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May 17, 2022

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Description

Presents quantitative approaches to measurement in the psychological and social sciences. Topics include the principles of psychometrics, including reliability and validity; the statistical basis for latent variable analysis, including exploratory and confirmatory factor analysis and latent class analysis; and item response theory. Draws examples from the social sciences, including stress and distress, social class and socioeconomic status, personality; consumer satisfaction, functional impairment and disability, quality of life, and the measurement of overall health status. Intended for doctoral students.

Session Topic Activities
1 Introduction to Measurement After this class students will be able to (1) briefly describe the concept of reliability in both intuitive and statistical terms, and (2) identify the key assumptions of classical test theory. Introduction Syllabus review Classical test theory Introduction to reliability
2 Measuring Association and Dimensionality After this class students will be able to (1) measure associations between continuous observed variables using covariances and correlations, (2) measure magnitudes of association between discrete observed variables, and (3) define multidimensionality regarding latent variables. Covariance Pearson & Spearman correlation Correlations with non-linear data Polychoric correlation Covariance, correlation, and odds ratio matrices Dimensionality
3 Principles of Psychometrics: Reliability I After this class students will be able to (1) describe two definitions of the concept of reliability, (2) predict how long a scale should be, and (3) estimate reliability for continuous and categorical measures Types of reliability (Interrater, test-retest reliability, internal consistency) Different types of reliability coefficients (correlation, split half measures, Alpha coefficient, Kuder Richardson Coefficient, Kappa
4 Principles of Psychometrics: Reliability II After this class students will be able to (1) describe the relationship of the intraclass correlation coefficient to other measures of reliability, and (2) correctly identify which intraclass correlation to use for different research designs ANOVA model for reliability Intra-class Correlation Coefficient Research Designs
5 Principles of Psychometrics: Validity I After this class students will be able to (1) distinguish four different types of validity, (2) describe the conceptual and quantitative relationship of reliability to validity, (3) estimate a true correlation from an observed correlation Types of Validity (face, content, criterion, construct) Relationship of Reliability to Validity Correction for attenuation
6 Principles of Psychometrics: Validity II After this class students will be able to evaluate the relative utility of different cutoffs for a measure in relation to a gold standard. Internal Construct Validity External Construct Validity Multi-trait Multimethod Matrix Sensitivity and Specificity ROC Curves
7 Scale Development After this class students will be able to describe procedures for constructing a scale from scratch Scale Construction
8 Factor Analysis I After this class students will be able to (1) identify when a factor analysis is appropriate and when it is not, (2) run a one-factor and multi-factor analysis, (3) interpret the results from a factor analysis Introduction to factor analysis The orthogonal factor model Loadings Principal components Eigenvalues Introduction to rotation Communalities/Uniqueness of items
9 Factor Analysis II After this class students will be able to (1) use the statistical procedure of rotation to aid in the interpretation of results from a factor analysis, (2) be able to apply both orthogonal and oblique rotations and identify the assumptions underlying each, and (3) apply the appropriate method of estimation for factor analysis Factor extraction Methods of estimation and rotation: orthogonal and oblique Choosing the number of factors Factor scores Confirmatory factor analysis Conditional independence Dichotomous factor analysis
10 Factor Analysis III In this class, students will (1) apply factor analysis to real data, and (2) critique published use of factor analysis Journal examples
11 Latent Class Analysis I After this class students will be able to (1) differentiate when to use factor analysis and when to use latent class analysis, and (2) interpret output from a latent class analysis The latent class model The response pattern matrix Choosing the number of classes Conditional probabilities Interpreting the model Examples: depression and functioning
12 Latent Class Analysis II After this class students will be able to (1) estimate a latent class analysis, and (2) to interpret different criterion to choose among alternative models Statistical model and assumptions Exploration: response patterns Issues of model fitting Identifiability Checking the model: tests and displays
13 Latent Class In this class students will (1) apply latent class analysis to real data, and (2) critique published use of latent class analysis Journal Examples
14 Sample Size in Reliability and Factor Analysis After this class students will be able to (1) estimate the sample size needed to for scales with targeted reliability levels, (2) estimate the sample size needed for pilot studies that will use factor analysis, and (3) understand issues involved with sample size in latent class analysis. Targeted Reliability Levels Pilot Studies Latent Class Analysis

Course Curriculum

    • Lecture 1: Introduction to Measurement 01:00:00
    • Lecture 3: Principles of Psychometrics: Reliability I 01:00:00
    • Lecture 4: Principles of Psychometrics: Reliability II 01:00:00
    • Lecture 5: Principles of Psychometrics: Validity I 01:00:00
    • Lecture 6: Principles of Psychometrics: Validity II 01:00:00
    • Lecture 8: Factor Analysis I 01:00:00
    • Lecture 9: Factor Analysis II 01:00:00

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