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Gentle Introduction to Stata, Sixth Edition 6th edition [Pehme köide]

(University of Aarhus, Denmark)
  • Formaat: Paperback / softback, 570 pages, kõrgus x laius: 246x189 mm, kaal: 1180 g
  • Ilmumisaeg: 12-Apr-2018
  • Kirjastus: Stata Press
  • ISBN-10: 1597182699
  • ISBN-13: 9781597182690
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  • Formaat: Paperback / softback, 570 pages, kõrgus x laius: 246x189 mm, kaal: 1180 g
  • Ilmumisaeg: 12-Apr-2018
  • Kirjastus: Stata Press
  • ISBN-10: 1597182699
  • ISBN-13: 9781597182690
Teised raamatud teemal:
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 xv
List of tables xxiii
List of boxed tips xxv
Preface xxix
Support materials for the book xxxv
Glossary of acronyms xxxix
Glossary of mathematical and statistical symbols xli
1 Getting started 1(20)
1.1 Conventions
1(3)
1.2 Introduction
4(3)
1.3 The Stata screen
7(2)
1.4 Using an existing dataset
9(2)
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(2)
2.2 An example questionnaire
23(1)
2.3 Developing a coding system
24(5)
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(3)
3.7 Saving some of your data
73(1)
3.8 Summary
74(1)
3.9 Exercises
75(2)
4 Working with commands, do-files, and results 77(16)
4.1 Introduction
77(1)
4.2 How Stata commands are constructed
78(4)
4.3 Creating a do-file
82(6)
4.4 Copying your results to a word processor
88(1)
4.5 Logging your command file
89(2)
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(7)
5.7 Summary
119(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(6)
6.3.1 Degrees of freedom
129(1)
6.3.2 Probability tables
129(4)
6.4 Percentages and measures of association
133(3)
6.5 Odds ratios when dependent variable has two categories
136(2)
6.6 Ordered categorical variables
138(3)
6.7 Interactive tables
141(2)
6.8 Tables-linking categorical and quantitative variables
143(3)
6.9 Power analysis when using a chi-squared test of significance
146(3)
6.10 Summary
149(1)
6.11 Exercises
149(2)
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(1)
7.5 One-sample test of a proportion
157(3)
7.6 Two-sample test of a proportion
160(4)
7.7 One-sample test of means
164(2)
7.8 Two-sample test of group means
166(10)
7.8.1 Testing for unequal variances
175(1)
7.9 Repeated-measures t test
176(2)
7.10 Power analysis
178(8)
7.11 Nonparametric alternatives
186(2)
7.11.1 Mann-Whitney two-sample rank-sum test
186(1)
7.11.2 Nonparametric alternative: Median test
187(1)
7.12 Video tutorial related to this chapter
188(1)
7.13 Summary
188(1)
7.14 Exercises
189(4)
8 Bivariate correlation and regression 193(28)
8.1 Introduction to bivariate correlation and regression
193(1)
8.2 Scattergrams
194(5)
8.3 Plotting the regression line
199(2)
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(54)
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(3)
9.6 Analysis of covariance
239(11)
9.7 Two-way ANOVA
250(6)
9.8 Repeated-measures design
256(5)
9.9 Intraclass correlation-measuring agreement
261(2)
9.10 Power analysis with ANOVA
263(8)
9.10.1 Power analysis for one-way ANOVA
264(2)
9.10.2 Power analysis for two-way ANOVA
266(2)
9.10.3 Power analysis for repeated-measures ANOVA
268(2)
9.10.4 Summary of power analysis for ANOVA
270(1)
9.11 Summary
271(1)
9.12 Exercises
271(4)
10 Multiple regression 275(64)
10.1 Introduction to multiple regression
275(1)
10.2 What is multiple regression?
276(1)
10.3 The basic multiple regression command
277(4)
10.4 Increment in R-squared: Semipartial correlations
281(2)
10.5 Is the dependent variable normally distributed?
283(3)
10.6 Are the residuals normally distributed?
286(5)
10.7 Regression diagnostic statistics
291(5)
10.7.1 Outliers and influential cases
291(2)
10.7.2 Influential observations: DFbeta
293(1)
10.7.3 Combinations of variables may cause problems
294(2)
10.8 Weighted data
296(3)
10.9 Categorical predictors and hierarchical regression
299(9)
10.10 A shortcut for working with a categorical variable
308(1)
10.11 Fundamentals of interaction
309(7)
10.12 Nonlinear relations
316(12)
10.12.1 Fitting a quadratic model
318(6)
10.12.2 Centering when using a quadratic term
324(2)
10.12.3 Do we need to add a quadratic component?
326(2)
10.13 Power analysis in multiple regression
328(5)
10.14 Summary
333(2)
10.15 Exercises
335(4)
11 Logistic regression 339(42)
11.1 Introduction to logistic regression
339(1)
11.2 An example
340(4)
11.3 What is an odds ratio and a logit?
344(3)
11.3.1 The odds ratio
346(1)
11.3.2 The logit transformation
346(1)
11.4 Data used in the rest of the chapter
347(2)
11.5 Logistic regression
349(11)
11.6 Hypothesis testing
360(4)
11.6.1 Testing individual coefficients
361(1)
11.6.2 Testing sets of coefficients
362(2)
11.7 Margins: More on interpreting results from logistic regression
364(8)
11.8 Nested logistic regressions
372(2)
11.9 Power analysis when doing logistic regression
374(3)
11.10 Next steps for using logistic regression and its extensions
377(1)
11.11 Summary
377(1)
11.12 Exercises
378(3)
12 Measurement, reliability, and validity 381(36)
12.1 Overview of reliability and validity
381(1)
12.2 Constructing a scale
382(3)
12.2.1 Generating a mean score for each person
383(2)
12.3 Reliability
385(10)
12.3.1 Stability and test-retest reliability
387(1)
12.3.2 Equivalence
388(1)
12.3.3 Split-half and alpha reliability-internal consistency
388(4)
12.3.4 Kuder-Richardson reliability for dichotomous items
392(1)
12.3.5 Rater agreement-kappa (K)
393(2)
12.4 Validity
395(7)
12.4.1 Expert judgment
396(1)
12.4.2 Criterion-related validity
397(1)
12.4.3 Construct validity
397(5)
12.5 Factor analysis
402(4)
12.6 PCF analysis
406(7)
12.6.1 Orthogonal rotation: Varimax
410(2)
12.6.2 Oblique rotation: Promax
412(1)
12.7 But we wanted one scale, not four scales
413(2)
12.7.1 Scoring our variable
414(1)
12.8 Summary
415(1)
12.9 Exercises
416(1)
13 Structural equation and generalized structural equation modeling 417(32)
13.1 Linear regression using sem
417(12)
13.1.1 Using the sem command directly
419(1)
13.1.2 SEM and working with missing values
420(6)
13.1.3 Exploring missing values and auxiliary variables
426(2)
13.1.4 Getting auxiliary variables into your SEM command
428(1)
13.2 A quick way to draw a regression model
429(3)
13.3 The gsem command for logistic regression
432(8)
13.3.1 Fitting the model using the logit command
432(2)
13.3.2 Fitting the model using the gsem command
434(6)
13.4 Path analysis and mediation
440(4)
13.5 Conclusions and what is next for the sem command
444(2)
13.6 Exercises
446(3)
14 Working with missing values-multiple imputation 449(22)
14.1 Working with missing values-multiple imputation
449(1)
14.2 What variables do we include when doing imputations?
450(2)
14.3 The nature of the problem
452(1)
14.4 Multiple imputation and its assumptions about the mechanism for missingness
453(2)
14.5 Multiple imputation
455(1)
14.6 A detailed example
456(12)
14.6.1 Preliminary analysis
457(3)
14.6.2 Setup and multiple-imputation stage
460(2)
14.6.3 The analysis stage
462(2)
14.6.4 For those who want an R2 and standardized βs
464(2)
14.6.5 When impossible values are imputed
466(2)
14.7 Summary
468(1)
14.8 Exercises
469(2)
15 An introduction to multilevel analysis 471(30)
15.1 Questions and data for groups of individuals
471(1)
15.2 Questions and data for a longitudinal multilevel application
472(1)
15.3 Fixed-effects regression models
473(1)
15.4 Random-effects regression models
474(2)
15.5 An applied example
476(4)
15.5.1 Research questions
476(1)
15.5.2 Reshaping data to do multilevel analysis
477(3)
15.6 A quick visualization of our data
480(1)
15.7 Random-intercept model
481(10)
15.7.1 Random intercept-linear model
481(3)
15.7.2 Random-intercept model-quadratic term
484(4)
15.7.3 Treating time as a categorical variable
488(3)
15.8 Random-coefficients model
491(3)
15.9 Including a time-invariant covariate
494(5)
15.10 Summary
499(1)
15.11 Exercises
500(1)
16 Item response theory (IRT) 501(38)
16.1 How are IRT measures of variables different from summated scales?
502(2)
16.2 Overview of three IRT models for dichotomous items
504(3)
16.2.1 The one-parameter logistic (1PL) model
504(2)
16.2.2 The two-parameter logistic (2PL) model
506(1)
16.2.3 The three-parameter logistic (3PL) model
507(1)
16.3 Fitting the 1PL model using Stata
507(9)
16.3.1 The estimation
510(2)
16.3.2 How important is each of the items?
512(2)
16.3.3 An overall evaluation of our scale
514(1)
16.3.4 Estimating the latent score
515(1)
16.4 Fitting a 2PL IRT model
516(6)
16.4.1 Fitting the 2PL model
517(5)
16.5 The graded response model-IRT for Likert-type items
522(7)
16.5.1 The data
522(2)
16.5.2 Fitting our graded response model
524(5)
16.5.3 Estimating a person's score
529(1)
16.6 Reliability of the fitted IRT model
529(3)
16.7 Using the Stata menu system
532(3)
16.8 Extensions of IRT
535(1)
16.9 Exercises
536(3)
A What's next? 539(12)
A.1 Introduction to the appendix
539(1)
A.2 Resources
539(8)
A.2.1 Web resources
540(2)
A.2.2 Books about Stata
542(2)
A.2.3 Short courses
544(1)
A.2.4 Acquiring data
545(1)
A.2.5 Learning from the postestimation methods
546(1)
A.3 Summary
547(4)
References 551(4)
Author index 555(2)
Subject index 557
Alan Acock is a sociologist and a University Distinguished Professor Emeritus in the School of Social and Behavioral Health Sciences at Oregon State University.