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The courses will be taught from 8:30am to 6:00 pm each day (see schedule
for details). The official language of the workshop is English.
This three-day workshop discusses advances in latent variable modeling made
possible by the general modeling framework of the Mplus
program. The generality of the Mplus
framework comes from the unique use of both continuous and categorical latent
variables. While continuous latent variables have seen frequent use in factor
analysis, structural equation modeling, and random effects growth modeling,
modeling that includes categorical latent variables is less widespread. The
workshop focuses on models that use categorical latent variables, either alone
or together with continuous latent variables. The theme is the use of
categorical latent variables to represent latent classes corresponding to
different groups of individuals and latent trajectory classes corresponding to
different types of development. An overview of conventional and new techniques
is given. For each topic, issues of model
specification, identification, estimation, testing, and model modification are
discussed. Several examples are examined. Modeling strategies are presented. Mplus
input setups are provided and Mplus
output is used for interpretation of analysis results. The presentation is in
lecture format with no need for computer analyses. Prerequisites: Intermediate
understanding of latent variable structural equation modeling or multilevel
modeling. Familiarity with categorical data analysis, especially logistic
regression.
- Instructors:
- Bengt Muthén and Linda Muthén
Topics:
Day 1 (September 10, 2007)
Observed And Latent Categorical Variable Modeling Using Mplus
 | Overview
of logit, multinomial logit, probit, censored-normal, Poisson, and
zero-inflated Poisson regression
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| Regression
mixture analysis
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| Non-compliance
and Complier-average causal effect (CACE) estimation in randomized
trials
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| Latent
class analysis
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| Latent
class analysis with covariates
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| Confirmatory
latent class analysis
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| Twin
modeling
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| Violations
of conditional independence
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| Factor
(IRT) mixture modeling
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Day 2 (September 11, 2007)
Longitudinal Modeling Using Mplus
| Hidden
Markov modeling, latent transition analysis
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| Growth
modeling with random effects
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| Latent
class growth analysis
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| Growth
mixture modeling with latent trajectory classes
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| Randomized
trials and treatment effects varying across latent trajectory
classes
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| Latent
class growth analysis vs. growth mixture modeling
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| Survival
analysis
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Day 3 (September 12, 2007)
Multilevel Modeling Using Mplus
| Complex
survey data analysis
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| Two-level
regression analysis
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| Two-level
structural equation modeling
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| Multivariate
approach to multilevel modeling
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| Two-level
growth modeling (3-level analysis)
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| Latent
class variables on level 1 and level 2
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| Two-level
latent transition analysis
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| Two-level
growth mixture modeling
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Background: suggested papers
to download the papers click here: http://www.statmodel.com/papers_date.shtml
Introductory:
Muthén, B. & Muthén, L. (2000). Integrating person-centered and
variable-centered analyses: Growth mixture modeling with latent trajectory
classes. Alcoholism: Clinical and Experimental Research, 24, 882-891.
Muthén, B. & Asparouhov, T. (2006). Item response mixture modeling:
Application to tobacco dependence criteria. Addictive Behaviors, 31, 1050-1066.
Nylund, K. (2007). Latent transition analysis: Modeling extensions and an
application to peer victimization. Doctoral dissertation, University of
California, Los Angeles.
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.
Kreuter, F. & Muthen, B. (2007). Analyzing criminal trajectory profiles:
Bridging multilevel and group-based approaches using growth mixture modeling.
Forthcoming in Journal of Quantitative Criminology (with commentary).
Intermediate:
Muthén, B. (2002). Beyond SEM: General latent variable modeling.
Behaviormetrika, 29, 81-117.
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. (2006). Latent variable hybrids: Overview of old and new
models. Forthcoming in Hancock, G. R., & Samuelsen, K. M. (Eds.). (2007).
Advances in latent variable mixture models. Charlotte, NC: Information Age
Publishing, Inc.
Advanced:
Muthén, B. & Asparouhov, T. (2007). Growth mixture modeling: Analysis with
non-Gaussian random effects. Forthcoming in Fitzmaurice, G., Davidian, M.,
Verbeke, G. & Molenberghs, G. (eds.), Advances in Longitudinal Data
Analysis. Chapman & Hall/CRC Press.
Asparouhov, T. & Muthen, B. (2006). Multilevel mixture models. Forthcoming
in Hancock, G. R., & Samuelsen, K. M. (Eds.). (2007). Advances in latent
variable mixture models. Charlotte, NC: Information Age Publishing, Inc.
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