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/
@wviechtb thanks, I appreciate the recommendations
Enders, C. K. (2022). Applied Missing Data Analysis, Second Edition (2nd ed.). The Guilford Press.
@wviechtb Thanks for this - it actually got me thinking more about some work I had planned. I'm looking at doing an IPD meta-analysis of just two of my own RCTs with the same intervention. Even though the samples are different, a random effects approach might not be suitable with just two studies. I appreciate your comments and the chance to learn a bit more.
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).
@wviechtb Yes, I agree, and I think the information we've provided is complementary. If the goal is to make inferences about only the specific set of studies included in the meta-analysis, a fixed-effects model can be used, even if the studies are not necessarily estimating a common effect size. However, I understand this is not usually the main aim in most meta-analyses, where a random-effects approach would better suit the type of inference needed.
Il n'y a pas de règle sans exception
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
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