References

 

(To request a Muthén paper, please email bmuthen@ucla.edu and refer to the number in parenthesis.)

 

Analysis With Categorical Outcomes

 

General

 

Agresti, A. (1990).  Categorical data analysis.  New York: John Wiley & Sons.

 

Hosmer, D. W. & Lemeshow, S. (2000).  Applied Logistic Regression.  Second edition.  New York: John Wiley & Sons.

 

Factor Analysis

 

Bartholomew, D.J. (1987).  Latent variable models and factor analysis. New York: Oxford University Press.

 

Bock, R.D, Gibbons, R., & Muraki, E. J. (1988).  Full information item factor analysis. Applied Psychological Measurement, 12, 261-280.

 

Millsap, R.E. & Yun-Tien, J. (2003).  Assessing factorial invariance in ordered-categorical measures.  Forthcoming in Multivariate Behavioral Research.

 

Mislevy, R. (1986).  Recent developments in the factor analysis of categorical variables.  Journal of Educational Statistics, 11, 3-31.

 

Muthén, B.  (1978).  Contributions to factor analysis of dichotomous variables.  Psychometrika, 43, 551-560. (#3)

 

Muthén, B., & Christoffersson, A.  (1981).  Simultaneous factor analysis of dichotomous variables in several groups.  Psychometrika, 46, 407-419. (#6)

 

Muthén, B.  (1989).  Dichotomous factor analysis of symptom data.  In Eaton & Bohrnstedt (Eds.), Latent Variable Models for Dichotomous Outcomes:  Analysis of Data from the Epidemiological Catchment Area Program (pp. 19-65), a special issue of Sociological Methods & Research, 18, 19-65. (#21)

 

Muthén, B. (1996).  Psychometric evaluation of diagnostic criteria:  Application to a two-dimensional model of alcohol abuse and dependence.  Drug and Alcohol Dependence, 41, 101-112. (#66)

 

Muthén, B. & Asparouhov, T.  (2002).  Latent variable analysis with categorical outcomes: Multiple-group and growth modeling in Mplus.  Mplus Web Note #4 (www.statmodel.com).

 

Muthén, B., & Kaplan D.  (1985).  A comparison of some methodologies for the factor analysis of non-normal Likert variables.  British Journal of Mathematical and Statistical Psychology, 38, 171-189.

 

Muthén, B., & Kaplan, D.  (1992).  A comparison of some methodologies for the factor analysis of non-normal Likert variables:  A note on the size of the model.  British Journal of Mathematical and Statistical Psychology, 45, 19-30.

 

Takane, Y. & DeLeeuw, J. (1987).  On the relationship between item response theory and factor analysis of discretized variables.  Psychometrika, 52, 393-408.

 

MIMIC

 

Gallo, J.J., Anthony, J. & Muthen, B. (1994).  Age differences in the symptoms of depression: a latent trait analysis. Journals of Gerontology: Psychological Sciences, 49, 251-264. (#52)

 

Muthén, B. (1989).  Latent variable modeling in heterogeneous populations. Psychometrika, 54, 557-585. (#24)

 

Muthén, B., Tam, T., Muthén, L., Stolzenberg, R. M., & Hollis, M.  (1993).  Latent variable modeling in the LISCOMP framework:  Measurement of attitudes toward career choice.  In D. Krebs, & P. Schmidt (Eds.), New Directions in Attitude Measurement, Festschrift for Karl Schuessler (pp. 277-290).  Berlin:  Walter de Gruyter. (#46)

 

IRT

 

Bock, R.D. (1977).  A brief history of item response theory.  Educational Measurement: Issues and Practice, 16, 21-33.

 

du Toit, M. (2003).  IRT from SSI.  Lincolnwood, IL: Scientific Software International, Inc. (BILOG, MULTILOG, PARSCALE, TESTFACT)

 

MacIntosh, R. & Hashim, S. (2003).  Variance estimation for converting MIMIC model parameters to IRT parameters in DIF analysis. Applied Psychological Measurement, 27, 372-379.

 

Muthén, B.  (1985).  A method for studying the homogeneity of test items with respect to other relevant variables.  Journal of Educational Statistics, 10, 121-132. (#13)

 

Muthén, B.  (1988).  Some uses of structural equation modeling in validity studies:  Extending IRT to external variables.  In H. Wainer, & H. Braun (Eds.), Test Validity (pp. 213-238).  Hillsdale, NJ: Erlbaum Associates. (#18)

 

Muthén, B.  (1989).  Using item-specific instructional information in achievement modeling.  Psychometrika, 54, 385-396. (#30)

 

Muthén, B.  (1994).  Instructionally sensitive psychometrics:  Applications to the Second International Mathematics Study.  In I. Westbury, C. Ethington, L. Sosniak, & D. Baker (Eds.), In Search of More Effective Mathematics Education:  Examining Data from the IEA Second International Mathematics Study (pp. 293-324).  Norwood, NJ: Ablex. (#54)

 

Muthén, B. & Asparouhov, T.  (2002).  Latent variable analysis with categorical outcomes: Multiple-group and growth modeling in Mplus.  Mplus Web Note #4 (www.statmodel.com).

 

Muthén, B., Kao, Chih-Fen, & Burstein, L.  (1991).  Instructional sensitivity in mathematics achievement test items:  Applications of a new IRT-based detection technique.  Journal of Educational Measurement, 28, 1-22.  (#35)

 

Muthén, B., & Lehman. J.  (1985).  Multiple-group IRT modeling:  Applications to item bias analysis.  Journal of Educational Statistics, 10, 133-142. (#15)

 

SEM

 

Browne, M.W. & Arminger, G. (1995).  Specification and estimation of mean- and covariance-structure models. In G. Arminger, C.C. Clogg & M.E. Sobel (Eds.), Handbook of statistical modeling for the social and behavioral sciences (pp. 311-359).  New York: Plenum Press.

 

Muthén, B.  (1979).  A structural probit model with latent variables.  Journal of the American Statistical Association, 74, 807-811. (#4)

 

Muthén, B. (1983).  Latent variable structural equation modeling with categorical data. Journal of Econometrics, 22, 48-65. (#9)

 

Muthén, B. (1984).  A general structural equation model with dichotomous, ordered categorical, and continuous latent variable indicators.  Psychometrika, 49, 115-132. (#11)

 

Muthén, B. (1989).  Latent variable modeling in heterogeneous populations. Psychometrika, 54, 557-585. (#24)

 

Muthén, B. (1993).  Goodness of fit with categorical and other non-normal variables.  In K. A. Bollen, & J. S. Long (Eds.), Testing Structural Equation Models (pp. 205-243).  Newbury Park, CA:  Sage.  (#45)

 

Muthén, B., & Speckart, G.  (1983).  Categorizing skewed, limited dependent variables:  Using multivariate probit regression to evaluate the California Civil Addict Program.  Evaluation Review, 7, 257-269. (#3)

 

Muthén, B., du Toit, S.H.C., & Spisic, D. (1997).  Robust inference using weighted least squares and quadratic estimating equations in latent variable modeling with categorical and continuous outcomes.  Accepted for publication in Psychometrika. (#75)

 

Xie, Y. (1989).  Structural equation models for ordinal variables.  Sociological Methods & Research, 17, 325-352.

 

Yu, C.-Y. & Muthén, B. (2002).  Evaluation of model fit indices for latent variable models with categorical and continuous outcomes.  Technical report.

 

Growth

 

Gibbons, R.D. & Hedeker, D. (1997).  Random effects probit and logistic regression models for three-level data.  Biometrics, 53, 1527-1537.

 

Hedeker, D. & Gibbons, R.D. (1994).  A random-effects ordinal regression model for multilevel analysis.  Biometrics, 50, 933-944.

 

Muthén, B. (1996).  Growth modeling with binary responses.  In A. V. Eye, & C. Clogg (Eds.), Categorical Variables in Developmental Research:  Methods of Analysis (pp. 37-54).  San Diego, CA: Academic Press.  (#64)

 

Muthén, B. & Asparouhov, T.  (2002).  Latent variable analysis with categorical outcomes: Multiple-group and growth modeling in Mplus.  Mplus Web Note #4 (www.statmodel.com).

 

 

Analysis With Multilevel Data

 

Cross-sectional Data

 

Harnqvist, K., Gustafsson, J.E., Muthén, B, & Nelson, G. (1994).  Hierarchical models of ability at class and individual levels.  Intelligence, 18, 165-187. (#53)

 

Heck, R.H. (2001).  Multilevel modeling with SEM.   In G. A. Marcoulides & R. E. Schumacker (eds.), New Developments and Techniques in Structural Equation Modeling (pp. 89-127).  Lawrence Erlbaum Associates. 

 

Hox, J. (2002).  Multilevel analysis.  Techniques and applications.  Mahwah, NJ:  Lawrence Erlbaum

 

Kaplan, D., & Elliott, P. R. (1997).  A didactic example of multilevel structural equation modeling applicable to the study of organizations.  Structural Equation Modeling: A Multidisciplinary Journal, 4, 1-24.

 

Kaplan, D., & Kreisman, M. B. (2000).  On the validation of indicators of mathematics education using TIMSS:  An application of multilevel covariance structure modeling.  International Journal of Educational Policy, Research, and Practice, 1, 217-242.

 

Kreft, I. & de Leeuw, J. (1998).  Introducing multilevel modeling. Thousand Oakes, CA: Sage Publications.

 

Longford, N. T., & Muthén, B. (1992).  Factor analysis for clustered observations. Psychometrika, 57, 581-597. (#41)

 

Muthén, B. (1989).  Latent variable modeling in heterogeneous populations.  Psychometrika, 54, 557-585. (#24)

 

Muthén, B.  (1990).  Mean and covariance structure analysis of hierarchical data.  Paper presented at the Psychometric Society meeting in Princeton, NJ, June 1990.  UCLA Statistics Series 62.  (#32)

 

Muthén, B. (1991).  Multilevel factor analysis of class and student achievement components.  Journal of Educational Measurement, 28, 338-354. (#37)

 

Muthén, B.  (1994).  Multilevel covariance structure analysis.  In J. Hox & I. Kreft (eds.), Multilevel Modeling, a special issue of Sociological Methods & Research, 22, 376-398.  (#55)

 

Muthén, B., Khoo, S.T. & Gustafsson, J.E. (1997).  Multilevel latent variable modeling in multiple populations.   (#74)

 

Muthén, B. & Satorra, A. (1995).  Complex sample data in structural equation modeling. In P. Marsden (ed.), Sociological Methodology 1995, 216-316.  (#59)

 

Raudenbush, S.W. & Bryk, A.S. (2002).  Hierarchical linear models: Applications and data analysis methods.  Second edition.  Newbury Park, CA: Sage Publications. 

 

Skinner, C.J., Holt, D. & Smith, T.M.F. (1989).  Analysis of complex surveys.  West Sussex, England:  Wiley.

 

Snijders, T. & Bosker, R. (1999).  Multilevel analysis. An introduction to basic and advanced multilevel modeling.  Thousand Oakes, CA: Sage Publications.

 

Longitudinal Data

 

Khoo, S.T. & Muthén, B. (2000).  Longitudinal data on families: Growth modeling alternatives.  Multivariate Applications in Substance use Research, J. Rose, L. Chassin, C. Presson & J. Sherman (eds.), Hillsdale, N.J.:  Erlbaum, pp. 43-78 (#79)

 

Muthén, B. (1997).  Latent variable modeling with longitudinal and multilevel data.  In A. Raftery (ed), Sociological Methodology (pp. 453-480). Boston: Blackwell Publishers.  (#73)

 

Muthén, B. (1997).  Latent variable growth modeling with multilevel data.  In M. Berkane (ed.), Latent Variable Modeling with Application to Causality (149-161), New York:  Springer Verlag. (#72) 

 

 

Analysis With Missing Data

 

Hedeker, D. & Rose, J.S. (2000).  The natural history of smoking:  A pattern-mixture random-effects regression model.  Multivariate Applications in Substance use Research, J. Rose, L. Chassin, C. Presson & J. Sherman (eds.), Hillsdale, N.J.:  Erlbaum, pp. 79-112.

 

Little, R.J., & Rubin, D.B.  (2002).  Statistical analysis with missing data.  2nd edition.  New York:  John Wiley & Sons.

 

Muthén, B., Kaplan, D., & Hollis, M.  (1987).  On structural equation modeling with data that are not missing completely at random.  Psychometrika, 42, 431-462. (#17)

 

Schafer, J.L. (1997).  Analysis of incomplete multivariate data.  London:  Chapman & Hall.

 

Schafer, J.L & Graham, J. (2002).  Missing data: Our view of the state of the art.  Psychological Methods, 7, 147- 177.