This course deals with the two fundamental cornerstones of research methodology: Measurement and causality. The first part of the lecture (chapters 2-5) provides an extensive introduction to the measurement of organizational concepts (e.g., salesperson motivation) and consumer psychological variables (e.g., customer satisfaction). This background is necessary because causal inference (chapters 6 and 7) requires valid measurement instruments.

1. Introduction

Relevance versus rigor: A misconception The relevance of rigorous measurement The relevance of rigor in causal inference Measurement and causality: An overview

2. Foundations of psychometric measurement

Observed variables Latent variables Classical latent variable theory Operationalism Properties of measurement models: Dimensionality, reliability, and validity

3. Dimensionality

Local independence Partial correlations The one-factor model Observed and implied covariance matrix Model identification Maximum likelihood estimation Model fit Exploratory factor analysis Confirmatory factor analysis

4. Reliability

Cronbach's alpha coefficient Composite reliability Indicator reliability Average variance extracted

5. Validity

Discriminant validity Criterion validity Content validity The process of scale validation

6. Structural equation modeling

Introduction of a structural or "causal" model component Observed and implied covariance matrix Model identification and estimation Model fit Interpretation of structural parameters Limitations and extensions

7. Experiments and Rubin's Causal Model

Classical conditions of causality Limitations of observational studies Advantages of experiments Rubin's Causal Model, individual causal effects, average causal effects Experimental design and analysis of experimental data Measurement models and causal models: An integrative perspective