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• 1. What is Power? Why is Power Important?
• 2. Chi-Square and Tests for Proportions.
• 3. Independent Samples and Paired t-tests.
• 4. Correlations and Differences between Correlations.
• 5. Between Subjects ANOVA (One Factor, Two or more Factors).
• 6. Within Subjects Designs.
• 7. Mixed Model ANOVA and Multivariate ANOVA.
• 8. Multiple Regression.
• 9. Covariate Analyses and Regression Interactions.
• 10. Precision Analysis for Confidence Intervals.
• 11. Additional Issues and Resources.
• (source: Nielsen Book Data)

This practical guide on conducting power analyses using IBM SPSS was written for students and researchers with limited quantitative backgrounds. Readers will appreciate the coverage of topics that are not well described in competing books such as estimating effect sizes, power analyses for complex designs, detailed coverage of popular multiple regression and multi-factor ANOVA approaches, and power for multiple comparisons and simple effects. Practical issues such as how to increase power without increasing sample size, how to report findings, how to derive effect size expectations, and how to support null hypotheses, are also addressed. Unlike other texts, this book focuses on the statistical and methodological aspects of the analyses. Performing analyses using software applications rather than via complex hand calculations is demonstrated throughout. Ready-to-use IBM SPSS syntax for conducting analyses are included to perform calculations and power analyses at http://www.psypress.com/applied-power-analysis . Detailed annotations for each syntax protocol review the minor modifications necessary for researchers to adapt the syntax to their own analyses. As such, the text reviews both power analysis techniques and provides tools for conducting analyses. Numerous examples enhance accessibility by demonstrating specific issues that must be addressed at all stages of the power analysis and providing detailed interpretations of IBM SPSS output. Several examples address techniques for estimation of power and hand calculations as well. Chapter summaries and key statistics sections also aid in understanding the material. Chapter 1 reviews significance testing and introduces power. Chapters 2 through 9 cover power analysis strategies for a variety of common designs. Precision analysis for confidence intervals around mean difference, correlations, and effect sizes is the focus of chapter 10. The book concludes with a review of how to report power analyses, a review of freeware and commercial software for power analyses, and how to increase power without increasing sample size. Chapters focusing on simpler analyses such as t-tests present detailed formulae and calculation examples. Chapters focusing on more complex topics such as mixed model ANOVA/MANOVA present primarily computer-based analyses. Intended as a supplementary text for graduate-level research methods, experimental design, quasi-experimental methods, psychometrics, statistics, and/or advanced/multivariate statistics taught in the behavioral, social, biological, and medical sciences, researchers in these fields also appreciate this book's practical emphasis. A prerequisite of introductory statistics is recommended.
(source: Nielsen Book Data)

• 1. What is Power? Why is Power Important?
• 2. Chi-Square and Tests for Proportions
• 3. Independent Samples and Paired t-tests
• 4. Correlations and Differences Between Correlations
• 5. Between Subjects ANOVA (One and Two Factors)
• 6. Within-Subjects Designs with ANOVA and Linear Mixed Models
• 7. Mixed Model ANOVA and Multivariate ANOVA
• 8. Multiple Regression
• 9. Analysis of Covariance, Moderated Regression, Logistic Regression, and Mediation
• 10. Precision Analysis for Confidence Intervals
• 11. Additional Issues and Resources.
• (source: Nielsen Book Data)

Applied Power Analysis for the Behavioral Sciences is a practical "how-to" guide to conducting statistical power analyses for psychology and related fields. The book provides a guide to conducting analyses that is appropriate for researchers and students, including those with limited quantitative backgrounds. With practical use in mind, the text provides detailed coverage of topics such as how to estimate expected effect sizes and power analyses for complex designs. The topical coverage of the text, an applied approach, in-depth coverage of popular statistical procedures, and a focus on conducting analyses using R make the text a unique contribution to the power literature. To facilitate application and usability, the text includes ready-to-use R code developed for the text. An accompanying R package called pwr2ppl (available at https://github.com/chrisaberson/pwr2ppl) provides tools for conducting power analyses across each topic covered in the text.
(source: Nielsen Book Data)

Interpreting and Using Regression sets out the actual procedures researchers employ, places them in the framework of statistical theory, and shows how good research takes account both of statistical theory and real world demands. Achen builds a working philosophy of regression that goes well beyond the abstract, unrealistic treatment given in previous texts.
(source: Nielsen Book Data)

• 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 labelsReverse-code variablesCreate and modify variablesCreate scalesSave some of your dataSummaryExercisesWORKING WITH COMMANDS, DO-FILES, 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 graphs-unordered categoriesStatistics and graphs-ordered categories and variablesStatistics and graphs-quantitative variablesSummaryExercisesSTATISTICS AND GRAPHS FOR TWO CATEGORICAL VARIABLESRelationship between categorical variablesCross-tabulationChi-squaredPercentages and measures of associationOrdered categorical variablesInteractive tablesTables-linking categorical and quantitative variablesSummaryExercisesTESTS FOR ONE OR TWO MEANSTests for one or two meansRandomizationHypothesesOne-sample test of a proportionTwo-sample test of a proportionOne-sample test of meansTwo-sample test of group meansRepeated-measures t testPower analysisNonparametric alternativesSummaryExercisesBIVARIATE CORRELATION AND REGRESSIONIntroduction to bivariate correlation and regressionScattergramsPlotting the regression lineCorrelationRegressionSpearman's rho: rank-order correlation for ordinal dataAlpha reliabilityKappa as a measure of agreement for categorical dataSummaryExercisesANALYSIS OF VARIANCE (ANOVA)The logic of one-way analysis of varianceANOVA exampleANOVA example using survey dataA nonparametric alternative to ANOVAAnalysis of covarianceTwo-way ANOVARepeated-measures designIntraclass correlation-measuring agreementSummaryExercisesMULTIPLE REGRESSIONIntroductionWhat is multiple regression?The basic multiple regression commandIncrement in R-squared: 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.
• (source: Nielsen Book Data)

Providing the basic collection of statistical procedures used by social scientists, "A Gentle Introduction to Stata" presents the fundamental tools to learn Stata. The book begins with showing how to enter and manage data as well as perform basic descriptive statistics and graphical analysis. It then examines standard statistical procedures from a t-test, nonparametric tests, measures of association, multiple regression, and logical regression. The book ends with guidelines for future work and advanced topics. This learning source is an excellent introduction for those with little statistical software experience while also a useful reference for more knowledgeable statisticians by offering a detailed index of commands.
(source: Nielsen Book Data)

• 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 Reverse-code variables Creating and modifying variables Creating scales Save some of your data Summary Exercises
• Working with commands, do-files, and results Introduction How Stata commands are constructed Creating a do-file 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 graphs-unordered categories Statistics and graphs-ordered categories and variables Statistics and graphs-quantitative variables Summary Exercises
• Statistics and graphs for two categorical variables Relationship between categorical variables Cross-tabulation Chi-squared test Degrees of freedom Probability tables Percentages and measures of association Odds ratios when dependent variable has two categories Ordered categorical variables Interactive tables Tables--linking categorical and quantitative variables Power analysis when using a chi-squared test of significance Summary Exercises
• Tests for one or two means Introduction to tests for one or two means Randomization Random sampling Hypotheses One-sample test of a proportion Two-sample test of a proportion One-sample test of means Two-sample test of group means Testing for unequal variances Repeated-measures t test Power analysis Nonparametric alternatives Mann--Whitney two-sample rank-sum 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: Rank-order correlation for ordinal data Power analysis with correlation Summary Exercises
• Analysis of variance The logic of one-way analysis of variance ANOVA example ANOVA example with nonexperimental data Power analysis for one-way ANOVA A nonparametric alternative to ANOVA Analysis of covariance Two-way ANOVA Repeated-measures design Intraclass correlation<-measuring agreement Power analysis with ANOVA Power analysis for one-way ANOVA Power analysis for two-way ANOVA Power analysis for repeated-measures 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 R-squared: 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 test-retest reliability Equivalence Split-half and alpha reliabilit--internal consistency Kuder-Richardson reliability for dichotomous items Rater agreement-kappa (K) Validity Expert judgment Criterion-related 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 values-multiple 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 multiple-imputation 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 Fixed-effects regression models Random-effects regression models An applied example Research questions Reshaping data to do multilevel analysis A quick visualization of our data Random-intercept model Random intercept-linear model Random-intercept model-quadratic term Treating time as a categorical variable Random-coefficients model Including a time-invariant 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 one-parameter logistic (PL) model The two-parameter logistic (PL) model The three-parameter 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 model-IRT for Likert-type 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.
• (source: Nielsen Book Data)

Alan C. Acock's A Gentle Introduction to Stata, Fifth 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 portion 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 structural equation 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 and 2006, 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 fifth edition of the book includes two new chapters that cover multilevel modeling and item response theory (IRT) models. The multilevel modeling chapter demonstrates how to fit linear multilevel models using the mixed command. Acock discusses models with both random intercepts and random coefficients, and he provides a variety of examples that apply these models to longitudinal data. The IRT chapter introduces the use of IRT models for evaluating a set of items designed to measure a specific trait such as an attitude, value, or a belief. Acock shows how to use the irt suite of commands, which are new in Stata 14, to fit IRT models and to graph the results. In addition, he presents a measure of reliability that can be computed when using IRT.
(source: Nielsen Book Data)

• Slippery numbers in the public arena: boldfaced lies-- something's amiss-- coming up with the numbers. Math model portraits: what's the cost of smoking
• Andy and Rasa take vacations-- math model development. The price tag: the numbers you don't knowcan hurt you-- consequences of math fiction. Preventative medicine - education: mathematics education.
• (source: Nielsen Book Data)

Highlighting how slippery numbers and questionable mathematical conclusions emerge and what can be done to safeguard against them, this book examines flawed usage of math in public affairs through actual cases of how mathematical data and conclusions can be distorted and misrepresented to influence public opinion. The author analyzes the cost of "slippery math" in terms of squandered resources and identifies common misperceptions about the role of math in public affairs. He discusses how math education can be reformed to sharpen public awareness of "slippery math", the development of math models and their use and misuse, and proper and improper polling methods.
(source: Nielsen Book Data)