- Chapter 1: Introduction
- CONCEPTS: Scaling vs. Classification; Meaningfulness and Usefulness; Standardization; Scales of Measurement.

- Chapter 2: Traditional Approaches to Scaling
- CONCEPTS: The Data Matrix; Model Evaluation; Threshold; Fullerton-Cattell Law; Judgments vs. Sentiments; Preferences vs. Similarities; Law of Comparative Judgment; Multi-item Measures; Item Characteristic Curves; Difficulty and Discrimination; Guttman Scale; The Linear Model.
- Chapter 3: Validity (of the first 3, this is for rereading)
- CONCEPTS: Theory vs. Instrument; Theories and Models: Measurement and Structure; Construct Validity: Domain of Observables, Relations among Constructs, Campbell and Fiske's Multitrait-Multimethod Matrix; Criterion-Related Validity: (Postdictive, Concurrent, & Predictive), The Criterion Problem; Content Validity; Substantive Theory vs. Measurement Theory.
- Chapter 4: Elements of Statistical Description and Estimation
- CONCEPTS: Ordered vs. Categorical Variables; Variance (Ordered and Dichotomous variables); Scale Transformations; Covariance and Correlation; Variations on Pearson: phi, point-biserial, and phi; "Assumptions underlying" Pearson correlation; Factors influencing r: restriction of range, distribution form; Non-linear measure of relationship: eta (correlation ratio); Regression; Standard Error of Estimate; Partitioning variance; Estimation procedures: Least squares, Maximum likelihood; Properties of estimators.
- Chapter 5: Linear Combinations, Partial and Multiple Correlation
- CONCEPTS: Variance of linear combinations; Covariance of linear combinations; Within and between covariance matrices; Partial correlation (Semi-partial correlation) [Work through example on pp 178-180], Multiple correlation and multiple regression (significance, suppression, categorical predictors, multicollinearity, importance of "predictors," etc.); Selection (hierarchical inclusion or elimination, moderated multiple regression); Related topics (ANCOVA, nonlinear relations, residual analysis, canonical analysis).

- Chapter 6: The Theory of Measurement Error
- CONCEPTS: The Concept of Measurement Error; Classical Test Theory; The Domain Sampling Model (multi-item measures, estimates of reliability); The Model of Parallel Tests; Test Length (Spearman-Brown prophecy formula); Coefficient Alpha; KR-20; Variance of True and Error Scores; The Standard Error of Measurement; Attenuation; Reliability as Stability over Time.
- Chapter 7: The Assessment of Reliability
- CONCEPTS: Sources of Error; Estimation of Reliability; Uses of the Reliability Coefficient (Correction for Attenuation, Confidence Intervals, Effects of Dispersion on Reliability); Making Measures Reliable (Test Length, Standards, Limitations); Reliability of Linear Combinations (Negative Elements, Weighted Sums, Principles); An Analysis of Variance Approach to Reliability; Generalizability Theory.
- Chapter 10: Recent Developments in Test Theory
- CONCEPTS: Item Response Theory (IRT) (Conditional Independence, One-Parameter Models, Two-Parameter Models, Three-Parameter Models, Item and Test Information); Differential Item Functioning (Item Bias); Tailored Tests and Computerized Adaptive Testing; Commentary on IRT.

- Chapter 11: Factor Analysis I: The General Model and Variance Condensation
- CONCEPTS: Uses of Factor Analysis; Condensing Variance in Exploratory Factor Analysis; Centroid Condensation; Principal Component and Principal Axis Condensation; Maximum Likelihood and Related Forms of Condensation; Determining the Number of Factors; Causal Indicators.
- Chapter 12: Exploratory Factor Analysis II: Rotation and Other Topics
- CONCEPTS: Factor Rotation; Analytic Rotations; Estimation of Factor Scores; The Common Factor Model; Factor Analytic Designs; Ad-Lib Factoring; Higher-Order Factors.
- Chapter 13: Confirmatory Factor Analysis
- CONCEPTS: Spearman's General Factor Solution; Comparing
Factors in Different Analyses; Testing Weak Theories;
Factoring Categorical Variables (Item Level Factoring);
Testing Strong Theories.

- Factor
Analysis Using SAS PROC FACTOR (Stat Services, UT-Austin)

Factor Analysis (Bertram Malle, University of Oregon)

A compilation of SEM resources (AERA SIG: Structural Equation Modeling)

I stole the following from an email msg posted to STAT-L by Eric Bohlman (ebohlman@netcom.com):

"H.G. Wells once said that he expected that in his lifetime, learning
to think statistically would become as important as learning to
read. Harold Hotelling once questioned how anyone could consider
himself (*sic*) in possession of a liberal education if he
(*sic*) didn't understand the nature of variation."

If you have difficulty with any of the concepts presented in the text or lectures, please consult the syllabus for one or more of the courses listed on my home page

Questions or comments to: daniel@hawaii.edu