*书籍名称：A Gentle Introduction to Stata, Sixth Edition*

*出版社：Stata Press*

*作者： Alan C. Acock*

*出版时间：2018-06-07*

*语种： 英语*

*页数： 570*

*印刷日期:2018-06-12*

*开本: 胶状纸 *

*纸张：570 I S B N： 978-1-59718-269-0 *

*装订: 平装*

Alan C. Acock's A Gentle Introduction to Stata, Sixth 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 part 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 multilevel 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, 2006, and 2016, 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 sixth edition incorporates new features of Stata 15. All menus, dialog boxes, and instructions for using the point-and-click interface have been updated. Power-and-sample-size calculations for linear regression are demonstrated using Stata 15's new power rsquared command. This edition also includes new sections that describe how to evaluate convergent and discriminant validity, how to compute effect sizes for t tests and ANOVA models, how to use margins and marginsplot to interpret results of linear and logistic regression models, and how to use full-information maximum-likelihood (FIML) estimation with SEM to address problems with missing data.

List of figures List of tables List of boxed tips Preface Support materials for the book Glossary of acronyms Glossary of mathematical and statistical symbols 1 Getting started 1.1 Conventions 1.2 Introduction 1.3 The Stata screen 1.4 Using an existing dataset 1.5 An example of a short Stata session 1.6 Video aids to learning Stata 1.7 Summary 1.8 Exercises 2 Entering data 2.1 Creating a dataset 2.2 An example questionnaire 2.3 Developing a coding system 2.4 Entering data using the Data Editor 2.4.1 Value labels 2.5 The Variables Manager 2.6 The Data Editor (Browse) view 2.7 Saving your dataset 2.8 Checking the data 2.9 Summary 2.10 Exercises 3 Preparing data for analysis 3.1 Introduction 3.2 Planning your work 3.3 Creating value labels 3.4 Reverse-code variables 3.5 Creating and modifying variables 3.6 Creating scales 3.7 Saving some of your data 3.8 Summary 3.9 Exercises 4 Working with commands, do-files, and results 4.1 Introduction 4.2 How Stata commands are constructed 4.3 Creating a do-file 4.4 Copying your results to a word processor 4.5 Logging your command file 4.6 Summary 4.7 Exercises 5 Descriptive statistics and graphs for one variable 5.1 Descriptive statistics and graphs 5.2 Where is the center of a distribution? 5.3 How dispersed is the distribution? 5.4 Statistics and graphs—unordered categories 5.5 Statistics and graphs—ordered categories and variables 5.6 Statistics and graphs—quantitative variables 5.7 Summary 5.8 Exercises 6 Statistics and graphs for two categorical variables 6.1 Relationship between categorical variables 6.2 Cross-tabulation 6.3 Chi-squared test 6.3.1 Degrees of freedom 6.3.2 Probability tables 6.4 Percentages and measures of association 6.5 Odds ratios when dependent variable has two categories 6.6 Ordered categorical variables 6.7 Interactive tables 6.8 Tables—linking categorical and quantitative variables 6.9 Power analysis when using a chi-squared test of significance 6.10 Summary 6.11 Exercises 7 Tests for one or two means 7.1 Introduction to tests for one or two means 7.2 Randomization 7.3 Random sampling 7.4 Hypotheses 7.5 One-sample test of a proportion 7.6 Two-sample test of a proportion 7.7 One-sample test of means 7.8 Two-sample test of group means 7.8.1 Testing for unequal variances 7.9 Repeated-measures t test 7.10 Power analysis 7.11 Nonparametric alternatives 7.11.1 Mann–Whitney two-sample rank-sum test 7.11.2 Nonparametric alternative: Median test 7.12 Video tutorial related to this chapter 7.13 Summary 7.14 Exercises 8 Bivariate correlation and regression 8.1 Introduction to bivariate correlation and regression 8.2 Scattergrams 8.3 Plotting the regression line 8.4 An alternative to producing a scattergram, binscatter 8.5 Correlation 8.6 Regression 8.7 Spearman’s rho: Rank-order correlation for ordinal data 8.8 Power analysis with correlation 8.9 Summary 8.10 Exercises 9 Analysis of variance 9.1 The logic of one-way analysis of variance 9.2 ANOVA example 9.3 ANOVA example with nonexperimental data 9.4 Power analysis for one-way ANOVA 9.5 A nonparametric alternative to ANOVA 9.6 Analysis of covariance 9.7 Two-way ANOVA 9.8 Repeated-measures design 9.9 Intraclass correlation—measuring agreement 9.10 Power analysis with ANOVA 9.10.1 Power analysis for one-way ANOVA 9.10.2 Power analysis for two-way ANOVA 9.10.3 Power analysis for repeated-measures ANOVA 9.10.4 Summary of power analysis for ANOVA 9.11 Summary 9.12 Exercises 10 Multiple regression 10.1 Introduction to multiple regression 10.2 What is multiple regression? 10.3 The basic multiple regression command 10.4 Increment in R-squared: Semipartial correlations 10.5 Is the dependent variable normally distributed? 10.6 Are the residuals normally distributed? 10.7 Regression diagnostic statistics 10.7.1 Outliers and influential cases 10.7.2 Influential observations: DFbeta 10.7.3 Combinations of variables may cause problems 10.8 Weighted data 10.9 Categorical predictors and hierarchical regression 10.10 A shortcut for working with a categorical variable 10.11 Fundamentals of interaction 10.12 Nonlinear relations 10.12.1 Fitting a quadratic model 10.12.2 Centering when using a quadratic term 10.12.3 Do we need to add a quadratic component? 10.13 Power analysis in multiple regression 10.14 Summary 10.15 Exercises 11 Logistic regression 11.1 Introduction to logistic regression 11.2 An example 11.3 What is an odds ratio and a logit? 11.3.1 The odds ratio 11.3.2 The logit transformation 11.4 Data used in the rest of the chapter 11.5 Logistic regression 11.6 Hypothesis testing 11.6.1 Testing individual coefficients 11.6.2 Testing sets of coefficients 11.7 Margins: More on interpreting results from logistic regression 11.8 Nested logistic regressions 11.9 Power analysis when doing logistic regression 11.10 Next steps for using logistic regression and its extensions 11.11 Summary 11.12 Exercises 12 Measurement, reliability, and validity 12.1 Overview of reliability and validity 12.2 Constructing a scale 12.2.1 Generating a mean score for each person 12.3 Reliability 12.3.1 Stability and test–retest reliability 12.3.2 Equivalence 12.3.3 Split-half and alpha reliability—internal consistency 12.3.4 Kuder–Richardson reliability for dichotomous items 12.3.5 Rater agreement—kappa (κ) 12.4 Validity 12.4.1 Expert judgment 12.4.2 Criterion-related validity 12.4.3 Construct validity 12.5 Factor analysis 12.6 PCF analysis 12.6.1 Orthogonal rotation: Varimax 12.6.2 Oblique rotation: Promax 12.7 But we wanted one scale, not four scales 12.7.1 Scoring our variable 12.8 Summary 12.9 Exercises 13 Structural equation and generalized structural equation modeling 13.1 Linear regression using sem 13.1.1 Using the sem command directly 13.1.2 SEM and working with missing values 13.1.3 Exploring missing values and auxiliary variables 13.1.4 Getting auxiliary variables into your SEM command 13.2 A quick way to draw a regression model 13.3 The gsem command for logistic regression 13.3.1 Fitting the model using the logit command 13.3.2 Fitting the model using the gsem command 13.4 Path analysis and mediation 13.5 Conclusions and what is next for the sem command 13.6 Exercises 14 Working with missing values—multiple imputation 14.1 Working with missing values—multiple imputation 14.2 What variables do we include when doing imputations? 14.3 The nature of the problem 14.4 Multiple imputation and its assumptions about the mechanism for missingness 14.5 Multiple imputation 14.6 A detailed example 14.6.1 Preliminary analysis 14.6.2 Setup and multiple-imputation stage 14.6.3 The analysis stage 14.6.4 For those who want an R2 and standardized βs 14.6.5 When impossible values are imputed 14.7 Summary 14.8 Exercises 15 An introduction to multilevel analysis 15.1 Questions and data for groups of individuals 15.2 Questions and data for a longitudinal multilevel application 15.3 Fixed-effects regression models 15.4 Random-effects regression models 15.5 An applied example 15.5.1 Research questions 15.5.2 Reshaping data to do multilevel analysis 15.6 A quick visualization of our data 15.7 Random-intercept model 15.7.1 Random intercept—linear model 15.7.2 Random-intercept model—quadratic term 15.7.3 Treating time as a categorical variable 15.8 Random-coefficients model 15.9 Including a time-invariant covariate 15.10 Summary 15.11 Exercises 16 Item response theory (IRT) 16.1 How are IRT measures of variables different from summated scales? 16.2 Overview of three IRT models for dichotomous items 16.2.1 The one-parameter logistic (1PL) model 16.2.2 The two-parameter logistic (2PL) model 16.2.3 The three-parameter logistic (3PL) model 16.3 Fitting the 1PL model using Stata 16.3.1 The estimation 16.3.2 How important is each of the items? 16.3.3 An overall evaluation of our scale 16.3.4 Estimating the latent score 16.4 Fitting a 2PL IRT model 16.4.1 Fitting the 2PL model 16.5 The graded response model—IRT for Likert-type items 16.5.1 The data 16.5.2 Fitting our graded response model 16.5.3 Estimating a person’s score 16.6 Reliability of the fitted IRT model 16.7 Using the Stata menu system 16.8 Extensions of IRT 16.9 Exercises A What’s next? A.1 Introduction to the appendix A.2 Resources A.2.1 Web resources A.2.2 Books about Stata A.2.3 Short courses A.2.4 Acquiring data A.2.5 Learning from the postestimation methods A.3 Summary References Author index Subject index