LCA News | The Methodology Center

LCA News

January 30, 2015

distal outcomes example diagramWe are pleased to announce the release of the latest version of the %LCA_Distal SAS macro. Latent class analysis allows researchers to divide subjects into underlying subgroups that cannot be directly observed. The %LCA_Distal SAS macro was created to allow researchers to estimate the impact of membership in a latent class on an outcome at a later time. The newly released version 3.0 of the macro allows users to generate standard errors for the binary case and provides more information in the onscreen results for all cases. PROC LCA version 1.3.2 or higher must be used in order to generate standard errors.  


Read more or download the macro

Bethany Bray, Ph.D.

January 26, 2015

As part of our annual series of one-credit courses in research methodology for Penn State graduate students, in fall 2015 we will offer, “Advanced topics in latent class analysis (LCA).” LCA is an analytic method used to identify hidden subgroups within a population based on individuals’ responses to multiple observed variables. This short course, taught by Methodology Center Investigator Bethany Bray, will build on the knowledge and skills presented in the short course, “An introduction to latent class and latent profile analysis.” Credit for that course is not a prerequisite for taking this course, but familiarity with LCA and baseline category multinomial logistic regression are prerequisites.


September 25, 2014
classroom with kids and teacherHead Start is the largest federally funded preschool program for disadvantaged children. Some people have questioned its worth because research has shown relatively small impacts on cognitive and social skills. In a forthcoming article in Child Development, Methodology Center Affiliate Brittany Rhoades Cooper and Methodology Center Scientific Director Stephanie Lanza perform latent class analysis (LCA) on the Head Start Impact Study dataset to identify subgroups of children who have similar home environments and caregivers.


The authors then examine cognitive, behavioral, and relationship skills measured two years later to determine whether the effects of Head Start vary across these subgroups. Their approach could have broad application by researchers interested in using latent class analysis to understand differential effects of an intervention by simultaneously considering multiple potential moderators. 


August 27, 2014

researchers Kari and AngieEach year, we hold regular meetings of special interest groups open to all researchers and graduate students working with and developing cutting edge research methods. The groups provide a forum for individuals to discuss their own research and to learn from others. Topics include analyzing complex data, mixture modeling, optimizing behavioral interventions, and causal analysis. 


The schedule for this fall and contact information is now available. For specific dates, please see The Methodology Center’s calendar.


Stephanie LanzaDonna CoffmanLinda Collins

April 23, 2014

Several Methodology Center principal investigators are being recognized this spring.  At the 2014 Society for Behavioral Medicine (SBM) Annual Meeting, Methodology Center Director Linda Collins was named a Fellow of SBM for her contributions to behavioral medicine. The Society for Prevention Research (SPR) has awarded Donna Coffman the Early Career Award for her outstanding research contributions to prevention science. SPR also recognized Scientific Director Stephanie Lanza with the Friend of Early Career Prevention Network (ECPN) award, which is presented for “promoting training, funding, or early career involvement in prevention efforts; or encouraging early career preventionists in their work”. Congratulations to all three!


Read more about Linda’s work on optimizing behavioral interventions, Donna’s work on causal inference, or Stephanie’s work on latent class analysis.

LCA Stata Plugin

April 2, 2014

The Methodology Center is pleased to release the latest version (1.1) of the LCA Stata plugin for conducting latent class analysis (LCA). The software is available for download free of charge. For an overview of the functionality of the LCA Stata plugin, please visit the download page. The new version includes functionality requested in our recent software survey, including

  • the ability to assess identification of models with covariates via multiple random starts,
  • an indication of which latent class is the best match for each individual, and
  • the option to generate 20 random draws for each individual’s class membership based on posterior probabilities.

The users’ guide has also been updated and revised based on user feedback. Please email with any questions.


Read more or download the software

PROC LCAJanuary 24, 2014

We have released PROC LCA v. 1.3.1 to fix a bug related to the seed_draws statement in v. 1.3.0 that sometimes causes an error. If you used seed_draws and did not receive an error message, then the output generated is reliable. There is no change in functionality between v. 1.3.0 and 1.3.1.

Download PROC LCA v. 1.3.1

Bethany BrayStephanie LanzaSeptember 24, 2013

Latent class analysis (LCA) has become a popular tool for identifying subgroups within populations. Despite the fact that these are latent subgroups and, therefore, membership in each class is unknown, research questions sometimes make it necessary to assign individuals to classes and then treat class membership as known in later analyses. Current standard practice is to retain from LCA each individual’s posterior probabilities of class membership, and then either assign them to the most likely class or to repeatedly assign them using pseudo-class draws. These practices, however, are known to attenuate estimates in subsequent analyses.


In a new article in Structural Equation Modeling, "Eliminating bias in classify-analyze approaches for latent class analysis," by Center Investigators Bethany Bray and Stephanie Lanza and Xianming Tan of McGill University, the authors propose a straightforward solution that includes all variables used in the subsequent analyses as covariates in the LCA. Then, class assignment is carried out using either the most likely class or pseudo-class draws. With adequate measurement quality and sample size, this approach substantially reduces the bias that results from common approaches to class assignment.

Nicole ButeraStephanie LanzaDonna Coffman

August 1, 2013

Donna Coffman and Stephanie Lanza lead the Methodology Center’s research projects on causal inference and latent class analysis (LCA), respectively. Recently, they began integrating modern causal inference techniques with LCA to reveal the impact that nonrandomized risks have on later, complex, behavioral outcomes. In a new article in Prevention Science, “A Framework for Estimating Causal Effects in Latent Class Analysis: Is There a Causal Link Between Early Sex and Subsequent Profiles of Delinquency?” Center researchers Nicole Butera, Stephanie, and Donna examine data on 1,890 adolescents from the National Longitudinal Study of Adolescent Health to determine whether early sexual initiation impacts later profiles of delinquent behavior.

Linda Collins, Stephanie Lanza, Donna Coffman, Bethany Bray, Kari Kugler, and Anne Fairlie

Society for Prevention Research (SPR) Annual Meeting

San Francisco, CA, May 28-31, 2013


The Methodology Center will be active at the upcoming annual meeting of the Society for Prevention Research (SPR).  At this year's conference, The Science of Prevention: Building a Comprehensive National Strategy for Well-Being, we will be presenting symposiums, a special interest group, paper talks, and multiple posters. Also look for us at the ECPN symposiums. We hope to see you there!

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