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Related Concept Videos

Statistical Analysis System (SAS)01:14

Statistical Analysis System (SAS)

SAS, short for Statistical Analysis System, is a powerful data analysis, management, and visualization tool. Developed by the SAS Institute in the early 1970s, SAS has evolved into a comprehensive software suite used across various industries for statistical analysis, business intelligence, and predictive modeling.
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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
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Regression Analysis

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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Calibration Curves: Linear Least Squares01:20

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Updated: Jun 18, 2026

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

PROC LCA: A SAS Procedure for Latent Class Analysis.

Stephanie T Lanza1, Linda M Collins, David R Lemmon

  • 1The Methodology Center, The Pennsylvania State University.

Structural Equation Modeling : a Multidisciplinary Journal
|September 28, 2011
PubMed
Summary
This summary is machine-generated.

Latent class analysis (LCA) identifies distinct groups from categorical data. This study introduces PROC LCA, a SAS procedure for advanced LCA, including multiple groups and covariates, demonstrated with alcohol use data.

Related Experiment Videos

Last Updated: Jun 18, 2026

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

Area of Science:

  • Statistics
  • Psychometrics
  • Data Analysis

Background:

  • Latent class analysis (LCA) is a statistical technique for identifying unobserved subgroups within a population based on observed categorical variables.
  • Existing methods for advanced LCA, such as multiple-group LCA and LCA with covariates, require specialized software or complex implementations.
  • There is a need for a user-friendly and integrated procedure to perform these advanced LCA models.

Purpose of the Study:

  • To introduce PROC LCA, a novel SAS procedure designed for conducting latent class analysis.
  • To enable the application of multiple-group LCA and LCA with covariates within a single procedure.
  • To provide a practical tool for researchers analyzing complex categorical data structures.

Main Methods:

  • The study presents the PROC LCA procedure implemented in SAS.
  • The procedure supports standard LCA, multiple-group LCA with invariance testing, and LCA with covariates.
  • Demonstration utilizes a national sample of high school seniors' alcohol use behavior data.

Main Results:

  • PROC LCA successfully performs latent class analysis, multiple-group LCA, and LCA with covariates.
  • The procedure allows for empirical testing of measurement invariance across groups.
  • The application to alcohol use data illustrates the utility of PROC LCA in identifying distinct behavioral patterns and their predictors.

Conclusions:

  • PROC LCA offers a comprehensive and accessible SAS procedure for advanced latent class analysis.
  • The availability of this procedure facilitates the robust analysis of subgroup structures and predictors of class membership.
  • This tool is expected to advance research in fields utilizing categorical data analysis, such as social sciences and psychology.