A Gentle Introduction to Stata, Revised Third Edition

Updated to reflect the new features of Stata 11, A Gentle Introduction to Stata, Third Edition continues to help new Stata users become proficient in Stata. After reading this introductory text, you will be able to enter, build, and manage a data set as well as perform fundamental statistical analyses. New to the Third Edition A new chapter on the analysis of missing data and the use of multiple-imputation methods Extensive revision of the chapter on ANOVA Additional material on the application of power analysis The book covers data management; good work habits, including the use of basic do-files; basic exploratory statistics, including graphical displays; and analyses using the standard array of basic statistical tools, such as correlation, linear and logistic regression, and parametric and nonparametric tests of location and dispersion. Rather than splitting these topics by their Stata implementation, the material on graphics and postestimation are woven into the text in a natural fashion. The author teaches Stata commands by using the menus and dialog boxes while still stressing the value of do-files. Each chapter includes exercises and real data sets are used throughout.

Table of Contents:
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 Develop a coding system Entering data using the Data Editor Value labels The Variables Manager The Data Editor (Browse) view Saving your dataset 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 Save 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 Degrees of freedom Probability tables 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 Testing for unequal variances Repeated-measures t test Power analysis Nonparametric alternatives Mann–Whitney two-sample rank-sum test Nonparametric alternative: Median test Summary Exercises Bivariate correlation and regression Introduction to bivariate correlation and regression Scattergrams Plotting the regression line 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 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 Outliers and influential cases Influential observations: DFbeta Combinations of variables may cause problems Weighted data Categorical predictors and hierarchical regression A shortcut for working with a categorical variable Fundamentals of interaction Power analysis in multiple regression Summary Exercises Logistic regression Introduction to logistic regression An example What is an odds ratio and a logit? The odds ratio The logit transformation Data used in rest of chapter Logistic regression Hypothesis testing Testing individual coefficients Testing sets of coefficients Nested logistic regressions Power analysis when doing logistic regression Summary Exercises Measurement, reliability, and validity Overview of reliability and validity Constructing a scale Generating a mean score for each person Reliability Stability and test–retest reliability Equivalence Split-half and alpha reliability—internal consistency Kuder–Richardson reliability for dichotomous items Rater agreement—kappa (K) Validity Expert judgment Criterion-related validity Construct validity Factor analysis PCF analysis Orthogonal rotation: Varimax Oblique rotation: Promax But we wanted one scale, not four scales Scoring our variable 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 Preliminary analysis Setup and multiple-imputation stage The analysis stage For those who want an R2 and standardized ßs When impossible values are imputed Summary Exercises A What’s next? Introduction to the appendix Resources Web resources Books about Stata Short courses Acquiring data Summary References Author index (pdf) Subject index(pdf)

About the Author :
Alan C. Acock is a University Distinguished Professor of Family Science and the Knudson Chair for Family Research in the College of Health and Human Sciences at Oregon State University. He has published more than 120 articles in leading social and behavioral sciences journals. Dr. Acock’s research interests encompass quantitative methodology and family studies.