A gentle introduction to Stata

著者

書誌事項

A gentle introduction to Stata

Alan C. Acock

Stata Press, 2014

4th ed

大学図書館所蔵 件 / 全14件

この図書・雑誌をさがす

注記

Includes bibliographical references and indexes

内容説明・目次

内容説明

A Gentle Introduction to Stata, Fourth Edition is for people who need to learn Stata but who may not have a strong background in statistics or prior experience with statistical software packages. After working through this book, you will be able to enter, build, and manage a dataset, and perform fundamental statistical analyses. This book is organized like the unfolding of a research project. You begin by learning how to enter and manage data and how to do basic descriptive statistics and graphical analysis. Then you learn how to perform standard statistical procedures from t tests, nonparametric tests, and measures of association through ANOVA, multiple regression, and logistic regression. Readers who have experience with another statistical package may benefit more by reading chapters selectively and referring to this book as needed. The fourth edition has incorporated numerous changes that were new with Stata 13. Coverage of the marginsplot command has expanded. This simplifies the construction of compelling graphs. There is a new chapter showing how to estimate path models using the sem (structural equation modeling) command. Menus have been updated, and several minor changes and corrections have been included based on suggestions from readers.

目次

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

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