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Latest in longitudinal data analysis

How to create reproducible workflows for longitudinal data analysis

Learn best practices for longitudinal data analysis, including Git, renv, and reproducible workflows to improve efficiency, accuracy, and collaboration.
How to include control variables in a cross-lagged panel model

Learn how to include control variables in a Cross-Lagged Panel Model (CLPM) in R, using time-constant and time-varying predictors to refine causal analysis.
Mixture Latent Growth Models R: A Step-by-Step Guide

The blog post discusses Mixture Latent Growth Models (MLGM) that enhance traditional longitudinal models by identifying latent subgroups with distinct trajectories over time. It details the implementation of MLGM in R using the tidySEM package and compares results from using R and Mplus for improved understanding of individual and aggregate changes.
How to estimate and interpret parallel Latent Growth Models (LGM) in R: a step-by-step guide

Learn how to estimate and interpret Parallel Latent Growth Models (LGM) in R and lavaan. This step-by-step guide explores modeling multiple trajectories, analyzing relationships over time, and visualizing results. Perfect for researchers in psychology, sociology, and public health using longitudinal data.
How to include time-varying predictors in Latent Growth Models (LGM) in R

This post explores how to incorporate time-varying predictors into Latent Growth Models (LGM) to analyse changes in variables over time. It discusses data preparation, using R code for modeling, and differentiating between concurrent and lagged effects.
Including time constant controls in Latent Growth Models

This post discusses incorporating time-constant predictors in Latent Growth Models (LGM) using R to better understand changes over time. It details coding in R using real data. It compares strategies for including these predictors and interpreting their effects on intercept and slope in LGMs.
Understanding time-varying predictors in multilevel models for longitudinal data

This post explains how to include time-varying predictors in multilevel models for analyzing dynamic processes over time. By incorporating variables that change over time, such as income or educational support, researchers can gain a nuanced understanding of factors influencing outcomes like mental health.
Understanding the longitudinal data workflow: a comprehensive guide

Unlock insights from longitudinal data with our step-by-step guide, from import to analysis. Master the workflow to elevate your research.
Complete guide to visualizing longitudinal data in R

Unlock the secrets of longitudinal data in R with this complete guide. Master the art of insightful visualizations for impactful analysis.
Exploring longitudinal data in R: tables and summaries

Unlock the power of R for longitudinal data analysis! Learn to navigate tables and summaries with ease in our latest guide.
Cleaning longitudinal data in R: a step-by-step guide

Explore key R commands for cleaning longitudinal data, including recoding variables and handling missing data effectively.
Preparing longitudinal data for analysis in R: a step-by-step guide

Explore the crucial steps of data preparation for longitudinal analysis in R, from importing to reshaping data, in this step-by-step guide.
Estimating and visualizing multilevel models for change in R

Learn how to analyse longitudinal data using the multilevel model for change with R. Hands on example using real world data and syntax.
Estimating non-linear change with Latent Growth Models in R

Learn how to estimate and visualize nonlinear Latent Growth Models (LGM) using R. Hands on example using real world longitudinal data and code
Comparing the multilevel model with the Latent Growth Model

Learn how the multilevel model for change and the latent growth models are different and when to use each one. Hands on example using real data and R
Understanding causal direction using the cross-lagged model

Learn how to investigate the causal direction of two variables using the cross-lagged model and longitudinal data. Hands on example using R and real data
Estimating non-linear change in time using the multilevel model for change

Learn how to model non-linear change in time using the multilevel model for change in R. Hands on tutorial with real data.
Explaining change using multilevel and time constant predictors

Learn how to explain change in time using time constant predictors using the multilevel model. Examples with real data and R code
Visualizing transitions in time using R and alluvial graphs

Learn how to visualize transitions in time for categorical data using Sankey plot/river/alluvial graphs and R. Easy hands one example using real data
Estimating and visualizing Latent Growth Models with R

Tutorial showing you how to visualize change in time and how to estimate it using Latent Growth Modelling with R. Hands on longitudinal data analysis.
Data structures for longitudinal analysis: wide versus long

Learn how to create, store and reshape longitudinal data. Reshaping data from wide to long data format and back using R. Hands on examples
Interview for Frontmatter podcast discussing survey research and longitudinal data analysis

Great conversation with Leanpub co-founder Len Epp for the Frontmatter podcast about my background, survey research and longitudinal data analysis.