Psychological Statistics and Psychometrics Using Stata

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Psychological Statistics and Psychometrics Using Stata by Scott Baldwin is a complete and concise resource for students and researchers in the behavioral sciences.
ISBN 13 2019
ISBN 10 978-1-59718-303-1
Pages 454; paperback
Copyright 2019
Book type Paperback

Psychological Statistics and Psychometrics Using Stata by Scott Baldwin is a complete and concise resource for students and researchers in the behavioral sciences.

Baldwin's primary goal in this book is to help readers become competent users of statistics. To that end, he first introduces basic statistical methods such as regression, t tests, and ANOVA. He focuses on explaining the models, how they can be used with different types of variables, and how to interpret the results. After building this foundation, Baldwin covers more advanced statistical techniques, including power-and-sample size calculations, multilevel modeling, and structural equation modeling. This book also discusses measurement concepts that are crucial in psychometrics. For instance, Baldwin explores how reliability and validity can be understood and evaluated using exploratory and confirmatory factor analysis. Baldwin includes dozens of worked examples using real data to illustrate the theory and concepts.

In addition to teaching statistical topics, this book helps readers become proficient Stata users. Baldwin teaches Stata basics ranging from navigating the interface to using features for data management, descriptive statistics, and graphics. He emphasizes the need for reproducibility in data analysis; therefore, he is careful to explain how version control and do-files can be used to ensure that results are reproducible. As each statistical concept is introduced, the corresponding commands for fitting and interpreting models are demonstrated. Beyond this, readers learn how to run simulations in Stata to help them better understand the models they are fitting and other statistical concepts.

This book is an excellent textbook for graduate-level courses in psychometrics. It is also an ideal reference for psychometricians and other social scientists who are new to Stata.

List of figures
List of tables
Acknowledgments
Notation and Typography

Getting oriented to Stata
1 Introduction
  • 1.1 Structure of the book
  • 1.2 Benefits of Stata
  • 1.3 Scientific context
2 Introduction to Stata
  • 2.1 Point-and-click versus writing commands
  • 2.2 The Stata interface
  • 2.3 Getting data in Stata
  • 2.4 Viewing and desribing data
  • 2.4.1 list, in, and if
  • 2.5 Creating new variables
  • 2.5.1 Missing data
  • 2.5.2 Labels
  • 2.6 Summarizing data
  • 2.6.1 summarize
  • 2.6.2 table and tabulate
  • 2.7 Graphing data
  • 2.7.1 Histograms
  • 2.7.2 Box plots
  • 2.7.3 Scatterplots
  • 2.8 Reproducible analysis
  • 2.8.1 Do-files
  • 2.8.2 Log files
  • 2.8.3 Project Manager
  • 2.8.4 Workflow
  • 2.9 Getting help
  • 2.9.1 Help documents
  • 2.9.2 PDF documentation
  • 2.10 Extending Stata
  • 2.10.1 Statistical Software Components
  • 2.10.2 Writing your own programs
Understanding relationships between variables
  • 3 Regression with continuous predictors
  • 3.1 Data
  • 3.2 Exploration
  • 3.2.1 Demonstration
  • Simulation program
  • 3.3 Bivariate regression
  • 3.3.1 Lines
  • 3.3.2 Regression equation
  • 3.3.3 Estimation
  • 3.3.4 Interpretation
  • Slope
  • Intercept
  • 3.3.5 Residuals and predicted values
  • 3.3.6 Partitioning variance
  • 3.3.7 Confidence intervals
  • 3.3.8 Null hypothesis significance testing
  • 3.3.9 Additional methods for understanding models
  • Using predicted scores to understand model implications
  • Composite contrasts
  • 3.4 Conclusions
4 Regression with categorical and continuous predictors
  • 4.1 Data for this chapter
  • 4.2 Why categorical predictors need special care
  • 4.3 Dummy coding
  • 4.3.1 Example: Incorrect use of categorical variable
  • 4.4 Multiple predictors
  • 4.4.1 Interpretation
  • Model fit
  • Intercept
  • Slopes
  • 4.4.2 Unique variance
  • 4.5 Interactions
  • 4.5.1 Categorical by continuous interactions
  • Dichotomous by continuous interactions
  • Polytomous by continuous interactions
  • Joint test for interactions with polytomous variables
  • 4.5.2 Continuous by continuous interactions
  • 4.6 Summary
5 t tests and one-way ANOVA
  • 5.1 Data
  • 5.2 Comparing two means
  • 5.2.1 t test
  • 5.2.2 Effect size
  • 5.3 Comparing three or more means
  • 5.3.1 Analysis of variance
  • 5.3.2 Multiple comparisons
  • Planned comparisons
  • Direct adjustment for multiple comparisons
  • 5.4 Summary
6 Factorial ANOVA
  • 6.1 Data for this chapter
  • 6.2 Factorial design with two factors
  • 6.2.1 Examining and visualizing the data
  • 6.2.2 Main effects
  • Testing the null hypothesis
  • 6.2.3 Interactions
  • 6.2.4 Partitioning the variance
  • 6.2.5 2 x 2 source table
  • 6.2.6 Using anova to estimate a factorial ANOVA
  • 6.2.7 Simple effects
  • 6.2.8 Effect size
  • 6.3 Factorial design with three factors
  • 6.3.1 Examining and visualizing the data
  • 6.3.2 Marginal means
  • 6.3.3 Main effects and interactions
  • 6.3.4 Three-way interaction
  • 6.3.5 Fitting the model with anova
  • 6.3.6 Interpreting the interaction
  • 6.3.7 A note about effect size
  • 6.4 Conclusion
7 Repeated-measures models
  • 7.1 Data for this chapter
  • 7.2 Basic model
  • 7.3 Using mixed to fit a repeated-measures model
  • 7.3.1 Covariance structures
  • Compound symmetry (exchangeable)
  • First-order autoregressive
  • Toeplitz
  • Unstructures
  • 7.3.2 Degrees of freedom
  • 7.3.3 Pairwise comparisons
  • 7.4 Models with multiple factors
  • 7.5 Estimating heteroskedastic residuals
  • 7.6 Summary
8 Planning studies: Power and sample-size calculations
  • 8.1 Foundational ideas
  • 8.1.1 Null and alternative distributions
  • 8.1.2 Simulating draws out of the null and alternative distributions
  • 8.2 Computing power manually
  • 8.3 Stata's commands
  • 8.3.1 Two-sample z test
  • 8.3.2 Two-sample t test
  • 8.3.3 Correlation
  • 8.3.4 One-way ANOVA
  • 8.3.5 Factorial ANOVA
  • 8.4 The central importance of power
  • 8.4.1 Type M and S errors
  • Type S errors
  • Type M errorss
  • 8.5 Summary
9 Multilevel models for cross-sectional data
  • 9.1 Data used in this chapter
  • 9.2 Why clustered data structures matter
  • 9.2.1 Statistical issues
  • 9.2.2 Conceptual issues
  • 9.3 Basics of a multilevel model
  • 9.3.1 Partitioning sources of variance
  • 9.3.2 Random intercepts
  • 9.3.3 Estimating random intercepts
  • 9.3.4 Intraclass correlations
  • 9.3.5 Estimating cluster means
  • Comparing pooled and unpooled means
  • 9.3.6 Adding a predictor
  • 9.4 Between-clusters and within-cluster relationships
  • 9.4.1 Partitioning variance in the predictor
  • 9.4.2 Total- versus level-specific relationships
  • 9.4.3 Exploring the between-clusters and within-cluster relationships
  • 9.4.4 Estimating the between-clusters and within-cluster effects
  • 9.5 Random slopes
  • 9.6 Summary
10 Multilevel models for longitudinal data
  • 10.1 Data used in this chapter
  • 10.2 Basic growth model
  • 10.2.1 Multilevel model
  • 10.3 Adding a level-2 predictor
  • 10.4 Adding a level-1 predictor
  • 10.5 Summary
Psychometrics through the lens of factor analysis
  • 11 Factor analysis: Reliability
  • 11.1 What you will learn in this chapter
  • 11.2 Example data
  • 11.3 Common versus unique variance
  • 11.4 One-factor model
  • 11.4.1 Parts of a path model
  • 11.4.2 Where do the latent variables come from?
  • 11.5 Prediction equation
  • 11.6 Using sem to estimate CFA models
  • 11.7 Model fit
  • 11.7.1 Computing χ²
  • 11.8 Obtaining σ²C and σ²U
  • 11.8.1 Computing R² for an item
  • 11.8.2 Computing σ²C and σ²U for all items
  • 11.8.3 Computing reliability—ω
  • 11.8.4 Bootstrapping the standard error and 95% confidence interval for ω
  • 11.9 Comparing ω with α
  • 11.9.1 Evaluating the assumption of tau-equivalence
  • 11.9.2 Parallel items
  • 11.10 Correlated residuals
  • 11.11 Summary
12 Factor analysis: Factorial validity
  • 12.1 Data for this chapter
  • 12.2 Exploratory factor analysis
  • 12.2.1 Common factor model
  • 12.2.2 Extraction methods
  • 12.2.3 Interpreting loadings
  • 12.2.4 Eigenvalues
  • 12.2.5 Communality and uniqueness
  • 12.2.6 Factor analysis versus principal-component analysis
  • 12.2.7 Choosing factors and rotation
  • How many factors should we extract?
  • Eigenvalue-greater-than-one rule
  • Scree plots
  • Parallel analysis
  • Orthogonal rotation—varimax
  • Oblique rotation—promax
  • 12.3 Confirmatory factor analysis
  • 12.3.1 EFA versus CFA
  • 12.3.2 Estimating a CFA with sem
  • 12.3.3 Mean structure versus variance structure
  • 12.3.4 Identifying models
  • Imposing constraints for identification
  • How much information is needed to identify a model?
  • 12.3.5 Refitting the model with constrained latent variables
  • 12.3.6 Standardized solutions
  • 12.3.7 Global fit
  • RMSEA
  • TLI
  • CFI
  • SRMR
  • A summary and a caution
  • 12.3.8 Refining models further
  • 12.3.9 Parallel items
  • 12.4 Summary
13 Measurement invariance
  • 13.1 Data
  • 13.2 Measurement invariance
  • 13.3 Measurement invariance across groups
  • 13.3.1 Configural invariance
  • 13.3.2 Metric invariance
  • 13.3.3 Scalar invariance
  • 13.3.4 Residual invariance
  • 13.3.5 Using the comparative fit index to evaluate invariance
  • 13.4 Structural invariance
  • 13.4.1 Invariant factor variances
  • 13.4.2 Invariant factor means
  • 13.5 Measurement invariance across time
  • 13.5.1 Configural invariance
  • Effects coding for identification
  • Effects-coding constraints in Stata
  • 13.5.2 Metric invariance
  • 13.5.3 Scalar invariance
  • 13.5.4 Residual invariance
  • 13.6 Structural invariance
  • 13.7 Summary
References
Author index
Subject index
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