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1. A gentle introduction to Stata [2016]
 Acock, Alan C., 1944 author.
 Fifth edition.  College Station, Texas : A Stata Press Publication, StataCorp LP, 2016.
 Description
 Book — xxxvi, 546 pages : illustrations ; 24 cm
 Summary

 Getting started Conventions Introduction The Stata screen Using an existing dataset An example of a short Stata session Video aids to learning Stata Summary Exercises
 Entering data Creating a dataset An example questionnaire Developing a coding system Entering data using the Data Editor Value labels The Variables Manager The Data Editor (Browse) view Saving your dataset Checking the data Summary Exercises
 Preparing data for analysis Introduction Planning your work Creating value labels Reversecode variables Creating and modifying variables Creating scales Save some of your data Summary Exercises
 Working with commands, dofiles, and results Introduction How Stata commands are constructed Creating a dofile Copying your results to a word processor Logging your command file Summary Exercises
 Descriptive statistics and graphs for one variable Descriptive statistics and graphs Where is the center of a distribution? How dispersed is the distribution? Statistics and graphsunordered categories Statistics and graphsordered categories and variables Statistics and graphsquantitative variables Summary Exercises
 Statistics and graphs for two categorical variables Relationship between categorical variables Crosstabulation Chisquared test Degrees of freedom Probability tables Percentages and measures of association Odds ratios when dependent variable has two categories Ordered categorical variables Interactive tables Tableslinking categorical and quantitative variables Power analysis when using a chisquared test of significance Summary Exercises
 Tests for one or two means Introduction to tests for one or two means Randomization Random sampling Hypotheses Onesample test of a proportion Twosample test of a proportion Onesample test of means Twosample test of group means Testing for unequal variances Repeatedmeasures t test Power analysis Nonparametric alternatives MannWhitney twosample ranksum test Nonparametric alternative: Median test Video tutorial related to this chapter Summary Exercises
 Bivariate correlation and regression Introduction to bivariate correlation and regression Scattergrams Plotting the regression line An alternative to producing a scattergram, binscatter Correlation Regression Spearman's rho: Rankorder correlation for ordinal data Power analysis with correlation Summary Exercises
 Analysis of variance The logic of oneway analysis of variance ANOVA example ANOVA example with nonexperimental data Power analysis for oneway ANOVA A nonparametric alternative to ANOVA Analysis of covariance Twoway ANOVA Repeatedmeasures design Intraclass correlation<measuring agreement Power analysis with ANOVA Power analysis for oneway ANOVA Power analysis for twoway ANOVA Power analysis for repeatedmeasures ANOVA Summary of power analysis for ANOVA Summary Exercises
 Multiple regression Introduction to multiple regression What is multiple regression? The basic multiple regression command Increment in Rsquared: Semipartial correlations Is the dependent variable normally distributed? Are the residuals normally distributed? Regression diagnostic statistics Outliers and influential cases Influential observations: DFbeta Combinations of variables may cause problems Weighted data Categorical predictors and hierarchical regression A shortcut for working with a categorical variable Fundamentals of interaction Nonlinear relations Fitting a quadratic model Centering when using a quadratic term Do we need to add a quadratic component? Power analysis in multiple regression Summary Exercises
 Logistic regression Introduction to logistic regression An example What is an odds ratio and a logit? The odds ratio The logit transformation Data used in the rest of the chapter Logistic regression Hypothesis testing Testing individual coefficients Testing sets of coefficients More on interpreting results from logistic regression Nested logistic regressions Power analysis when doing logistic regression Next steps for using logistic regression and its extensions Summary Exercises
 Measurement, reliability, and validity Overview of reliability and validity Constructing a scale Generating a mean score for each person Reliability Stability and testretest reliability Equivalence Splithalf and alpha reliabilitinternal consistency KuderRichardson reliability for dichotomous items Rater agreementkappa (K) Validity Expert judgment Criterionrelated validity Construct validity Factor analysis PCF analysis Orthogonal rotation: Varimax Oblique rotation: Promax But we wanted one scale, not four scales Scoring our variable Summary Exercises
 Working with missing valuesmultiple imputation The nature of the problem Multiple imputation and its assumptions about the mechanism for missingness What variables do we include when doing imputations? Multiple imputation A detailed example Preliminary analysis Setup and multipleimputation stage The analysis stage For those who want an R and standardized sss When impossible values are imputed Summary Exercises
 The sem and gsem commands Linear regression using sem Using the SEM Builder to fit a basic regression model A quick way to draw a regression model and a fresh start Using sem without the SEM Builder The gsem command for logistic regression Fitting the model using the logit command Fitting the model using the gsem command Path analysis and mediation Conclusions and what is next for the sem command Exercises
 An introduction to multilevel analysis Questions and data for groups of individuals Questions and data for a longitudinal multilevel application Fixedeffects regression models Randomeffects regression models An applied example Research questions Reshaping data to do multilevel analysis A quick visualization of our data Randomintercept model Random interceptlinear model Randomintercept modelquadratic term Treating time as a categorical variable Randomcoefficients model Including a timeinvariant covariate Summary Exercises
 Item response theory (IRT) How are IRT measures of variables different from summated scales? Overview of three IRT models for dichotomous items The oneparameter logistic (PL) model The twoparameter logistic (PL) model The threeparameter logistic (PL) model Fitting the PL model using Stata The estimation How important is each of the items? An overall evaluation of our scale Estimating the latent score Fitting a PL IRT model Fitting the PL model The graded response modelIRT for Likerttype items The data Fitting our graded response model Estimating a person's score Reliability of the fitted IRT model Using the Stata menu system Extensions of IRT Exercises
 What's next? Introduction to the appendix Resources Web resources Books about Stata Short courses Acquiring data Learning from the postestimation methods Summary.
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2. A gentle introduction to Stata [2006]
 Acock, Alan C., 1944
 College Station, Tex. : StataCorp LP, c2006.
 Description
 Book — xx, 289 p. : ill. ; 24 cm.
 Summary

 PrefaceSupport Materials for the BookGETTING STARTEDIntroductionThe Stata screenUsing an existing datasetAn example of a short Stata sessionConventionsChapter summaryExercisesENTERING DATACreating a datasetAn example questionnaireDevelop a coding systemEntering dataSaving your datasetChecking the dataChapter summaryExercisesPREPARING DATA FOR ANALYSISIntroductionPlan your workCreate value labelsReversecode variablesCreate and modify variablesCreate scalesSave some of your dataSummaryExercisesWORKING WITH COMMANDS, DOFILES, AND RESULTSIntroductionHow Stata commands are constructedGetting the command from the menu systemSaving your resultsLogging your command fileSummaryExercisesDESCRIPTIVE STATISTICS AND GRAPHS FOR A SINGLE VARIABLEDescriptive statistics and graphsWhere is the center of a distribution?How dispersed is the distribution?Statistics and graphsunordered categoriesStatistics and graphsordered categories and variablesStatistics and graphsquantitative variablesSummaryExercisesSTATISTICS AND GRAPHS FOR TWO CATEGORICAL VARIABLESRelationship between categorical variablesCrosstabulationChisquaredPercentages and measures of associationOrdered categorical variablesInteractive tablesTableslinking categorical and quantitative variablesSummaryExercisesTESTS FOR ONE OR TWO MEANSTests for one or two meansRandomizationHypothesesOnesample test of a proportionTwosample test of a proportionOnesample test of meansTwosample test of group meansRepeatedmeasures t testPower analysisNonparametric alternativesSummaryExercisesBIVARIATE CORRELATION AND REGRESSIONIntroduction to bivariate correlation and regressionScattergramsPlotting the regression lineCorrelationRegressionSpearman's rho: rankorder correlation for ordinal dataAlpha reliabilityKappa as a measure of agreement for categorical dataSummaryExercisesANALYSIS OF VARIANCE (ANOVA)The logic of oneway analysis of varianceANOVA exampleANOVA example using survey dataA nonparametric alternative to ANOVAAnalysis of covarianceTwoway ANOVARepeatedmeasures designIntraclass correlationmeasuring agreementSummaryExercisesMULTIPLE REGRESSIONIntroductionWhat is multiple regression?The basic multiple regression commandIncrement in Rsquared: semipartial correlationsIs the dependent variable normally distributed?Are the residuals normally distributed?Regression diagnostic statisticsWeighted dataCategorical predictors and hierarchical regressionFundamentals of interactionSummaryExercisesLOGISTIC REGRESSIONIntroductionAn exampleWhat are an odds ratio and a logit?Data used in rest of chapterLogistic regressionHypothesis testingNested logistic regressionsSummaryExercisesWHAT'S.
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