 
Abstract/Syllabus:

330.657
Statistics in Psychosocial Research: Measurement
Staff
Instructors: William Eaton, Elizabeth GarrettMayer, and JeannieMarie Leoutsakos
Originally Offered
Fall 2006
Offered By
Department of Mental Health
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.
OCW offers a snapshot of the content used in courses offered by JHSPH. OCW materials are not for credit towards any degrees or certificates offered by the Johns Hopkins Bloomberg School of Public Health.
Schedule

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 nonlinear 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, testretest 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
Intraclass 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
Multitrait 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 onefactor and multifactor 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

15 
Review 

16 
Final Exam 




Further Reading:

Readings
Readings are shown below. Click here for a printerfriendly version.
Textbooks
Required
Richard G. Netemeyer, William O. Bearden, Subhash Sharma. Scaling Procedures: Issues and Applications. Thousand Oaks, CA: Sage Publications, 2003.
A.L. McCutcheon. Latent Class Analysis. Newbury Park: Sage Publications, 1987.
Highly recommended
Robert F. DeVellis. Scale Development: Theory and Applications. Newbury Park: Sage Publications, 1991.
Jaeon Kim and Charles W. Mueller. Factor Analysis: Statistical Methods and Practical Issues. Beverly Hills, CA: Sage Publications, 1978.

1 
Intorduction to Measurement

No Reading 
2 
Measuring Association and Dimensionality

Required:
Netemeyer. Pages xiiixiv and 140.
Recommended:
Pagano, Gauvreau. Correlation. In: Principles of Biostatistics. Belmont, CA: Duxbury Press, 1993; 363378.
Digby PGN. Approximating the tetrachoric correlation coefficient.
Biometrics 1983;39:753757.

3 
Principles of Psychometrics: Reliability I 
Required:
Netemeyer. Pages 4159.
Recommended:
DeVellis. Pages 1842
Anastasi A. Reliability. In: Psychological Testing , 6th edition. New York : Macmillan, 1988.
Carmines EG, Zeller RA. Reliability and Validity Assessment . Beverly Hills, CA: Sage, 1979.
Bohrnstedt G. Measurement. In: Rossi PH, Wright JD, Anderson AB, eds. Handbook of Survey Research. Orlando , FL : Academic Press, 1983.
Shrout PE , Fleiss JL. Intraclass Correlations: uses in assessing rater reliability. Psychological Bulletin 1979;86:420428.

4 
Principles of Psychometrics: Reliability II 
No Reading 
5 
Principles of Psychometrics: Validity I 
Required:
Netemeyer. Pages 7194.
Recommended:
DeVellis. Pages 4350.
Anastasi A. Validity: basic concepts. In: Psychological Testing , 6th edition. New York, Macmillan, 1988.
Carmines EG, Zeller RA. Reliability and Validity Assessment. Beverly Hills, CA: Sage, 1979.
McCrae RR, Costa PT. Validation of the fivefactor model of personality across instruments and observers. Journal of Personality and Social Psychology 1987;52:8190.
Bohrnstedt G. Measurement. In: Rossi PH, Wright JD, Anderson AB, Eds. Handbook of Survey Research, Orlando, FL: Academic Press, 1983.

6 
Principles of Psychometrics: Validity II 
No Reading 
7 
Scale Development 
Required:
Netemeyer. Pages 94107.
Streiner DL, Norman GR. From items to scales. In: Health Measurement Scales: A Practical Guide to Their Development and Use. 2nd edition. New York: Oxford University Press, 1995; 96102.
Murphy JM, et al. Performance of screening and diagnostic tests. Archives of General Psychiatry 1987;44:550555.

8 
Factor Analysis I 
Required:
Netemeyer. Pages 115170.
Recommended:
DeVellis. pages 91109.
Kim JO, Mueller CW. Factor Analysis: Statistical Methods and Practical Issues, Beverly Hills, CA: Sage Publications, 1988.
Everitt B, Dunn G. Factor analysis. In: Applied Multivariate Data Analysis. London: Arnold Press, 2001.

9 
Factor Analysis II 
Recommended:
Everitt B, Dunn G. Principal components analysis. In: Applied Multivariate Data Analysis. London: Arnold, 2001.
Fisher LD, Van Belle G. Principal component analysis and factor analysis. In: Biostatistics: A Methodology for the Health Sciences, New York: John Wiley & Sons, 1993; 692762.
Johnson RA, Wichern DW. Principal components analysis (Chapter 8) and Factor Analysis and Inference for Structured Covariance Matrices (Chapter 9) In: Applied Multivariate Statistical Analysis, 2nd edition. Englewood Cliffs, NJ: Prentice Hall, 1988.
Long JS. Confirmatory Factor Analysis: A Preface to LISREL. Beverly Hills, CA: Sage, 1983.
Muthen B. A general structural equation model with dichotomous, ordered categorical, and continuous latent variable indicators. Psychometrika 1984;49:115132.
Eaton WW, Bohrnstedt GW, eds. Latent Variable Models for Dichotomous Outcomes: Analysis of Data from the NIMH Epidemiologic Catchment Area Program. Newbury Park: Sage Publications, 1989

10 
Factor Analysis III 
Required:
Shapiro, Lasarev, McCauley. Factor Analysis of Gulf War Illness: What does it add to our understanding of possible health effects of deployment. Am J Epidemiol 2002;156:578585.
Lakka, Laaksonen, Lakka, Niskanen, Kumpausalo, Tuomilehto, Salonen. The metabolic syndrome and total and cardiovascular disease mortality in middleaged men. JAMA 2002;288:27092716.
Hoodin, Kalbfleisch. Factor analysis and validity of the Transplant Evaluation Rating Scale in a large bone marrow transplant sample. Journal of Psychosomatic Research 2003;54:465473.
Costa PT, McCrae RR Four ways five factors are basic. In: Personality and Individual Differences 1992;13:653665

11 
Latent Class Analysis I 
Required:
McCutcheon A. Chapters 1 and 2. Latent Class Analysis , Newbury Park : Sage, 1987.
Recommended:
Uebersax J. LCA Frequently Asked Questions.

12 
Latent Class Analysis II 
Required:
McCutcheon A. Chapter 3. In: Latent Class Analysis, Newbury Park, Sage, 1987.

13 
Latent Class 
Required:
Nestadt, Addington, Samuels, Liang, Bienvenu, Riddle, Grados, HoehnSaric, Cullen. The Identification of OCDRelated Subgroups Based on Comorbidity. Biological Psychiatry 2003;53:914920.
Eaton WW, Dryman A, Sorenson A, McCutcheon A. DSMIII major depressive disorder in the community. A latent class analysis of data from the NIMH epidemiologic catchment area programme. British Journal of Psychiatry. 1989;155:4854.
Sullivan PF, Kessler RC, Kendler KS . Latent class analysis of lifetime depressive symptoms in the national comorbidity survey. Am J Psychiatry 1998; 155;13971406.
Reboussin BA, Song EY, Shrestha A, Lohman KK, Wolfson M. A latent class analysis of underage problem drinking : Evidence from a community sample of 1620 year olds. Drug and Alcohol Dependence 2006 ;83:199209.

14 
Sample Size in Reliability and Factor Analysis 
Required:
MacCallum RC, Widaman KF, Shaobo Z, Hong S. Sample size in factor analysis. Psychological Methods 1999;4:8499.




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