| 000 | 06898nam a2200181 4500 | ||
|---|---|---|---|
| 020 | _a9781597181426 (pb) | ||
| 040 | _cCUS | ||
| 082 |
_a005.55 _bACO/G |
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| 100 |
_aAcock, Alan C. _918999 |
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| 245 |
_aA gentle introduction to stata / _cAlan C. Acock |
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| 250 | _a4th ed. | ||
| 260 |
_bStata press , _c2014. |
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| 300 |
_axxiii, 468 p. _bill. ; |
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| 504 | _aIncludes index | ||
| 505 | _aGetting started 1.1 Conventions 1.2 Introduction 1.3 The Stata screen 1.4 Using an existing datiuset 1.5 An example of a short Stata session 1.6 Summary 1.7 Exercises . Entering data 2.1 Creating a datiuset 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 tlu; data 2.9 Summary 2.10 Exercises 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 . 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 . 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. Statistics and graphs for two categorical variables 6.1 Relationsliip between categorical variables 6.2 Cross-tabulation 6.3 Clii-squared tost 6.3.1 Degrees of freedom 6.3.2 Probability tal>les . 6.4 Percentages and measures of association 6.5 Odds ratios when dependent variable hiis 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. 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-samj)le 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 . 5 Bivaxiate correlation and regression 8.1 Introduction to bivfiiialc correlation and regression 8.2 Scattorgrains 8.3 Plotting the regression line 8.4 An alternative to producing a scattcrgrain. l^inscattcr 8.5 Correlation 8.6 R.cgiTssion . 8.7 Speannan's rho: Riiiik-order correlation for ordinal data . 8.8 Summary 8.9 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 noiiparainetric alternative to ANOVA 9.5 Analysis of covariancc 9.6 Two-way ANOVA 9.7 Repeated-measurers design 9.8 Intraclass correlation measuring agreement . 9.9 Power analysis witli ANOVA 9.9.1 One-way ANOVA Power analysis for two-way ANOVA 9.9.2 Power analysis for rei)eated-m(ra.sure.s ANOVA . 9.9.3 Summary of power analysis for ANOVA 9.10 S\imniary 9.11 Exercises. 10 Multiple regression 10.1 Introduction to multiple regression 10.2 What is multiple r(?gressi()n? 10.3 The imsic mult iple regression eoiiimaiid 10.4 Incrcinont in Il-scinarcd: Soniipartial correlatioiivS 10.5 Is the (lepcncleiit variabk.' nonnally distributed? . 10.6 Are the residuals nonnally distributed? . 10.7 Regression diagnostic statistics 10.7.1 Outliers and influential Cciaes 10.7.2 Influent ial ob.servations: DFbcta 10.7.3 Combinations of variables may cause proPieius 10.8 Weighted data . 10.9 Categorical predictors and hierarchical regression 10.10 A sliortcut for working with a categorical variable 10.11 Fundamentals of interaction 10.12 NonliiK^ar relations 10.12.1 Fitting a quadratic model 10.12.2 Centering when using a quadratic term . 10.12.3 Do we iukhI to add a quadratic component? . 10.13 Power analysis in multii)]e regression 10.14 Summary 10.15 Exer(;ises 11 Logistic regression 11.1 Introduct ion to logistic regn^ssion 11.2 An exam})le 11.3 What is an odds ratio and a logit? 11.3.1 The odds ratio 11.3.2 Tlie logit transformation 11.4 Data u.sed in the rest of the chapter 11.5 Logist ic regression ll.G Hypotiiesis testing ll.G.l Testing individual coeflicients 11.6.2 Test ing sets of coeflicients 11.7 More on interpret ing results from logistic regres.sioii 11.8 Ni'.stc(l logistic rcgrc.ssioiis 11.9 Power analysis when doing logist ic regression 11.10 Snnnnary 11.11 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 Ecini valence 12.3.3 Split-half and alpha reliability— internal consistency 12.3.4 Ktider 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 Oblicine rotation: Promax 12.7 But we wanted one .scale, not four scales 12.7.1 Scoring our variabl(> 12.8 Summary 12.9 Exercises. 13 Working with missing values—multiple imputation 1.1.1 The nature of the problem 13.2 Multiple imputation and its a.s.sumptions about the mechani.sm 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 B? and standardized /3s 13.5.5 When impossible values are imputed 13.6 Summary 13.7 Exercises . 14 The sem and gsem commands 14.1 Ordinary least-squares regression models using sem 14.1.1 Using the SEM Builder to fit a basic regression model 14.2 A quick way to draw a regression model and a fresh start 14.2.1 Using sem without the SEM Builder 14.3 The gsem command for logistic regression 14.3.1 Fitting the model using the logit command 14.3.2 Fitting the model using the gsem command 14.4 Path analysis and mediation 14.5 Conclusions and what is next for the sem command . 14.6 Exercises A What's next? A.l 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 | ||
| 650 |
_aComputer Programming _9587 |
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_cL2C2 _02 |
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_c3478 _d3478 |
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