lcsm: An R package and tutorial on latent change score modelling.

Area of Science:

• Behavioral Sciences
• Psychometrics
• Quantitative Psychology

Background:

• Latent change score models (LCSMs) are crucial for studying temporal dynamics of constructs in behavioral research.
• These models allow for univariate (single construct) and bivariate (two-construct) analyses of change over time.
• Accurate modeling and visualization are essential for robust interpretation of longitudinal data.

Purpose of the Study:

• Introduce the R package 'lcsm' designed to facilitate the application and understanding of latent change score models.
• Provide tools for generating model syntax and visualizing various LCSM specifications.
• Enhance transparency and accessibility in reporting longitudinal analyses within behavioral sciences.

Main Methods:

• Development of an R package 'lcsm' offering functions for univariate and bivariate LCSM syntax generation.
• Implementation of visualization tools for presenting different model specifications through path diagrams.
• Creation of an interactive application to demonstrate package functionality and model variations.

Main Results:

• The 'lcsm' package enables users to generate syntax for basic and complex LCSMs.
• Users can visualize different model specifications using simplified path diagrams.
• An interactive application aids in understanding how model specifications impact syntax and visualizations.

Conclusions:

• The 'lcsm' R package serves as a valuable resource for researchers in behavioral sciences.
• It aims to demystify latent change score modeling and improve the clarity of reporting.
• The package promotes greater transparency and provides an accessible learning tool for longitudinal data analysis.