References
(To request a Muthén paper, please email bmuthen@ucla.edu and refer to the number in parenthesis.)
Analysis
With Categorical Latent Variables (Mixture Modeling)
General
Agresti, A. (1990).
Categorical data analysis.
Everitt,
B.S. & Hand, D.J. (1981). Finite mixture distributions.
McLachlan, G.J. &
Peel, D. (2000). Finite
mixture models.
Muthén, L.K. & Muthén, B. (1998-2001). Mplus User’s Guide.
Schwartz, 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.
Lo, Y., Mendell, N.R. & Rubin, D.B. (2001). Testing the number of
components in a normal mixture. Biometrika, 88, 767-778.
Vuong, Q.H. (1989). Likelihod ratio tests for model selection and non-nested
hypotheses. Econometrica,
57, 307-333.
Asparouhov, T. & Muthén, B. (2002). Skew and kurtosis tests in
mixture modeling.
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.
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. (2002).
Applied latent class analysis.
Cambridge: 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.
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.
Mooijaart, A. (1998). Log-linear and
Markov modeling of categorical longitudinal data. In Bijleveld,
C. C. J. H., & van der Kamp,
T. (eds). Longitudinal data analysis: Designs,
models, and methods. Newbury Park: Sage.
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)
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.
Jo, B. (1999). Estimation of intervention effects with
noncompliance: Alternative model specifications. Forthcoming in Journal of Educational and
Behavioral Statistics (will appear with comments).
Jo, B. (2002). Statistical power in
randomized intervention studies with noncompliance. Psychological Methods,
7, 178-193.
Jo, B. (2002). Model
misspecification sensitivity analysis in estimating causal effects of
interventions with noncompliance. Statistics
in Medicine, 21, 3161 - 3181.
Jo, B. & Muthén, B.
(2000). Longitudinal studies with
intervention and noncompliance:
Estimation of causal effects in growth mixture modeling. 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.
Factor Mixture Modeling, SEMM
Jedidi, K., Jagpal. H.S. & DeSarbo, W.S. (1997).
Finite-mixture structural equation models for response-based
segmentation and unobserved heterogeneity.
Marketing Science, 16, 39-59.
Lubke, G. & Muthén, B. (2003).
Performance of factor mixture models.
Under review, Multivariate Behavioral Research. (#94)
Growth Mixtures, Latent Class Growth Analysis
Jones, B.L., Nagin, D.S. &
Roeder, K. (2001). A SAS procedure based
on mixture models for estimating developmental trajectories. Sociological
Methods & Research, 29, 374-393.
Land, K.C. (2001).
Introduction to the special issue on finite mixture models. Sociological Methods & Research,
29, 275-281.
Li, F., Duncan, T.E, Duncan, S.C. & Acock, A. (2001).
Latent growth modeling of longitudinal data: a finite growth mixture
modeling approach. Structural
Equation Modeling, 8, 493-530.
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). 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. (2001).
Two-part growth mixture modeling.
University of California, Los Angeles. (#95)
Muthén, B. (2002). Beyond SEM:
General latent variable modeling. Behaviormetrika, 29, 81-117. (#96)
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. (2000). General growth mixture modeling for randomized preventive interventions. Accepted for publication in Biostatistics. (#87)
Muthén, B., Khoo, S.T., Francis, D.
& Kim Boscardin, C. (2002). 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.S. & Tremblay, R.E. (2001). Analyzing developmental trajectories of
distinct but related behaviors: A
group-based method. Psychological
Methods, 6, 18-34.
Nagin, D.S., Farrington, D. & Moffitt, T.
(1995). Life-course trajectories of
different types of offenders. Criminology, 33, 111-139.
Nagin, D.S. & Tremblay, R.E. (2001). Analyzing developmental trajectories of
distinct but related behaviors: a group-based method. Psychological Methods, 6, 18-34.
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, May,
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.
Allison, P.D.
(1984). Event History Analysis. Regression for Longitudinal Event Data. Quantitative Applications in the Social
Sciences, No. 46. Thousand Oaks: Sage Publications.
Muthén, B. & Masyn, K. (2001). Discrete-time survival mixture analysis.
Singer,
J.D., and Willett, J.B. (1993). It’s
about time: Using discrete-time survival
analysis to study duration and the timing of events. Journal of Educational Statistics,
18(2), 155-195.
Vermunt, J.K. (1997). Log-linear models for event histories. Advanced quantitative techniques in the
social sciences, vol 8. Thousand Oaks: Sage Publications.