Chapter One: Introduction to Factor AnalysisLatent and observed variablesThe importance of theory in doing factor analysisComparison of exploratory and confirmatory factor analysisA brief word about softwareOutline of the bookChapter Two: Mathematical Underpinnings of Factor AnalysisCorrelation and covariance matricesThe common factor modelCorrespondence between the factor model and the covariance matrixEigenvaluesError variance and communalitiesSummaryChapter Three: Methods of Factor Extraction in Exploratory Factor AnalysisEigenvalues, factor loadings, and the observed correlation matrixMaximum LikelihoodPrincipal Axis FactoringPrincipal components analysisPrincipal components versus factor analysisOther factor extraction methodsExampleSummaryChapter Four: Methods of Factor RotationSimple structureOrthogonal versus oblique rotation methodsCommon orthogonal rotationsVarimax rotationQuartimax rotationEquamax rotationCommon Oblique RotationsPromax rotationGeomin rotationTarget factor rotationsBifactor rotationExampleDeciding which rotation to useSummaryChapter Five: Methods for determining the number of factors to retain in exploratory factor analysisScree plot and eigenvalue greater than 1 ruleObjective methods based on the scree plotEigenvalues and the proportion of variance explainedResidual correlation matrixChi-square goodness of fit test for maximum likelihoodParallel analysisMinimum average partialVery Simple StructureExampleSummaryChapter Six: Final Issues in Factor AnalysisFactor scoresPower analysis and a priori sample size determinationDealing with missing dataExploratory structural equation modelingMultilevel EFAProper reporting practices for factor analysisEFA and other multivariate data reduction techniquesSummary.