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Last updated:
May 17, 2022
Duration:
Unlimited Duration
FREE
This course includes:
Unlimited Duration
Badge on Completion
Certificate of completion
Unlimited Duration
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 |
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. Jae-on Kim and Charles W. Mueller. Factor Analysis: Statistical Methods and Practical Issues. Beverly Hills, CA: Sage Publications, 1978.| Session | Topic | Readings |
|---|---|---|
| 1 | Intorduction to Measurement | No Reading |
| 2 | Measuring Association and Dimensionality | Required: Netemeyer. Pages xiii-xiv and 1-40. Recommended: Pagano, Gauvreau. Correlation. In: Principles of Biostatistics. Belmont, CA: Duxbury Press, 1993; 363-378. Digby PGN. Approximating the tetrachoric correlation coefficient. Biometrics 1983;39:753-757. |
| 3 | Principles of Psychometrics: Reliability I | Required: Netemeyer. Pages 41-59. Recommended: DeVellis. Pages 18-42 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;86:420-428. |
| 4 | Principles of Psychometrics: Reliability II | No Reading |
| 5 | Principles of Psychometrics: Validity I | Required: Netemeyer. Pages 71-94. Recommended: DeVellis. Pages 43-50. 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 five-factor model of personality across instruments and observers. Journal of Personality and Social Psychology 1987;52:81-90. 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 94-107. 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; 96-102. Murphy JM, et al. Performance of screening and diagnostic tests. Archives of General Psychiatry 1987;44:550-555. |
| 8 | Factor Analysis I | Required: Netemeyer. Pages 115-170. Recommended: DeVellis. pages 91-109. Kim J-O, 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; 692-762. 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;49:115-132. 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;156:578-585. Lakka, Laaksonen, Lakka, Niskanen, Kumpausalo, Tuomilehto, Salonen. The metabolic syndrome and total and cardiovascular disease mortality in middle-aged men. JAMA 2002;288:2709-2716. Hoodin, Kalbfleisch. Factor analysis and validity of the Transplant Evaluation Rating Scale in a large bone marrow transplant sample. Journal of Psychosomatic Research 2003;54:465-473. Costa PT, McCrae RR Four ways five factors are basic. In: Personality and Individual Differences 1992;13:653-665 |
| 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:Â Latent Class Analysis, Newbury Park, Sage, 1987. |
| 13 | Latent Class | Required: Nestadt, Addington, Samuels, Liang, Bienvenu, Riddle, Grados, Hoehn-Saric, Cullen. The Identification of OCD-Related Subgroups Based on Comorbidity. Biological Psychiatry 2003;53:914-920. Eaton WW, Dryman A, Sorenson A, McCutcheon A. DSM-III major depressive disorder in the community. A latent class analysis of data from the NIMH epidemiologic catchment area programme. British Journal of Psychiatry. 1989;155:48-54. Sullivan PF, Kessler RC, Kendler KS . Latent class analysis of lifetime depressive symptoms in the national comorbidity survey. Am J Psychiatry 1998; 155;1397-1406. Reboussin BA, Song E-Y, Shrestha A, Lohman KK, Wolfson M. A latent class analysis of underage problem drinking : Evidence from a community sample of 16-20 year olds. Drug and Alcohol Dependence 2006 ;83:199-209. |
| 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;4:84-99. |
Web Links
» Mplus Web site
Mplus is a statistical modeling program that provides researchers with a flexible tool to analyze their data. Mplus offers researchers a wide choice of models, estimators, and algorithms in a program that has an easy-to-use interface and graphical displays of data and analysis results.
» Reliability Analysis
Reliability is the correlation of an item, scale, or instrument with a hypothetical one which truly measures what it is supposed to.
» The Multitrait-Multimethod Matrix
The Multitrait-Multimethod Matrix (MTMM) is an approach to assessing the construct validity of a set of measures in a study.
» ROC Curves
Receiver Operating Characteristic curves.
Course Curriculum
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- Lecture 1: Introduction to Measurement 01:00:00
- Lecture 3: Principles of Psychometrics: Reliability I 01:00:00
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- 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
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