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Introduction to Multilevel/Hierarchical/Mixed Models

Workshop

Multilevel models (also referred to as hierarchical models or mixed models) are a class of statistical models that can be used when observations are not independent. Non-independence can occur when data is clustered due to the study design (e.g., data collected on households and their individual members or blocked agricultural studies) or when data is […]

Analysis of Pre-test-Post-test Data

Workshop

A pretest-posttest research design is a simple form of a repeated-measures design where a baseline measurement is taken on subjects randomized to control and intervention groups followed by a post-intervention measurement on the same groups. Generally, the analysis is based on testing three hypotheses concerning (1) the difference in pretest measurements between the control and […]

Introduction to Longitudinal Data Analysis

Workshop

In a longitudinal study data has been collected on the same observation over time. Since the repeated measures taken on the same observations are not independent from each other this is a type of multilevel data and special care needs to be taken during the analysis to account for this non-independence. These types of data […]