A Gentle Introduction to Stata, Fourth Edition

Alan Acock is a sociologist and a University Distinguished Professor in the School of Social and Behavioral Health Sciences at Oregon State University. He held the Knudson Chair in Family Research and was also recognized as the Alumni Distinguished Professor based on his work with students. He is the author of Discovering Structural Equation Modeling Using Stata, Revised Edition. He has published more than 150 articles in leading journals across the social and behavioral sciences, including Structural Equation Modeling, Psychological Bulletin, Multivariate Behavioral Research, Journal of Gerontology, Journal of Adolescence, American Journal of Public Health, American Sociological Review, Journal of Marriage and Family, Social Forces, Drug and Alcohol Dependence, Educational and Psychological Measurement, Journal of Politics, Prevention Science, American Journal of Preventive Medicine, and many others. With this broad experience, Acock brings examples from a variety of disciplines.

List of figures


List of tables


List of boxed tips


Preface


Support materials for the book


Getting started


Conventions


Introduction


The Stata screen


Using an existing dataset


An example of a short Stata session


Summary


Exercises


Entering data


Creating a dataset


An example questionnaire


Developing a coding system


Entering data using the Data Editor


The Variables Manager


The Data Editor (Browse) view


Saving your


Checking the data


Summary


Exercises


Preparing data for analysis


Introduction


Planning your work


Creating value labels


Reverse-code variables


Creating and modifying variables


Creating scales


Saving some of your data


Summary


Exercises


Working with commands, do-files, and results


Introduction


How Stata commands are constructed


Creating a do-file


Copying your results to a word processor


Logging your command file


Summary


Exercises


Descriptive statistics and graphs for one variable


Descriptive statistics and graphs


Where is the center of a distribution?


How dispersed is the distribution?


Statistics and graphs—unordered categories


Statistics and graphs—ordered categories and variables


Statistics and graphs—quantitative variables


Summary


Exercises


Statistics and graphs for two categorical variables


Relationship between categorical variables


Cross-tabulation


Chi-squared test


Percentages and measures of association


Odds ratios when dependent variable has two categories


Ordered categorical variables


Interactive tables


Tables—linking categorical and quantitative variables


Power analysis when using a chi-squared test of significance


Summary


Exercises


Tests for one or two means


Introduction to tests for one or two means


Randomization


Random sampling


Hypotheses


One-sample test of a proportion


Two-sample test of a proportion


One-sample test of means


Two-sample test of group means


Repeated-measures t test


Power analysis


Nonparametric alternatives


Summary


Exercises


Bivariate correlation and regression


Introduction to bivariate correlation and regression


Scattergrams


Plotting the regression line


An alternative to producing a scattergram, binscatter


Correlation


Regression


Spearman’s rho: Rank-order correlation for ordinal data


Summary


Exercises


Analysis of variance


The logic of one-way analysis of variance


ANOVA example


ANOVA example using survey data


A nonparametric alternative to ANOVA


Analysis of covariance


Two-way ANOVA


Repeated-measures design


Intraclass correlation—measuring agreement


Power analysis with ANOVA


Power analysis for two-way ANOVA


Summary


Exercises


Multiple regression


Introduction to multiple regression


What is multiple regression?


The basic multiple regression command


Increment in R-squared: Semipartial correlations


Is the dependent variable normally distributed?


Are the residuals normally distributed?


Regression diagnostic statistics


Weighted data


Categorical predictors and hierarchical regression


A shortcut for working with a categorical variable


Fundamentals of interaction


Nonlinear relations


Power analysis in multiple regression


Summary


Exercises


Logistic regression


Introduction to logistic regression


An example


What is an odds ratio and a logit?


Data used in the rest of the chapter


Logistic regression


Hypothesis testing


More on interpreting results from logistic regression


Nested logistic regressions


Power analysis when doing logistic regression


Summary


Exercises


Measurement, reliability, and validity


Overview of reliability and validity


Constructing a scale


Reliability


Validity


Factor analysis


PCF analysis


But we wanted one scale, not four scales


Summary


Exercises


Working with missing values—multiple imputation


The nature of the problem


Multiple imputation and its assumptions about the mechanism for missingness


What variables do we include when doing imputations?


Multiple imputation


A detailed example


Summary


Exercises


The sem and gsem commands


Ordinary least-squares regression models using sem


A quick way to draw a regression model and a fresh start


The gsem command for logistic regression


Path analysis and mediation


Conclusions and what is next for the sem command


Exercises


What’s next?


Introduction to the appendix


Resources


Summary


References


Author index


Subject index