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References

References for Analysis with Continuous Outcomes
References for Analysis with Categorical Outcomes
References for Analysis with Longitudinal Data
References for Analysis with Multilevel Data
References for Analysis with Missing Data
References for Analysis with Categorical Latent Variables (Mixture Modeling)
References for Design and Power Issues
References for Randomized Trials
References for Growth Mixture Modeling

References for Analysis with Continuous Outcomes

Note: Click here to download Muthén papers.

EFA

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

Fabrigar, L.R., Wegener, D.T., MacCallum, R.C. & Strahan, E.J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods , 4, 272-299.

Gorsuch, R.L. (1983). Factor analysis. 2nd edition. Hillsdale, N.J.: Lawrence Erlbaum.

Harman, H.H. (1976). Modern factor analysis. 3rd edition. Chicago: The University of Chicago Press.

Holzinger, K. J. & Swineford, F. (1939). A study in factor analysis: The stability of a bi-factor solution. Supplementary Educational Monographs. Chicago, Ill.: The University of Chicago.

Joreskog, K.G. (1977). Factor analysis by least-squares and maximum-likelihood methods. In Statistical Methods for Digital Computers, K. Enslein, A. Ralston, and H.S. Wilf (Eds.). New York: John Wiley & Sons, pp. 125-153.

Joreskog, K.G. (1979). Author's addendum. In Advances in Factor Analysis and Structural Equation Models, J. Magidson (Ed.). Cambridge, Massachusetts: Abt Books, pp. 40-43.

Kim, J.O. & Mueller, C.W. (1978). An introduction to factor analysis: What it is and how to do it. Sage University Paper series on Quantitative Applications in the Social Sciences, No 07-013. Beverly Hills, CA: Sage.

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

Mulaik, S. (1972). The foundations of factor analysis. McGraw-Hill.

Schmid, J. & Leiman, J.M. (1957). The development of hierarchical factor solutions. Psychometrika, 22, 53-61.

Spearman, C. (1927). The abilities of man. New York: Macmillan.

Thurstone, L.L. (1947). Multiple factor analysis. Chicago: University of Chicago Press.

Tucker, L.R. (1971). Relations of factor score estimates to their use. Psychometrika, 36, 427-436.

CFA

Joreskog, K.G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34, 183-202.

Joreskog, K.G. (1971). Simultaneous factor analysis in several populations. Psychometrika, 36, 409-426.

Lawley, D.N. & Maxwell, A.E. (1971). Factor analysis as a statistical method. London: Butterworths.

Long, S. (1983). Confirmatory factor analysis. Sage University Paper series on Quantitative Applications in the Social Sciences, No 33. Beverly Hills, CA: Sage.

Lord, F.M. & Novick, M.R. (1968). Statistical theories of mental test scores. Reading, Mass.: Addison-Wesley Publishing Co.

Meredith, W. (1964). Notes on factorial invariance. Psychometrika, 29, 177-185.

Meredith, W. (1993). Measurement invariance, factor analysis and factorial invariance. Psychometrika, 58, 525-543.

Muthén, B. (1989). Factor structure in groups selected on observed scores. British Journal of Mathematical and Statistical Psychology, 42, 81-90. (#23)

Muthén, B. (1989). Multiple-group structural modeling with non-normal continuous variables. British Journal of Mathematical and Statistical Psychology, 42, 55-62. (#26)

Sorbom, D. (1974). A general method for studying differences in factor means and factor structure between groups. British Journal of Mathematical and Statistical Psychology, 27, 229-239.

MIMIC

Hauser, R.M. & Goldberger, A.S. (1971). The treatment of unobservable variables in path analysis. In H. Costner (Ed.), Sociological Methodology (pp. 81-117). American Sociological Association: Washington, D.C.

Joreskog, K.G. & Goldberger, A.S. (1975). Estimation of a model with multiple indicators and multiple causes of a single latent variable. Journal of the American Statistical Association, 70, 631-639.

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

SEM

Amemiya, T. (1985). Advanced econometrics. Cambridge, Mass.: Harvard University Press.

Bollen, K.A. (1989). Structural equations with latent variables. New York: John Wiley.

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.

Browne, M.W. & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. Bollen & K. Long (Eds.), Testing structural equation models (pp. 136-162). Newbury Park: Sage.

Joreskog, K.G. (1973). A general method for estimating as linear structural equation system. In Structural Equation Models in the Social Sciences, A.S. Goldberger and O.D. Duncan Eds.). New York: Seminar Press, pp. 85-112.

Joreskog, K.G. & Sorbom, D. (1979). Advances in factor analysis and structural equation models. Cambridge, MA: Abt Books.

Kaplan, D. (2000). Structural equation modeling. Foundations and extensions. Thousand Oaks, CA: Sage.

Klein, A. & Moosbrugger, H. (2000). Maximum likelihood estimation of latent interaction effects with the LMS method. Psychometrika, 65, 457-474.

Kline, R.B. (1998). Principles and practice of structural equation modeling. New York, NY: Guilford Press.

MacCallum, R. C. & Austin, J. T. (2000). Applications of structural equation modeling in psychological research. Annual Review of Psychology, 51, 201-226.

Raykov, T. & Marcoulides, G. A. (2000). A first course in structural equation modeling. Mahwah, NJ: Erlbaum.

Sorbom, D. (1989). Model modification. Psychometrika, 54, 371-384.

Wheaton, B., Muthén, B., Alwin, D. & Summers, G. (1977). Assessing reliability and stability in panel models. In D.R. Heise (Ed.), Sociological Methodology 1977 (pp. 84-136). San Francisco: Jossey-Bass. (#1)

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References for Analysis with Categorical Outcomes

Note: Click here to download Muthén papers.

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.

Maddala, G.S. (1983). Limited-dependent and qualitative variables in econometrics. Cambridge: Cambridge University Press.

Factor Analysis

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

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)

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. & Muthén, 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

Lord, F.M. & Novick, M.R. (1968). Statistical theories of mental test scores. Reading, Mass.: Addison-Wesley Publishing Co.

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., 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. (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. & 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.

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. (#8)

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.

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References for Analysis with Longitudinal Data

Note: Click here to download Muthén papers.

Introductory

Goldstein, H. (1995). Multilevel statistical models. London: Edward Arnold.

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

Jennrich, R.I. & Schluchter, M.D. (1986). Unbalanced repeated-measures models with structured covariance matrices. Biometrics, 42, 805-820.

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)

Laird, N.M. & Ware, J.H. (1982). Random-effects models for longitudinal data. Biometrics, 38, 963-974.

Lindstrom, M.J. & Bates, D.M. (1988). Newton-Raphson and EM algorithms for linear mixed-effects models for repeated-measures data. Journal of the American Statistical Association, 83, 1014-1022.

Littell, R., Milliken, G.A., Stroup, W.W. & Wolfinger, R.D. (1996). SAS system for mixed models. Cary NC: SAS Institute.

McArdle, J.J. & Epstein, D. (1987). Latent growth curves within developmental structural equation models. Child Development, 58, 110-133.

Meredith, W. & Tisak, J. (1990). Latent curve analysis. Psychometrika, 55, 107-122.

Muthén, B. (1991). Analysis of longitudinal data using latent variable models with varying parameters. In L. Collins & J. Horn (eds.), Best Methods for the Analysis of Change. Recent Advances, Unanswered Questions, Future Directions (pp. 1-17). Washington DC: American Psychological Association. (#33)

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. (2000). Methodological issues in random coefficient growth modeling using a latent variable framework: Applications to the development of heavy drinking. In Multivariate Applications in Substance use Research, J. Rose, L. Chassin, C. Presson & J. Sherman (eds.), Hillsdale, N.J.: Erlbaum, pp. 113-140. (#81)

Muthén, B. & Curran, P. (1997). General longitudinal modeling of individual differences in experimental designs: A latent variable framework for analysis and power estimation. Psychological Methods, 2, 371-402. (#71)

Muthén, B. & Khoo, S.T. (1998). Longitudinal studies of achievement growth using latent variable modeling. Learning and Individual Differences, Special issue: Latent growth curve analysis, 10, 73-101. (#80)

Muthén, B. & Muthén, L. (2000). The development of heavy drinking and alcohol-related problems from ages 18 to 37 in a U.S. national sample. Journal of Studies on Alcohol, 61, 290-300. (#83)

Rao, C.R. (1958). Some statistical models for comparison of growth curves. Biometrics, 14, 1-17.

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

Singer, J.D. (1999). Using SAS PROC MIXED to fit multilevel models, hierarchical models, and individual growth models. To appear in Journal of Educational and Behavioral Statistics, Spring 1999.

Tucker, L.R. (1958). Determination of parameters of a functional relation by factor analysis. Psychometrika, 23, 19-23.

Advanced

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

Muthén, B. & Curran, P. (1997). General longitudinal modeling of individual differences in experimental designs: A latent variable framework for analysis and power estimation. Psychological Methods, 2, 371-402. (#71)

Muthén, B. & Muthén, L. (2000). The development of heavy drinking and alcohol-related problems from ages 18 to 37 in a U.S. national sample. Journal of Studies on Alcohol, 61, 290-300. (#83)

Olsen, M. K, & Schafer, J., L. (2001). A two-part random effects model for semicontinuous longitudinal data. Journal of the American Statistical Association, 96, 730-745.

Satorra, A. & Saris, W. (1985). Power of the likelihood ratio test in covariance structure analysis. Psychometrika, 51, 83-90.

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References for Analysis with Multilevel Data

Note: Click here to download Muthén papers.

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. (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.

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)

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References for Analysis with Missing Data

Note: Click here to download Muthén papers.

Little, R.J., & Rubin, D.B. (2002). Statistical analysis with missing data. Second 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.

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References for Analysis with Categorical Latent Variables (Mixture Modeling)

Note: Click here to download Muthén papers. To request a Jo paper, please contact the author at booil@stanford.edu.

General

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

Everitt, B.S. & Hand, D.J. (1981). Finite mixture distributions. London: Chapman and Hall.

Hosmer, D. W. (1973). A comparison of iterative maximum likelihood estimates of the parameters of a mixture of two normal distributions under three different types of sample. Biometrics, 29, 761-770.

McLachlan, G.J. & Peel, D. (2000). Finite Mixture Models. New York: Wiley & Sons.

Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6, 461-464.

Titterington, D.M., Smith, A.F.M. & Makov, U.E. (1985). Statistical analysis of finite mixture distributions. Chichester, U.K.: John Wiley & Sons.

Latent Class Analysis

Bandeen-Roche, K., Miglioretti, D.L., Zeger, S.L. & Rathouz, P.J. (1997). Latent variable regression for multiple discrete outcomes. Journal of the American Statistical Association, 92, 1375-1386.

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

Bucholz, K.K., Heath, A.C., Reich, T., Hesselbrock, V.M., Kramer, J.R., Nurnberger, J.I. & Schuckit, M.A. (1996). Can we subtype alcoholism? A latent class analysis of data from relatives of alcoholics in a multi-center family study of alcoholism. Alcohol Clinical Experimental Research, 20, 1462-1471.

Clogg, C.C. (1995). Latent class 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.

Clogg, C.C. & Goodman, L.A. (1985). Simultaneous latent structural analysis in several groups. In Tuma, N.B. (ed.), Sociological Methodology, 1985 (pp. 81-110). San Francisco: Jossey-Bass Publishers.

Dayton, C.M. & Macready, G.B. (1988). Concomitant variable latent class models. Journal of the American Statistical Association, 83, 173-178.

Formann, A. K. (1992). Linear logistic latent class analysis for polytomous data. Journal of the American Statistical Association, 87, 476-486.

Goodman, L.A. (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika, 61, 215-231.

Hagenaars, J.A. & McCutcheon, A.L. (2002). Applied latent class analysis. Cambridge, UK: Cambridge University Press.

Heijden, P.G.M., Dressens, J. & Bockenholt, U. (1996). Estimating the concomitant-variable latent-class model with the EM algorithm. Journal of Educational and Behavioral Statistics, 21, 215-229.

Lazarsfeld, P.F. & Henry. N.W. (1968). Latent structure analysis. New York: Houghton Mifflin.

Muthén, B. (2001). Latent variable mixture modeling. In G. A. Marcoulides & R. E. Schumacker (eds.), New Developments and Techniques in Structural Equation Modeling (pp. 1-33). Lawrence Erlbaum Associates. (#86)

Muthén, B. & Muthén, L. (2000). Integrating person-centered and variable-centered analysis: Growth mixture modeling with latent trajectory classes. Alcoholism: Clinical and Experimental Research, 24, 882-891. (#85)

Nestadt, G., Hanfelt, J., Liang, K.Y., Lamacz, M., Wolyniec, P. & Pulver, A.E. (1994). An evaluation of the structure of schizophrenia spectrum personality disorders. Journal of Personality Disorders, 8, 288-298.

Rindskopf, D. (1990). Testing developmental models using latent class analysis. In A. von Eye (Ed.), Statistical methods in longitudinal research: Time series and categorical longitudinal data (Vol 2, pp. 443-469). Boston: Academic Press.

Rindskopf, D. & Rindskopf, W. (1986). The value of latent class analysis in medical diagnosis. Statistics in Medicine, 5, 21-27.

Stoolmiller, M. (2001). Synergistic interaction of child manageability problems and parent-discipline tactics in predicting future growth in externalizing behavior for boys. Developmental Psychology, 37, 814-825.

Uebersax, J.S. & Grove, W.M. (1990). Latent class analysis of diagnostic agreement. Statistics in Medicine, 9, 559-572.

Vermunt, J.K. & Magidson, J. (2002). Latent class cluster analysis. In J.A. Hagenaars & A.L. McCutcheon (Eds.), Applied latent class analysis (pp. 89-106). Cambridge, UK: Cambridge University Press.

Latent Transition Analysis

Collins, L.M. & Wugalter, S.E. (1992). Latent class models for stage-sequential dynamic latent variables. Multivariate Behavioral Research, 27, 131-157.

Collins, L.M., Graham, J.W., Rousculp, S.S. & Hansen, W.B. (1997). Heavy caffeine use and the beginning of the substance use onset process: An illustration of latent transition analysis. In K. Bryant, M. Windle & S. West (eds.), The Science of Prevention: Methodological Advances from Alcohol and Substance Use Research. Washington DC: American Psychological Association. pp. 79-99.

Graham, J.W., Collins, L.M., Wugalter, S.E., Chung, N.K. & Hansen, W.B. (1991). Modeling transitions in latent stage- sequential processes: A substance use prevention example. Journal of Consulting and Clinical Psychology, 59, 48-57.

Kandel, D.B., Yamaguchi, K. & Chen, K. (1992). Stages of progression in drug involvement from adolescence to adulthood: Further evidence for the gateway theory. Journal of Studies of Alcohol, 53, 447-457.

Reboussin, B.A., Reboussin, D,M., Liang, K.Y. & Anthony, J.C. (1998). Latent transition modeling of progression of health-risk behavior. Multivariate Behavioral Research, 33, 457-478.

Noncompliance (CACE)

Jo, B. (2002). Estimation of intervention effects with noncompliance: Alternative model specifications. Journal of Educational and Behavioral Statistics, 27, 385-409.

Jo, B. & Muthén, B. (2000). Intervention studies with noncompliance: Complier Average Causal Effect Estimation in Growth Mixture Modeling. Draft. To appear in N. Duan and S. Reise (Eds.), Multilevel Modeling: Methodological Advances, Issues, and Applications, Multivariate Applications Book Series, Lawrence Erlbaum Associates.

Jo, B. & Muthén, B. (2001). Modeling of intervention effects with noncompliance: A latent variable approach for randomized trials. In G. A. Marcoulides & R. E. Schumacker (eds.), New Developments and Techniques in Structural Equation Modeling (pp.57-87). Lawrence Erlbaum Associates. (#90)

Little, R.J. & Yau, L.H.Y. (1998). Statistical techniques for analyzing data from prevention trials: Treatment of no-shows using Rubin's causal model. Psychological Methods, 3, 147-159.

Growth Mixtures, Latent Class Growth Analysis

Jones, B.L., Nagin, D.S. & Roeder, K. (1998). A SAS procedure based on mixture models for estimating developmental trajectories.

Moffitt, T.E. (1993). Adolescence-limited and life-course persistent antisocial behavior. Psychological Review, 100, 674-701.

Muthén, B. (2000). Methodological issues in random coefficient growth modeling using a latent variable framework: Applications to the development of heavy drinking. In Multivariate Applications in Substance use Research, J. Rose, L. Chassin, C. Presson & J. Sherman (eds.), Hillsdale, N.J.: Erlbaum, pp. 113-140. (#81)

Muthén, B. (2001). Latent variable mixture modeling. In G. A. Marcoulides & R. E. Schumacker (eds.), New Developments and Techniques in Structural Equation Modeling (pp. 1-33). Lawrence Erlbaum Associates. (#86)

Muthén, B. (2001). Second-generation structural equation modeling with a combination of categorical and continuous latent variables: New opportunities for latent class/latent growth modeling. In Collins, L.M. & Sayer, A. (eds.), New Methods for the Analysis of Change (pp. 291-322). Washington, D.C.: APA. (#82)

Muthén, B. (2004). Latent variable analysis: Growth mixture modeling and related techniques for longitudinal data. In D. Kaplan (ed.), Handbook of quantitative methodology for the social sciences (pp. 345-368). Newbury Park, CA: Sage Publications.

Muthén, B. & Muthén, L. (2000). Integrating person-centered and variable-centered analysis: Growth mixture modeling with latent trajectory classes. Alcoholism: Clinical and Experimental Research, 24, 882-891. (#85)

Muthén, B. & Shedden, K. (1999). Finite mixture modeling with mixture outcomes using the EM algorithm. Biometrics, 55, 463-469. (#78)

Muthén, B., Brown, C.H., Masyn, K., Jo, B., Khoo, S.T., Yang, C.C., Wang, C.P., Kellam, S., Carlin, J. & Liao, J. (2002). General growth mixture modeling for randomized preventive interventions. Biostatistics, 3, 459-475. (#87)

Muthén, B., Khoo, S.T., Francis, D. & Kim Boscardin, C. (2003). Analysis of reading skills development from Kindergarten through first grade: An application of growth mixture modeling to sequential processes. In S.R. Reise & N. Duan (eds), Multilevel Modeling: Methodological Advances, Issues, and Applications (pp. 71-89). Mahaw, NJ: Lawrence Erlbaum Associates (#77).

Nagin, D.S. (1999). Analyzing developmental trajectories: A semi-parametric, group-based approach. Psychological Methods, 4, 139-157.

Nagin, D.S. & Land, K.C. (1993). Age, criminal careers, and population heterogeneity: Specification and estimation of a nonparametric, mixed Poisson model. Criminology, 31, 327-362.

Nagin, D.S. & Tremblay, R.E. (1999). Trajectories of boys' physical aggression, opposition, and hyperactivity on the path to physically violent and non violent juvenile delinquency. Child Development, 70, 1181-1196.

Nagin, D., Farrington, D. & Moffitt, T. (1995). Life-course trajectories of different types of offenders. Criminology, 33, 111-139.

Pearson, J.D., Morrell, C.H., Landis, P.K., Carter, H.B. & Brant, L.J. (1994). Mixed-effect regression models for studying the natural history of prostate disease. Statistics in Medicine, 13, 587-601.

Porjesz, B. & Begleiter, H. (1995). Event-related potentials and cognitive function in alcoholism. Alcohol Health & Research World, 19, 108-112.

Roeder, K., Lynch, K.G. & Nagin, D.S. (1999). Modeling uncertainty in latent class membership: A case study in criminology. Journal of the American Statistical Association, 94, 766-776.

Schulenberg, J., O'Malley, P.M., Bachman, J.G., Wadsworth, K.N. & Johnston, L.D. (1996). Getting drunk and growing up: Trajectories of frequent binge drinking during the transition to young adulthood. Journal of Studies on Alcohol, 57, 289-304.

Verbeke, G. & Lesaffre, E. (1996). A linear mixed-effects model with heterogeneity in the random-effects population. Journal of the American Statistical Association, 91, 217-221.

Zucker, R.A. (1994). Pathways to alcohol problems and alcoholism: A developmental account of the evidence for multiple alcoholisms and for contextual contributions to risk. In: R.A. Zucker, J. Howard & G.M. Boyd (Eds.), The development of alcohol problems: Exploring the biopsychosocial matrix of risk (pp. 255-289) (NIAAA Research Monograph No. 26). Rockville, MD: U.S. Department of Health and Human Services.

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References for Design and Power Issues

Note: Click here to download Muthén papers.

Brown, C.H. & Liao, J. (1999). Principles for designing randomized preventive trials in mental health: An emerging developmental epidemiologic perspective. American Journal of Community Psychology, Special Issue on Prevention Science, 27, 673-709.

Jo, B. (2002). Statistical power in randomized intervention studies with noncompliance. Psychological Methods, 7, 178-193.

Miyazaki, Y. & Raudenbush, S.W. (2000). A test for linkage of multiple cohorts from an accelerated longitudinal design. Psychological Methods, 5, 44-63.

Muthén, B. & Curran, P. (1997). General longitudinal modeling of individual differences in experimental designs: A latent variable framework for analysis and power estimation. Psychological Methods, 2, 371-402. (#71)

Raudenbush, S.W. (1997). Statistical analysis and optimal design for cluster randomized trials. Psychological Methods, 2(2), 173-185.

Raudenbush, S.W. & Liu, X. (2000). Statistical power and optimal design for multisite randomized trials. Psychological Methods, 5, 199-213.

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References for Randomized Trials

Note: Click here to download Muthén papers. To request a Jo paper, please contact the author at booil@stanford.edu.

Angrist, J.D., Imbens, G.W., Rubin, D.B. (1996). Identification of causal effects using instrumental variables. Journal of the American Statistical Association, 91, 444-445.

Brown, C.H. & Liao, J. (1999). Principles for designing randomized preventive trials in mental health: An emerging developmental epidemiologic perspective. American Journal of Community Psychology, special issue on prevention science, 27, 673-709.

Jo, B. (2002). Estimation of intervention effects with noncompliance: Alternative model specifications. Journal of Educational and Behavioral Statistics, 27, 385-409.

Jo, B. (2002). Model misspecification sensitivity analysis in estimating causal effects of interventions with noncompliance. Statistics in Medicine, 21, 3161-3181.

Jo, B. (2002). Statistical power in randomized intervention studies with noncompliance. Psychological Methods, 7, 178-193.

Jo, B. & Muthén, B. (2003). Longitudinal studies with intervention and noncompliance: Estimation of causal effects in growth mixture modeling. In N. Duan and S. Reise (Eds.), Multilevel Modeling: Methodological Advances, Issues, and Applications, Multivariate Applications Book Series (pp. 112-139). Lawrence Erlbaum Associates.

Jo, B. & Muthén, B. (2001). Modeling of intervention effects with noncompliance: A latent variable approach for randomized trials. In G. A. Marcoulides & R. E. Schumacker (eds.), New Developments and Techniques in Structural Equation Modeling (pp.57-87). Lawrence Erlbaum Associates. (#90)

Little, R.J. & Yau, L.H.Y. (1998). Statistical techniques for analyzing data from prevention trials: Treatment of no-shows using Rubin's causal model. Psychological Methods, 3, 147-159.

Muthén, B., Jo, B. & Brown, H. (2003). Comment on the Barnard, Frangakis, Hill & Rubin article, Principal stratification approach to broken randomized experiments: A case study of school choice vouchers in New York City, Journal of the American Statistical Association, 98, 311-314. (#99)

Muthén, B., Brown, C.H., Masyn, K., Jo, B., Khoo, S.T., Yang, C.C., Wang, C.P., Kellam, S., Carlin, J. & Liao, J. (2002). General growth mixture modeling for randomized preventive interventions. Biostatistics, 3, 459-475. (#87)

Raudenbush, S.W. (1997). Statistical analysis and optimal design for cluster randomized trials. Psychological Methods, 2, 173-185.

Raudenbush, S.W. & Liu, X. (2000). Statistical power and optimal design for multisite randomized trials. Psychological Methods, 5, 199-213.

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References for Growth Mixture Modeling

Note: Click here to download Muthén papers.

Muthén, B. (2000). Methodological issues in random coefficient growth modeling using a latent variable framework: Applications to the development of heavy drinking. In Multivariate Applications in Substance use Research, J. Rose, L. Chassin, C. Presson & J. Sherman (eds.), Hillsdale, N.J.: Erlbaum, pp. 113-140. (#81)

Muthén, B. (2001). Second-generation structural equation modeling with a combination of categorical and continuous latent variables: New opportunities for latent class/latent growth modeling. In Collins, L.M. & Sayer, A. (eds.), New Methods for the Analysis of Change (pp. 291-322). Washington, D.C.: APA. (#82)

Muthén, B. (2001). Latent variable mixture modeling. In G. A. Marcoulides & R. E. Schumacker (eds.), New Developments and Techniques in Structural Equation Modeling (pp. 1-33). Lawrence Erlbaum Associates. (#86)

Muthén, B. (2004). Latent variable analysis: Growth mixture modeling and related techniques for longitudinal data. In D. Kaplan (ed.), Handbook of quantitative methodology for the social sciences (pp. 345-368). Newbury Park, CA: Sage Publications.

Muthén, B. & Masyn, K. (2004). Discrete-time survival mixture analysis. Journal of Educational and Behavioral Statistics, in press. (#92)

Muthén, B. & Muthén, L. (2000). Integrating person-centered and variable-centered analysis: Growth mixture modeling with latent trajectory classes. Alcoholism: Clinical and Experimental Research, 24, 882-891. (#85)

Muthén, B. & Shedden, K. (1999). Finite mixture modeling with mixture outcomes using the EM algorithm. Biometrics, 55, 463-469. (#78)

Muthén, B., Khoo, S.T., Francis, D. & Kim Boscardin, C. (2003). Analysis of reading skills development from Kindergarten through first grade: An application of growth mixture modeling to sequential processes. In S.R. Reise & N. Duan (eds), Multilevel Modeling: Methodological Advances, Issues, and Applications (pp. 71-89). Mahaw, NJ: Lawrence Erlbaum Associates (#77).

Muthén, B., Brown, C.H., Masyn, K., Jo, B., Khoo, S.T., Yang, C.C., Wang, C.P., Kellam, S., Carlin, J. & Liao, J. (2002). General growth mixture modeling for randomized preventive interventions. Biostatistics, 3, 459-475. (#87)



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