An Introduction to Intermediate and Advanced Statistical Analyses for Sport and Exercise Scientists

 
 
Wiley (Verlag)
  • erschienen am 24. November 2015
  • |
  • 312 Seiten
 
E-Book | PDF mit Adobe DRM | Systemvoraussetzungen
978-1-118-96206-0 (ISBN)
 
Ntoumanis and Myers have done sport and exercise science researchers and students a tremendous service in producing An Introduction to Intermediate and Advanced Statistical Analyses for Sport and Exercise Scientists. This book has an outstanding compilation of comprehensible chapters dealing with the important concepts and technical minutia of the statistical analyses that sport and exercise science scholars use (or should be using!) in their efforts to conduct meaningful research in the field. It is a resource that all sport and exercise scientists and their students should have on their book shelves. Robert Eklund, School of Sport, University of Stirling, UK
Motivating, to have a statistics text devoted to enabling researchers studying sport and exercise science to apply the most sophisticated analytical techniques to their data. Authors hit the mark between using technical language as necessary and user-friendly terms or translations to keep users encouraged. Text covers traditional and well-used tools but also less common and more complex tools, but always with familiar examples to make their explanations come alive. As a dynamic systems theorist and developmentalist, I would love to see more researchers in my area create study designs that would enable the use of tools outlined here, such as multilevel structural equation modeling (MSEM) or mediation & moderation analyses, to uncover cascades of relations among subsystems contributing to motor performance, over time. This text can facilitate that outcome. Beverly D. Ulrich, School of Kinesiology, University of Michigan, USA
The domain of quantitative methods is constantly evolving and expanding. This means that there is tremendous pressure on researchers to stay current, both in terms of best practices and improvements in more traditional methods as well as increasingly complex new methods. With this volume Ntoumanis and Myers present a nice cross-section of both, helping sport and exercise science researchers to address old questions in better ways, and, even more excitingly, to address new questions entirely. I have no doubt that this volume will quickly become a lovingly dog-eared companion for students and researchers, helping them to continue to move the field forward. Gregory R. Hancock, University of Maryland and Center for Integrated Latent Variable Research (CILVR), USA
weitere Ausgaben werden ermittelt
1 - Title Page [Seite 5]
2 - Copyright Page [Seite 6]
3 - Contents [Seite 9]
4 - About the editors [Seite 15]
5 - List of contributors [Seite 17]
6 - Foreword [Seite 21]
7 - Preface [Seite 23]
8 - Chapter 1 Factorial ANOVA and MANOVA [Seite 27]
8.1 - General Introduction [Seite 27]
8.1.1 - Hypothesis Testing [Seite 28]
8.1.2 - Alpha Level [Seite 28]
8.1.3 - Assumptions [Seite 29]
8.1.4 - Further Considerations [Seite 30]
8.2 - Utility in Sport and Exercise Sciences [Seite 32]
8.2.1 - Treatment Conditions [Seite 32]
8.2.2 - Existing Conditions [Seite 32]
8.2.3 - Individual Characteristics [Seite 33]
8.2.4 - Recent Usage [Seite 33]
8.3 - The Substantive Example [Seite 33]
8.3.1 - Univariate: Factorial ANOVA [Seite 34]
8.3.2 - Univariate Assumptions [Seite 34]
8.4 - The Synergy [Seite 36]
8.4.1 - Factorial ANOVA Analysis Plan [Seite 36]
8.4.2 - Example of a Write-Up Compatible with the APA Publication Manual [Seite 37]
8.4.3 - Factorial MANOVA Analysis Plan [Seite 39]
8.4.4 - Example of a Write-Up Compatible with the APA Publication Manual [Seite 39]
8.5 - Summary [Seite 42]
8.6 - Acknowledgment [Seite 44]
8.7 - References [Seite 44]
9 - Chapter 2 Repeated measures ANOVA and MANOVA [Seite 45]
9.1 - General Introduction [Seite 45]
9.1.1 - Between- versus Within-Subjects Variables [Seite 45]
9.1.2 - Hypothesis Testing [Seite 46]
9.1.3 - Assumptions [Seite 46]
9.1.4 - Further Considerations [Seite 47]
9.2 - Utility in Sport and Exercise Sciences [Seite 48]
9.2.1 - Multiple Treatment Conditions [Seite 49]
9.2.2 - Multiple Assessments [Seite 49]
9.2.3 - Longitudinal Studies [Seite 49]
9.2.4 - Recent Usage [Seite 50]
9.3 - The Substantive Example [Seite 50]
9.3.1 - Univariate: Repeated Measures ANOVA [Seite 50]
9.3.2 - Univariate Assumptions [Seite 51]
9.3.3 - Multivariate: Repeated Measures MANOVA [Seite 52]
9.3.4 - Multivariate Assumptions [Seite 52]
9.4 - The Synergy [Seite 53]
9.4.1 - Repeated Measures ANOVA Analysis Plan [Seite 53]
9.4.2 - Example of a Write-Up Compatible with the APA Publication Manual [Seite 55]
9.4.3 - Repeated Measures MANOVA Analysis Plan [Seite 55]
9.4.4 - Example of a Write-Up Compatible with the APA Publication Manual [Seite 57]
9.5 - Summary [Seite 58]
9.6 - Acknowledgment [Seite 60]
9.7 - References [Seite 60]
10 - Chapter 3 Mediation and moderation via regression analysis [Seite 61]
10.1 - General Introduction [Seite 61]
10.2 - Utility of the Methods in Sport and Exercise Science [Seite 62]
10.3 - The Substantive Example [Seite 64]
10.3.1 - Mediation [Seite 64]
10.4 - The Synergy [Seite 64]
10.4.1 - Mediation [Seite 64]
10.5 - The Substantive Example [Seite 70]
10.5.1 - Moderation [Seite 70]
10.6 - The Synergy [Seite 71]
10.6.1 - Moderation [Seite 71]
10.7 - Summary [Seite 79]
10.8 - References [Seite 81]
11 - Chapter 4 Item response theory and its applications in Kinesiology [Seite 83]
11.1 - General Introduction [Seite 83]
11.2 - What Is IRT? [Seite 85]
11.3 - Other Commonly Used IRT Models [Seite 86]
11.4 - Assumptions Related to IRT [Seite 88]
11.4.1 - Unidimensionality [Seite 88]
11.4.2 - Local Independence [Seite 88]
11.5 - Addressing Model-Data Fit [Seite 88]
11.5.1 - Inspecting Model Assumptions [Seite 89]
11.5.2 - Inspecting Expected Model Features [Seite 89]
11.5.3 - Inspecting Overall Model-Data Fit [Seite 90]
11.5.4 - Computer Simulation for Model-Data Fit Testing [Seite 90]
11.6 - Unique Features and Advantages of IRT [Seite 91]
11.6.1 - Estimation Invariance [Seite 91]
11.6.2 - Common Metric Scale [Seite 91]
11.6.3 - Item and Test Information [Seite 92]
11.6.4 - Test Relative Efficiency [Seite 94]
11.6.5 - Global "Reliability" Is no Longer a Concern [Seite 95]
11.6.6 - Item Bank and IRT-Based Test Construction [Seite 95]
11.6.7 - Parameter Estimation and Software [Seite 97]
11.7 - Utility of the Methodology in Kinesiology [Seite 97]
11.8 - IRT Limitations and Future Direction [Seite 98]
11.9 - Conclusion [Seite 99]
11.10 - References [Seite 100]
12 - Chapter 5 Introduction to factor analysis and structural equation modeling [Seite 105]
12.1 - General Introduction [Seite 105]
12.2 - Utility of the Method in Sport and Exercise Science [Seite 106]
12.3 - Terminology and Methodology [Seite 109]
12.3.1 - Evaluating Model Fit [Seite 112]
12.3.2 - Interpreting Parameter Estimates [Seite 114]
12.4 - The Substantive Example [Seite 115]
12.5 - The Synergy [Seite 117]
12.5.1 - EFA: Establishing the Factor Structure [Seite 117]
12.5.2 - CFA: Testing the Measurement Models [Seite 119]
12.5.3 - Structural Equation Modeling: Adding the Regression Paths [Seite 122]
12.6 - Summary [Seite 124]
12.7 - References [Seite 125]
13 - Chapter 6 Invariance testing across samples and time: Cohort-sequence analysis of perceived body composition [Seite 127]
13.1 - General Introduction to the Importance of Measurement Invariance [Seite 128]
13.1.1 - Cohort-Sequential Designs: Longitudinal Invariance across Samples and Time [Seite 132]
13.2 - Substantive Application: Physical Self-Concept [Seite 133]
13.3 - Methodology [Seite 137]
13.3.1 - The PSDQ Instrument [Seite 137]
13.3.2 - Statistical Analyses [Seite 137]
13.3.3 - Goodness of Fit [Seite 138]
13.4 - Results [Seite 139]
13.4.1 - Basic Cohort-Sequence Model: Four Cohort Groups and Four Waves [Seite 139]
13.4.2 - Cohort-Sequence Design of Multiple Indicators, Multiple Causes Models [Seite 141]
13.4.3 - Use of Model Constraint with Orthogonal Polynomial Contrasts to Evaluate Cohort Sequence and MIMIC Latent Means [Seite 142]
13.4.4 - Use of Latent Growth Curve Models to Evaluate Stability/Change over Time [Seite 145]
13.4.5 - LGC Results [Seite 149]
13.5 - Summary, Implications, and Further Directions [Seite 149]
13.5.1 - Methodological Implications, Limitations, and Further Directions [Seite 149]
13.6 - References [Seite 151]
14 - Chapter 7 Cross-lagged structural equation modeling and latent growth modeling [Seite 157]
14.1 - General Introduction [Seite 157]
14.2 - A Theoretical Framework for the Study of Change [Seite 158]
14.3 - Utility of the Method in Sport and Exercise Science [Seite 158]
14.3.1 - Analysis of Change [Seite 158]
14.4 - The Substantive Example [Seite 160]
14.4.1 - Theoretical Background [Seite 160]
14.4.2 - The Data: Participants and Measurement [Seite 160]
14.5 - The Synergy [Seite 161]
14.5.1 - CLPM [Seite 161]
14.5.2 - CLPM Example [Seite 163]
14.5.3 - Latent Growth Modeling [Seite 166]
14.5.4 - LGM Example [Seite 167]
14.5.5 - Model 2a: Unconditional LGM [Seite 169]
14.5.6 - Model 2b: Conditional LGM [Seite 171]
14.5.7 - Model 2c: Unconditional LGM with TVCs [Seite 171]
14.5.8 - Model 3: Parallel Process LGM [Seite 172]
14.5.9 - Model 4: Second-Order LGM [Seite 174]
14.6 - Summary [Seite 176]
14.7 - References [Seite 177]
15 - Chapter 8 Exploratory structural equation modeling and Bayesian estimation [Seite 181]
15.1 - General Introduction [Seite 181]
15.2 - Utility of the Methods in Sport and Exercise Science [Seite 182]
15.3 - The Substantive Example(s) [Seite 185]
15.4 - The Motivational Correlates of Mentally Tough Behavior [Seite 185]
15.5 - Developing Synergies through Statistical Modeling [Seite 187]
15.5.1 - ESEM [Seite 187]
15.5.2 - Bayesian Estimation [Seite 194]
15.6 - Summary [Seite 205]
15.7 - References [Seite 206]
16 - Chapter 9 A gentle introduction to mixture modeling using physical fitness performance data [Seite 209]
16.1 - General Introduction [Seite 209]
16.2 - Utility of the Method in Sport and Exercise Science [Seite 212]
16.3 - The Substantive Example(s) [Seite 213]
16.3.1 - Class Enumeration in Mixture Models [Seite 214]
16.3.2 - The Estimation of Mixture Models [Seite 216]
16.4 - The Synergy [Seite 216]
16.4.1 - LPA of Grade 5 Students and Tests of Invariance across Gender Groups [Seite 216]
16.4.2 - Inclusion of Covariates in LPA Solutions [Seite 221]
16.4.3 - LTA [Seite 222]
16.4.4 - Mixture Regression Analyses of Grade 5 Students [Seite 224]
16.4.5 - Latent Basis Growth Mixture Analyses: Cardiovascular Fitness [Seite 228]
16.4.6 - Piecewise Growth Mixture Analyses: Physical Strength [Seite 229]
16.5 - Summary [Seite 230]
16.6 - Acknowledgments [Seite 231]
16.7 - References [Seite 232]
17 - Chapter 10 Multilevel (structural equation) modeling [Seite 237]
17.1 - General Introduction [Seite 237]
17.1.1 - Multilevel Structural Equation Modeling [Seite 238]
17.2 - Utility of the Methodology in Sport and Exercise Science [Seite 240]
17.3 - The Substantive Examples [Seite 241]
17.3.1 - Coaching Competency-Collective Efficacy-Team Performance: 1-1-2 [Seite 242]
17.3.2 - Action Planning Intervention-Physical Activity Action Plans-Physical Activity: 2-1-1 [Seite 243]
17.4 - The Synergy [Seite 244]
17.4.1 - Coaching Competency-Collective Efficacy-Team Performance: 1-1-2 [Seite 245]
17.4.2 - Action Planning Intervention-Physical Activity Action Plans-Physical Activity: 2-1-1 [Seite 248]
17.5 - Summary [Seite 255]
17.6 - References [Seite 256]
18 - Chapter 11 Application of meta-analysis in sport and exercise science [Seite 259]
18.1 - General Introduction [Seite 259]
18.1.1 - Stages of Meta-Analysis [Seite 259]
18.1.2 - Key Elements of Meta-Analysis [Seite 260]
18.1.3 - Goals of Meta-Analysis [Seite 262]
18.2 - Utility of the Methodology in Sport and Exercise Science [Seite 264]
18.3 - The Substantive Example [Seite 264]
18.4 - The Synergy [Seite 267]
18.4.1 - Univariate Meta-Analysis [Seite 267]
18.4.2 - Multivariate Meta-Analysis [Seite 271]
18.5 - Summary [Seite 275]
18.6 - Acknowledgment [Seite 277]
18.7 - References [Seite 277]
19 - Chapter 12 Reliability and stability of variables/instruments used in sport science and sport medicine [Seite 281]
19.1 - Introduction [Seite 281]
19.1.1 - A. Assessment of Test-Retest Agreement Using Interval/Ratio Data [Seite 282]
19.1.2 - A Worked Example Using the Test-Retest Differences of the Biceps Skinfold Measurements [Seite 283]
19.1.3 - B. Utility of the Assessment of Test-Retest Stability Using Categorical/Likert-Type Data [Seite 286]
19.2 - The Substantive Example [Seite 287]
19.2.1 - Utility of the Test-Retest Stability Using Nonparametric Data [Seite 287]
19.3 - The Synergy [Seite 288]
19.3.1 - Utility of the Item by Item Approach to Test-Retest Stability [Seite 289]
19.4 - The Synergy [Seite 289]
19.5 - Summary [Seite 291]
19.6 - References [Seite 292]
20 - Chapter 13 Sample size determination and power estimation in structural equation modeling [Seite 293]
20.1 - General Introduction [Seite 293]
20.1.1 - Power [Seite 294]
20.1.2 - Power Analysis in SEM [Seite 294]
20.2 - Utility of the Methodology in Sport and Exercise Science [Seite 295]
20.2.1 - Power Analysis Regarding Model-Data Fit: An Introduction [Seite 295]
20.2.2 - Power Analysis Regarding Focal Parameters: An Introduction [Seite 296]
20.3 - The Substantive Example [Seite 298]
20.3.1 - Bifactor Model in Sport and Exercise Science [Seite 298]
20.3.2 - Bifactor Model and the PETES [Seite 299]
20.4 - The Synergy [Seite 301]
20.4.1 - Power Analysis Regarding Model-Data Fit: A Demonstration [Seite 302]
20.4.2 - Power Analysis Regarding Focal Parameters: A Demonstration [Seite 304]
20.5 - Summary [Seite 307]
20.6 - References [Seite 308]
21 - Index [Seite 311]
22 - EULA [Seite 315]

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