89... La... u magi... 9 2.. o t... .5. 2.3;. fin 1%. V . .1. . rk$flfi in. r .3? ”hm-ml: ...r 5m I! 3 £2 {a fir. a. 5.! ; c . 1.4.1.193... in... we}. . 0 .t 81..) 1.1.1 Nut. . .1; 3.2... .15.) $11.12.} 0.5. .1 Splff tv‘ bill‘s . {\A :l g9§:\55i [JENRAWTY MiChigan State This is to certify that the University dissertation entitled ANTHROPOMETRIC DETERMINANTS OF PERFORMANCE IN THE STANDING LONG JUMP presented by David A. Kinnunen has been accepted towards fulfillment of the requirements for the Ph. D. degree in Kinesiology WW ‘J Major Professor’s Signature 4g/Qg/ék3 Date MSU is an Affirmative Action/Equal Opportunity Institution v v v v“ v v ~ ‘ PLACE IN RETURN BOX to remove this checkout from your record. To AVOID FINES return on or before date due. MAY BE RECALLED with earlier due date if requested. DATE DUE DATE DUE DATE DUE 6/01 C'JCIRC/DateDuepSS-p. 15 ANTHROPOMETRIC DETERMINANTS OF PERFORMANCE IN THE STANDING LONG IUMP by David A. Kinnunen A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of Doctor of PhiIOSOphy Department of Kinesiology 2003 ABSTRACT ANTHROPOMETRIC DETERMINANTS OF PERFORMANCE IN THE STANDING LONG JUMP by David A. Kinnunen The purpose of this study was to examine the relationship of structural- maturational (SM) variables to performance in the standing long jump from a dynamic systems perspective. The SM variables to be studied were: weight, standing height, sitting height, acrom-radiale length, radio-stylion length, biacromial width, bicristal width, arm girth, thigh girth, calf girth, triceps skinfold, subscapular skinfold, and umbilical skinfold. Derived variables included in'the study were: body mass index, sum of skinfolds, triceps + subscapular skinfolds, sit/stand ratio and hip/shoulder ratio. Dynamic systems theory predicts that change results when one or more control parameters are altered (Clark & Phillips, 1993; Peitgen, Jurgens, & Saupe, 1992; Thelen, 1985; Kelso, 1984). Haubenstricker and Branta (1997) suggested that further research into jumping behavior should concentrate on determining the variables, or control parameters, that enhance or limit performance. An analysis of the anthropometric measures on the standing long jump aids in identifying the factors that may drive changes in performance. A systems approach allows us to look at how the many subsystems involved act together to impact performance and at the same time identifies the subsystems where small changes may influence development or performance. In order to fully understand the changes in developing systems, the system sensitive control parameters (e.g., changes in the muscular-skeletal system, the masses and length of the limbs, or other physical characteristics) that drive the system to reorganization should be examined (Clark, 1986). This study included 487 Caucasian participants, 234 males (47%) and 258 females (53%), a subset of the longitudinal Motor Performance Study (MPS) at Michigan State University. Ages ranged from 7 through 18 years. Data were longitudinal in nature and collected semi annually. Regression analysis suggested the following factors act as control parameters for females at age 7 - radio-stylion length; females at age 12 - triceps + subscapular skinfolds; females at'age l6 -— sum of skin folds and standing height. The percent variance explained by the variables was 10.6%, 9.5%, and 15.3% respectively. The results for the study suggested the following factors act as control parameters for males the age groups and corresponding factors were: at age 7 - subscapular skinfolds; age 14 - triceps skinfolds, biacromial width and umbilical skinfolds; age 18— triceps + subscapular skinfolds and sitting height. The percent varianv\ce explained by the variables was 11.8%, 25.9%, and 19.4% respectively. Copyright David A. Kinnunen 2003 This work is dedicated to my parents, my mother Barbara, and my father James. They gave me so much over the year, I never knew just how much. They continue to do so in my heart. Everything I am or shall ever be I owe to them. I love you both, you are always with me. Most of all, this work is dedicated to Dawn-Kimberly, you never gave up on me. You caught me when I fell and held me upby the strength of your love. For that, and so much more. . .thank you. Thank you for loving methe way I have always dreamed it could be. You have my love, now, forever, and always... ‘. ...I have promises to keep, and miles to go before I sleep. (Robert Frost) Acknowledgements There are so many to whom I owe thanks, at Michigan State University: my Advisor Dr. Crystal Branta, who never let me quit, my committee members Dr. John Haubenstricker, Dr. Martha Ewing, and Dr. Alice Whiren. I must also thank my friends ' Frank and Linda Boster, and Rodney Wilson. From Idaho State University I must thank my dearest friend for so many years Dr. James ‘Byrd’ Yizar. I must also acknowledge the work of Dr. Vern Seefeldt, Dr. John Haubenstricker and Dr. Crystal Branta for conducting the Motor Performance Study. Their hard work and dedication are an inspiration and provided the early data and the means by which this study could be conducted. I must also thank all of the other individuals who, contributed in so many ways for so many years to the Motor Performance Study. Their efforts have been, and will continue to be, invaluable. vi TABLE OF CONTENTS .b.) [\J H t—4 LIST OF TABLES ' ix LIST OF FIGURES- x INTRODUCTION 1 Rationale 1 Background of the Study 3 Statement Of Purpose ' 4- Description of the Standing Long Jump 4 Standing Long Jump - 6 Significance of the Study , 7' Research Questions 12 Scope and Limitations of the Study ' 12 Terms and Definitions 13 REVIEW OF LITERATURE ‘ 15 Background ~ ‘ 15 Plasticity ' 17 Dynamic Systems 19 Growth and Dynamic Systems 26 METHODS 3 Participants 3 Data Collection/Procedures 3 Data Analysis 3 RESULTS Age groupings in six month categories 36 Male performance means and standard deviations Female performance means and standard deviations 38 Means and standard deviations for anthropometric variables 39 Group correlations ' 41 Male correlations 44 Female correlations 47 Seven year old female correlations 51 Twelve year old female correlations 52 Sixteen year old female correlations 53 Seven year old male correlations 55 vii b) \3 Fourteen year old male correlations Eighteen year old male correlations Regression Means and standard deviations for 7 year old females Regression analysis for 7 year old females Means and standard deviations for 7 year old males Regression analysis for 7 year old males Means and standard deviations for 12 year old females Regression analysis for 12 year old females Means and standard deviations for 14 year old males Regression analysis for 14 year old males Means and standard deviations for 16 year old females Regression analysis for 16 year old females Means and standard deviations for 18 year old males Regression analysis for 18 year old males Summary regression table DISCUSSION Descriptive results Correlations Regression Future research Summary APPENDIX A: Descriptions of measurements APPENDDi B: Means and standard deviations for male and female anthropometric variables by age group APPENDIX C: Correlations between performance on the standing long jump and the anthr0pometric variables across age groups APPENDDI D: Specific age group correlation matrices APPENDIX E: Longitudinal changes across variables REFERENCES viii 55 56 58 58 59 6O 6O 61 61. 62 63 65 66 67 68 68 68 69 79 84 85 91 94 102 109 LIST OF TABLES Table l — Age groupings in six month categories Table 2 - Vleans and Standard deviations for males by age Table 3 - Means and standard deviations for females by age Table 4 - Means and standard deviations for anthropometric variables Table 5 -— Group correlations between SLJ and anthropometric variables Table 6 - Male correlations between SLJ and anthropometric variables Table 7 - Female correlations between SLJ and anthropometric variables _ Table 8 — Seven year old female correlations between SLJ and anthro. variables Table 9 - Twelve year old female correlations between SLJ and anthro. variables Table 10 - Sixteen year old female correlations between SLJ and anthro. variables , 'Table 11 - Seven year old male correlations between SLJ and anthro. variables Table 12 - Fourteen year Old male correlations between SLJ and anthro. variables Table 13 — Eighteen year old male correlations between SLJ and anthro. variables Table 14 - Means and standard deviations for 7 year old females . Table 15 ¥Regression analysis for 7 year old females ' Table 16 - Means and standard deviations for 7 year old males Table 17 - Regressionanalysis for 7 year old males Table ‘18 - Means and standard deviations for 12 year old females Table 19 - Regression analysis for 12 year old females 7 Table 20 — Means and standard deviations for 14 year old males Table 21 - Regression analysis for 14 year old males Table 22 - Means and standard deviations for females 16 year. old females Table 23 - Regression analysis for 16 year old females Table 24 -— Means and standard deviations for 18 year old males Table 25 - Regression analysis for 18 year Old males Table 26 — Summary regression table LIST OF FIGURES Figure l — Standing long jump Figure 2 - Sample scatterplot/performance portrait 49 CHAPTER 1 INTRODUCTION Rationale Dynamic systems theory is an attempt to understand and explain complex, nonlinear change over time (Ulrich, 1989; Clark. a Phillips, 1993, Thelen & Ulneh, 1991; Crutchfield, Farmer, Packard, & Shaw, 1987; Rosen, 1970). Dynamic systems theory views human motor develOpment' as behavior that arises from the collective dynamics of Contributing subsystems, including the central nervous system and the musculoskeletal system, and predicts that change may result when one or more control parameters are altered (Clark & Phillips, 1993; Peitgen, Jurgens, & Saupe, .1992; Thelen, 1985; Kelso, 1984). For example, these systems are thought to be dynamic, relational, and multileveled in nature (Fentress, 1986). Systems theory proposes that any new _ organization, or reorganization, of a system can only come about from perturbations that disrupt the stability of an older system (Brown, 1995; Thelen & Ulrich, 1991; KelSo, . SChroner, Scholz & Haken, 1987). These perturbations may include properties of the - envirOnment. or the organism (Hamilton, Pankey & Kinnunen, 2003; Goldfield, Kay, & Warren, 1993;7Newell, 1986). Specifically, they may include environmental surfaces and objects, gravity, the central nervous system, the musculoskeletal system, and the masses and length of the limbsrxinnunen, 2001; Goldfield, Kay,.& Warren, 1993). The study of motor development from a dynamic systems perspective is still relatively new (Ulrich 1989; Clark & Phillips, 1993). Although physical growth and motor development achievements may not have changed significantly over recent years, further study of the factors influencing growth, development, and performance is needed (Halverson, 1966; Pipho, 1971; Wilson, 1993). Movement is made possible by the musculoskeletal system (Ford & Lerner, 1999). This system provides the strengdr and structural stability that allows the body to generate movement. These movements or patterns must be coordinated dynamically with a flow of environmental events, requiring coordination between action and environment (Ford & Lerner, 1992). For example, these patterns are controlled by a complex interaction between the central nervous system and psychological processes (Garcia-Ruiz, Louis, Meakin, & Sander, 1993; Kelso, Holt, Rubin, & Kugler, 1981). These patterns are achieved by combining conceptual informatiOn with perceptions regarding the environmental dynamics and the movements or patterns themselves (Ford & Lerner, 1992). Traditional approaches, such as information processing and maturational theories, have not satisfactorily explained the mechanisms of change underlying human development or performance (Thelen, 1986). While motor development tends to follow a sequential order much like physical development, the timing and rate of develOpment varies among individuals (Garrison, 1952; Pipho, 1971). A systems approach allows researchers to look {at how the many subsystems involved act together to impact performance and at the same time identifies the subsystems where small changes may influence larger development or performance. ' Dynamic systems theory holds that one component or subsystem might be the key determinant in forcing a system into sometype of change (Haubenstricker & Branta, 1997; Ulrich, 1989). The use of longitudinal data offers two advantages when employing a dynamic systems perspective. First, because develOpment occurs on such a long time scale, the assembly and tuning processes, such as the central nervous system, the musculoskeletal system, the masses and length of the limbs (Goldfield, Kay, & Warren, 1993), practice, strength, motivational changes, sensory or perceptual abilities, or physical characteristics (Thelen & Ulrich, 1991), may cause change to occur in any of the variables themselves, particularly when looking for the emergence of sudden changes. Secondly, changes in constraints may drive the system changes (Goldfield, Kay, & Warren, 1993), and these - changes may be more readily observable with longitudinal research. Background of the Study The idea for this study originated in fall of 1993 while observing two seminal figures in the field of motor development discuss and contrast their theoretical ' perspectives regarding motor skill performance and how it develops and changes over time. It struck me that there must be some type of bridge between the component and composite models of motor develOpment. The motor development writing group at Michigan State provided a number of perspectives concerning motor development and performance, including both the composite and component views, along with the ‘ influence of dynamic syStems as a means of examining both perspectives. This study is an attempt to look at motor performance from a dynamic systems perspective. Now completing its 36th year, the Motor Performance Study (MPS) was begun in December of 1967. The MPS is a longitudinal project examining the impact of physical growth and biological maturation on motOr performance. Data for the MPS are collected semi- annually during June/July and December/January. Semi-annual growth measurements are taken on the participants beginning at the age of two years. Data are collected on thirteen measures of growth. These structural-maturational variables include: weight, standing height, sitting height, biacromial width, bicristal width, acrom-radiale length, radio-stylion length, arm girth, thigh girth, calf girth, triceps skinfold, subscapular skinfold, and umbilical skinfold. Semi-annual motor performance data are also collected on the participants. Data were taken on seven motor performance tasks (flexed arm hang, jump and reach, thirty yard dash, sit and reach, agility shuttle run, standing long jump, and an endurance shuttle run). The subjects continued in the study until they showed little or no growth in height for three consecutive measurement periods. Purpose of the Study The purpOse of this present study was to examine the relationship of structural- maturational (SM) variables to performance in the standing long jump from a dynamic systems perspective. The SM variables studied were: weight, standing height, sitting height, sit/stand ratio, hip/shoulder ratio, acrom-radiale length, radio-stylion length, biacromial width, bicristal width, arm girth, thigh girth, calf girth, triceps skinfold, subscapular skinfold, umbilical skinfold, body mass index, sum of skinfolds, and triceps + subscapular skinfolds. The performance'measure was the distance attained on the standing long jump. . Description of the Standing Long Jump The standing long jump has been studied by a number of researchers. The jump is an explosive movement that requires a coordinated effort of all parts of the body (Gallahue & Ozmun, 2002). A standing long jump is performed, by first taking a starting position behind a mark or line on the ground. This starting position begins with the toes of both feet at the very edge of the takeoff line. The, jumpers should bend their knees slightly as they swing their arms in a back and forth rocking motion in order to build as much forward momentum as possible. Inexperienced jumpers may find it difficult not to take a preliminary step forward with one of the feet, almost as a preparatory movement (Gallahue & Ozmun, 2002). The takeoff portion of the jump is accomplished by the simultaneous extension of the knees combined with a vigorous forward arm swing (Phillips, Clark & Petersen, 1985). The forward sWing of the arms will help to pull the jumper up and outward. In flight, the legs should be brought forward and extended in order to gain as much distance as possible. Landing should be made with the knees slightly bent and the heels of the feet as even as possible. Overall, the total jump distance evaluates performance in the standing long jump, which is the horizontal distance from the takeoff line to the nearest point of contact made by the heels at landing. Figure 1- Standing long lump gab . A. Use u;— ip‘ rte-5y of H. ‘ .F., C Significance of the Study Traditional research in motor develOpment has focused on describing the actions involved in the development of specific movement patterns. Frequently, these descriptions have been in the form of develOpmental sequences (Clark, Phillips, & Petersen, 1989; Roberton, 1989a). Over the last century, little longitudinal motor development research has examined the processes underlying changes and movement sequences (Roberton, 1989b). Few contempOrarymotor development researchers take the opportunity to study age changes, as opposed to age differences, or the processes involved in those changes (Roberton, 1989b). . The principles of dynamic systems suggest that development is not an outcome, but a transitional state. Dynamic systems theorists hold that the primary thrust of development is to generate new structures and behaviors (Peitgen, J urgens, & Saupe, 1992; Thelen & Smith, .1994). This development is driven by parallel develOping subsystems, each with its own trajectory (Thelen, 1988; Thelen & Ulrich, 1991). While in theory each subsystem is an equal contributor, the system as a whole may be more sensitive to changes in certain subsystems rather than in others at any given point in time (Thelen & Ulrich, 1991). As such, certain subsystems may act as control parameters on the system as a whole. These subsystems must therefore change in order to drive the system to reorganize. For nonlinear systems, certain parameter changes can alter the system's behavior qualitatively. At critical points of reorganization, the system is said to undergo a phase transition. By examining a range of parameters, one can determine the structural stability of particular systems and learn about transitions from one phase to another (Goldfield, Kay, & Warren, 1993; Clark, 1995). These phase transitions, known in dynamic systems as shifts or bifurcations, generally result from increasing amounts of noise to a system (Peitgen, Jurgens, & Saupe, 1992; Thelen & Smith, 1994). Noise can come from a number of variables, such as practice; strength, motivational changes, sensory or perceptualabilities, or physical characteristics (Thelen & Ulrich, 1991), and may cause change to occur in any of the variables themselves. There may even be critical values within each of the component subsystems where the stability of the entire system is overwhelmed and is driven to reorganize (Gleick, 1987; Peitgen, Jurgens, & Saupe, 1992; Thelen & Smith, 1994). These changing internal or external variables drive the system into new behavioral configurations (Thelen & Ulrich, 1991). These changes may include environmental factors, the central nervous system, the musculoskeletal system, the mass of the body as a wholeand the masses and length of the limbs (Goldfield, Kay, & Warren, 1993). . These variables can be regarded as either rate limiters or rate attractors, depending on the particular impact they have on the system as a whole. The changes these variables create, or contribute to, are referred to as phase shifts (Gleick, 1987). Phase shifts may result in increased variability, as the system displays. changes from, or wobbles within, its current relatively stable state. These phase shifts may be driven by changes in anthropometric measures thatmay also impact motor performance. It is the interactions of these factors that determine the next level of reorganization. However, little is known about the relations between these factors and their effect on performance (Thelen, 1986). The concept of nonlinearityassociated with dynamic systems theory suggests such changes in the subsystems may not be smooth. These changes, rather than following a simple trajectory, may occur with spurts, plateaus, and even regressions (Thelen & Smith, 1994): Bernstein held that there must be a close and mutual link between the brain and the mechanical pr0perties of the body, they must act and deve10p together (Lockman & Thelen, 1993; Bernstein, 1967). The key for using dynamic systems to study motor develOpment is to identify the variables involved, to describe the associated attractor states as they change over time, and to discover phase shifts where the system is assuming new forms (Thelen & Smith, . 1994). The manner in which complex biological systems are coordinated to produce movement remains one of the great-unsolved problems of biology (FentreSs, 1986; Schoner & Kelso, 1988). It is important that research in motor develOpment begin to employ a dynamic systems perspectiveto understand the underlying physical'causes of changes in movement (Zemicke & Schneider, 1993). Research by Haubenstricker and Branta (1997) found that age, gender, and the developmental level. of the movement patterns used impacted the distance young children achieved in long jumping. Their findings also suggested that factors other than age, gender, and developmentallevel, such as body size or body fat, might influence jumping performance (Haubenstricker & Branta, 1997). Haubenstricker and Branta (1997) also suggested that further research into jumping behavior should concentrate on determining the variables, or control parameters, that enhance or limit performance. Because one specific component might be the critical element driving a system developmentally, the factors controlling the periods of stability and transition need to be better understood (Haubenstricker & Branta, 1997). The identifiCation of phase shifts is of importance because it is at these points where we learn more about what drives the system to reorganize (Thelen, 1995). The question becomes what is changing that generates a shift into new forms? Can specific rate attractors or rate limiters, also known as control parameters, be identified through longitudinal anthropometric data? Phase shifts can be driven by changesin certain physical variables (Thelen & Ulrich, 1991); therefore, understanding developmental change through dynamic systems involves identifying the control parameters that enable oredrive phase shifts. While it is well established that jumping performance generally improves across the growing years, attempts should be made to determine what factors underlie or drive this improvement (Glassow & Kruse, 1960). The scale and composition of the body imposes important constraints on . movement (Thelen, 1986; Clark, 1995). An analysis of anthropometric measurements on the standing long jump will aid in identifying the factors that may drive changes in performance. The control parameters that are responsible for shifts in the system remain to be identified (Clark, Phillips, & Petersen, 1990; Thelen & Smith, 1994). The objective is to discover the points of change so that underlying control parameters that drive the phase shifts can be identified. In order to understand the changes in developing systems, the system sensitive control parameters (changes in the muscular-skeletal system, the masses and length of the limbs, or other physical characteristics) that drive the system to reorganization should be examined (Clark, 1986). Research Questions The primary purpose of the present study was to assess the influence of specific anthropometric characteristics on performance of the standing long jump. Dynamic systems research suggests that it should be possible to identify the various components that may influence the performance of specific motor skills. Little of the motor development research has examined the underlying changes that drive performance. An additional purpose was to attempt to determine if anthropometric control parameters exist, and if so, could they be specifically identified. A systems approach should allow investigators to examine how many subsystems or factors act together to impactperformance and at the same time help to identify the subsystems influencing that performance. Specifically, this investigation attempts to determine if anthropometric control parameters can be identified with regards to the standing long jump. Newell (1984) suggested that various factors can and will greatly influence the task at hand. Dynamic systems theory holds that one component or I subsystem or group of subsystems might be the key determinants influencing a system (Haubenstricker & Branta, 1997; Ulrich, 1989). This investigation is an initial step in an attempt to utilize dynamic systems theory to identify variables acting as control parameters and the various subsystems that might influence or control performance in the standing long jump. 10 Research Questions This study will address the following questions: 1. To what extent were the selected anthropometric parameters related to the performance variations on the standing long jump (Clark, 1986)? Can the subsystems that influence performance in the standing long jump be identified? Can one or more of the selected anthrOpometric variables be identified as a control parameter in performance of the standing long jump. Delimitations of the Study The study is delimited by the following factors: Only subjects who participated in the Michigan State University Motor Performance Study are included; only subjects with complete data records are included; only Caucasian participants were selected. Limitations of the Study ‘ The study was conducted under the following limitations: 1. Subjects may not have given their best effort even though assessors provided positive encouragement during the skill testing. Any additional practice, training, or experience by the subjects outside of the testing setting could not be controlled. Data were collected and recorded by different individuals over the length of the study. Although each assessor was given training by a senior investigator some measurement error may be present in the data. 11 Definitions Attractor State -- a mode of behavior a system prefers above all others (For example, the definitive stages of fundamental motor skills may be viewed as attractor states). Body mass index - a method for calculating the relationshipbetween weight and stature (weight in kilograms divided by stature in centimeters squared). Chaos - study of nonlinear systems that change. Control parameter — a variable that controls changes in performance or the overall collective behavior of the system. Dynamic systems - the theoretical perspective that new forms of behavior emerge from the COOperative interactions of multiple subsystems. Fractals — geometric shapes found and used in higher math, nature, chaos theory and dynamic systems. Girth - the relative diameter. Growth - an increase in the size of the body as a whole or the size attained by specific parts of the body. Hip/shoulder ratio - a derived variable, a ratio of the hip width divided by standing height. This measure provides a relative idea about the overall proportions of the subject. Scores would typically fall between .6 and 1.4. Horizontal decalage - a type of hierarchical system ordering where no one factor or variable lays claim to being in control Mass — a measure of weight. Mass equals the weight of an object or individual, divided by gravity (32.2 feet per second squared). Performance portrait - An overview of performance results viewed as a scatter plot, or longitudinal distance curve. Perturbation - disruptions in stability, can be either natural or induced. Phase shift — system reorganizations resulting from small changes in one or a few component variables - changes or shifts in performance directly related to changes in the anthropometric measures. Phase portrait — similar to a performance portrait. May be comprised of scatter plots or. distance curves depending on the data being plotted and observed. Rate attractor - a component that pushes the reorganization of a system or changes in performance. Rate controller — similar to the control parameter. May be a single variable or a combination of varibles Rate limiter - a component that prevents or slows the reorganization of a system or changes in performance. Sit/stand ratio - sitting height divided by standing height, this provides an idea of the relative contribution of the lower body to overall stature. Skinfold - an indicator of subcutaneous fat, calipers are used to measure the thickness of a double fold of skin and the subcutaneous tissue at various sites. State space - an abstract construct of a space whose coordinates define the components of a system (Thelen & Smith, 1994). Sum of skinfolds (sumsf) - a derived variable, a total of the Skinfold measurements. This measure is a reflection of the relative level of adipose tissue present. Trisubsf - a derived variable, triceps + subscapular skinfold. This measure is a reflection of the relative level of adipose tissue present. 14 CHAPTER 2 REVIEW OF LITERATURE Background The standing long jump may be defined as a jump in which the take-off is from both feet and the landing is on both feet simultaneously (Pipho, 1971). It is a somewhat complicated modification of the movement patterns previously established through walking and running. In her study on the development of jumping skills in children, Wilson (1945) observed that a two-foot take-off and landing appeared at abOut the age of three or four years in a series of short jumps. Hellebrandt, Rarick, Glassow, and Came (1961) studied the growth and development of horizontal jumping using the standing long jump. Their research indicated that the level of performance is related to a variety of factors, such as height, weight, and fitness. They further suggested that these factors should be identified, specifically those that impact the performance of the standing long jump (Hellebrandt, Rarick, Glassow & Came, 1961; Pipho, 1971). Quantifying these variables might help in identifying the cause and significance of typical and atypical. motor development. 1 Traditionally, developmental changes in motor ability were attributed to maturational processes in'the central nervous system (Bushnell & Boudreau, 1993; McGraw, 1940, 1941, 1943). Although interest in motor development began as part of the field of child development (Roberton, 1989a), the research was primarily descriptive, and closely connected to the question of the effects of maturation versus environment. Pioneering developmental scientists such as Shirley, Gesell, and McGraw spent the 19203 15 through the 19405 researching how control is gained over movements (Thelen, 1995, 1986a; McGraw, 1940, 1941, 1943; Gesell, 1928, 1933,1939). Much of the work was longitudinal, and the appearances of stage-like sequences - of new motor milestones were taken as evidence for the hierarchical maturation of the brain (Schneider, Zenicke, Ulrich, JenSen, & Thelen, 1990; Roberton, 1989a; McGraw, ' 1932). Gesell (1928, 1933, 1939) was particularly clear in assigning developmental control to the changes in the nervous system. Perceptual and social incentives and information-processing theories were also used to explain motor development (Bower, 1974; Bruner, 1973; Zelazo, 1976). Although not recognized at the time, Bernstein's (1923 — translated in 1967) work in the early part of this century also examined the way in which systems helped to organize and control movement. Von Holst conducted other early work. regarding interlimb phase control during the 19305 (von Holst, 1973). 3 Dynamic systems is grounded in the belief that movements are not represented centrally in a motor program, schema, or other form, but are an emergent prOperty of the dynamics of the underlying systems (Abernathy & Sparrow, 1992). From a dynamic systems perspective, motor development is not'seen as pre-programmed behavior, rather motor development proceeds due to adjustments and reorganizations of components intrinsic to the functioning motor system (Bushnell & Boudreau, 1993). For the purposes .of this study, the components undergoing adjustments and reorganizations consist of the selected anthropometric parameters. Traditional descriptive or information processing approaches has not satisfactorily explained the underlying mechanisms of change involved in movement (Thelen, 1986). From the traditional points of view, motor development is viewed as a derivative of 16 processes that occur at some higher level. This traditional neuro-maturational perspective is lacking in two ways. First, there is no account for process, of how new form and function are realized over time; and second, there is no consideration for how the central nervous system learns to control limbs and body segments (Schneider, Zenicke, Ulrich, Jensen, & Thelen, 1990). Although the maturation of the central nervous system is certainly essential to motor develOpment, the inherent determinism and singular causality implied by the neuro-maturational perspective has been questioned over the past few years (Clark, Phillips, & Petersen, 1989). Plasticity Motor systems remain plastic throughout life, ready to compensate for change (Spoms & Edelman, 1992). There is ovens/helming evidence that the emergence of coordinated movements is tied togthe growth and maturation of the musculoskeletal system (Schoner & Kelso, 1988). Schneider et al. (1990) confirmed that the development of skill involved the efficient use of inter-segrnental dynamics. Other recent findings reveal that well understood neural circuits show a surprising degree of plasticity (Schoner & Kelso, 1988), and may ultimately be related to other concepts of nonlinearity (Peitgen, Jurgens, & Saupe, 1992). Variations in neuro-structural components were major factors contributing to changes in performance. Edelman (1992) proposed a theory of neuronal group selection (T NGS) to integrate neuro-anatomy, neuro-embryology, and developmental psychology. TNGS holds that in the central nervous system (CNS), categories of actions are self- organizing, in that the system is attracted to one preferred configuration out of many 17 possible states. For example, stages of fundamental motor skills may be thought of as preferred configurations or attractor states. Additionally, TNGS holds that these categories of actions are as dependent on the morphology of non-neural structures as on the CNS (Ulrich, 1989). TNGS places a large emphasis on the structural variability of the brain's circuitry. During development, neuronal circuits are not precisely wired at a micro anatomical level. Therefore, the brain allows for structural variability that can give rise to dynamic variability in its output. These variant circuits form what Edelman calls neuronal groups (Sporns & Edelman, 1993). These groups are considered to be the basic functional units of selection, and tend to share functional properties and discharge in a temporally correlated fashion (Spoms & Edelman, 1993). These groups have been identified in several cerebral cortical areas (Gray & Singer, 1989; Spoms & Edelman, 1993). ‘ Neuronal groups are arranged in the cortex in neural maps. While these maps may be functionally segregated and occupy specific regions of the cortex, they are coupled through reciprocal long-range connections (Spoms & Edelman, 1993). This reciprocal arrangement between neuronal groups in distant sensory and motor regions gives rise to new dynamic properties and temporal correlations (Spoms & Edelman, 1993). Neuronal groups are subject to selection when their activation in a given context matches given environmental and internal constraints. Particular groupsmay be selected for their contributions to specific tasks. Selection in the nervous system is done through synaptic change, leading to the amplification or dimming of neuronal group responses. 18 This selection ultimately allows for the discrimination and categorization of sensory input and the integration of sensory and motor processes in order to result in adaptive behavior (S poms & Edelman, 1993). According to Edelman’s selection model, stable categories of behavior can emerge over time. As actions are repeated over and over, synaptic connections will be strengthened. Therefore, efficacious movements would be gradually carved out from the myriad of less functional options. Dynamic systems theory predicts that, under such conditions, systems will automatically seek stable solutions (Thelen, 1989). The study of classic dynamics is concerned with how various forces in a system evolve over time in order to produce motion (Goldfield, Kay, & Warren, 1993). When dynamics are used to analyze the human body and its movements, the segments of the body are approximated as rigid bodies or interconnected links (Zernicke & Schneider, 1993; Bernstein, 1967). The complex multi-joint nature of normal human movement means that results utilizing dynamics .are not intuitively obvious, due to the fact there are no simple relationships between the movements of individual segments of the body (Zernicke & Schneider, 1993). Schneider et a1. (1989) confirmedBemstein's concept that ; becoming skilled involved the efficient use of inter-segmental dynamics. Dynamic Systems Systemic research into motor behavior is typically thought to have begun during the 19305 (Bushnell & Boudreau, 1993), with the work of Gesell and McGraw (Gesell, 1933; McGraw, 1940). Although the concept of general systems theory is typically credited to Ludwig von Bertalanffy (Laszlo & Laszlo, 1997; Bertalanffy, 1968; Brown, 1995), some researchers point to the concepts, ideas, and results of the French 19 mathematician Poincare (Peitgen, Jurgens, & Saupe, 1997; Brown, 1995). Poincares’ theory of dynamics was concerned with understanding the nature and origin of the properties within a system (Peitgen, Jurgens, & Saupe, 1997; Brown, 1995: Kugler, 1986). Thompson proposed a theory of growth and form, arguing that the form of an object is intimately linked to its dynamic properties (Thompson, 1917/1942; Kugler, 1986). Thompson argued that understanding dynamic properties required an examination of the system’s geometry. Bernstein's work (1967) regarding the coordination and regulation of movement is also viewed as pioneering the concept of dynamic systems theories as they apply to increasing understanding of the organization and plasticity of development (Thelen, 1995). In any event, long before the time of Bernstein and Von Bertalanffy, researchers recognized that there must be a link between the movements of the body and neural A control (Schneider, Zemicke, Ulrich, Jensen, & Thelen, 1990). Since that time, there has been an increasing interest in the area of motor development across the lifespan (Bushnell & Boudreau, 1993). Researchers have attempted to link coordinated human movements to the concepts of nonlinear systems theory (Spoms & Edelman, 1993; Kelso & Tuller, 1984; Schoner & Kelso, 1.988). These nonlinear theories imply that coordinated movement is made with a number of interacting and related components, creating a nonlinear system capable of attaining a number of dynamic states (Sporns & Edelman, 1993). Systems theory developed out of a number of areas of study (Levine & Fitzgerald, 1992), including engineering, mathematics, and biology. In the late 19203, Cannon (1939) noted that animals seek to maintain their state conditions, even when faced with major variations in their environment. A systems approach to research-attempts to view the world in terms of irreducibly integrated systems, focusing attention on both the whole and the complex interrelationships (Laszlo & Laszlo, 1997). Von Bertalanffy’s first statements on the subject date from 1925-1926, at about the same time as Bernstein was beginning to formulate his ideas and theories (Laszlo, 1972a; Laszlo & Laszlo, 1997). Von Bertalanffy recognized relationships between several areas in biology, and in 1937 referred to the concept as general systems theory (Levine & Fitzgerald, 1992). General systems theory stresses looking at wholes composed of many different but interrelated parts or systems (Levine & Fitzgerald, 1992; Peitgen, Jurgens, & Saupe, 1997). Systems theory predicts that transitions from one stable phase to another may not be linear or continuous (T helen, 1986). Small changes in A one element or factor may be a product of the dynamic, relational, multileveled interaction of those systems. 9 While Von Bertalanffy's work was originally presented in 1937, it was not until after World War II that his first writings on the subject began to be published (Laszlo & Laszlo, 1997; Fivaz, 1997). By the late 19405 and early 19505, Cannon's animal work began to be linked to other areas of research (Levine & Fitzgerald, 1992) involving biological state changes, feedback and control, and dynamic relationships among variables. By the early 19603, systems theory had begun to be recognized as a serious attempt to integrate a variety of theories from across scientific fields (Gleick, 1987; Laszlo & Laszlo, 1997; Peitgen, Jurgens, & Saupe, 1992). An early area of eoncentration was in the prediction of weather. Edward Lorenz's now famous work attempting to predict long-range weather patterns may have been one of the first studies to utilize what is now referred to as dynamic systems (Gleick, 1987). During the mid 19605, Von Bertalanffy and others began to suggest that growth and develOpment could also be examined using dynamic systems theories (Levine & Fitzgerald, 1992). Bernstein's central insight regarding systems was that motor development emerged from continual and intimate interactions between the nervous system and the limbs and body (Lockman & Thelen, 1993). By the early 19705, researchers argued that developmental change in motor skills resulted from the increased ability to integrate movement subroutines into larger units of action (Clark & Whitall, 1989; Bruner, 1973). Bernstein's work has had adramatic effect on the field of motor development. One of Bernstein's theories of motor development is that movement patterns emerge through a dynamic interaction between the organism and the environment (Zernicke & Schneider, 1993). Therefore, movement is not believed to be imposed on the organism by an autonomously developing brain, but blended into the neuromuscular system by interactions with various feedback mechanisms and other forces (Thelen, Zemicke, Schneider, Jensen, Kamm, & Corbetta, 1992). This dynamic process is one in which functional strategies are formed in the context of change. This change consists of the reorganization of various parameters, including environmental and internal influences, in order to simplify the control required by reducing the number of parameters needing to be coordinated (Zernicke & Schneider, 1993). This reduction of parameters has come to be referred to as reducing the degrees of freedom involved in movement. Nonlinear dynamic systems demonstrate that motor activity demonstrates periods of regularity and irregularity, demonstrated as stability and instability (Lockman 8; Thelen, 1993). It is possible these periods of stability and instability are the system's attempt at controlling the degrees of freedom (Schoner & Kelso, 1988; Clark & Philips, 1993). Dynamic systems theory specifically offers a set of principles for studying the emergence of new forms. It attempts to explain change. Included among these principles are attempts to identify the collective variable involved, the points of transition, and to identify potential control parameters (Thelen & Ulrich, 1991). Without reducing the study to physics, a dynamic system offers a powerful conceptual approach for understanding the interrelationships that exist in motor development (Laszlo & Laszlo, 1997). By definition, a dynamic system is one that changes over time (Rosen, 1970). In dynamic systems, specific prOpositions are made about the relative stability or loss of stability (Schoner & Kelso, 1988). An unstable system is said to be in transition, allowing the system to move to another stable attractor state. Unstable systems demonstrate increased variability when compared to stable systems. A system may move into transition when a control parameter crosses a critical threshold. Evidence that a specific parameter acts as a control may be found by looking at that parameter's effect on the system as a whole when the parameter changes (Clark & Philips, 1993). Dynamic systems theory predicts that change results from the scaling of one or more control parameters (Clark & Philips, 1993), and that a period of instability would occur at the onset of a new form. Then, over time, the system can be expected to [\J (J) stabilize into an attractor State (Clark & Philips, 1993). Esther Thelen, perhaps the most well known proponent of using dynamic systems to study motor development, emphasizes the importance of all the subsystems, rather than a dominant central nervous system (Clark & Whitall, 1989). Development might be best understood as a temporal sequence of attractor states (Thelen, 1990). The transition from one state, stable or unstable, to another is under the control of any number of deVelopmental control parameters. These control parameters may .have a single component or several, and there is no one-to-one relationship between subsystems and their components (Levine & Fitzgerald, 1992). Any one subsystem or component may act as a rate-limiting factor (301], 1979; Thelen, 1986). Certain elements related to performance, may change or appear early, and initially seem to be disassociated from the performance in question, or used for another function. Thelen identifies stable states as attractors, because the system settles into that pattern from a wide variety of initial positions and tends to return to that pattern if perturbed (Thelen, 1995). Thelen believes a develOping system is dynamic in that patterns of behavior act as attractor states for the component parts within the environment and task constraints. These attractor patterns are preferred under certain circumstanCes. Other patterns are possible but performed with more difficulty and are more easily disrupted or perturbed. The relative stability of a behavioral system is a function of its history, current status, the intention of the individual, and the context (Thelen, 1993). The use of a dynamic systems perspective places an emphasis on process, rather than the more traditional performance variables. Process accounts provide explanations of not just what behaviors are performed, but how they are assembled and how they change over time (Lockman & Thelen, 1993; Whitall & Clark, 1994). The advantage of systems sciences is the potential for providing a cross-disciplinary framework for critical exploration of relationships (Laszlo & Laszlo, 1997). In order to understand a dynamic, relational, multileveled system, it is necessary to try to identify the rate-controllin g components involved and their interactions (Thelen, 1986). Performance is the system’s product of the changes in status of the individual components. No one component determines the overall performance of the system. However, in combination, one component may support,inhibit, or mask the expression of another component (Thelen, 1986; Schoner & Kelso, 1988;1‘Zernicke & Schneider, 1993). Over time, these relationships may shift and flow, depending on the rate of develOpment of the various components. Because of the dynamic, relational, multileveled relationship of the system, even small changes in one component may alter the entire performance or system (Thelen, 1986; Schoner & Kelso, 1988; Zernicke & Schneider, 1993). Dynamic systems allows us to view how many levels may act together and at the same time identify the subsystems where small changes result in major consequences (Thelen, 1986). Shifts in long jump performance can be examined to. determine if they are influenced by anthr0pometric measurement data. Performance can be represented in terms of a position in state space (Smith, 1994; Thelen & Smith, 1994). State space is defined as an abstract construct of a Spacewhose coordinates define the components of a system (Thelen & Smith, 1994). Conceptuall'y, it is similar to a three dimensional Cartesian coordinate system. A specific performance, or an average, on the long jump can be located or represented by a point on a graph. A dynamic system refers to this point as existing in state space. A scatter plot can illustrate the individual or group l\) U1 performance. The scatter plot of these performances is made, the locations of the responses are found, and then the performance area is identified (Smith, 1994). These scatter plots, representing state space, serve as an index of the developmental landscape. The shape of this landscape is determined by the location of the various performances on the scatter plot. The size of the performance area indicates the shape of the develOpmental landscape. An area that appears as a narrow and deep valley indicates that all the performances were similar and a strong attractor or attractors are suggested. A broad shallow plain indicates the performances were scattered widely and a weak attractor, or attractors, is suggested. An overview of the results is referred to as a phase portrait. The parameters responsible for shifts in the system remain to be identified (Thelen & Smith, 1994). The point is to discover the points of change so the underlying control parameters directing the phase shifts can be identified. Growth and Dynamic Systems Developmental changes are not planned but come about as the product of a number of developing elements (Thelen, 1995). These elements, or constraints, are typically structural in nature (Newell, 1986) and include variables such as body weight, height, strength, mass, or limb length (Goldfield, Kay, & Warren, 1993; Jensen, Phillips & Clark, 1994). From a nonlinear systems perspective, certain parameter changes can alter the entire system's behavior (Goldfield, Kay, & Warren, 1993; Schoner, Haken & Kelso, 1986). Maturational changes in these constraints differ over the course of growth and development (Goldfield, Kay, & Warren, 1993) resulting in different organization at various times or stages. Changes in growth and form are particularly evident in infancy, early childhood, and adolescence (Newell, 1986). These changes may have an impact on the constraints involved in action or performance. A major consequence of growth is the change in the absolute and relative size of respective body parts (Newell, 1986; Malina & Bouchard, 1991). These changes in size may act as rate limiters or rate attractors on the constraints of the system. Thelen (1985) noted that components may compete with, inhibit, or facilitate each other with implications for performance, and any one component may act as a rate-limiting factor. Von Hofsten (1989) agreed, implying that when a critical value in size is reached, the stability of a movement pattern is disrupted. In fact, size can be viewed as a scaling factor, if the system is scaled to some critical value; the system changes (Clark, 1986). Thelen suggests physical sizemight be a sensitive scaling factor, disrupting the entire system when changes occur (Thelen, 1984; Clark, 1986). These overall changes are due to the system reorganizing in response to specific changes in size and mass. This disruption forces the system to find a new more stable state. However, because all aspects of the system are not subject to change, identifying those aspeCts that actually change and those that do not becomes increasingly important (Von Hofsten, 1989). One example of this type of change or organization involving growth is the concept of adolescent awkwardness. The term adolescent awkwardness has been used to describe a period of time during the adolescent growth spurt where a temporary disruption in motor performance may occur (Garcia-Ruiz, Louis, Meakin, & Sander, 1993; Malina & Bouchard, 1991). This disruption does not appear universally and does not seem to impact males and females equally. The awkwardness or reorganization may reflect a period of readjustment due to the relatively rapid changes that may be occurring in the body at this time. Developmental change can be seen as a series of stability, instability, and phase shifts, with change being predicted by a loss of stability (Thelen, 1995). Each component in the system is both cause and product (T helen, 1995). Bones and muscles are continually in a state of change, although some changes thatoccur may take place at a slower pace and therefore be more difficult to observe. While dynamic systems theory can provide an explanation for why transitions occur, it cannot tell us when those changes occur, or their time course (Von Hofsten, 1989). The states of the factors feeding into the system at a specific time are generally not known. 0 While many aspects of motor development have been studied, a logical step would be to define the component elements that may influence performance of specific motor skills (Pipho, 1971). A classic study by Rarick and Oyster (1964) was One of the first to determine that a number of factors might have an influence on performance. This study looked at the effects of physiCal maturity and muscular strength on motor performance in boys (Rarick & Oyster, 1964; Erbaugh, 1997). Rarick and Oyster found that age, height, and weight had an impact on strength. Espenschade (1963) looked at the relationship of height and weight and motor performance within age groups. Earlier work by Seils (1951) revealed no significant relationship between stature, body weight and performance in the standing long jump. The findings of other studies have been inconsistent with the effects of various factors (Pipho, 1971, Latchaw, 1954, Berg, 1968). Malina (1975) summarized much of the research concerning develOpment and motor performance. Research by Malina indicated that fatness has a negative impact on motor performance in tasks involving movement of the body through space (Malina, 1975; Erbaugh, 1997). Additionally, Malina's work found that body size is positively related to performance on tasks requiring strength. Further research has examined the influence of somatotype, body composition, and size on motor performance (Slaughter, Lohman, & Misner, 1980). These findings indicated that lean body mass was a key predictor of performance. In 1982, Hensley, East, and Stillwell looked at the relationship between body fatness and motor performance, and found significant performance differences between boys and girls in some tasks. Erbaugh (1984) investigated the relationship between the physical growth and stability performance of preschool children. Much like Malina’s (1975) earlier work, the results of this study found that body composition, diameters, and circumference measurements were the most important variables. Malina and Bushang (1985) examined growth, strength, and motor performance in groups of children from Mexico and Philadelphia and found that little performance variation was explained by a number of anthropometric variables. However, Eoff (1985) found that performance was influenced by structural-maturational variables, specifically, the length and weight of a limb was found to have an effect on overall performance in throwing for both boys and girls. DeveIOpmental change may be linear and gradual, such as the usual growth increments in body weight or size (Thelen, 1992; Thelen & Ulrich, 1991). But developmental change may frequently show discontinuities. A phase shift suggests a transition from one stable mode to another, with the intermediate stage being more unstable and transitory (Turvey & Fitzpatrick, 1993; Kapitianiak, 1990). Only one or a few of the components of the system control parameters can bring about these phase shifts (T helen, 1992; Thelen & Ulrich, 1991). The study of motor development and performance from a dynamic systems perspective is still relatively new (Ulrich 1989; Clark & Phillips, 1993). The purpose of this study ass to examine the relationship of structural-maturational variables to performance in the standing long jump from a dynamic systems perspective. Dynamic systems research suggests that it should be possible to identify the various subcomponents that may influence the performance of specific motor skills. A systems approach should allow investigators to examine how many subsystems or factors act together to impact performance and at the same time help to identify the subsystems influencing that performance. In addition, this investigation attempted to determine if anthropometric control parameters could be identified with regards to the standing long jump. Newell (1984) suggested that various factors can and will greatly influence the task at hand. Dynamic systems theory holds that one component or subsystem or group of subsystems might be the key determinants influencing a system (Haubenstricker & Branta, 1997; Ulrich, 1989). This investigation was an initial step invan attempt to utilize ' dynamic systems theory to identify variables acting as control parameters and the various subsystems that might influence or control performance in the standing long jump. CHAPTER 3 METHODS Participants A sub-sample of 487 participants in the Michigan State University Motor Performance Study (MPS) was chosen for the investigation. The majority (97.5%) of the subjects in the Motor Performance Study are Caucasian; therefore, only Caucasian participants were selected for this study. The subjects included 224 males (46%) and 263 females (54%), ranging in age from 14 months to nearly 23 years of age (M = 10.897) years. The participants selected for this study presented consistent participation records over time, missing no testing or measurement periods. The minimum performance data recorded for a participant selected for this study was five years while the maximum was twenty years. The subjects continue in the study until they show little or no growth in height for three consecutive measurement periods. Semi-annual growth measurements are taken on the participants beginning at the age of two years. Data are collected on thirteen measures of growth. These structural-maturational variables include: weight, ' standing height, sitting height, biacromial (shoulder) width, bicristal (hip) width, acrom- radiale (upper arm) length, radio-stylion (lower arm) length, arm girth, thigh girth, calf girth, triceps skinfold, subscapular skinfold, and umbilical skinfold. Semi-annual motor performance data are also collected on the participants beginning at the age of five years. Data are collected on seven motor performance tasks, including: flexed arm hang, jump and reach, thirty yard dash, sit and reach, agility shuttle run, standing long jump, and an endurance shuttle run. For the purpose of this study only 31 the standing long jump was examined. The 487 participants provided a total of 12,752 standing long jump records. . Data Collection Structural-maturational data on each subject were obtained prior to performance data. All measurements were taken from the left side of the body. Research suggests the consequences of taking anthropometric measurements on one or the other side of the body is limited and does not seem to be biologically significant (Moreno, Rodriguez, Guillen, Rabanaque, Leon & Arino, 2002). All measurements were rounded to the nearest one half millimeter with the exception of weight, which is rounded to the nearest pound, and skinfolds which were rounded to the nearest half millimeter. Growth and motor performance measurements for participants in the MPS were collected semi- annually (June/July and December/January). Descriptions of how each structural maturational measurement was taken are provided in Appendix A. During measurement, the subjects were barefoot and wore swimsuits, or shorts and a light shirt. Performance data were obtained after the structural—maturational measures were completed. The motor performance tasks were performed in the following order: flexed arm hang, jump and reach, thirty yard dash, sit and reach, agility shuttle run, standing long jump, and endurance shuttle run. All performance data were collected in a gymnasium setting. For the purposes of this study, only the long jump was utilized. The protocol for the standing long jump consists of three trials, with the subjects beginning with the toes of both feet placed behind a starting line. A two-foot takeoff and landing are required. 32 The takeoff is from behind a restraining line on the floor, and the landing is on a two-inch thick mat. Following the jump the measurement is taken with the back of the heels marking the actual distance covered. Distance is rounded to the nearest one/half inch. All of the successful jumps are recorded, with the participant’s longest recorded jump being used for this study Data Analysis The structural-maturational measures and the additional derived variables of body mass index, sit/stand ratio, hip/shoulder ratio, triceps + subscapular Skinfold measurements and sum of skinfolds served as independent variables. The motor performance task, the standing long jump distance, was the dependent variable. All statistical analyses were conducted using the Statistical Package for the Social Sciences (SPSS Version 10.0/ 10.4). Descriptive statistics, correlations, and regression analysis were conducted to address the hypotheses and research questions for this study. The significance level for all cases throughout the various analyses was set at the .05 level.- The participants were divided into age groups corresponding with the testing periods plus or minus three months. None of the participants exceeded the age groupings or categories listed, however, certain age groups were subsequently removed from analysis due to extremely low numbers of participants having performance records during those time periods. For the males, the age groupings removed from analysis were 33 - 35. These groupings constituted a total of five subjects being removed from the analysis and represented approximately the ages of twenty one to twenty two years of age. Therefore, the analysis for male participants stops at age group 32, which represents long jump performance from 243 to 248 months or approximately twenty years of age. 33 For females, the ages removed from analysis were nineteen to twenty years. Twelve subjects were removed from the analysis. CHAPTER 4 RESULTS The results of this study are presented in regard to performance on the standing long jump. First, age categories used for analysis are listed with the mean and standard deviations regarding performance on the standing long jump by age category. Second, descriptive statistics for the anthropometric variables are presented. Third, the correlations for the total group, male participants, and female participants are presented. Fourth, regression analyses for the male and female participants are discussed regarding performance on the SLJ performance. The MPS groupings listed in Table l were used for analyses regarding performance in the standing long jump for this study. All numbers represent months in age, i.e., LJ 57-62 refer to long jump performances for a participant or group of participants at 5-years of age. Table 2 presents mean long jump performances for males across the age groups. Table 3 presents means and standard deviation long jump performancesfor females across the age groups. Table 4 presents means and standard deviations for the anthrOpometric variables. Table 1 Ageggroupings in six month categories Age cate (1982). Anthropometric, body composition, and maturity characteristics of selected school-age athletes. Pediatric clinics of north america, 29, 1305-1323. 113 Malina, R.M., & Rouche, AF. (1982). Manual of physical status and performance in childhood : Vol. 2. Physical Performance. New York, Plenum. Malina, R.M., & Buschang, RH. (1985). Growth, strength and motor performance of Zapotec children, Oazaca, Mexico. Human Biology, 57, 163-181. Malina, R.M., & Bouchard, C. (1991). Growth, maturation, and physical activity. Champaign, IL: Human Kinetics. McGraw, MB. (1932). From reflex to muscular control in the assumption of an erect posture and ambulation in the human infant. Child Development, 3, 291-297. McGraw, MB. (1940). N euromuscular development of the human infant as exemplified in the achievement of erect locomotion. Journal of Pediatrics, 17, 747-771. McGraw, MB. (1941). Development of neuromuscular mechanisms as reflected in the crawling and creeping behavior of the human infant. Journal of Genetic Psychology, 58, 83-111. McGraw, M. B. (1943). The neuromuscular maturation of the human infant. New York, NY: Columbia University Press. Moreno, LA; Rodriguez G.; Guillen'J; Rabanaque'MJ; Leon'JF; & Arino A. (2002). European Journal of Clinical Nutrition. Dec; 56 (12):1208-15. National Center for Health Statistics (1982). Boys: 2 to 18 years physical growth ' percentiles. Adapted from P.V.V. Hamill, T.A. Drizd, C.L. Johnson, R.B Reed, A.F. Roche, & W.M. Moore (1979), Physical Growth: National Center for Health Statistics percentiles. American Journal of Clinical Nutrition, 32, 607-629. Data . from the National Center for Health Statistics (N CHS). Hyattsville, MD. National Center for Health Statistics (1982). Girls: 2 to 18 yeas physical growth percentiles. Adapted from P.V.V. Hamill, T.A. Drizd, C.L. Johnson, R.B Reed, A.F. Roche, & W.M. Moore (1979), Physical Growth: National Center for Health Statistics percentiles. American Journal of Clinical Nutrition, 32, 607-629. Data from the National Center for Health Statistics (NCHS). Hyattsville, MD. Newell, K. (1986). Constraints on the development of coordination. In M. Wade & H. T. A. Whiting (Eds. ), Motor development m children: Aspects of coordination and control (pp. 341 -.360) Nijhoff, Netherlands. Nugent, W.R. (1996). Integrating single-case and group-comparison designs for evaluation research. Journal of Applied Behavioral Science, 32, 209-226. 114 Osgood, D.W., & Smith, G. L. (1995). Applying hierarchical linear modeling to extended longitudinal evaluations: The boys town follow-up study. Evaluation Review, 19, 3-38. Oesterreich, L. (1995). Ages & stages- six through eight-year-olds. In L. Oesterreich, B. Holt, & S. Karas, Iowa famzly child care handbook [Fm 1541] (pp. 211-.212) Ames, IA. Iowa State University Peitgen, H.O., Jurgens, H., & Saupe, D. (1992). Chaos and fractals: New frontiers of science. New York, NY: Springer. Phillips, S. J, Clark, J. E., & Petersen, R. D. (1985). DeveIOpmental differences in standing Iona gjump takeoff parameters. Journal of Human Movement Studies, 11, 75- 87. . Pipho, A. (1971). An investigation of the relationships between selected physical growth and developmental measures and performance in the standing long jump by five- year-old children. Unpublished doctoral dissertation, University of Oregon. Rarick, G.L., & Oyster, N. (1964). Physical maturity, muscular strength, and motor performance of young school-age boys. Research Quarterly, 35, 523-531. Roberton, M.A. (1977). Stability of stage categorization across trials: Implications for the stage theory of overann throw development. Journal of Human Movement Studies, 3, 49-59. Roberton, M.A. (1978). Stages in motor develOpment. In M.V. Ridenour (ed.), Motor development: Issues and implications. (pp. 63-81). Princeton, NJ: Princeton Books. Roberton, M.A. (1989a). Motor development: Recognizing our roots, charting our future. Quest, 4], 213-223. Roberton, M.A. (1989b). Developmental changes in the relative timing of locomotion. In M. Wade & H.T.A. Whiting (Eds.), Themes in motor development (pp. 279- 293). Nijhoff, Netherlands: Rosen, R. (1970). Dynamical system theory in biology_( Vol. I). New York, NY: Wiley. Schneider, K., Zemicke, R.F., Ulrich, B.D., Jensen, J .L., & Thelen, E. (1990). Understanding movement control in infants through the analysis of limb intersegrnental dynamics. Journal of Motor Behavior, 22, 493-520. Schoner, G., Haken, H., & Kelso, J AS. (1986). A stochastic theory of phase transitions in human hand movement. Biological Cybernetics, 53, 247-257. 115 Schoner, G., & Kelso, J AS. (1988). Dynamic pattern generation in behavioral and neural systems. Science, 239, 1513-1520. Seefeldt, V. (1979). Developmental motor patterns : Implications for elementary school physical education. In C. Nadeau, W. Holliwell, K. Newell, & G. Roberts (Eds.) Psychology of motor behavior and sport — 1979. (pp. 314-323). Champaign, IL: Human Kinetics. Seefeldt V, & Haubenstricker J. (1982). Patterns, phases or stages: An analytical model for the study of developmental movement. In Kelso J AS, Clark JE, eds., The ~ Development of Movement Control and Coordination (pp. 309—318). New York, NY: John Wiley and Sons. Seils, LG. (1951). The relationship between measures of physical growth and gross motor performance of primary-grade school children. Research Quarterly, 22, 244-260. Slaughter, M.,H. Lohman, T.G., & Misner, J .E. (1980). Association of somatotype and body composition to physical performance in 7-12 year-old girls. Journal of Sports Medicine and Physical Fitness, 20, 189-198. Smith, LB. (1994). Stability and variability: The geometry of children’s novel word interpretations. In F. Gilgen and F. Abrahan (Eds.), Chaos theory in psychology. Westport, CT: Greenwood Press. 3011, DR. (1979). Timers in developmental systems. Science, 203, 841-849. Spiegel, MR. (1996). Statistics (2nd ed.). New York, NY: McGraw-Hill. Sporns, O., & Edelman, GM. (1993). Solving Bernstein's problem: A proposal for the development of coordinated movement by system. Child Development, 64, 960- 981. ‘ Thelen, E. (1984). Learning to walk: ecological demands and phylogenetic constraints. In L.P. Lipsitt and C. Rovee-Collier (Eds.), Advances in infancy research, Vol. 3 (pp. 51-98). Norwood, NJ: Ablex. Thelen, E. (1985). Developmental origins of motor coordination: Leg movements in human infants. Developmental Psychobiology, 18, 1-22. Thelen, E. (1986). Development of coordinated movement: lrnplications for early human development. In M. Wade & H.T.A. Whiting (Eds.), Motor develOpment in children: Aspects of coordination and control (pp. 107-124), Nijhoff, Netherlands. 116 Thelen, E. (1988). Dynamical approaches to the development of behavior. In J.A.S. Kelso, A.J. Mandell, & M.F. Shiesinger (Eds.), Dynamic patterns in complex systems (pp. 348-369. Singapore: World Scientific. Thelen, E. (1989). The (re) discovery of motor development: Learning new things from an old field. Developmental Psychology, 25, 946-949. Thelen, E. (1992). Development as a dynamic system; Current Directions in Psychological Science, I, 189-193. Thelen, E. (1995). Motor develOpment: A new synthesis. American Psychologist, 79- 95. - Thelen, E., & Smith, LB. (1994). A dynamic systems approach to the development of cognition and action. Cambridge, MA: MIT. Thelen, 13., & Ulrich, ED. (1991). Hidden Skills: Monographs ofthe society for research in child development, Serial no. 23, Vol. 56. Chicago, IL: University of Chicago Press. ‘ Thelen, E., Zemicke, R.F., Schnieder, K., Jensen, J.L., Karnrn, K., & Corbetta, D. (1992). The role of intersegmental dynamics in infant neuromotor development. In G.E. Stelrnach & J. Requin (Eds.), Tutorial in Motor Behavior II (pp. 53-548). Amsterdam, Netherlands: Elsevier. Thompson, D.L. (1917/1942). 0n growth and form. London, Cambridge University Press. Turvey, M.T., & Fitzpatrick, P. (1993). Commentary: Development of perception-action systems and general principles of pattern formation. Child Development, 64, 1 175-1 190. Ulrich, B. (1989). Development of stepping patterns in human infants: A dynamical systems perspective. Joumal of Motor Behavior, 21, 392-408. Van Geert, P. (1994). Dynamic systems of development: Change between complexity and chaos. New York, NY: Harvester. Von Hofsten, C. (1989). Motor development as the development of systems: Comments on the special section. Developmental psychology, 25, 950-953. Von Holst, E. (1939/1973). Relative coordination as a phenomenon and as a method of analysis of central nervous function. In: R. Martin (Ed). The collected papers of Erich von Holst (pp. 33-135). Coral Gables, FL: University of Miami. 117 Whitall, J ., & Clark, J. E. (1994). The development of bipedal interlimb coordination. In S. P. Swinnen, H. Heurer, J. Massion, & P. Casaer (Eds.), Interlimb coordination: Neural, dynamical, and cognitive constraints. NY: Academic Press. Williams, F. (1992). Reasoning with statistics: How to read quantitative research (4th ed.). New York, NY: Harcourt Brace J anovitch. Wilson, D. J. (1993). An investigation of a developmental sequence of the standing long jump using multidimensional scaling. Unpublished Doctoral Dissertation, Michigan State University. Wilson, M (1945). Development of jumping skill in children. Unpublished Doctoral Dissertation, University of Iowa. Zelazo, PR. (1976). From reflexive to instrumental behavior. In L. Lipsitt (Ed.), Developmental psychobiology: The significance of infancy (pp. 87-104). Hillsdale, NJ: Erlbaum. Zemicke, R.F., & Schneider, K. (1993). Biomechanics and develOpmental neuromotor control. Child Development, 64,_982-1004. 118 IIIIIIIIIIIIIIIIIIIIII 1111111121111111111