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基本説明
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Full Description
Multivariable Modeling and Multivariate Analysis for the Behavioral Sciences shows students how to apply statistical methods to behavioral science data in a sensible manner. Assuming some familiarity with introductory statistics, the book analyzes a host of real-world data to provide useful answers to real-life issues.The author begins by exploring the types and design of behavioral studies. He also explains how models are used in the analysis of data. After describing graphical methods, such as scatterplot matrices, the text covers simple linear regression, locally weighted regression, multiple linear regression, regression diagnostics, the equivalence of regression and ANOVA, the generalized linear model, and logistic regression. The author then discusses aspects of survival analysis, linear mixed effects models for longitudinal data, and the analysis of multivariate data. He also shows how to carry out principal components, factor, and cluster analyses. The final chapter presents approaches to analyzing multivariate observations from several different populations.Through real-life applications of statistical methodology, this book elucidates the implications of behavioral science studies for statistical analysis. It equips behavioral science students with enough statistical tools to help them succeed later on in their careers. Solutions to the problems as well as all R code and data sets for the examples are available at www.crcpress.com
Contents
Data, Measurement, and ModelsIntroductionTypes of StudyTypes of MeasurementMissing ValuesThe Role of Models in the Analysis of DataDetermining Sample SizeSignificance Tests, p-Values, and Confidence IntervalsLooking at DataIntroductionSimple Graphics-Pie Charts, Bar Charts, Histograms, and BoxplotsThe Scatterplot and BeyondScatterplot MatricesConditioning Plots and Trellis GraphicsGraphical DeceptionSimple Linear and Locally Weighted RegressionIntroductionSimple Linear RegressionRegression DiagnosticsLocally Weighted RegressionMultiple Linear RegressionIntroductionAn Example of Multiple Linear RegressionChoosing the Most Parsimonious Model When Applying Multiple Linear RegressionRegression DiagnosticsThe Equivalence of Analysis of Variance and Multiple Linear Regression, and An Introduction to the Generalized Linear ModelIntroductionThe Equivalence of Multiple Regression and ANOVAThe Generalized Linear ModelLogistic RegressionIntroductionOdds and Odds RatiosLogistic RegressionApplying Logistic Regression to the GHQ DataSelecting the Most Parsimonious Logistic Regression ModelSurvival AnalysisIntroductionThe Survival FunctionThe Hazard FunctionCox's Proportional Hazards ModelLinear Mixed Models for Longitudinal DataIntroductionLinear Mixed Effects Models for Longitudinal DataHow Do Rats Grow?Computerized Delivery of Cognitive Behavioral Therapy-Beat the BluesThe Problem of Dropouts in Longitudinal StudiesMultivariate Data and Multivariate Analysis IntroductionThe Initial Analysis of Multivariate DataThe Multivariate Normal Probability Density FunctionPrincipal Components AnalysisIntroductionPCAFinding the Sample Principal ComponentsShould Principal Components Be Extracted from the Covariance or the Correlation Matrix?Principal Components of Bivariate Data with Correlation Coefficient rRescaling the Principal ComponentsHow the Principal Components Predict the Observed Covariance MatrixChoosing the Number of ComponentsCalculating Principal Component ScoresSome Examples of the Application of PCAUsing PCA to Select a Subset of the VariablesFactor AnalysisIntroductionThe Factor Analysis ModelEstimating the Parameters in the Factor Analysis ModelEstimating the Numbers of FactorsFitting the Factor Analysis Model: An ExampleRotation of FactorsEstimating Factor ScoresExploratory Factor Analysis and PCA Compared Confirmatory Factor AnalysisCluster AnalysisIntroductionCluster AnalysisAgglomerative Hierarchical Clusteringk-Means ClusteringModel-Based ClusteringGrouped Multivariate DataIntroductionTwo-Group Multivariate DataMore Than Two GroupsReferencesAppendix: Solutions to Selected ExercisesIndexA Summary and Exercises appear at the end of each chapter.
