@GuyProchilo profile picture
Dr. Guy Prochilo πŸ³οΈβ€πŸŒˆ @GuyProchilo

Psychological Scientist | Lecturer of Research Methods | Fourth Year Psychology (Honours) Program Coordinator at @ISNPsych | guyprochilo.com

Joined January 2013
  • Tweets 2,778
  • Following 189
  • Followers 2,906
1,230 Photos and videos
Many power analyses I read in published manuscripts are not reported with sufficient transparency to understand what on earth they are doing! What is the effect estimate? Where did it come from? For what test does it apply? Etc. Here is a reporting guide I give students #phdchat
18
201
812
On the lookout for academics who want to pick up some psychology marking work at ISN Psychologyβ€”research proposals, theses, and reportsβ€”at $72/hour. Must hold a PhD in Psychology and be based in Australia. DM for details. #PsychologyJobs #PhDJobs #PhDChat #RemoteWork
1
513
Almost ready to submit my paper on a randomized trial! Every number, figure, table, and even the CONSORT diagram will be fully reproducible and can be generated on the fly. Can't wait to share it! #phdchat #rstats
5
871
🧡 Little's MCAR test is often used to check if missing data is "Missing Completely at Random" (MCAR). However, it might not actually do what many researchers think it does. Let's look at the details! #phdchat #statstwitter
1
1
1
646
more replies
To sum it up: Missing data is complicated. Little’s test probably isn’t doing what you think it is, and MCAR is often unlikely. Don’t rely on Little's test as an excuse to avoid properly handling your missing data.
1
372
note: all these images are just random screenshots of papers where the methods section makes claims about Little's test that it doesn't actually provide. Some even claim it provides support for MAR...
1
285
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. #phdchat #statstwitter
1
5
875
Enders, C. K. (2022). Applied Missing Data Analysis, Second Edition (2nd ed.). The Guilford Press.
238
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). #phdchat
3
471
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. #phdchat
1
2
354
Borenstein, M. (2019). Common Mistakes in Meta-Analysis and How to Avoid Them. Biostat Inc.
182
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
2
4
22
2,473
Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2009). Introduction to Meta-Analysis (1st ed.). Wiley.
203
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 #statstwitter
1
4
35
10,952
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.
3
324
Load more