• Table of Contents
  • Foreword
  • Chapter 1. Introduction to Structural Equation Modeling
  • Chapter 2. Center and Spread
  • Chapter 3. Type of Data, Distributions, Graphs
  • Chapter 4. Covariance and Correlation
  • Chapter 5. Directionality and Causality
  • Chapter 6. Standard Errors and p-values
  • Chapter 7. Linear Regression
  • Appendix A. Introduction to SPSS
  • Appendix B. Introduction to AMOS
  • Appendix C. Common Formulas
  • Translations
  • Foreword

    To provide the reader with a unifying structure to understand statistics, data analysis, and inference that spans the enormity of the science in a way that is accessible to the layman.

    Intended audience: graduate students who have not been exposed to statistics

    Learning Objectives

    • How to describe one variable (Univariate Descriptives)
      • Describe data distributions via graphical (e.g., histograms) and summary statistics (e.g., mean, standard deviation) techniques that evaluate center and spread.
    • How to describe how two variables relate (Bivariate associations)
      • Describe bivariate relationships via graphical (e.g., scatterplots) and summary statistics (e.g., covariance, correlation) techniques that evaluate the strength of linear associations.
    • How to construct graphics to show relationships between variables (Structural Equation Modeling Diagrams)
      • Show hypothesized causal and associative relationships between variables through accepted SEM diagramming techniques.
    • Sampling Distributions
      • Describe the process of the creation of sampling distributions and describe their distributional properties (e.g., spread or standard error).
    • Statistical language fluency
      • Demonstrate mastery of statistical terminology (e.g., p-value, standard errors) through the application and the implication on the stories we can tell from data.
    • Regression Betas
      • Recite from memory the definition of an unstandardized and standardized beta in a linear regression context and apply its definition in understanding its implication in data analysis.
    • Relate to traditional statistics
      • Demonstrate mastery of how SEM techniques are equivalent to traditional statistical methodology (e.g., ANOVA, t-test, etc).

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    Access it online or download it at https://edtechbooks.org/sem/purpose.