 -II 10 -8 I ' ■■ ■— ___ ____ ___________________ _________ ) u u a I 10 3 -3 a 4 -II 2 2V 4 14 7 -12 3 -14 3-11 21 -12 -II 3 11 21 -2 14 -22 -2 -4 -14 0 -9 -2 44 14 -4 9 -2 I -10 -1 1 -II -3 -3 -I -ii -a -3 -4 ■14 - 7 -I -9 - 4 - I I - 4 -3 -II - 9 19 -3 17 7 ia 2 3 10 is 17 14 14 17 39 3 3 9 14 33 39 IS 10 0 14 9 4 13 22 14 21 -3 10 14 17 2 3 22 14 8 a 13 1 3 -I 13 14 13 a 4 -i •17 -a 2 -19 •13 -8 -4 -II -II 10 2 7 a 3 .7 9 -4 9 2 -I 13 7 II -3 -2 -23 -13 ■19 -11 -IS -12 -21 -21 -14 -4 -7 -4 -2 -10 7 0 -3 1 -7 •10 -7 -4 -II 4 -10 1 -4 -3 10 a -l It 27 -0 3 10 -3 12 IB 7 -0 -2 -10 13 -1 a 2 -10 -4 I II 19 14 19 -14 4 2 -a -4 -II 14 14 19 a 14 -2 14 -I 0 -4 0 33 -10 12 2 -13 -I •32 14 -20 -33 -24 9 3 -7 4 -II -IB -30 -21 -30 -I -29 -I -1 14 -2 3 -1 27 23 ia 2 3 -II 12 -9 4 44 -7 24 -11 •21 - U -41 27 7 IB 0 3 a 10 -a 4 4 4 -2 -3 13 4 3 -a 30 37 4 23 11 13 IB 11 3 II ■19 -30 -IB -3 -7 13 9 30 14 24 11 7 20 9 1 -4 4 -7 12 41 22 13 -4 -21 3 27 20 IB 15 44 41 47 2 2a 4 12 -1 13 47 29 29 23 17 17 12 21 11 28 - 3 IB •10 7 -4 - 7 -4 - I -20 - 7 -7 -1 4 7 -10 4 - 7 -1 4 4 24 7 20 - 3 24 7 14 7 12 4 21 11 20 14 22 15 34 I 4 -II -I -a -3 14 34 -1 13 ■II - 7 -IS -1 4 I I 17 14 20 20 13 -12 13 19 0 4 4 13 13 0 4 3 II II -2 -7 -2 -22 -1 4 24 22 41 20 27 48 13 33 4 34 ■17 - 3 9 37 21 3B 14 2 3 2 ' 33 34 39 23 -ia - ii -ts -19 - 1 9 -13 -21 -23 - 1 6 -13 -is • t o •12 - 4 • 1 3 -13 - 8 -1 1 -a - i 9 • 1 3 • f • 1 2 -4 - 4 -1 1 •8 -14 - 1 3 - 1 4 • 4 ■32 - 7 - 0 • 7 3 10 1 11 o a 0 15 V 4 -5 14 II 14 to 17 22 21 2 25 41 47 28 47 43 34 37 32 3 4 44 40 34 3 7 40 24 14 3 2 34 14 24 44 23 10 24 24 34 33 20 37 31 30 24 19 24 24 27 14 33 17 23 21 19 14 15 la 22 30 29 30 20 24 10 -4 12 14 2 - 4 -B - 4 - 4 -4 -3 4 -2 -21 - 4 - 9 -1 9 fl -1 4 -3 4 7 -2 3 - 2 IB -3 3 13 1 -13 -2 -2 -1 8 22 IB 23 10 19 32 21 3 32 29 24 1 10 IB 18 1 23 4 8 0 22 29 27 19 24 IB 14 24 21 12 13 21 33 29 23 34 -3 4 0 to -13 - 4 • 1 2 8 0 - 3 - I I -1 7 -1 9 •1 1 14 13 14 13 21 -14 - 9 • 1 2 -1 4 -21 -20 -2 -2 1 37 20 17 19 17 13 23 28 19 10 18 I I 32 22 33 -1 43 12 27 t 24 V 14 3 0 7 7 11 13 a 8 -t 13 I I 17 IB 1 . ‘ 3 13 - 1 4 9 13 • 1 14 ■31 -2 7 -2 4 v-20 7 17 a 28 29 31 10 34 47 44 31 31 40 47 30 41 SO 32 40 29 -10 7 8 -20 39 40 38 10 3V 29 27 40 -4 2 - 1 9 29 7 0 43 43 40 *9 •7 -IB - 1 0 -1 9 1 10 22 14 29 44 23 19 24 21 13 24 30 23 19 25 27 -7 3 -3 •2 7 -22 •2 0 -3 •It 8 -2 11 8 14 24 to -8 2 2 2 4 29 24 34 33 20 13 20 29 14 28 4 32 28 10 7 4 13 12 8 20 11 22 11 13 9 11 24 23 22 30 12 -4 -3 4 -7 -9 4 -1 7 -1 12 23 37 31 30 24 24 29 39 34 37 34 32 24 3 -4 -3 -1 9 3 3 0 2 34 27 19 IS 10 33 12 10 24 -3 -7 -5 •2 13 -0 •1 4 7 24 14 IS 13 12 11 -2 0 9 -1 4 -3 14 IS -1 3 -7 -2 0 -1 0 37* 0 14 4 7 10 23 30 23 2 24 0 S 3 4 3 10 •3 14 12 11 14 ? 10 - S -1 It 3 - 2 5 - 2 0 -21 -3 -3 9 29 7 9 44 44 32 42 33 43 39 44 42 -24 24 -4 21 27 44 14 36 22 -9 U -1 4 91 33 47 - 1 3 -1 •1 1 - 1 2 10 B 13 14 8 3 7 13 13 -1 17 17 19 14 24 33 24 17 27 25 30 23 14 20 34 37 34 35 33 33 30 27 30 19 IB 6 5 -4 •20 -7 - 1 9 -1 • 1 7 -4 -2 3 - I I - 2 3 -1 1 -S - I -1 4 -1 3 7 11 3 18 •4 1 2 21 -4 17 18 13 8 14 - 3 12 9 20 0 -4 -2 4 -3 -4 7 8 17 8 - 9 -1 4 -1 9 -2 1 24 14 23 31 3 11 1 -2 20 I I 31 1 23 10 4 22 17 10 -2 0 1 10 3 -1 9 -2 7 22 20 9 14 39 •40 SO 57 33 20 -3 4 -1 3 7 23 4 23 4 -7 48 43 -9 •S -7 -4 -IB 13 13 7 II 12 21 21 19 14 13 19 4 34 30 27 27 23 17 10 -3 -1 -1 3 4 3 1 -4 20 14 14 IS 10 21 2 0 14 -1 4 -1 4 -1 4 6 2 -9 -1 7 14 U 10 2 13 17 4 17 4 7 •3 -1 8 24 19 30 52 30 -2 20 8 -3 4 38 - 2 0 - 8 -2 - I -1 3 - 9 9 7 -1 3 -1 3 2 3 -2 0 -1 4 -1 2 • 3 -20 • 1 3 - 4 - 3 14 -1 0 -1 4 8 4 -1 • 1 7 9 -4 -2 3 -1 9 -3 11 - 3 - 1 0 - 4 28 18 7 -7 38 30 - 4 - 4 22 20 12 - 8 30 34 14 - 4 29 10 2 -4 23 27 • 7 3 32 28 18 7 32 24 3 -4 30 18 - 4 -2 0 19 8 3 -7 23 17 10 - 3 47 44 1 -1 3 44 47 II -4 1 11 34 24 -1 3 - 4 24 24 -2 -1 1 23 34 - 4 -t 32 24 -2 0 28 21 0 0 23 22 3 • 4 37 14 4 2 14 22 23 14 IS 7 12 17 14 - 4 14 19 23 9 8 8 32 3 IS 12 9 2 23 20 0 4 9 21 4 -2 13 14 1 0 29 17 2 -1 4 *3 -12 20 8 - 8 -1 **7 - 3 - 4 - 4 •4 a 11 - a -4 2 21 12 0 -a -1 4 -4 -1 7 3 - 1 0 •4 10 -3 10 13 -1 2 - 1 9 20 14 4 1 19 4 - 4 -1 IS 30 9 14 22 I I - I S -1 7 24 11 -1 2 - 3 4 10 -1 3 - 4 4 -1 -7 -1 1 14 4 -II -2 -1 -9 9 10 5 3 5 -3 -33 -2 3 •3 3 22 •1 -1 7 -38 4 •4 -19 -1 4 32 40 11 •4 45 29 4 -IS 47 47 9 -1 3 -i -3 tali 51 30 26 29 7 34 24 3 -8 7 -7 -11 -9 33 34 -13 -7 3 12 II I 9 4 -1 -3 4 I 4 0 -7 -I -a 2 -19 -IS -23 1-14 a -4 -I -4 -3 4 -2 -3 4 -3 -1 9 -3 -I -2 -II 21 34 21 24 13 13 17 2B -1 -2 0 -7 -2 3 -4 -v -1 3 -2 7 13 -1 9 -1 7 -4 -1 3 -4 -I 32 24 24 21 23 14 14 14 4 -2 -4 4 -14 9 -9 -3 o a -is -a -7 -a -7 -7 -4 -4 4 -10 7 4 -2 -3 10 3 7 13 -21 I -7 -3 -3 4 a -1 4 -2 2 -20 a 12 3 3 - 2 3 -23 -II -II 1 4 0 -2 0 0 20 23 21 22 13 13 23 14 20 31 33 20 IB 24 14 15 -1 23 19 2 3 17 32 10 3 23 31 2 4 15 13 7 12 -2 -2 1 -4 I -1 7 -3 9 7 4 9 1 -a 7 3 -13 14 9 27 I -1 -7 a -4 -12 3 I -I -4 -2 I -7 -3 -a -ii -ii -4 0 11 9 7 4 19 I 3 -2 -14 -20 -I 2 -7 2 -a -a -3 2o 0 -3 -I 1 3 -4 37 14 17 14 IB 24 19 19 14 3 -4 II 3 II 3 a 14 4 -2 1 IS 3 -II -10 -13 -7 7 -3 13 3 14 I 19 -B -14 -24 -19 -13 1 -10 -1 2 - 1 3 - 3 - 9 - 4 - 4 -3 a -1 2 -B 9 -2 4 -a 0 - 7 ia -I 23 23 I -II -20 3 9 -1 7 -1 3 -2 -II -10 4H 4 3 7 -3 a i o 11 -21 -24 -34 -9 -1 8 -3 -25 -12 -4 -I 44 43 I 24 -12 II 30 -24 -1 8 -2 -7 ia -10 4 -21 -U 0 -II -1 10 -13 O 45 32 II -II -4 -2 -I 44 7 9 28 -a Continued on next Table B .1. 27 -II 17 0 •5 14 0 •4 -2 -1 0 -7 -1 4 -1 3 -2 -2 -1 0 -II II 2 -1 4 -IS -4 4 2 14 22 20 14 14 13 19 14 7 -2 0 -4 -1 4 -3 3 2 0 4 4 4 -2 -4 20 0 -1 5 -1 0 1 3 -1 4 -2 2 -1 -2 1 -2 -3 13 9 —4 -1 4 -1 3 -1 3 4 30 -4 3 14 •2 4 44 41 43 -1 4 3 -7 -1 4 - 7 - 1 4 - 1 0 -1 4 - 1 7 -1 7 -4 -1 3 -4 0 9 0 13 -7 •1 3 - I I 0 -5 • 1 0 7 1 -7 4 7 -3 24 20 24 22 19 32 10 32 29 23 21 24 10 3 1 0 -2 II 22 I I 13 34 27 19 3 -4 7 II 10 1 20 14 14 23 12 14 14 17 19 13 14 23 7 -4 9 2 10 -1 -1 3 a 23 17 23 19 32 31 14 3 1 7 -2 0 47 44 30 44 44 42 30 42 34 39 30 41 24 32 20 34 22 13 9 A 9 II 7 0 22 10 10 5 12 - 1 0 - 1 0 -2 1 - 2 8 • 3 8 •3 2 -2 3 - 2 -1 1 -8 9 4 -3 -3 -2 -7 -2 0 -9 11 14 A 1 0 3 14 27 14 44 5 1 -2 A 2 10 0 3 -3 20 24 12 0 7 12 A 20 11 - 7 -I A - 3 -2 9 - 9 - 2 3 -3 -4 -2 9 A -0 33 27 39 31 20 12 29 22 24 22 19 27 49 40 40 20 13 17 -3 9 •4 9 -4 2 39 22 44 I 29 94 49 91 2 9 -1 0 - 4 -1 4 -9 -1 0 - I I -2 7 -1 0 -1 2 -1 9 -1 3 - 7 - 2 - I 4 -0 3 10 14 22 21 12 IS 21 12 22 10 -3 12 0 13 14 1 0 1 -2 •Ii 1 -4 * 7 12 23 A 0 0 14 II 10 -4 17 10 13 12 9 -4 10 II 20 -1 11 -0 0 3 7 0 -2 3 -0 9 21 23 12 U 39 14 -3 0 30 10 0 IS -4 7 10 15 13 4 0 2 13 14 10 7 7 -4 14 12 IS 10 0 -1 - 3 - 1 9 13 •12 10 11 11 A 9 3 -5 0 -9 24 1 -4 0 - 2 4 12 13 9 -1 1 - 1 7 -1 4 - 2 4 -1 4 A 10 0 2 I I 32 21 19 43 34 32 10 37 19 0 20 3 2 2 2 21 1 2 0 9 41 32 13 14 4 27 75 A3 - 3 0 - 1 0 2 2 -1 4 1ft 3A 27 2 4 2 -2 -2 7 1 12 -1 -A I I -I A -3 24 34 32 22 20 13 23 17 17 9 9 10 2 It 3 19 7 23 -A - 2 -0 0 -2 1 - 1 3 -1 4 4 11 10 - 5 -4 92 - to -7 -1 4 -1 0 - 1 4 -2 4 -1 4 - t o 7 -A -It 4 7 IA 10 10 10 1 0 9 13 2 21 IS 0 0 32 3 2 4 19 13 -3 -A 39 30 41 33 29 17 4 5 23 4 •2 1 -3 7 4 to -1 7 3 •3 10 0 4 21 22 29 27 19 24 24 33* 10 13 21 23 20 0 4 1 4 -1 II 27 13 20 24 18 14 24 10 23 12 3 14 2 9 21 4 -2 7 90 4 9 9 7 3 11 3 2 9 11 7 4 9 0 9 4 2 3 11 19 94 49 49 43 49 41 II -4 4 9 1 -1 3 4 -1 4 20 4 -1 10 A 2 3 4 A 3 7 •0 -2 1 4 -3 A 0 *10 •2 0 12 4 0 14 29 4 11 0 -2 0 -5 -1 0 12 12 10 -1 ts -II 34 .-1 35 -1 3 29 -A 25 -1 2 34 0 It 9 30 -4 24 -a - 7 9 -A- - 2 20 0 4 14 —14 - 1 4 29 - 3 - 0 17 -1 2 - 1 2 20 - 7 •1 4 0 -3 3 9 -7 27 0 14 3 •4 7 - 3 -1 2 3 0 14 0 4 4 22 5 •1 0 10 12 -2 1 10 -1 0 - 2 0 23 4 -2 1 7 -A • 0 29 - 2 0 49 11 4 41 - 4 9 43 - 3 2 - 3 17 - 5 2 - 9 19 -0 7 13 3 10 - 3 4 0 0 •B - 1 13 1 IS 4 0 2 20 9 2 19 1 - 4 - 1 3 t3 • 4 -1 3 14 4 •9 A -1 3 -1 5 13 4 2 10 2 5 0 7 -1 8 -1 • 9 - 0 10 13 19 -2 4 9 24 5 2 •10 2 7 -1 - 1 0 91 2 -5 29 - 1 0 - 0 33 -1 1 - 7 I 20 - 3 34 1 -2 9 49 2 A -1 7 42 39 40 4 -2 1 IA 17 14 7 1 12 12 14 22 14 5 -4 0 2 -1 0 -8 -3 -3 -It -1 7 . 0 •1 4 -3 -3 -3 •A -I A -4 -A -4 9 3 1 -1 •4 4 4 -3 0 -3 2 -2 3 -3 7 -2 1 -1 5 1 -1 3 -0 7 19 0 -1 -0 2 0 9 0 -3 -1 4 A -II 2 -1 0 0 -1 4 10 29 4 -IS -1 4 -1 4 -0 2 -SO -1 0 20 -It -• -1 -4 -3 -5 0 -4 1 II 14 -1 -1 9 -1 1 14 -3 A -2 7 0 A 11 •-0 -4 2 -2 1 -7 •3 -2 -2 -2 -It -0 4 -1 4 4 A -1 5 10 -3 -1 3 14 -2 -7 -1 11 19 -1 0 12 10 4 12 4 -1 2 II -9 4 22 3 0 2 -5 -0 9 14 29 -a t -IA -1 4 -2 4 -1 3 21 If 14 19 3A 13 IS 14 13 21 14 15 13 17 0 2 21 12 0 -3 -0 -1 1 -II -4 3 -4 9 4 -5 10 II 10 4 20 4 0 0 -4 -A 11 * -2 29 -1 2 -1 4 4 2 -II *7 -1 4 -9 -1 2 -1 4 •13 -7 *0 -9 -1 4 -9 -1 4 -4 -1 7 9 11 9 7 0 15 20 -5 -2 -7 -1 7 •5 -4 4 -1 -0 -1 13 2 -2 -1 2 0 9 -7 -1 4 -5 -A •0 0 3 -1 2 -3 -9 7 II -IA -7 -2 0 -IB -1 5 14 -1 2 -2 3 -2 1 ' i 21 23 4 14 -1 1 -• -4 -1 3 -IS -IA -2 1 -2 0 -2 -2 1 -2 0 -1 0 -1 4 -1 9 -2 1 -1 7 -1 0 -4 10 •3 A 19 1 3 9 0 -2 -4 -II •A •5 -1 4 -1 0 -2 0 -2 0 1 IS 0 -7 -9 9 0 -1 4 -3 -7 -1 2 -7 -4 -9 -7 -1 2 9 14 14 19 13 17 37 20 17 19 37 0 7 24 14 14 10 IS -1 2 -If -2 -1 4 -2 0 -1 3 -1 0 -1 5 0 •0 14 0 -1 2 -9 0 19 20 -4 13 0 9 0 1 -1 4 •3 7 39 44 27 10 29 -1 1 IS 14 3 30 -5 -1 0 -9 -3 -4 •1 4 -1 29 -1 4 -2 3 -2 0 -3 1 -1 0 13 -1 3 -2 0 2 -3 4 -» 30 20 35 47 32 - 1 4 -1 3 -I A - 1 3 -1 1 - 2 -1 0 - 1 4 -1 4 1 -5 -1 5 -3 -4 - 1 4 -1 1 - 1 7 - 2 11 23 - 3 14 0 14 - 9 7 14 -1 - 9 10 -2 0 -4 13 14 20 • 1 2 19 20 15 IS 13 17 19 32 43 13 10 22 12 9 23 IB 3« 20 I I * » 27 14 7 2S 23 A 10 30 2 24 14 IS 15 25 3 1 20 -2 31 I I 11 14 10 2 13 20 I f 15 22 14 A 30 I I A •A 9 -IS 14 - 1 7 1 -1 -I 2* 14 - 0 2 -0 1 -1 4 - 7 - 0 19 -2 4 -1 3 7 15 - 1 9 - 7 -S 9 -1 3 -IA 1 3 -IA 14 0 27 5 4 3 IA 1 1 14 44 - 2 to I I 20 -1 4 14 -4 7 10 B IB IA 7 12 IS 2 13 13 7 IS 20 19 13 14 9 -4 -4 A 2 - 1 3 -1 3 - 9 0 -3 -1 4 A 11 IS - 1 0 12 15 20 - 4 13 11 -4 10 0 15 •4 4 0 2 9 -1 0 -7 -1 4 -3 -7 0 A 10 4 10 32 10 22 13 0 22 2 30 13 11 22 1 13 -2 10 7 3 9 -7 14 IA 9 S -to It •1 18 9 - 1 4 - 1 7 -2 1 25 25 24 24 10 14 39 31 23 24 42 10 25 27 10 -2 9 -0 -5 a 17 IS 7 23 17 -1 10 1 0 47 42 34 -1 2 22 2 0 11 9 24 4 7 -1 22 -1 -2 4 -1 4 -1 3 40 7 33 21 42 13 -3 2 34 -5 30 13 II 9 II 7 II IS 14 -1 1 -1 •1 4 29 24 33 25 25 -3 4 , 5 20 34 33 53 40 45 0 -3 3 -2 -4 14 e 10 20 4 0 0 7 7 11 3 4 5 23 10 A A 10 -1 3 •4 •9 -A -A -9 0 -1 0 3 -9 1 0 2 A -1 4 -4 -0 A 9 A 4 13 0 0 -1 10 -3 14 4 22 17 4 •1 -7 -II -1 2 -0 9 -2 4 -21 20 24 12 0 11 11 A 9 10 2 9 -1 0 12 0 -1 2 -5 3 9 -7 7 IS 4 9 A 3 4 0 -2 -1 -1 -1 0 -1 4 0 9 4 4 1 11 9 13 13 17 10 12 || U 17 10 4 U A -II -2 -0 0 -7 -II -2 0 -2 0 7 12 3 3 -5 0 -9 0 7 -1 0 0 4 1 -3 -4 14 14 9 -1 14 0 0 3 2 -9 -5 -to 14 to 23 21 IA •1 7 0 -4 0 -1 4 -2 2 -2 0 5 7 0 13 24 9 IA 3 24 0 s 31 1 17 24 11 -1 2 -5 -1 2 -1 5 -5 1 -1 0 -2 2 A 2 10 11 -8 10 -1 9 0 13 -1 12 -5 10 A 13 -IS 4 -IS 2 -1 1 2 10 4 0 5 0 3 -1 0 -9 22 - 2 1 10 13 7 24 4 7 11 7 5 9 4 4 9 0 0 A -1 1 -IS 17 -2 1 -3 3 23 -S 12 20 12 24 9 10 24 I f 29 2A 12 2 -2 1 -9 - 1 3 12 • 4 24 0 17 12 -1 4 0 2 2A 25 24 50 34 8 3 -9 12 -» A 10 3 -9 -2 -2 1 - 2 IS -3 A -4 4 11 - 7 -2 •2 3 -3 -3 14 13 9 -U 13 2 -1 10 | | -s 3 10 -2 0 1 5 7 -3 •I 9 -9 J 9 5 10 • 3 10 3 ~| 0 | 25 25 - 7 1 IB - S 13 II •7 A -1 4 20 - 3 7 0 24 - 2 4 1 12 -4 13 9 0 -1 10 -5 13 -B 19 -1 4 IB II -1 2 -1 4 •V 7 - 1 4 -1 5 -1 -1 0 10 II 5 22 -1 -1 3 -1 A -1 5 -1 1 17 4 0 -9 -5 0 -1 4 0 -1 4 •4 9 -5 IA A -3 2 || -7 7 -0 A 22 10 21 - 1 3 • 3 20 -4 4 54 IS - 7 A - 0 - 2 9 -3 1 -21 2 - 7 -10 -5 -24 5 - 9 - 3 0 - 1 4 -1 4 14 -2 1 -10 23 -17 -II -10 - 1 8 -1 3 -2 0 3 - 3 0 -29 44 -23 -0 -IJ -20 - 1 3 - 2 5 - 7 - 1 7 -13 -7 -14 0 -10 -21 -22 • 2 4 4 - | | -V 24 -41 S3 12 2 14 9 •11 4 -13 27 12 50 4 II 10 11 - 1 9 - 1 0 -7 9 14 37 25 13 1 1 3 - 3 0 - 9 13 30 24 13 At 22 13 - 4 -2 1 3 27 20 10 9 21 U 34 41 27 15 4 - 1 7 32 32 20 40 33 34 - 3 37 30 2 33 7 29 47 40 SO - 1 0 39 39 -4 70 7 40 29 2 45 17 31 44 47 32 9 10 31 30 40 0 30 27 -19 45 91 34 31 41 29 -20 10 40 29 40 -A -I -3 9 22 30 24 27 -1 29 7 9 9 IA 19 A -4 -17 - 1 9 -30 -14 -17 • 1 9 -21 -20 -21 -3 4 -9 0 -14 - I I -A - 1 4 -3 9 -A A -14 -0 9 2 11 32 -2 A 10 0 21 s 2 -10 4 4 3? -IA -IA 23 23 24 -14 29 3 23 -3 14 -3 IA 27 -1 -1 0 -1 3 22 44 -7 -2 3 24 10 14 -1 3 24 12 20 12 10 A -3 *0 -2 4 too 30 30 100 19 41 32 23 IA 2 -43 -3 1 -0 7 10 9 40 29 41 42 39 33 44 43 42 50 33 57 20 52 30 43 4 7 29 47 A 9 -A - 1 5 -1 3 -2 • I I - 1 0 - 2 3 -A -5 -1 2 -1 - 1 3 10 - 2 - 1 4 -1 3 -1 5 33 31 29 27 20 35 12 24 21 23 12 19 10 20 43 37 32 34 15 32 21 51 29 35 2 - 1 0 •II -3 -0 -7 - 1 4 -1 4 • 8 3 2 27 10 25 -20 •10 - 1 3 -20 -3 1 39 24 25 31 42 27 23 10 40 7 33 33 25 23 24 24 24 9 19 12 10 25 2 23 21 14 2 -7 -0 11 A 7 - 4 2 -3 0 - 4 3 19 32 14 41 23 2 100 70 70 59 40 53 -4 -2 3 -4 54 43 43 30 30 0 3 -2 3 07 77 47 44 44 32 39 40 30 52 40 -2 4 24 -4 -3 4 -1 3 •2 -3 -3 50 51 40 44 45 43 44 30 22 19 27 U 1 2 0 3 1 II -IB -1 0 22 8 22 20 8 -10 10 100 2i 40 100 21 U -9 SI 27 30 13 33 44 22 -14 47 4 4 40 7 23 23 -7 43 0 -34 30 20 30 24 7 99 20 2A -7 3A 29 3 - I t -IS 7 -0 -9 -7 4 3 7-5 I -12 30 -18 24 13 -?A -2 32 11 -31 -4 7 9 20 -0 -A 3 14 -2A 49 20 -39 39 40 13 -49 22 40 17 -A2 44 59 u -SO 30 41 4 -30 32 27 -10 13 75 22 14 45 -14 34 45 -17 I 2 42 - 3 -29* 4 3V 2 - 3 0 -10 20 -5 -0 3 14 -11 15 14 3 14 -1 2 -2 3 13 - 1 3 -20 2 -2 9 3 7 -1 -0 17 23 10 -5 13 17 10 21 42 13 -32 -3 4 9 20 34 -21 12 -9 -1 3 -1 7 -4 24 0 22 21 10 -1 3 7 -3 5 -4 3 -0 -3 1 9 - 4 94 - 2 3 43 - 4 43 100 2 7 27 100 7 31 1 -7 3 -2 3 99 0 17 12 -4 -3 20 •32 7 40 SO 24 30 7 31 100 •4 49 -14 0 2 0 -44 54 33 10 29 3 -23 0 1 -73 -4 100 19 4 -21 -It 25 30 -21 -34 47 42 34 34 33 24 23 24 19 -7 A -49 40 41 07 77 47 -23 59 49 19 100 178 9 11 10 4 0 2 5 3 13 13 22 42 19 21 31 33 34 40 37 33 38 44 57 24 34 20 29 34 39 23 27 44 41 43 23 54 49 31 30 32 10 4 14 17 14 7 1 12 30 20 33 47 32 S3 40 43 SO 24 301 502 5 03 504 90S 504 5 07 300 309 510 511 Con tinued• ™ le B'2' Consistency) (or the A — flit DIAGONAL VAIUC USCB IN THIS ANALT0IO U A 0 . BOUAAtD 0* •HATAII 9 II 311 100 SO 71 60 03 •43 -23 •41 -42 •39 •40 •74 •72 -71 -40 -?J -47 -73 -74 •49 -49 -01 -71 -3 24 24 47 13 •0 100 90 74 70 -39 t -27 -41 -39 -44 -71 -47 -47 •33 -44 •30 -40 -33 -42 -43 -47 -44 -2 33 20 92 -4 -21 19 44 -49 •39 •43 -43 -43 *49 -32 -49 -49 31 42 73 -24 -43 31 74 -33 -32 -42 -41 -31 -44 •29 -44 -30 •24 21 09 -32 -34 -49 -47 -40 10 •70 •47 47 -49 2 4 2V -39 -39 •44 •47 -40 •71 -S3 -41 -43 19 49 mn -42 -74 91 73 -42 -70 -73 -33 -43 -40 -44 -39 -39 3 •II 00 -44 -37 -73 •73 -7* 24 -37 •44 30 -77 0 10 71 90 100 73 79 -31 -3 -24 -43 -41 -47 -72 -71 -72 -SI -43 -40 -49 -SI -4J -?| -40 -43 -13 19 21 30 -14 -31 21 41 -74 -48 -73 -70 -44 *70 -37 -71 -74 30 73 00 -22 -47 37 >8 -93 -53 -44 -40 -43 -44 -30 -44 -50 -30 24 90 -31 -34 -71 -49 -49 14 -70 -70 92 -71 00 74 73 100 74 -43 •12 •30 *40 -39 -40 -70 -71 -71 •41 -43 -40 -46 -40 -SO -43 -70 -70 -20 14 14 33 -4 -19 -4 19 -47 -31 •57 -50 -40 -44 •54 -42 -43 II S3 43 -33 •74 40 44 -32 •43 -74 •39 -52 -44 -35 -34 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-14 -53 72 41 40 42 37 00 41 45 02 -10 -40 -30 30 77 -74 -01 77 07 70 42 74 73 41 47 71 -I 13 -12 42 40 72 100 70 -35 72 74 -21 72 -7 7 -4 1 -4 7 -7 4 -7 7 44 27 41 44 30 70 72 70 72 00 OS 04 72 01 03 02 74 74 15 •20 -22 -4 4 -4 4 1 -1 7 75 42 47 41 40 17 47 71 ■4 -1 4 -4 4 -3 7 27 72 -4 7 -7 7 74 ■1 70 74 71 73 52 37 47 2 10 -0 4 47 37 73 70 100 -10 73 00 -2 4 71 24 II 14 0 23 -4 0 -3 7 -7 1 -5 2 -4 0 -1 7 -2 7 -1 2 -3 -4 1 -4 4 13 •1 7 -5 7 -3 4 -2 0 -2 1 -1 0 ■1 04 07 00 ■7 70 77 74 20 25 20 22 II -I -7 -2 -7 34 -I -7 -3 4 -4 2 34 37 -4 0 •3 1 -2 0 -3 7 -7 0 -7 0 -4 1 -4 4 13 -II | 23 41 75 75 77 04 47 42 74 01 44 44 70 72 74 44 3 3 -2 4 41 S3 12 -2 4 75 73 73 73 04 II 51 44 74 -1 7 -4 7 -0 4 4 47 -3 5 -4 2 42 54 50 29 05 •4 4 32 -4 4 30 -4 4 17 -2 4 40 -4 0 43 41 47 2 -2 0 20 -7 7 -4 0 - I I -S 3 17 -2 1 74 -3 5 72 - I I 73 100 17 17 100 - I 41 -2 4 - 4 0 -3 2 70 -44 -47 -70 -43 -41 II 11 43 43 47 ■4 03 04 01 01 72 01 70 57 74 47 77 75 IS -21 -10 -42 10 17 14 -21 47 SI SI 50 40 04 71 73 74 0 -32 -51 37 70 -47 -71 40 74 70 42 43 47 51 31 43 7 30 -75 42 70 04 74 00 -0 40 100 -II ■I 20 -77 47 -47 52 -71 20 -71 32 -73 -7 54 14 31 27 54 IS 42 0 71 -21 72 -24 71 -24 72 -31 04II II -14 II -17 77 -12 71 -1 . 81 -14 01 -37 15 -17 72 -30 03 -11 1 -17 -7 10 -47 -34 4 13 -57 -45 -47 -44 -72 -27 I -12 -17 42 45 74 42 2 30 24 2 O -14 -54 11 -14 27 -10 -I -44 97 37 22 4 -20 -21 -74 -24 -40 -II -42 -12 -57 -14 -4 -10 -41 70 SI SO 37 M 00 71 70 83 -II -41 -37 41 10 -74 -OS. II 00 04 44 02 77 42 44 74 2 22 -04 41 74 71 72 71 -32 70 II 100 -21 -21 100 00 o 181 Table B.3. Second Order Cluster Analysis of the Clusters Formed from the A Posteriori Cluster Analysis. Inter-Correlations Matrix and Similarity Coefficient Matrix. FACTOR INTERCORRELATIONS AND LOADING MATRIX COMMONALITY IN THE DIAGONAL S04 506 505 508 501 509 507 502 503 601 602 504 506 505 508 501 509 507 502 503 601 602 86 68 70 58 42 58 6 19 41 93 37 68 56 55 45 43 38 4 16 2 75 13 70 58 55 45 48 46 46 42 38 40 26 33 23 -26 32 -8 23 9 69 65 44 -14 42 43 38 40 30 32 7 8 24 55 22 58 6 38 4 26 23 33 -26 7 32 27 -7 -7 43 7 43 40 31 52\ 2 22 | 66 19 41 93 37 16 . 2 75 13 32 23 69 44 -8 9 65 -14 8 24 55 22 7 40 52 22 43 31 2 66 41 30 18 64 30 23 34 47 18 34 100 30 64 -4Z. 30 100 STANDARD SCORE COEFFICIENT ALPHAS 84 * 61» THE DIAGONAL VALUE USED IN THIS ANALYSIS WAS *40 SQUARED R— MATRIX 504 506 505 508 501 509 507 502 503 601 602 t 504 100 93 93 87 98 93 J 31 59 77 91 62 506 93 100 94 93 96 92 18 48 71 93 56 505 93 94 100 81 96 9 31 43 69 83 93 71 508 87 93 81 100 90 89 * -9 20 50 88 31 501 98 96 96 90 100 96 j 28 56 78 95 61 509 93 92 93 89 96 LOOj. 25 52 80 94 58 507 31 18 43 ~ -9' 28 25' loo" 94 69 26 83 502 59 48 69 20 56 52 94 100 85 54 92 503 77 71 83 50 78 8 0 1 69 85 100 74 89 601 91 93 93 88 95 94 26 54 74 100 59 602 62 56 71 31 61 58 83 92 89 59 100 COLUMN SUMS OF SQUARES OF INPUT R-MATRIX 3.164 1.014 2.074 1.058 2.267 1.118 1.709 3.302 1.341 1.789 1.407 APPENDIX C TABLES OF CLUSTER ANALYSIS FROM SPSS RELIABILITY PROGRAM Table C.l. Scales (Clusters) formed from Reliability Analysis with Mean, Standard Devation for each Variable, Scales Means. Variances, Correlations, and Alphas. * * * * * * * * « R E L 1. 2. 3. 4. S. I A B I L I T Y AN A L Y S I S FOR S C A LE V9 Vll V10 V6 V8 MEANS V9 Vll V10 V6 V8 ITEM-TOTAL STATISTICS V9 Vll V10 V6 V8 STD DEV 4.03061 4.54592 4.53061 4.38776 3.79082 CASES .89390 .75985 .68991 .71083 .80519 196.0 196.0 196.0 196.0 196.0 SCALE MEAN IF ITEM DELETED SCALE VARIANCE IF ITEM DELETED CORRECTED ITEMTOTAL CORRELATION SQUARED MULTIPLE CORRELATION ALPHA IF ITEM DELETED 17.25510 16.73980 16.75510 16.89796 17.49490 3.85254 4.17297 4.33972 4.36902 4.27177 .38426 .40254 .41201 .37882 .32446 .16504 .24971 .25076 .16140 .11091 .56952 .55734 .55601 .57016 .59743 A VALUE OF 99.0 IS PRINTED IF A COEFFICIENT CANNOT BE COMPUTED RELIABILITY COEFFICIENTS ALPHA - .42375 5 ITEMS STANDARDIZED ITEM ALPHA = .62873 Continued on next page 183 1. 2. 31 4. G• ( A T C 0 M P 1 Table C.l. Continued. A B I L I T V 1. 2. 3. ANA L Y S I S FOR S C A L E < A T C 0 M P 2 ) * * * « * * » * « V2 V5 V3 MEANS 1. 2. 3. V2 VS V3 STD DEV 2.40816 3.28061 2.86735 ITEM-TOTAL STATISTICS .96436 .99116 1.18657 196.0 196.0 196.0 SCALE MEAN IF ITEH DELETED SCALE VARIANCE IF ITEH DELETED CORRECTED ITEMTOTAL CORRELATION SQUARED HULTIPLE CORRELATION ALPHA IF ITEM DELETED 6.14796 S.27551 5.6887B 2.83441 2.87755 2.51290 .35112 .31064 .26148 .13098 .11262 *07020 .31334 .37505 .47795 A VALUE OF 99.'0 IS PRINTED IF A COEFFICIENT CANNOT BE COHPUTED RELIABILITY COEFFICIENTS ALFHA - .48451 ( t t U M U R E L I 1. 2. 3 ITEMS STANDARDIZED ITEH ALPHA - A B I L I T Y ANA L Y S I S FOR S C A L E < A T C 0 M P 3 ) * * * * * * * * * V13 VI5 MEANS I. 2. .49496 V13 V15 2.65816 2.23469 STD DEV .91174 1.01072 CASES 196.0 196.0 184 V2 VS V3 CASES Table C.l. Continued. ITEM-TOTAL STATISTICS V13 VIS SCALE MEAN IF ITEH DELETED SCALE VARIANCE IF ITEH DELETED CORRECTED ITEHTOTAL CORRELATION SQUARED HULTIPLE CORRELATION ALPHA IF ITEH DELETED 2.23469 2.65816 1.02156 .83127 .34350 .34350 .11799 .11799 99.00000 99.00000 A VALUE OF 99.0 IS PRINTED IF A COEFFICIENT CANNOT BE COHPUTED RELIABILITY COEFFICIENTS ALPHA * ELI 1. 2. 3. 4. 5. 6. 7. 2 ITEHS .50933 STANDARDIZED ITEH ALPHA A B I L I T Y A N A L Y S I S V22 V42 V19 V21 V31 V55 V34 V22 V42 V19 V21 V31 V55 V34 FOR • HEANS 1. 2. 3. 4. 5. 6* 7. .51135 2.18367 2.76020 2.28571 2.17347 2.58673 3.04592 2.17857 STD DEV .64604 .79656 .70165 .61645 .83367 .61011 .65925 CASES 196.0 196.0 196.0 196.0 196.0 196.0 196.0 Continued on next page Table C.l. Continued. SCALE MEAN IF ITEM DELETED SCALE VARIANCE IF ITEM DELETED CORRECTED ITEMTOTAL CORRELATION SQUARED MULTIPLE CORRELATION ALPHA IF ITEH DELETED 15.03061 14.45408 14.92857 15.04082 14.62755 14.16837 15.03571 7.45547 6.99788 7.24615 8.01884 7.25031 8.14074 8.08590 .58985 .55084 .58647 .44540 .44738 .41386 .38230 .39279 .35582 .39427 .26991 .27569 .23864 .18670 .71573 .72130 .71416 .74417 .74798 .74985 .75605 8 ITEM-TOTAL STATISTICS V22 V42 V19 V21 V31 V55 V34 A VALUE OF 99.0 IS PRINTED IF A COEFFICIENT CANNOT BE COHPUTED RELIABILITY COEFFICIENTS ALPHA = .76511 7 STANDARDIZED ITEH ALPHA ■ R E L I A B I L I T Y 1. 2 . 3. 4. ITEMS A N A L Y S I S 1. 3. 4. FOR S C A L E ( A T T E L P 2 ) * * « * < * $ * * V40 V37 V33 V38 MEANS 2. .76716 V40 V37 V33 V38 2.26531 2.76531 2.33673 2.31633 STD DEV .68014 .69135 .62337 .61764 CASES 196.0 196.0 196.0 196.0 Continued on next page Table C.l. Continued. :s ITEM-TOTAL STATISTICS V40 V37 V33 V38 SCALE MEAN IF ITEH DELETED SCALE VARIANCE IF ITEM DELETED CORRECTED ITEMTOTAL CORRELATION SQUARED MULTIPLE CORRELATION ALPHA IF ITEM DELETED 7.41837 6.91837 7.34694 7.36735 1.96766 2.04458 2.19696 2.23359 .49046 .42665 .42238 *40680 .24171 .18266 .18358 .16877 .54859 .59567 .59737 .60739 A VALUE OF 99.0 IS PRINTED IF A COEFFICIENT CANNOT BE COMPUTED RELIABILITY COEFFICIENTS ALPHA - .65974 4 STANDARDIZED ITEH ALPHA M M I t l t t R E L I A l I L I T Y 1. ITEMS A N A L Y S I S .65561 FOR S C A L E I A T T E L P 3 V44 V57 2. MEANS 1. 2.50163 2.61735 V44 V57 2. ITEM-TOTAL STATISTICS V44 V57 STD DEV CASES 196.0 196.0 .71502 .69541 SCALE MEAN IF ITEM DELETED SCALE VARIANCE IF ITEH DELETED CORRECTED ITEMTOTAL CORRELATION SQUARED MULTIPLE CORRELATION ALPHA IF ITEH DELETED 2.61735 2.58163 .48359 .51125 .41896 .41896 .17553 .17553 99.00000 99.00000 A VALUE OF 99.0 IS PRINTED IF A COEFFICIENT CANNOT BE COMPUTED Table C.l. Continued. RELIABILITY COEFFICIENTS ALPHA - ,59036 2 STANDARDIZED ITEH ALPHA - * * « * * * * * * R E L I A B I L I T Y 1. 2. 3. 4. 5. 6. 7. 8. ITEHS A N A L Y S I S FOR S C A L E ( V24 V56 V2B 029 036 039 V23 027 MEANS 1. 2. 3. 4. 5* 6* 7. 8. 024 056 028 029 036 039 023 027 STATISTICS - *59052 024 056 028 029 036 039 023 027 STD DEV 2.12245 2.80102 2.88265 2.87245 2.36735 2.62755 2.38776 2.87245 CASES .76815 .78851 *77889 1.01226 .76309. .68617 .73216 .84067 196.0 196.0 196.0 196.0 196.0 196.0 196.0 196,0 SCALE HEAN IF ITEH DELETED SCALE VARIANCE IF ITEH DELETED CORRECTED ITEHTOTAL CORRELATION SQUARED MULTIPLE CORRELATION ALPHA IF ITEH DELETED 18.81122 18.13265 18.05102 18.06122 18.56633 18.30612 18.54592 18.06122 9.89751 10.12590 10.31533 9.41162 10.26737 10.74683 10.72096 10.45777 .47859 .40912 .37550 .38069 .39896 ,35188 .32197 .30093 .29655 .20745 .19856 .18010 .28156 .25419 .17502 .11499 .63045 .64620 .65405 .65665 •64B89 .65989 .66576 .67220 A VALUE OF 99.0 IS PRINTED IF A COEFFICIENT CANNOT BE COMPUTED Table C.l. Continued. RELIABILITY COEFFICIENTS ALPHA - .48404 8 ITEMS STANDARDIZED ITEH ALPHA * * * * * * * * * R E L I A B X L I T Y A N A L Y S I S FOR .48815 S C A L E ( A T T E L P 5 V44 041 043 025 054 049 I. 2* 3. 4. 5. 6• STD DEO HEANS 1. 2. 3. 4. 5. i 4. 044 041 043 025 054 049 ■TOTAL STATISTICS 044 041 043 025 054 049 CASES 194.0 194.0 194.0 194.0 194.0 194.0 .47757 .70304 .83432 .72420 .82851 .54544 1.58473 2.05412 2.31433 1.70408 2.42347 2.09184 SCALE MEAN IF ITEH DELETED SCALE OARIANCE IF ITEH DELETED CORRECTED ITEMTOTAL CORRELATION SQUARED MULTIPLE CORRELATION ALPHA IF ITEH DELETED 10.59184 10.12245 9.84224 10.47449 9.75510 10.08473 5.43255 5.45140 5.14554 5.58394 5.71408 4.50013 .54154 .52374 .47340 .45280 .31934 .29324 .34395 .28939 .24835 .22294 .11210 .13217 .42248 .43274 .44820 .45478 .70313 .49874 A OALUE OF 99.0 IS PRINTED IF A COEFFICIENT CANNOT BE COMPUTED RELIABILITY COEFFICIENTS ALPHA - .70119 4 ITEHS STANDARDIZED ITEH ALPHA - .70490 Continued on next page Table C.l. Continued. R E L I A B I L I T Y 1. 2. 3. A N A L Y S I S FOR S C A L E >** V51 V50 V52 MEANS 1. 2. 3. V51 V50 V52 ITEM-TOTAL STATISTICS V51 V50 V52 STD DEV 1.95916 1.90306 2.01531 RELIABILITY COEFFICIENTS .71703 CASES 196.0 196.0 196.0 .67053 .57805 .58596 SCALE MEAN IF ITEH DELETED SCALE VARIANCE IF ITEM DELETED CORRECTED ITEMTOTAL CORRELATION SQUARED HULTIPLE CORRELATION ALPHA IF ITEM DELETED 3.91037 3.97449 3.86224 .95740 1.17370 1.15529 .57330 .52011 .52448 .32868 .27250 .27728 .58472 .64879 .64319 A VALUE OF 99.0 IS PRINTED IF A COEFFICIENT CANNOT BE COMPUTED ALPHA - ( A T T E L P i 3 ITEMS STANDARDIZED ITEM ALPHA ■ .71793 APPENDIX D CLUSTERS FORMED FROM STRUCTR AND BC TRY (FIGURES) 192 FROXIHITJCS ARE CORRELATIONS BETWEEN VARIABLES COMPLETE-LINK CLUSTERING IDISCCJURAGES CLUSTERING) 00 00 11 11 01 7 a :t ts 00 00 4 3 .353 .358 .348 .353 .344 .348 .339 .344 .334 .33? .329 .334 .325 .32? .320 .325 .315 .320 .310 .315 .306 .310 .301 .304 .296 .301 .291 •2?A .287 .291 .282 .287 .277 .282 .272 .277 .267 .272 .267 .263 .258 .263 .253 .258 .253 ' .248 .244 .248 .239 .244 .234 .23? .229 .234 .225 .22? .220 .225 .215 .220 .210 .215 .206 .210 .201 .206 .196 .201 .191 .196 .186 .191 .182 .186 .177 .182 .172 .177 .167 .172 .163 .167 .158 .163 .153 .158 .148 .153 .144 .148 .13? .144 .134 .139 .129 .134 .12? .125 .120 .125 .115 .120 .110 .115 .105 .110 .101 .105 .096 .101 .091 .096 .086 .091 .082 .086 .077 .082 .072 .077 .067 .072 .063 .067 .058 .063 .053 .058 .048 .053 .044' .048 .039 .044 .034 .039 .029 .034 .025 .02? .025 .020 .015 .020 .010 .015 .005 .010 .001 .005 Figure D.l. t t t : j t t t ( t : t t t t t Ii t t i :i ti : t : i t t i t t i i t :i t i ii :i :t T T T r T ? T r ? T T ? T r r t ? T T T T T T T Clusters Formed from the Computer Items (STRUCTR Used) Complete-Link, Single-Link, and UPGMA. 193 SINGLE-LINK CLUSTERING (ENCOURAGES CLUSTERING! 00 000 0 1000 3 25 12 J 4 • 4 44 4 ♦ 4 44 .355 : S4 .358 4 4 .353 : 66 4 .355 .350 : •t 1 .353 .348 I *: t .350 44 4 .345 : 8• .343 4 4 4 .343 : S4 .345 4 44 .340 :i6 .343 4i 1 .338 t 6 .340 * 4 • 4 4 .335 : 1 .338 • • 4 .333 :1 4 .335 1 4 4 .330 : 4t .333 • 4 6 4 .328 t11• .330 •• 44 4 .325 : 6 .323 4 4: i .325 .323 t * * 4 « .320 I 44 .323 44 4 44 .318 1 • .320 4 4 .315 (1i•« .318 .313 t18t S .315 4• 4 .310 :114 .313 64 4 4I .308 : 14 .310 4 .305 (t4: : .308 4 4 4 4 .303 t 14 .305 4 44 4 .300 :14 .303 4 4 4 4 .298 S 1 .300 •: : .298 .295 : 4 .295 .293 ::::t i i :I .290 : .293 44 4 .290 .287 : t *4 4 4 4 4 1i .285 :i4 .287 4 4 4 4 44 .282 : 4 .285 4 .280 :i44 4 .282 4 : t .277 S: 4 .280 4 444 .275 : 4 .277 4 t 1 .272 ::4 44 .275 4 44 4 .270 ti4 .272 .267 : 4: t .270 t t .265 :i: .267 4:: .262 : i4 .265 4 4 « ^ 4 .260 1 4i .262 44 4 4 .257 :t1 .260 4 .255 :•4 .257 4 4 4•4 .252 : •• .255 4:: .250 : • 4 .252 .247 :* iiI .250 .245 1 1tt .247 4ti .242 : • •« .245 .240 1 tt i: .242 .237 t t1 i i .240 44 4 .235 1: 44 .237 .232 :t 111 .235 .230 : 1tt .232 41t .227 : •« .230 .225 1 ti:: .227 : : .222 : 4i: .225 .219 : i4i— .222 4: t .2 1 7 :t 4 .219 .2 1 4 : i 1: 7 .217 .212 tt 11 7 .214 ,209 t 1S 7 .212 .207 t * • 1 -- — .209 .204 : t1 7 .207 .204 .202 : • • .1 9 9 : * .202 .197 :t .199 .194 : .197 .192 : .194 .189 t .192 .187 .189 T .184 .187 .182 .184 .179 .182 .177 .179 .174 .177 .172 .174 .169 .172 Figure D.l. Continued. 11 01 i: il1 1 t 4S6 7 8 t • I I I II T 7 7 T 194 SPECIAL METHOD.CURRENTLY UPCHA OOOOOOOOOOOOOOOOOO 11 78 .350 .354 .350 .346 .342 .338 .334 .330 .326 .322 .317 .313 .30? .305 .301 .297 .293 .289 .285 .281 .277 .273 .269 .265 .261 .257 .253 .249 .245 .241 .236 .232 .228 .224 .220 .216 .212 .208 .204 .200 .196 .192 .188 .184 .180 .176 .172 .168 .164 .160 .155 .151 .147 .143 .139 .135 .131 .127 .123 .119 .115 .111 .107 .103 .09? ’ .095 .091 .087 .083 .079 .075 .070 .066 .062 .058 Figure D.l. .354 .350 .346 .342 .338 .334 .330 .326 .322 .317 .313 .309 .305 .301 .297 .293 .289 .285 .281 .277 .273 .269 .265 .261 .257 .253 .249 .245 .241 .236 .232 .228 .224 .220 .216 .212 .208 .204 .200 .196 .192 .188 .184 .180 .176 .172 .168 .164 .160 .155 .151 .147 .143 .13? .135 .131 ".127 .123 .119 .115 .111 .107 .103 .09? .095 .091 .087 .083 .079 .075 .070 .066 .062 .058 .054 Continued. II 195 rKOXIHITJES ARE CORRELATIONS BETUCCN VARIABLES CONrLETE-LINK CLUSIERIHO (DISCOURAGES CLUSTERING! 00O000 0000 0 0 2 112 2 1 0 1 2 3 2 7 5 9 0 2 2 4 3 9 2 1 1 479 473 466 460 4S4 447 441 43S 428 422 415 fo o f 409 403 396 390 383 377 371 364 358 351 345 339 332 326 320 313 307 300 294 208 281 275 268 262 256 249 243 236 230 224 217 211 Ln 00 O rt 3* S? 205 98 92 85 79 73 66 60 53 47 41 34 28 22 iV) 15 09 02 096 090 083 077 070 064 058 051 045 038 032 026 019 013 007 .473 .466 .460 .454 .447 .441 .435 .428 .422 .415 .409 .403 .396 .390 .383 .377 .371 .364 .358 .351 .345 .339 .332 .326 .320 .313 .307 .300 .294 .288 .281 .275 .268 .262 .256 0000 00 12 1366 1 oo 2 3 4 7 : ooo 00 12 3 3 4 5 6 0 a t • t 1 : : i :: :— .249 .243 .236 .230 .224 .217 t t .211 .205 .198 .192 .185 .179 .173 .166 .160 .153 .147 .141 .134 .128 .102 .115 .109 .102 .096 .090 .083 .077 .070 .064 .058 .051 .045 .038 .032 .026 .019 .013 .007 .000 Figure D.2. T T T T T T T T T T T T T T T T T T T T T ? T T T T T T T T T Clusters Formed from the Telplan Items (STRUCTR Used) Complete-Link, Single-Link, and UPGMA. 196 SINGLE-LINK CLUSTERING (ENCOURAGES CLUSTERING) OOO o o o o o o 2 0 3 3 03 0 0 0 7 2 S 0 819 S 6 J : : : :! : : t .479 .473 .471 .468 .464 .460 .436 .432 .448 .444 .440 .437 .433 .429 .425 .421 .417 .413 .409 .405 .402 .398 .394 .390 .386 .302 .378 .374 .370 .367 .363 .339 .353 .331 .347 .343 .339 .336 .332 .328 .324 .320 .316 .312 .308 .304 .301 .297 .293 .289 .283 .281 .277 .273 .269 .266 .262 .258 .234 .230 .246 .242 .238 .235 .231 .227 .223 .219 .213 .211 .207 .203 .200 .196 .192 .475 .471 .468 .464 .460 .456 .432 .448 .444 .440 .437 .433 .429 .423 .421 .417 .413 .409 .405 .402 .398 .394 .390 .386 .382 .378 .374 .370 .367 .363 .359 .335 .331 .347 .343 .339 .336 .332 .328 .324 .320 .316 .312 .308 .304 .301 .297 .293 .289 .283 .201 .277 .273 .269 .266 .262 .238 .234 .230 .246 .242 .238 .233 .231 .227 .223 .219 .213 .211 .207 .203 .200 .196 .192 .188 Figure D.2. o o o o o o o o o o o o o t3 12111023232112 0 8 8 1 4 2 7 7 3 3 8 6 0 5 9 2 • 6 9 : t s •• • 9 • t t : : : : •« 6 * : t • ** 9 i: • 9 i » :: t : • 99 9 :: •8 9 8 :i • •« 9 t: : s :: • 9 •9 t : 8 99 •• •• 8 :: : :: :i t t > it : t : :: : 8 t 1 s : : : 98 8 • ::i 0 0 0 0 6 * •9 ♦ * *« 8 8 8 8 8 * 98 9 99 9 9 9 8 9 9 9 8 9 9 9 99 99 9 9 9 99 9 99 9 9 9 9 9 9 9 99 9 9 99 9 9 99 9 9 9 9 9 9 8 99 : : it :t :t :t 7 7 7 7 7 7 7 7 Continued. o o o o o o o o o o o o o o 33301200122334 23430614394790 i s : s s s : i -- S I T S I T S I T 8 ? : T T 7 7 T T I ! S I 1 I 7 7 7 7 8 t * 1 7 7 7 8 8 8 8 8 7 7 f 7 7 7 7 j 8 8 8 8 7 7 7 7 7 7 T 7 7 7 T 7 7 7 7 T ■T T T t 7 T 7 7 7 7 7 197 SPECIAL METHOD.CURRENTLY UPGMA OOO 724 1 2 0 1 .479 .474 .468 .463 .498 .452 .447 .442 .436 .431 .425 .420 .415 .409 .404 .398 .393 .388 .382 .377 .371 .366 .361 .355 .330 .345 .339 .334 .328 .323 .318 .312 .307 .301 .296 .291 .285 .280 .274 .269 .264 .258 .253 .248 .242 .237 .231 .226 .221 .215 .210 .204 .199 .194 .188 .183 .177 .172 .167 .161 .156 .151 .145 .140 .134 .129 .124 .118 .113 .107 .102 .097 ,091 .006 .081 0 00 0 00 00 01 43 i: 2 23 1 2 4 3 86 8 .474 .468 .463 .458 .452 .447 .442 .436 .431 .425 .420 .415 .409 .404 .398 .393 .388 .302 .377 .371 .366 .361 .355 .350 .345 .339 .334 .328 .323 .318 .312 .307 .301 .296 .291 .285 .280 .274 .269 .264 •25B .253 .24B .242 .237 .231 .226 5 :: : t : — i : : : .221 .215 .210 .204 .199 .194 .188 .183 .177 .172 .167 .161 .156 .151 .145 .140 .134 .129 .124 .118 .113 .107 T T T T T T T T ? T T II T T .102 .097 .091 .086 .081 .075 Figure D.2. Continued. T T T T 7 7 7 7 7 T 7 Clusters frora iJC TRY. 0 0 0 0 0 0 0 00000 0 0 0 0 000 0 0 0000 0 e 000 0 00000 00000 ooooooo o oooooo o 0 o o o o 0 0 ooooooo 0 0 o 0 o ooooooo 0 ooooooo 0 0 0 00 0 0 00 0 0 0 0 0 o 00000000 000000000 0 0 0 0 0 0 0 0 0 0 0 0 o 0n 0 0 0 0 6 0 oooooo 0000 0 0 0 0 0 000000000 0 0 0 0 0 00300 0 0 0 000000000 oooooo 000 0 00 0 0 0 0 0 ( 0 0 0 000 0 0 0 0 0 0 0 0. 0_ 0 )0 0 0 0 00 ooooooo 0 0 0 0 00003 0 0 0 0 0 0 0 0000 0 0 0 00000 0 0 0 0 0 0 0 0 0 ooooooo 0 0 0 0 0 0 0 0 0 0 000 00000000 PROBLEM-EMPRICAL CLUSTER ANALISIS OF COMPUTERS AN) TELPLAN DATA 198 . — ..Pit p13SR ftt1 SELECTS SUCCESSIVE SUBSPACES WHICH ACCOUNT FOR THE MAXIMUM TOTAL DIMENSIONAL SPACE THE VARIABLES. A SUBSPACE IS A PART OF THE TOTAL 9 IN THIS PROBLEM* THE PROGRAM ONLY SELECTS 3 DIMENSIONAL SUBSPACES. SPHERES ARC, PRINTED FOR THE 3 DIMENSIONAL S'JBSPACES. EACH OF THESE PICTURES INCLUDES PLOTTING OF ONLY THOSE VARIABLES WHICH HAVE AT LEAST BO PERCENT OF THEIR COMMUNALITY IN THE SU3SPACES. IN THE SPHERES THE CO-ORDINATE LINES FORMED 7 IT i b ? SlftVS ARE^ARBITRARY AND SHCULO b e IGNORED. THE POINTS* DENOTED X, Y, AND 5? ?!$P*,ftW&"s’i5kE§ES}fE&L8?,iS«8a!!‘ THE” tUME"IED CONTROL OPTIONS USED IN THIS RUN. THE MODE OF SELECTION ( (1) BY SPAN* (2>BY ANALYST* <5)90TH> THE SOURCE Oc FACTOR COEFFICIENTS ( C D U F A C T 1 * C2)RFACT1) NSTART - THE LOWEST DESIRED DIMENSIONALITY OF A SUBSPACE NFIN - THE HIGHEST DESIRED DIMENSIONALITY OF A SUBSPACE MINEW - THE MINIMAL NUMBER OF NEW VARIABLES INCLUDED IN A SUBSPACECLOPR - LOWEST PERCENTAGE OF COMMUNALITY OF A VARIABLE IN A SUPSPACE M1NC0M - LOWEST BOUND OF COMMUNALITY OF A VARIABLC IN A SUBSPACE AS MARKED IN SPOTTER RIGIO ROTATION > (0) ON ALL PLOTTED POINTS* Cl) ON DIMENSION DEFINERS ONLYS - 1 1 3 3 3 -BO .10 -0 SET 1 8Y 1 - AXIS DIM* 1Z 6 1 2 2 3 4 3 4 5 5 6 7 6 7 a a 9 9 10 10 11 11 12 12 13 14 13 14 15 16 17 IB 19 25. 15 IS IB 17 V43 V46 19 \ 20 20 21 21 22 22 \ 23 24 25 26 27 28 29 30 23 24 25 26 27 2A 31 32 33 34 30 V49/* VIO 36 37 36 39 40 y Vll 41 42 43 44 45 V6 46 47 46 ZR 49 50 51 52 53 V8 V9‘ 54 55 56 57 58 59 60 61 • 29 30 31 32 33 34 35 76 37 38 39 48 41 42 43 44 45 46 47 4B 49 50 51 52 53 54 55 56 57 58 59 60 61 Figure D.3. Clusters Formed from BC TRY. (O «0 Figure D.3. Continued. Figure D.3. Continued. APPENDIX E LETTERS TO EXTENSION AGENTS 203 COOPERATIVE EXTENSION SERVICE MICHIGAN STATE UNIVERSITY and U.S. DEPARTMENT OF AGRICULTURE COOPERATING OFFICE OF THE DIRECTOR EAST LANSING • MICHIGAN ■ 48824 July 13, 1978 Dear Colleague: "If you want to get something done, ask a busy person." As Extension workers we get more than our share of surveys. However, we do try to screen them the best we can and this one seems especially deserving. You will soon be receiving a background questionnaire and attitude scale from Mr. Mehdi Ghods who is studying our Telplan Computer System. He is anxious for you to respond because the information can be of con­ siderable importance. Equally significant is the fact that the results can be extremely useful to us as we think about future Extension computer programs. I know how committed your time is, but Mr. Ghods indicates the time required to complete the questionnaire is from five minutes to a maximum of twenty minutes. I urge you to complete the form as soon as you can work it into your busy schedules. Thank you for your cooperation.. Very truly yours, Fred J. Peabody ^ Associate Director, Administration FJP:dc 204 M I C H I G A N S T A T E U N I V E R S I T Y b a st l a n s in g • Mi c h i g a n l s b i 4 C O N TIN U IN G EDUCATION SERVICE • OFFICE O F THE DIRECTOR • KELLOGG CENTER July 24, 1978 Recently, you received a letter from Mr. Fred J. Peabody regarding a doctoral study now underway by Mr. Mehdl Ghods. As the doctoral com­ mittee chairman for Mr. Ghods, I wish to formally introduce him to you and to urge your cooperation with him in this study. Mehdi is a native of Iran with Bachelors and Masters Degrees in Physics from Tehran Univer­ sity and a Master of Science in Computer Science and a Masters in Contin­ uing Education from Michigan State University. He has now completed all of his course work for his Ph.D in Continuing Education and Administration and Higher Education with an excellent academic record and upon satis­ factory completion of his research will return to his home country. In searching for a research topic which would tend to have high practical value in his country, the Telplan Computer System now in use within the Cooperative Extension Service became of greatest interest to him. In addition, we realize that the study will be of significant value to the Cooperative Extension Service at Michigan State University. His method of gathering information has been pre-tested to the extent that it gathers the basic necessary information with a minimum of time effort on your part. I appreciate your willingness to assist in this study and want to assure you that Mehdi will treat all responses in strict confidence and will also provide results of the study to the Cooperative Extension Service. Thank you for your cooperation. Sincerely Floyd G. Parker, Associate Director Continuing Education Service and Professor, Education and Continuing Education FGP/cg APPENDIX F SYSTEM DESIGN AND FLOWCHART OF A STATEWIDE ADULT BASIC EDUCATION (ABE) COMPUTERIZED SYSTEM 206 Table F.l. I. CONCEPTION 1. II. Goal Setting - establish goals of federal, state, and local agencies for project. RESEARCH 1. 2. 3. 4. 5. III. Gather reporting forss and reporting requirements of federal agency, state agency, local agency, sad individual learning sites. Research similar systems in industrial and educational setting. Determine information sought for reporting purposes. Determine time requirements for reporting. Research most cost effective approach to data onalyls and report writing. DECISION 1. IV. Decide on most cost effective computer system for data analysis and retrieval requirements. DESIGN 1. 2. 3. V. System Design of a Statewide ABE Computerized Data Collection, Analysis, and Retrieval System (Paeschke, 1976). Design computer configuration for data analysis and-retrieval. Design personnel and staffing requirements necessary for implementation of the application. Design system flow including data collection procedures, report generation, and report dissemination procedures. DECISION 1. 2. VI. Decide on adequacy of design. Decide on suitable computer facility with appropriate hardware and software for computer application. (Host likely this decision will be based on competitive bidding.) DEVELOPMENT 1. 2. 3. 4. Develop Develop Develop Develop data gathering instruments. collection procedures for Instruments. dissemination procedures. computer documentation. VII. TESTING 1. 2. 3. 4. 5. VIII. IMPLEMENTATION 1. 2. IX. Computer program debugging. Field Test instruments at selected sites. Field Test data collection procedures at selected sites. Field Test reports and dissemination procedures at selected sites. Obtain feedback from local, state, and federal agencies. Implement data collection, analysis, and retrieval system for all sites. Implement staff development needed to maintain the system. EVALUATION 1. 2. 3. Evaluate system design. Evaluate system Implementation. Evaluate report collection and generation. 207 CONCEPTION RESEARCH DECISION DESIGN sachines personnel systea flow DECISION DEVELOPMENT prograa drafts fores/Instruments docusentat ion TESTING IMPLEMENTATION EVALUATION Figure F.l. Flowchart Showing a Computerized Data Collection, Analysis, and Retrieval System (Paeschke* 1976). APPENDIX G MICHIGAN COUNTIES AND STATES USING THE TELPLAN SYSTEM AND M.S.U. INDEX OF TELPLAN PROGRAMS 209 K*oat<*r r MAtMMAC I OiCQOA m MAMIS. rM r M /SUUAteAC3COM CLAM weiAMO Figure G.l. Michigan Counties with Computer T e r m i n a l s (1978 D a t a ) . Figure G.2. States Using the Telplan System (1978 Data). 211 0:301 (Rsv. 9-1-77) Table G.l. MSU Index o£ Telplan Programs*. PROQRAM FORM HO. HO. PROGRAH TITLE PROGRAM is DSHD TO! USER MANUAL PAGES AND LAST DATE OP REVISION OUTPUT OPTIONS** 01 0 Compound Interest Model compute the future value of a sum of money using tbe com­ pound Interest formula or to dlseouat future money streams. 03 1 Investment Planning For Hew Dairy Systems Dairy Systran Analysis determine tbe total investment capital, feed storage capaci­ ties, acreage and labor re­ quired on a new or expanded dairy faro. 02:1 TO 03:13 Nov. 10, 1975 PH.BCR 03' 3 Capital Investmsnt Model evaluate the Investment of capi­ tal to reduce or eliminate coats Including custom hire and leasing, or to generata new Income. 03:1 TO 03:13 Jan. IS, 1973 PB.BCS 04 0 Air-Blast Spraysr Calibration compute discharge rate from one side of an air-blast sprayer. (Input form self-exT'l.) PH,ECS 09 7 laeoae Tax Management Analysis compute an estimate of the cur­ rent year's Income tax, next year's tax and tbe appropriate' tax strategy to be used in making yoar-end tax management decisions, 09:1 TO 05:10 Nov. 30, 1976 PH.HCR.HCS PH.HCR.HCS 06 0 Apple Scab Spraying* determine degree of Infection expected and spray chemical to use. (Input form self-expl.) PB.HCS 07 0 Spray Compatibility* determine spray cbemlcal com­ patibility and tolerances If used together. (Input form self-expl.) PB.BCS 09 0 Teed Sprayer Calibration compute nozzle spacing and gallons per acre applied with specified settings. (Input form self-expl.) PH.HCS 00 0 Plant Disease Identification identify several plant diseases derived from entering symptoms. 10 0 Soybean Herbicide. Recommendation * select a soybean herbicide pro­ gram based on weeds present, soil type, crop history, etc. (Input form self-expl.) PH.HCS 11 0 General Linear Programing solve various least-cost or pro­ fit maximization problems after setting up budgets. (Input form self-expl.) PB.BCS 13 1 Seine Ration Formulation formulate the least-cost com­ bination of feed Ingredients that meet the nutrient re­ quirements for growing and finishing rations. 12:1 TO 12:08 Jan. 26, 1B73 PB.BCR.BCS PB.BCS ’Prepared by Stephen B. Harsh, Department of Agricultural Economics, Michigan State University. PH “ Voice output, touch-tone Input. BCR ~ Hard-copy terminal Input and output with description of output. HC3 - Hard-copy terminal input and output with shorten output. *Vsed primarily for demonstration purposes. 212 0::202 (Rev. 5-1-77) ROORAM HO. FORM HO. PROGRAM TITLE PROGRAM IS USED TO: USER MANUAL PAGES AND LAST DATE OF REVISION OUTPUT OPTIONS** 14 1 Fertilizer Recommenda­ tions compute amounts of N, P. K. lime and magnesium required from given soli tent results. 14:1 TO 14:08 Jan. 2, 1973 PH.HCS 15 1 Poultry And Game Bird Ration Formulation evaluate tbe nutrient content of an existing ration or to foraulate a balanced, leastcost ration for specific birds given feeds available, their prices, and special restric­ tions. (Rough Draft Exists) June 15, 1977 PH.HCS 16 0 Corn Herbicide Recom­ mendations* /select a corn herbicide program based on weeds present, soil type, crop history, etc. (Input form self-expl.) PH.HCS 17 0 Beef-Prlee Forecasting Model forecast future expected prices of beef cattle. (Input form self-expl.) PH.HCR.HCS 16 2 Corn-Dean Enterprise Planning Guide determine the best corn and soy­ bean production systems and enterprise mix. (See Program IS, Form 1 Manual) PH.HCR.HCS 10 0 Labor Estimator estimate total farm labor re­ quirements given size and kinds of crop and livestock enterprises. (Input form self-expl.) PB.BCS 20 0 Livestock Feeding Planning Guide compare profits from alternative feeding programs 20:1 TO 20:07 Feb. 15, 1971 PH.HCS 21 1 Livestock Farm Planning Guide determine the most profitable fed beef, corn grain and corn allage enterprise mix given expected prices, yields, pro­ duction costs, machinery per­ formance, field time and til­ lable land available. (See For 22, And 1) Manuals Programs Form 0 28, Form PH.HCS 22 0 Corn Enterprise Planning Guide determine the best corn produc­ tion system Including machin­ ery complement and hybrid selection, 22:1 TO 22:24 Dec. 20, 1971 PH.HCS 23 0 Dairy Coe Cost/ Return Model evaluate the economics of selec­ ted dairy cows, given the associated milk production factors and costs. PB.HCS 24 1 Seine Finishing Planning Guide compute profits under alterna- ' tlve feeding programs. PH.HCS 25 0 Bent Depreciation Method select tbe best depreciation SMthod considering one’s tax bracket and other uses for capital. PH.HCS 26 1 Best Ration And Feeder Type Soleotlon Model determine the most profitable type of ration to feed and type of leader to buy, given feed supplies, purehnse and sale options and feedlot ca­ pacity. See Pag* 0:201. +Bee Pi|t 0:201. 26:1 TO 28:21 Sept. 1, 1972 PH.HCS 213 0:303 (Rev. 3-1-77) PROGRAM HO. f o rm PROGRAM 13 USED TO; USER MANUAL PAGES AND LAST DATE or REVISION OUTPUT OPTIONS** KO. PROGRAM TITLE 87 0 Corporation P r o m s 4 compare annual tares paid by farm business for various or­ ganisational structures. 37:1 TO 27:06 Jan. 1, 1971 PH.HCS as 1 Survivor's lncomo Protection project additional survivor's Income needs for tbe family In case a wage earner pre­ maturely passes away. 23:1 TO 23:06 Jan.. 1977 PB.BCR.HCS . 39 3 Infergeneration.Transfer Coat Eatlaator identify specific costs of transferring an estate from one generation to the next and to Illustrate bow much these costs can be re­ duced by estate planning. PH.HCR.HCS 30 0 Boot Cow Planning Guido eompute profits under alterna­ tive feeding systems, calving rata and calf weights. PB.BCS 31 3 Least-Cost Dairy Ration formulate and evaluate the least-cost combination of available feed ingredients that meet tbe nutrient re­ quirements of milking cows, dry-cows, and dairy heifers. (See Manual for Program 31, Form 1 fc Sup­ plemental Feed Sheet For Form 3) PH.HCR.HCS 33 0 Amortized Loan Calculator calculate the total Interest paid and annual Interest rate on an amortized loan. 32:1 TO 32:07 Mar. 1, 1972 PH.HCS 33 0 Tot Corn Buying Guide eompute the effective equi­ valent price of U.S. #2 corn from wet corn. 33:1 TO 33:08 Mar. 1, 1972 PH.HCS 34 1 Machinery Replacement Program dotermlne the optimum time to replace machinery and the associated cost. 34:1 TO 34:13 May IS, 1971 PB.HCS 33 0 Loan Refinance And Evaluation Model decide whether to refinance an existing loan, or to com­ pare costs of two different loan plana. 35:1 TO 35:06 Jan. 15, 1971 PH.HCS 33 0 financial Long-Range Tholo-Farm Budgeting compare alternative longrange plans for a complete farm business. The primary comparisons relate to. the financial consequences asso­ ciated with each plan. .36:1 TO 36:18 Jan. 1, 1974 37 0 General Leaot-Cost Rations formulate general least-cost rations, the user must spec­ ify tbe nutrients of each the feeds to be considered and the ration requirements. 37:1 TO 37:16 Feb. 15, 1972 PH.HCS 38 1 Silo Capacity/Cost Analysis determine size of tower or bunk silos needed to meet silage and/or high moisture corn storage requirements for dairy and beef animals. 38:1 TO 38:15 Apr. 1. 1972 PH.HCS **8ee Page 0:201. 4Bee Pago 0:201. PH.HCR.HCS 214 0:204 (Ki t . 8-1-77) PROGRAM HO. FORM KO. PROGRAM 18 USED TO: PROGHAM TITLE USER MANUAL PAGES AND LAST DATE OF REVISION OUTPUT OPTIONS** 99 0 Income Possibilities For Crops And Livestock provide a basis for estimating specific returns from a farm business including crop and livestock. 39:1 TO 39:0S Sept. 1, 1971 PH.HCS 40 0 Bssf Expansion Cost Model determine costs. Investments, annual costs and debt repay­ ment for a particular beef feeding system. 40:1 TO 40:08 Nov. 1 1971 PH.HCS 41 0 Inpaot Of Corn: bsan Mix determine the Impact on returns to. machinery, Improvements, and land of (1) allocation of tillable acreage between corn and soybeans, and (2) nitrogen allocation. 41:1 TO 41:16 Feb., 1974 PH.HCR.HCS 42 0 Dalry Pedigree Evalua­ tion Model obtain an objective measure of as animal's breeding merit based on the animal’s own performance and on that of Its offspring and ancestors. 42:1 TO 42:12 June 1, 1971 PH.HCS 43 0 Machine Coot Calculator compute ownership and operating costs for various types of equipment. 4 3 :1 TO 4 3 :0 7 Feb. 1 . 1978 PH.HCB.HCS 44 1 Bssf Batlon Formulation formulate the least-cost com­ bination of feed Ingredients that meet tbe nutrient re­ quirements of growing and finishing beef feeders. 4 4 :1 TO 4 4 :8 9 Dec. 1 . 1975 PH.HCS 48 0 Sett tag And Ventilation Requirements For Cattle Shelters eompute heating and ventila­ tion requirements to control moisture and to maintain a minimum temperature in cattle shelters. 4 8 :1 TO 4 8 :0 8 June,, 1973 PH.HCS 48 3 Michigan Dairy Farm Planner eompute an annual whole farm budget resulting In manage­ ment Income, the feed balance made up of corn equi­ valents, hay equivalents, and pounds of crude protein and a labor balance given livestock numbers and acreages of spe­ cific crops for a dairy farm. 4 6 :1 TO 4 6 :1 4 Jan. a s ,, 1977 PH.HCR.HCS 47 2 Caleltne For Consumers compute weekly Recommended Dietary Allowances (R.D.A.) for calcium Intake, and weekly coat savings In re­ ducing overconsumption or cost Increases in making up calcium deficits. Computation is baded on tbe needs for one person for one week. 47:1 TO 47:07 PH.HCR.HCS **8ee Page 0:201, Soy- 215 208 (l*T. 8-1-77) pr og r a m HO. form PROGRAM 18 U3EP TO; USER MANUAL PAGES AMD LAST DATE OP REVISION OUTPUT OPTIONS*e MO. PROGRAM TITLE 0 Protein For Consumer* ealeulat* th* recoamended and aetual eonnumptlon of protoln for on* day, given a person’* dally eonnumptlon of protein, age. and sex. Results are stated In terms of percentage of the U.S. Recommended Dally Allowances (U.S. RDA). 40 f ully Plannelnl Planning calculate a monthly cash balance given fully income by source and time period and cash out­ flow by month. Individual monthly details and change In net worth for the year are given. 80 Monthly Dairy Bord Growth project a farm's monthly live­ stock Inventory, given cur­ rent Inventories, planned purchases, cull rates, calving Interval, and heifer freshen­ ing age. Output options are livestock numbers, gross In­ come, feed required and manure generated in any specified 12 month period. 83 Zapaet Of Nitrogen On Corn Yield* And Profit* 83:1 TO 53:09 determine the rate of nitrogen Feb. 1. 1974 fertilizer which maximizes net returns per acre or to determine the expected yield from a specified rate of nitrogen fertilizer. Expected yields and added returns to tbe last 10 lbs of nitrogen are given for 10 and 20 pounds on each aide of tbe most profit­ able rate OR th* specified rate. PH.HCR.HCS 84 Llf*-Cyeln Management Of Bwls* develop schedules for breeding, farrowing, nursing, weaning, feeding and marketing swine. HCR 88 P*ad*r Enterprise Planning Guld* compare the profitability and .break-oven prices for alterna­ tive feeder types, feeding syetcms, and marketing systems. A comparative analysis of alternatlvr systems can be carried out by doing a base analysis followed by subse­ quent adjusted analyses. 85:1 TO 88:11 Feb. IS, 1973 PH.HCR.HCS 88 Simulation Of Feedlot Performance Of Growing And Finishing Cattlo. calculate the expected paywelght dally gain, feed conversion, and feed disappearance given ration sequence, feedor type, feeder condition, and en­ vironment. 86:1 TO 86:IB Feb. 1, 1977 HCR 87 P**dnh**t Calculation Tor Bonf Batlon* calculate the percentage com­ position and scale readings on an as-fed basis for alter­ native feed truck load sizes. 87:1 TO 57:08 June 1, 1977 HCR 41 **8ee Pag* 0:201. 48:1 TO 48:04 June 10, 1974 PH.HCR.HCS PH.HCR.HCS 82:1 TO 52:28 Nov. 1, 1978 HCR 216 0:206 (lev. 0- 1- 77) PROGRAM BO. M ro w WO. 0 BO PROGRAM TITLE USER MANUAL PACES AMD LAST DATE OF BEV1SIOM PROGRAM 13 USED TO: OUTPUT OPTIONS** PH.HCR Batch Cad Crossflow Cora Dryers aealet In understanding hoe the cost per bushel Xor drying high nolature shelled corn la affected by changing tbe operating conditions of the drying equipment. Dollar Vetch compute an estimate of a monthly 60:1 TO 60:13 budget by family size, income and whether or not a family has a ear payment. To compare that budget In dollars and percents with a "typical" budget for urban families of similar size and Income based on Bureau of Labor Statistics figures and University of Michigan Consumer Finance studies and farm fami­ lies on Income to tbe Farm-Operator Family Living Expenditures. The aim la to encourage families to begin thinking about how their sioney Is being spent, not to offer a specific plan. PH.HCR.HCS HCR 58:1 TO 88:38 Mar. IS, 1976 U 1 Optima Furniture Cutting Program determine which grade(s) of lumber are least expensive In meeting the needs of the rough mill cutting bill. as 1 Taking Charge OX Tour Pood Dollar design a personalized spending plan for food for your family, based on the number of per­ sona In your household, the number of meals they usually eat at home each week, and Individual nutritional needs. 64 0 Data Expansion Program expand on the Input section of TELPLAN programs that are de­ signed for and need a larger Input section than the basic program allows. NOTE: Should be used only by more experi­ enced TELPLAN user's. 60 0 Dairy Para Linear Programing compute tbe most profitable dairy herd size, amount of purchasod■feeds, and crop com­ binations given available land, labor plus any special restrictions set by the user. 68 0 in Tho Bank Or Cp The Chimney help you figure out what it might cost to add these energysavers to your house, how much each might save on heating costs,.and bow long It would take to pay off your Initial Investment. HCR 60 0 Dairy Health And Breeding Management decldo what spoclflc animals should be bred, receive health treatments or require special management attention, given a herd of dairy cows. Designed for dally use out of s farm milk house office. ECR Horse Ration Formulation formulate the least-cost com­ bination of feed Ingredients that meet tbe nutrient re­ quirements of growing and finishing horses. Ebould I Participate In The Food And Agri­ culture Act of 1977? evaluate the return to "fixed” factors of participating vs not participating in the wheat and corn "price support" program. 70 71 0 **8ee Page 0:201. 63:1 TO 63:14 Nov., 1976 HCR PH.HCR.HCS 69:1 TO 68:48 Sept. 15. 1976 HCR HCR (Input form self-expl.) PH,HCR