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书籍名称:A Gentle Introduction to Stata, Sixth Edition
出版社:Stata Press
作者: Alan C. Acock
语种: 英语
页数: 570
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
    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
    Author index
    Subject index