**A Gentle Introduction to Stata, Sixth Edition**

87,800원

Author: Alan C. Acock Publisher: Stata Press Copyright: 2018 ISBN-13: 978-1-59718-269-0 Pages: 570; paperback

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.

Alan Acock is a sociologist and a University Distinguished Professor Emeritus in the School of Social and Behavioral Health Sciences at Oregon State University. He held the Knudson Chair in Family Research and was also recognized as the Alumni Distinguished Professor based on his work with students. He is the author of *Discovering Structural Equation Modeling Using Stata, Revised Edition*. He has published more than 150 articles in leading journals across the social and behavioral sciences, including *Structural Equation Modeling, Psychological Bulletin, Multivariate Behavioral Research, Journal of Gerontology, Journal of Adolescence, American Journal of Public Health, American Sociological Review, Journal of Marriage and Family, Social Forces, Drug and Alcohol Dependence, Educational and Psychological Measurement, Journal of Politics, Prevention Science, American Journal of Preventive Medicine*, and many others. With this broad experience, Acock brings examples from a variety of disciplines.

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

2.2 An example questionnaire

2.3 Developing a coding system

2.4 Entering data using the Data Editor

2.6 The Data Editor (Browse) view

2.7 Saving your dataset

2.8 Checking the data

2.9 Summary

2.10 Exercises

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

4.2 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

5.2 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

6.2 Cross-tabulation

6.3 Chi-squared test

6.3.2 Probability tables

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

7.2 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

7.10 Power analysis

7.11 Nonparametric alternatives

7.11.2 Nonparametric alternative: Median test

7.13 Summary

7.14 Exercises

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

9.2 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

9.10.2 Power analysis for two-way ANOVA

9.10.3 Power analysis for repeated-measures ANOVA

9.10.4 Summary of power analysis for ANOVA

9.12 Exercises

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

10.7.2 Influential observations: DFbeta

10.7.3 Combinations of variables may cause problems

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

10.12.2 Centering when using a quadratic term

10.12.3 Do we need to add a quadratic component?

10.14 Summary

10.15 Exercises

11.2 An example

11.3 What is an odds ratio and a logit?

11.3.2 The logit transformation

11.5 Logistic regression

11.6 Hypothesis testing

11.6.2 Testing sets of coefficients

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

12.2 Constructing a scale

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*)

12.4.2 Criterion-related validity

12.4.3 Construct validity

12.6 PCF analysis

12.6.2 Oblique rotation: Promax

12.9 Exercises

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

13.3 The gsem command for logistic regression

13.3.2 Fitting the model using the gsem command

13.5 Conclusions and what is next for the sem command

13.6 Exercises

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

14.6.2 Setup and multiple-imputation stage

14.6.3 The analysis stage

14.6.4 For those who want an

*R*

^{2}and standardized

*β*s

14.6.5 When impossible values are imputed

14.8 Exercises

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

15.5.2 Reshaping data to do multilevel analysis

15.7 Random-intercept model

15.7.2 Random-intercept model—quadratic term

15.7.3 Treating time as a categorical variable

15.9 Including a time-invariant covariate

15.10 Summary

15.11 Exercises

16.2 Overview of three IRT models for dichotomous items

16.2.2 The two-parameter logistic (2PL) model

16.2.3 The three-parameter logistic (3PL) model

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

16.5.2 Fitting our graded response model

16.5.3 Estimating a person’s score

16.7 Using the Stata menu system

16.8 Extensions of IRT

16.9 Exercises

A.2 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