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Statistical Concepts, 4th Edition

Book Description

Statistical Concepts consists of the last 9 chapters of An Introduction to Statistical Concepts, 3rd ed. Designed for the second course in statistics, it is one of the few texts that focuses just on intermediate statistics. The book highlights how statistics work and what they mean to better prepare students to analyze their own data and interpret SPSS and research results. As such it offers more coverage of non-parametric procedures used when standard assumptions are violated since these methods are more frequently encountered when working with real data. Determining appropriate sample sizes is emphasized throughout. Only crucial equations are included.

The new edition features:

  • New co-author, Debbie L. Hahs-Vaughn, the 2007 recipient of the University of Central Florida's College of Education Excellence in Graduate Teaching Award.
  • A new chapter on logistic regression models for today's more complex methodologies.
  • Much more on computing confidence intervals and conducting power analyses using G*Power.
  • All new SPSS version 19 screenshots to help navigate through the program and annotated output to assist in the interpretation of results.
  • Sections on how to write-up statistical results in APA format and new templates for writing research questions.
  • New learning tools including chapter-opening vignettes, outlines, a list of key concepts, "Stop and Think" boxes, and many more examples, tables, and figures.
  • More tables of assumptions and the effects of their violation including how to test them in SPSS.
  • 33% new conceptual, computational, and all new interpretative problems.
  • A website with Power Points, answers to the even-numbered problems, detailed solutions to the odd-numbered problems, and test items for instructors, and for students the chapter outlines, key concepts, and datasets.

Each chapter begins with an outline, a list of key concepts, and a research vignette related to the concepts. Realistic examples from education and the behavioral sciences illustrate those concepts. Each example examines the procedures and assumptions and provides tips for how to run SPSS and develop an APA style write-up. Tables of assumptions and the effects of their violation are included, along with how to test assumptions in SPSS. Each chapter includes computational, conceptual, and interpretive problems. Answers to the odd-numbered problems are provided. The SPSS data sets that correspond to the book’s examples and problems are available on the web.?

The book covers basic and advanced analysis of variance models and topics not dealt with in other texts such as robust methods, multiple comparison and non-parametric procedures, and multiple and logistic regression models. Intended for courses in intermediate statistics and/or statistics II taught in education and/or the behavioral sciences, predominantly at the master's or doctoral level. Knowledge of introductory statistics is assumed.

Table of Contents

  1. Front Cover
  2. Statistical concepts
  3. Title Page
  4. Copyright
  5. Dedication
  6. Contents
  7. Preface
  8. Acknowledgements
  9. 1. One-Factor Analysis of Variance: Fixed-Effects Model
    1. 1.1 Characteristics of One-Factor ANOVA Model
    2. 1.2 Layout of Data
    3. 1.3 ANOVA Theory
    4. 1.4 ANOVA Model
    5. 1.5 Assumptions and Violation of Assumptions
    6. 1.6 Unequal n’s or Unbalanced Procedure
    7. 1.7 Alternative ANOVA Procedures
    8. 1.8 SPSS and G*Power
    9. 1.9 Template and APA-Style Write-Up
    10. 1.10 Summary
    11. Problems
  10. 2. Multiple Comparison Procedures
    1. 2.1 Concepts of Multiple Comparison Procedures
    2. 2.2 Selected Multiple Comparison Procedures
    3. 2.3 SPSS
    4. 2.4 Template and APA-Style Write-Up
    5. 2.5 Summary
    6. Problems
  11. 3. Factorial Analysis of Variance: Fixed-Effects Model
    1. 3.1 Two-Factor ANOVA Model
    2. 3.2 Three-Factor and Higher-Order ANOVA
    3. 3.3 Factorial ANOVA With Unequal n’s
    4. 3.4 SPSS and G*Power
    5. 3.5 Template and APA-Style Write-Up
    6. 3.6 Summary
    7. Problems
  12. 4. Introduction to Analysis of Covariance: One-Factor Fixed-Effects Model With Single Covariate
    1. 4.1 Characteristics of the Model
    2. 4.2 Layout of Data
    3. 4.3 ANCOVA Model
    4. 4.4 ANCOVA Summary Table
    5. 4.5 Partitioning the Sums of Squares
    6. 4.6 Adjusted Means and Related Procedures
    7. 4.7 Assumptions and Violation of Assumptions
    8. 4.8 Example
    9. 4.9 ANCOVA Without Randomization
    10. 4.10 More Complex ANCOVA Models
    11. 4.11 Nonparametric ANCOVA Procedures
    12. 4.12 SPSS and G*Power
    13. 4.13 Template and APA-Style Paragraph
    14. 4.14 Summary
    15. Problems
  13. 5. Random- and Mixed-Effects Analysis of Variance Models
    1. 5.1 One-Factor Random-Effects Model
    2. 5.2 Two-Factor Random-Effects Model
    3. 5.3 Two-Factor Mixed-Effects Model
    4. 5.4 One-Factor Repeated Measures Design
    5. 5.5 Two-Factor Split-Plot or Mixed Design
    6. 5.6 SPSS and G*Power
    7. 5.7 Template and APA-Style Write-Up
    8. 5.8 Summary
    9. Problems
  14. 6. Hierarchical and Randomized Block Analysis of Variance Models
    1. 6.1 Two-Factor Hierarchical Model
    2. 6.2 Two-Factor Randomized Block Design for n = 1
    3. 6.3 Two-Factor Randomized Block Design for n > 1
    4. 6.4 Friedman Test
    5. 6.5 Comparison of Various ANOVA Models
    6. 6.6 SPSS
    7. 6.7 Template and APA-Style Write-Up
    8. 6.8 Summary
    9. Problems
  15. 7. Simple Linear Regression
    1. 7.1 Concepts of Simple Linear Regression
    2. 7.2 Population Simple Linear Regression Model
    3. 7.3 Sample Simple Linear Regression Model
    4. 7.4 SPSS
    5. 7.5 G*Power
    6. 7.6 Template and APA-Style Write-Up
    7. 7.7 Summary
    8. Problems
  16. 8. Multiple Regression
    1. 8.1 Partial and Semipartial Correlations
    2. 8.2 Multiple Linear Regression
    3. 8.3 Methods of Entering Predictors
    4. 8.4 Nonlinear Relationships
    5. 8.5 Interactions
    6. 8.6 Categorical Predictors
    7. 8.7 SPSS
    8. 8.8 G*Power
    9. 8.9 Template and APA-Style Write-Up
    10. 8.10 Summary
    11. Problems
  17. 9. Logistic Regression
    1. 9.1 How Logistic Regression Works
    2. 9.2 Logistic Regression Equation
    3. 9.3 Estimation and Model Fit
    4. 9.4 Significance Tests
    5. 9.5 Assumptions and Conditions
    6. 9.6 Effect Size
    7. 9.7 Methods of Predictor Entry
    8. 9.8 SPSS
    9. 9.9 G*Power
    10. 9.10 Template and APA-Style Write-Up
    11. 9.11 What Is Next?
    12. 9.12 Summary
    13. Problems
  18. Appendix: Tables
  19. References
  20. Odd-Numbered Answers to Problems
  21. Author Index
  22. Subject Index