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Home  Software Downloads SAS Procedures for LCA & LTA
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We are pleased to announce that the book Latent Class and Latent Transition Analysis: With Applications in the Social, Behavioral, and Health Sciences, by L. M. Collins and S. T. Lanza, is now available for pre-order from the publisher (Wiley) and other online vendors.
Note that the procedures must be reinstalled after upgrading from SAS1 version 9.1 to version 9.2. Please send questions or comments about PROC LCA or PROC LTA to
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. If your organization does not permit download and installation of files, please contact us with a shipping address and we will provide a CD containing all downloadable files.
PROC LCA and PROC LTA are new SAS1 procedures for latent class analysis (LCA) and latent transition analysis (LTA) developed by The Methodology Center. These straightforward procedures make it possible to pre-process data, fit a variety of latent class and latent transition models, and post-process the results without leaving the SAS environment. Features include:
- Simple model specification
- Multiple-groups LCA and LTA
- LCA and LTA with covariates (prediction of latent class membership and transitions)
- Baseline-category multinomial logit model or binary logit model for prediction
- Posterior probabilities saved to SAS data file
- Parameter estimates saved to SAS data file
- Optional Bayesian stabilizing prior to handle sparseness issues in estimation
PROC LCA for Latent Class Analysis
In its simplest form, PROC LCA allows the user to fit a latent class model by specifying a SAS data set, the number of latent classes, the items measuring the latent variable, and the number of response categories for each item. Multiple-groups LCA can be run using the GROUPS statement; users can examine measurement invariance across groups by adding the MEASUREMENT statement. Additional parameter restrictions can be provided in a SAS data file.
Continuous and categorical covariates can be included in the COVARIATES statement in order to examine the relation between each covariate and the probability of latent class membership. Prediction can be modeled using a baseline-category multinomial logit model or a binary logit model.
A Bayesian stabilizing prior can be invoked with the STABILIZE statement when sparseness is an issue for parameter estimation. Random starting values can be generated by the program, or the user can provide starting values in a SAS data file.
An empirical demonstration of PROC LCA appeared in the journal Structural Equation Modeling:
Lanza, S. T., Collins, L. M., Lemmon, D. R., & Schafer, J. L. (2007). PROC LCA: A SAS procedure for latent class analysis. Structural Equation Modeling, 14(4), 671-694. RefWorks Link
PROC LTA for Latent Transition Analysis
LTA is a longitudinal extension of LCA used to estimate latent status membership probabilities at Time 1, along with probabilities of transitions in latent status membership over time. The transition probabilities characterize development over time based on longitudinal data.
In its simplest form, PROC LTA allows the user to fit a latent transition model by specifying a SAS data set, the number of latent statuses, the number of times, the items measuring the latent variable, and the number of response categories for each item. Multiple-groups LCA can be run using the GROUPS statement, and users can examine measurement invariance across groups or times by adding the MEASUREMENT statement. Additional parameter restrictions can be provided in a SAS data file.
Covariates can be included in the model to examine the relation between each covariate and the probability of latent status membership and/or transition probabilities. Prediction can be modeled using a baseline-category multinomial logit model or a binary logit model, adding to the set of questions that can be addressed in LTA. A Bayesian stabilizing prior can be invoked when sparseness is an issue for parameter estimation.
An empirical demonstration of PROC LTA appeared in the journal Developmental Psychology:
Lanza, S. T. and Collins, L. M. (2008), A new SAS procedure for latent transition analysis: Transitions in dating and sexual behavior. Developmental Psychology, 42(2) 446-456. RefWorks Link
EXAMPLE OF NEW SAS PROCEDURE FOR LATENT TRANSITION ANALYSIS;
PROC LTA DATA=SEX OUTPOST=SEX_POST;
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TITLE1 'Time 1 substance use predicting Time 1 risky sex, by gender';
TITLE2 'Measurement invariance across times and groups';
TITLE3 'Posterior probabilities saved to SAS data file';
NSTATUS 5;
NTIMES 3;
ITEMS date_98 sex_98 partners_98 exposed_98 date_99 sex_99 partners_99 exposed_99 date_00 sex_00 partners_00 exposed_00;
CATEGORIES 3 2 3 2;
GROUPS gender;
GROUPNAMES male female;
MEASUREMENT TIMES GROUPS;
COVARIATES1 cig_98 drunk_98 marij_98;
REFERENCE1 1;
SEED 409621; |
| RUN; |
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