Missing at Random (MAR) means that the probability of data being missing is purely random after conditioning on observed data (e.g., in multiple imputation). That is to say, it depends only on the observed data, not the missing values themselves.
Dr. Guy Prochilo 🏳️🌈
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Psychological Scientist | Lecturer of Research Methods | Fourth Year Psychology (Honours) Program Coordinator at ISN Psychology | http://guyprochilo.com | https://www.linkedin.com/in/guyprochilo/
Enders, C. K. (2022). Applied Missing Data Analysis, Second Edition (2nd ed.). The Guilford Press.
Just wrapped up today's lecture on missing data! Covered item- and construct-level missingness, detection, mechanisms, analysis (using Welch's t-tests with missingness indicators), solutions, and a theory intro to multiple imputation (since #jamovi lacks modules).
Borenstein, M. (2019). Common Mistakes in Meta-Analysis and How to Avoid Them. Biostat Inc.
Don't use a significance test of heterogeneity (eg Cochran's Q) to choose between fixed or random effects meta-analysis. If studies estimate a single common effect size, use fixed effects. If studies differ in populations, measures, or protocols, opt for random effects.
Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2009). Introduction to Meta-Analysis (1st ed.). Wiley.
A fixed effects meta-analysis assumes a single common effect size across all studies, which is often unrealistic given differences in interventions, populations, and measurements. In psychology especially, a random effects meta-analysis is usually the more plausible
#phdchat
#phdchat
Legha, A., Riley, R. D., Ensor, J., Snell, K. I. E., Morris, T. P., & Burke, D. L. (2018). Individual participant data meta‐analysis of continuous outcomes: A comparison of approaches for specifying and estimating one‐stage models. Statistics in Medicine, 37(29), 4404–4420.
A one-stage IPD meta-analysis aggregates data across all studies and can be analyzed using a linear mixed model with the study ID as a random effect. This aligns with a random effects meta-analysis where we assume the true effect size differs across studies #phdchat
Riley, R., Tierney, J., & Stewart, L. (2021). Individual Participant Data Meta-Analysis: A Handbook for Healthcare Research. Wiley.