*书籍名称：A Gentle Introduction to Stata, 3rd Edition *

*出版社：Stata Press *

*作者： Alan C. Acock *

*出版时间：2012*

*语种： 英文*

*页数： 401*

*印刷日期:2012*

*开本: 胶版纸 *

*纸张：401 I S B N： 978-1-59718-109-9 *

*装订: 平装*

Alan C. Acock’s A Gentle Introduction to Stata, Revised Third Edition is aimed at new Stata users who want to become proficient in Stata. After reading this introductory text, new users not only will be able to use Stata well but also will learn new aspects of Stata easily. 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). 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 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 psychology and the 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. The revised third edition of the book has been updated to reflect the new features available in Stata 12 and Stata 11. The ANOVA chapter has been revised to incorporate the pwmeans command, to do mean comparisons, and the marginsplot command, which simplifies the construction of graphs showing interaction effects. Menus and screenshots have also been updated. As in the third edition, an entire chapter is devoted to the analysis of missing data and the use of multiple-imputation methods. Factor-variable notation is introduced as an alternative to the manual creation of interaction terms. The new Variables Manager and revamped Data Editor are featured in the discussion of data management.

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 Summary 1.7 Exercises 2 Entering data 2.1 Creating a dataset 2.2 An example questionnaire 2.3 Develop 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 Save 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 Summary 7.13 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 Correlation 8.5 Regression 8.6 Spearman’s rho: Rank-order correlation for ordinal data 8.7 Summary 8.8 Exercises 9 Analysis of variance 9.1 The logic of one-way analysis of variance 9.2 ANOVA example 9.3 ANOVA example using survey data 9.4 A nonparametric alternative to ANOVA 9.5 Analysis of covariance 9.6 Two-way ANOVA 9.7 Repeated-measures design 9.8 Intraclass correlation—measuring agreement 9.9 Summary 9.10 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 Power analysis in multiple regression 10.13 Summary 10.14 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 rest of chapter 11.5 Logistic regression 11.6 Hypothesis testing 11.6.1 Testing individual coefficients 11.6.2 Testing sets of coefficients 11.7 Nested logistic regressions 11.8 Power analysis when doing logistic regression 11.9 Summary 11.10 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 (K) 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 Working with missing values—multiple imputation 13.1 The nature of the problem 13.2 Multiple imputation and its assumptions about the mechanism for missingness 13.3 What variables do we include when doing imputations? 13.4 Multiple imputation 13.5 A detailed example 13.5.1 Preliminary analysis 13.5.2 Setup and multiple-imputation stage 13.5.3 The analysis stage 13.5.4 For those who want an R2 and standardized βs 13.5.5 When impossible values are imputed 13.6 Summary 13.7 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.3 Summary