The Scientific Use of Factor Analysis in Behavioral and Life Sciences [Paperback]

I.- 1 The Position of Factor Analysis in Psychological Research.- 1.1. The ANOVA and CORAN Methods of Finding Significant Relations in Data.- 1.2. The Reason for the Salient Role of Statistics in the Biosocial Sciences.- 1.3. What Is an Experiment? Bivariate and Multivariate Designs.- 1.4. Dimensions of Experiment and Their Relation to Historic Areas of Research.- 1.5. The Relation of Experiment to the Inductive-Hypothetico-Deductive Method.- 1.6. The Relative Power and Economies and the Mutual Utilities of ANOVA and CORAN Methods.- 1.7. Summary.- 2 Extracting Factors: The Algebraic Picture.- 2.1. The Aims of Multivariate CORAN: Component Analysis, Cluster Analysis, and Factor Analysis.- 2.2. The Basic Factor Proposition: Correlation Size Related to Common Factor Size.- 2.3. The Centroid or Unweighted Summation Extraction of Factor Components.- 2.4. The Principal Components or Weighted Summation Extraction..- 2.5. Communality: Common (Broad) and Unique Variances.- 2.6. Checking Back from Factor Matrices to Correlation Matrices.- 2.7. The Specification and Estimation Equations Linking Factors and Variables.- 2.8. Summary.- 3 Rotating Factors: The Geometric Picture.- 3.1. The Correlation Coefficient Geometrically Represented.- 3.2. Plotting Variables in Hyperspace from the V0 Matrix.- 3.3. The Relation of Cluster Analysis to Factor Analysis.- 3.4. The Effect of Factor Rotation on Factor Patterns.- 3.5. The Possibility of a Unique Rotation and the Need to Find It ..- 3.6. Summary.- 4 Fixing the Number of Factors: TheScientific Model 52.- 4.1. A Return to the Number of Factors Issue.- 4.2. The Alternatives of Statistical and Psychometric Bases of Decision on Factor Number.- 4.3. Broader View of the Number of Factors Problem in the Light of the Scientific Model.- 4.4. Practical Decision by the Scree Test and Maximum Likelihood..- 4.5. The Contrasting Properties of the Principal Components and Factor Models.- 4.6. Summary.- 5 Fixing the Number of Factors: The Most PractlĂ#