Choose, fit and interpret the right longitudinal model for your research Five Mondays · 12 Oct - 9 Nov 2026 · 09:00–16:00 UK · Live on Zoom · £750 · 20 places Full refund within 14 days of booking or ...

Choose, fit and interpret the right longitudinal model for your research A live online course with Prof. Alexandru Cernat, author of Longitudinal Data Analysis Using R. Learn how to use longitudinal a...

Choose, fit and interpret the right longitudinal model for your research A live online course with Prof. Alexandru Cernat, author of Longitudinal Data Analysis Using R. Learn how to use longitudinal a...

An AI agent for data analysis can write code quickly, but researchers still have to build expertise and understand what decisions are made

Learn how to do causal mediation in R using real-world panel data. Learn how to interpret ACME, ADE and how to use binary mediators

Before visualising and analysing longitudinal data, it is essential to prepare it correctly. In this guide, we cover data cleaning tips before visualisation

Learn how to merge data when you have information in different files, at different levels and at multiple time points. Explore four real-world scenarios

Master longitudinal data wrangling in R: clean variables, merge and reshape panel data, and automate data preparation workflows.

Attrition bias explained: how participant dropout occurs, how to measure it, and why it matters for valid longitudinal research.

Mediating vs moderating variable with examples. Learn how to distinguish between mediation and moderation, choose the right model, and avoid common mistakes.

Amazon.com: Longitudinal Data Analysis Using R: 9798261825210: Cernat, Alexandru: Books

Introduction to R for social researchers. Learn data preparation, visualisation, and core statistical methods in R with practical, applied examples.

A practical guide to moderated mediation, explaining how indirect effects vary across contexts, with clear examples and applied modeling strategies.

Learn how to run longitudinal mediation analysis in R using lavaan. Step-by-step guide to model indirect effects and causal pathways over time.

Introduction to path analysis and mediation analysis in R. Estimate direct, indirect, and total effects with diagrams, examples, and code.

Unlock the power of R for longitudinal data analysis! Learn to navigate tables and summaries with ease in our latest guide.

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

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

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 understa

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 th

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 effec

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 psycholog

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 co

Learn how to prepare, explore, and analyse longitudinal data using R. The book covers the basics of R and regression to advanced statistical modelling.

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.

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.

Learn how to prepare, explore, and analyse longitudinal data using R. The book covers the basics of R and regression to advanced statistical modelling.