*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.

© Copyright 1996–2023 StataCorp LLC

**List of figures**

**List of tables**

**Acknowledgments**

**Notation and Typography**

**Getting oriented to Stata**

1.2 Benefits of Stata

1.3 Scientific context

2.2 The Stata interface

2.3 Getting data in Stata

2.4 Viewing and desribing data

2.5 Creating new variables

2.5.2 Labels

2.6 Summarizing data

2.6.2 table and tabulate

2.7 Graphing data

2.7.2 Box plots

2.7.3 Scatterplots

2.8 Reproducible analysis

2.8.2 Log files

2.8.3 Project Manager

2.8.4 Workflow

2.9 Getting help

2.9.2 PDF documentation

2.10 Extending Stata

2.10.2 Writing your own programs

**Understanding relationships between variables**

3.2 Exploration

3.3 Bivariate regression

3.3.2 Regression equation

3.3.3 Estimation

3.3.4 Interpretation

Intercept

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

Composite contrasts

3.4 Conclusions

4.2 Why categorical predictors need special care

4.3 Dummy coding

4.4 Multiple predictors

Intercept

Slopes

4.5 Interactions

Polytomous by continuous interactions

Joint test for interactions with polytomous variables

4.6 Summary

5.2 Comparing two means

5.2.2 Effect size

5.3 Comparing three or more means

5.3.2 Multiple comparisons

Direct adjustment for multiple comparisons

5.4 Summary

6.2 Factorial design with two factors

6.2.2 Main effects

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

6.3.2 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

7.2 Basic model

7.3 Using mixed to fit a repeated-measures model

First-order autoregressive

Toeplitz

Unstructures

7.3.3 Pairwise comparisons

7.4 Models with multiple factors

7.5 Estimating heteroskedastic residuals

7.6 Summary

8.1 Foundational ideas

8.1.2 Simulating draws out of the null and alternative distributions

8.2 Computing power manually

8.3 Stata’s commands

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

Type M errorss

8.5 Summary

9.2 Why clustered data structures matter

9.2.2 Conceptual issues

9.3 Basics of a multilevel model

9.3.2 Random intercepts

9.3.3 Estimating random intercepts

9.3.4 Intraclass correlations

9.3.5 Estimating cluster means

9.4 Between-clusters and within-cluster relationships

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

10.2 Basic growth 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**

11.2 Example data

11.3 Common versus unique variance

11.4 One-factor 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

11.8 Obtaining σ²_{C} and σ²_{U}

11.8.2 Computing σ²

_{C}and σ²

_{U}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

11.9.2 Parallel items

11.10 Correlated residuals

11.11 Summary

12.2 Exploratory factor analysis

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

Eigenvalue-greater-than-one rule

Scree plots

Parallel analysis

Orthogonal rotation—varimax

Oblique rotation—promax

12.3 Confirmatory factor analysis

12.3.2 Estimating a CFA with sem

12.3.3 Mean structure versus variance structure

12.3.4 Identifying models

How much information is needed to identify a model?

12.3.6 Standardized solutions

12.3.7 Global fit

TLI

CFI

SRMR

A summary and a caution

12.3.9 Parallel items

12.4 Summary

13.2 Measurement invariance

13.3 Measurement invariance across groups

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

13.4.2 Invariant factor means

13.5 Measurement invariance across time

Effects-coding constraints in Stata

13.5.3 Scalar invariance

13.5.4 Residual invariance

13.6 Structural invariance

13.7 Summary

**References**

**Author index**

**Subject index**

© Copyright 1996–2023 StataCorp LLC