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Gentle Introduction to Stata, Revised Sixth Edition 6th edition [Minkštas viršelis]

(Oregon State University, Corvallis, USA)
  • Formatas: Paperback / softback, 566 pages, aukštis x plotis: 246x189 mm, weight: 1209 g
  • Išleidimo metai: 19-Jan-2023
  • Leidėjas: Stata Press
  • ISBN-10: 1597183679
  • ISBN-13: 9781597183673
Kitos knygos pagal šią temą:
  • Formatas: Paperback / softback, 566 pages, aukštis x plotis: 246x189 mm, weight: 1209 g
  • Išleidimo metai: 19-Jan-2023
  • Leidėjas: Stata Press
  • ISBN-10: 1597183679
  • ISBN-13: 9781597183673
Kitos knygos pagal šią temą:

Alan C. Acock's A Gentle Introduction to Stata, Revised 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 to not only use Stata well but also 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. 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 explaining 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 Stata commands and 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 naturally. 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 revised sixth edition is fully up to date for Stata 17, including updated discussion and images of Stata's interface and modern command syntax. In addition, examples include new features such as the table command and collect suite for creating and exporting customized tables as well as the option for creating graphs with transparency.



Ready to learn Stata? The revised edition of Stata Press' long-time best seller, 'A Gentle Introduction to Stata' is now available and fully updated for Stata 17.

List of figures
xv
List of tables
xxiii
List of boxed tips
xxv
Preface xxix
Acknowledgments xxxiii
Support materials for the book xxxv
1 Getting started
1(20)
1.1 Conventions
1(3)
1.2 Introduction
4(3)
1.3 The Stata screen
7(3)
1.4 Using an existing dataset
10(1)
1.5 An example of a short Stata session
11(7)
1.6 Video aids to learning Stata
18(1)
1.7 Summary
19(1)
1.8 Exercises
19(2)
2 Entering data
21(30)
2.1 Creating a dataset
21(3)
2.2 An example questionnaire
24(1)
2.3 Developing a coding system
25(4)
2.4 Entering data using the Data Editor
29(5)
2.4.1 Value labels
33(1)
2.5 The Variables Manager
34(6)
2.6 The Data Editor (Browse) view
40(1)
2.7 Saving your dataset
41(2)
2.8 Checking the data
43(7)
2.9 Summary
50(1)
2.10 Exercises
50(1)
3 Preparing data for analysis
51(26)
3.1 Introduction
51(1)
3.2 Planning your work
52(5)
3.3 Creating value labels
57(3)
3.4 Reverse-code variables
60(5)
3.5 Creating and modifying variables
65(5)
3.6 Creating scales
70(4)
3.7 Saving some of your data
74(1)
3.8 Summary
75(1)
3.9 Exercises
76(1)
4 Working with commands, do-files, and results
77(16)
4.1 Introduction
77(1)
4.2 How Stata commands are constructed
78(5)
4.3 Creating a do-file
83(5)
4.4 Copying your results to a word processor
88(2)
4.5 Logging your command file
90(1)
4.6 Summary
91(1)
4.7 Exercises
92(1)
5 Descriptive statistics and graphs for one variable
93(30)
5.1 Descriptive statistics and graphs
93(1)
5.2 Where is the center of a distribution?
94(4)
5.3 How dispersed is the distribution?
98(2)
5.4 Statistics and graphs---unordered categories
100(10)
5.5 Statistics and graphs---ordered categories and variables
110(2)
5.6 Statistics and graphs---quantitative variables
112(8)
5.7 Summary
120(1)
5.8 Exercises
120(3)
6 Statistics and graphs for two categorical variables
123(28)
6.1 Relationship between categorical variables
123(1)
6.2 Cross-tabulation
124(3)
6.3 Chi-squared test
127(5)
6.3.1 Degrees of freedom
129(1)
6.3.2 Probability tables
129(3)
6.4 Percentages and measures of association
132(3)
6.5 Odds ratios when dependent variable has two categories
135(2)
6.6 Ordered categorical variables
137(3)
6.7 Interactive tables
140(2)
6.8 Tables---linking categorical and quantitative variables
142(3)
6.9 Power analysis when using a chi-squared test of significance
145(3)
6.10 Summary
148(1)
6.11 Exercises
148(3)
7 Tests for one or two means
151(42)
7.1 Introduction to tests for one or two means
151(3)
7.2 Randomization
154(2)
7.3 Random sampling
156(1)
7.4 Hypotheses
156(2)
7.5 One-sample test of a proportion
158(3)
7.6 Two-sample test of a proportion
161(4)
7.7 One-sample test of means
165(2)
7.8 Two-sample test of group means
167(10)
7.8.1 Testing for unequal variances
176(1)
7.9 Repeated-measures t test
177(2)
7.10 Power analysis
179(8)
7.11 Nonparametric alternatives
187(2)
7.11.1 Mann--Whitney two-sample rank-sum test
187(1)
7.11.2 Nonparametric alternative: Median test
188(1)
7.12 Video tutorial related to this chapter
189(1)
7.13 Summary
189(1)
7.14 Exercises
190(3)
8 Bivariate correlation and regression
193(28)
8.1 Introduction to bivariate correlation and regression
193(1)
8.2 Scattergrams
194(6)
8.3 Plotting the regression line
200(1)
8.4 An alternative to producing a scattergram, binscatter
201(4)
8.5 Correlation
205(5)
8.6 Regression
210(5)
8.7 Spearman's rho: Rank-order correlation for ordinal data
215(1)
8.8 Power analysis with correlation
216(2)
8.9 Summary
218(1)
8.10 Exercises
218(3)
9 Analysis of variance
221(52)
9.1 The logic of one-way analysis of variance
221(1)
9.2 ANOVA example
222(9)
9.3 ANOVA example with nonexperimental data
231(3)
9.4 Power analysis for one-way ANOVA
234(2)
9.5 A nonparametric alternative to ANOVA
236(2)
9.6 Analysis of covariance
238(11)
9.7 Two-way ANOVA
249(6)
9.8 Repeated-measures design
255(5)
9.9 Intraclass correlation---measuring agreement
260(2)
9.10 Power analysis with ANOVA
262(8)
9.10.1 Power analysis for one-way ANOVA
263(2)
9.10.2 Power analysis for two-way ANOVA
265(2)
9.10.3 Power analysis for repeated-measures ANOVA
267(2)
9.10.4 Summary of power analysis for ANOVA
269(1)
9.11 Summary
270(1)
9.12 Exercises
270(3)
10 Multiple regression
273(64)
10.1 Introduction to multiple regression
273(1)
10.2 What is multiple regression?
274(1)
10.3 The basic multiple regression command
275(4)
10.4 Increment in R-squared: Semipartial correlations
279(2)
10.5 Is the dependent variable normally distributed?
281(3)
10.6 Are the residuals normally distributed?
284(6)
10.7 Regression diagnostic statistics
290(6)
10.7.1 Outliers and influential cases
290(2)
10.7.2 Influential observations: DFbeta
292(2)
10.7.3 Combinations of variables may cause problems
294(2)
10.8 Weighted data
296(2)
10.9 Categorical predictors and hierarchical regression
298(9)
10.10 A shortcut for working with a categorical variable
307(1)
10.11 Fundamentals of interaction
308(7)
10.12 Nonlinear relations
315(12)
10.12.1 Fitting a quadratic model
317(6)
10.12.2 Centering when using a quadratic term
323(2)
10.12.3 Do we need to add a quadratic component?
325(2)
10.13 Power analysis in multiple regression
327(5)
10.14 Summary
332(1)
10.15 Exercises
333(4)
11 Logistic regression
337(40)
11.1 Introduction to logistic regression
337(1)
11.2 An example
338(4)
11.3 What is an odds ratio and a logit?
342(3)
11.3.1 The odds ratio
344(1)
11.3.2 The logit transformation
344(1)
11.4 Data used in the rest of the chapter
345(2)
11.5 Logistic regression
347(10)
11.6 Hypothesis testing
357(4)
11.6.1 Testing individual coefficients
358(1)
11.6.2 Testing sets of coefficients
359(2)
11.7 Margins: More on interpreting results from logistic regression
361(8)
11.8 Nested logistic regressions
369(2)
11.9 Power analysis when doing logistic regression
371(3)
11.10 Next steps for using logistic regression and its extensions
374(1)
11.11 Summary
374(1)
11.12 Exercises
375(2)
12 Measurement, reliability, and validity
377(34)
12.1 Overview of reliability and validity
377(1)
12.2 Constructing a scale
378(3)
12.2.1 Generating a mean score for each person
379(2)
12.3 Reliability
381(8)
12.3.1 Stability and test-retest reliability
382(1)
12.3.2 Equivalence
383(1)
12.3.3 Split-half and alpha reliability---internal consistency
383(3)
12.3.4 Kuder--Richardson reliability for dichotomous items
386(1)
12.3.5 Rater agreement---kappa (κ)
387(2)
12.4 Validity
389(7)
12.4.1 Expert judgment
390(1)
12.4.2 Criterion-related validity
391(1)
12.4.3 Construct validity
391(5)
12.5 Factor analysis
396(4)
12.6 PCF analysis
400(7)
12.6.1 Orthogonal rotation: Varimax
404(2)
12.6.2 Oblique rotation: Promax
406(1)
12.7 But we wanted one scale, not four scales
407(2)
12.7.1 Scoring our variable
408(1)
12.8 Summary
409(1)
12.9 Exercises
410(1)
13 Structural equation and generalized structural equation modeling
411(30)
13.1 Linear regression using sem
412(9)
13.1.1 Using the sem command directly
413(1)
13.1.2 SEM and working with missing values
414(5)
13.1.3 Exploring missing values and auxiliary variables
419(2)
13.1.4 Getting auxiliary variables into your SEM command
421(1)
13.2 A quick way to draw a regression model
421(4)
13.3 The gsem command for logistic regression
425(7)
13.3.1 Fitting the model using the logit command
425(3)
13.3.2 Fitting the model using the gsem command
428(4)
13.4 Path analysis and mediation
432(5)
13.5 Conclusions and what is next for the sem command
437(2)
13.6 Exercises
439(2)
14 Working with missing values---multiple imputation
441(22)
14.1 Working with missing values---multiple imputation
441(1)
14.2 What variables do we include when doing imputations?
442(2)
14.3 The nature of the problem
444(1)
14.4 Multiple imputation and its assumptions about the mechanism for missingness
445(2)
14.5 Multiple imputation
447(1)
14.6 A detailed example
448(12)
14.6.1 Preliminary analysis
449(3)
14.6.2 Setup and multiple-imputation stage
452(2)
14.6.3 The analysis stage
454(2)
14.6.4 For those who want an R2 and standardized (3s
456(2)
14.6.5 When impossible values are imputed
458(2)
14.7 Summary
460(1)
14.8 Exercises
461(2)
15 An introduction to multilevel analysis
463(30)
15.1 Questions and data for groups of individuals
463(1)
15.2 Questions and data for a longitudinal multilevel application
464(1)
15.3 Fixed-effects regression models
465(1)
15.4 Random-effects regression models
466(2)
15.5 An applied example
468(4)
15.5.1 Research questions
468(1)
15.5.2 Reshaping data to do multilevel analysis
469(3)
15.6 A quick visualization of our data
472(1)
15.7 Random-intercept model
473(10)
15.7.1 Random intercept---linear model
473(3)
15.7.2 Random-intercept model---quadratic term
476(4)
15.7.3 Treating time as a categorical variable
480(3)
15.8 Random-coefficients model
483(3)
15.9 Including a time-invariant covariate
486(5)
15.10 Summary
491(1)
15.11 Exercises
492(1)
16 Item response theory (IRT)
493(38)
16.1 How are IRT measures of variables different from summated scales?
494(2)
16.2 Overview of three IRT models for dichotomous items
496(4)
16.2.1 The one-parameter logistic (1PL) model
496(2)
16.2.2 The two-parameter logistic (2PL) model
498(1)
16.2.3 The three-parameter logistic (3PL) model
499(1)
16.3 Fitting the 1PL model using Stata
500(8)
16.3.1 The estimation
502(2)
16.3.2 How important is each of the items?
504(2)
16.3.3 An overall evaluation of our scale
506(1)
16.3.4 Estimating the latent score
507(1)
16.4 Fitting a 2PL IRT model
508(7)
16.4.1 Fitting the 2PL model
509(6)
16.5 The graded response model---IRT for Likert-type items
515(7)
16.5.1 The data
515(2)
16.5.2 Fitting our graded response model
517(5)
16.5.3 Estimating a person's score
522(1)
16.6 Reliability of the fitted IRT model
522(3)
16.7 Using the Stata menu system
525(3)
16.8 Extensions of IRT
528(1)
16.9 Exercises
529(2)
A What's next?
531(10)
A.1 Introduction to the appendix
531(1)
A.2 Resources
531(8)
A.2.1 Web resources
532(2)
A.2.2 Books about Stata
534(2)
A.2.3 Short courses
536(1)
A.2.4 Acquiring data
537(1)
A.2.5 Learning from the postestimation methods
538(1)
A.3 Summary
539(2)
Glossary of acronyms 541(2)
Glossary of mathematical and statistical symbols 543(2)
References 545(6)
Author Index 551(2)
Subject Index 553
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.