A Gentle Introduction to Stata (5th Revised edition)
By: Alan C. Acock (author)Paperback
Alan C. Acock's A Gentle Introduction to Stata, Fifth Edition, is aimed at new Stata users who want to become proficient in Stata. After reading this introductory text, new users will be able not only to use Stata well but also to learn new aspects of Stata. Acock assumes that the user is not familiar with any statistical software. This assumption of a blank slate is central to the structure and contents of the book. Acock starts with the basics; for example, the portion of the book that deals with data management begins with a careful and detailed example of turning survey data on paper into a Stata-ready dataset on the computer. When explaining how to go about basic exploratory statistical procedures, Acock includes notes that will help the reader develop good work habits. This mixture of explaining good Stata habits and good statistical habits continues throughout the book. Acock is quite careful to teach the reader all aspects of using Stata.
He 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 (correlation, linear and logistic regression, and parametric and nonparametric tests of location and dispersion). He also successfully introduces some more advanced topics such as multiple imputation and structural equation modeling in a very approachable manner. Acock teaches Stata commands by using the menus and dialog boxes while still stressing the value of do-files. In this way, he ensures that all types of users can build good work habits. Each chapter has exercises that the motivated reader can use to reinforce the material. The tone of the book is friendly and conversational without ever being glib or condescending. Important asides and notes about terminology are set off in boxes, which makes the text easy to read without any convoluted twists or forward-referencing.
Rather than splitting topics by their Stata implementation, Acock arranges the topics as they would appear in a basic statistics textbook; graphics and postestimation are woven into the material in a natural fashion. Real datasets, such as the General Social Surveys from 2002 and 2006, are used throughout the book. The focus of the book is especially helpful for those in the behavioral and social sciences because the presentation of basic statistical modeling is supplemented with discussions of effect sizes and standardized coefficients. Various selection criteria, such as semipartial correlations, are discussed for model selection. Acock also covers a variety of commands available for evaluating reliability and validity of measurements. The fifth edition of the book includes two new chapters that cover multilevel modeling and item response theory (IRT) models. The multilevel modeling chapter demonstrates how to fit linear multilevel models using the mixed command. Acock discusses models with both random intercepts and random coefficients, and he provides a variety of examples that apply these models to longitudinal data.
The IRT chapter introduces the use of IRT models for evaluating a set of items designed to measure a specific trait such as an attitude, value, or a belief. Acock shows how to use the irt suite of commands, which are new in Stata 14, to fit IRT models and to graph the results. In addition, he presents a measure of reliability that can be computed when using IRT.
Getting started Conventions Introduction The Stata screen Using an existing dataset An example of a short Stata session Video aids to learning Stata Summary Exercises Entering data Creating a dataset An example questionnaire Developing 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 Video tutorial related to this chapter 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 Power analysis with correlation Summary Exercises Analysis of variance The logic of one-way analysis of variance ANOVA example ANOVA example with nonexperimental data Power analysis for one-way ANOVA 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 one-way ANOVA Power analysis for two-way ANOVA Power analysis for repeated-measures ANOVA Summary of power analysis for 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 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 Nonlinear relations Fitting a quadratic model Centering when using a quadratic term Do we need to add a quadratic component? 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 the rest of the chapter Logistic regression Hypothesis testing Testing individual coefficients Testing sets of coefficients More on interpreting results from logistic regression Nested logistic regressions Power analysis when doing logistic regression Next steps for using logistic regression and its extensions 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 reliabilit--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 R and standardized sss When impossible values are imputed Summary Exercises The sem and gsem commands Linear regression using sem Using the SEM Builder to fit a basic regression model A quick way to draw a regression model and a fresh start Using sem without the SEM Builder The gsem command for logistic regression Fitting the model using the logit command Fitting the model using the gsem command Path analysis and mediation Conclusions and what is next for the sem command Exercises An introduction to multilevel analysis Questions and data for groups of individuals Questions and data for a longitudinal multilevel application Fixed-effects regression models Random-effects regression models An applied example Research questions Reshaping data to do multilevel analysis A quick visualization of our data Random-intercept model Random intercept-linear model Random-intercept model-quadratic term Treating time as a categorical variable Random-coefficients model Including a time-invariant covariate Summary Exercises Item response theory (IRT) How are IRT measures of variables different from summated scales? Overview of three IRT models for dichotomous items The one-parameter logistic (PL) model The two-parameter logistic (PL) model The three-parameter logistic (PL) model Fitting the PL model using Stata The estimation How important is each of the items? An overall evaluation of our scale Estimating the latent score Fitting a PL IRT model Fitting the PL model The graded response model-IRT for Likert-type items The data Fitting our graded response model Estimating a person's score Reliability of the fitted IRT model Using the Stata menu system Extensions of IRT Exercises What's next? Introduction to the appendix Resources Web resources Books about Stata Short courses Acquiring data Learning from the postestimation methods Summary
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- ID: 9781597181853
5th Revised edition
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