Spotlight: Graduate Student

Diego G. Campos

Tell us a bit about yourself.

My name is Diego G. Campos. I am a Ph.D. Candidate at the Centre for Educational Measurement at the University of Oslo (CEMO). I am working with Prof. Ronny Scherer and Prof. Mike Cheung to develop, use, and evaluate meta-analytic approaches to synthesize individual participant data from complex survey studies. We have outstanding educational data from large-scale assessments collected at the national and international levels. However, we have not successfully integrated those data sources in systematic reviews and meta-analyses. Individual participant data meta-analysis can provide us with tools to test relationships and describe distributions in these large, high-quality samples. Results from that enterprise can inform educational practices and develop educational policies. In my Ph.D. project, I hope to build and offer clear guidelines to use that data in systematic reviews and meta-analyses.

How did you get into systematic review and/or meta-analysis research?

While I was doing my master’s, there was a lot of fuzz around the replication crisis. It was challenging to decide between contradictory evidence and identify the best evidence-based practices in teaching and learning. The open science movement changed my mind about science. I learned about the importance of the replicability, robustness, and generalizability of empirical findings. Then, in conversation with my Ph.D. supervisor, he pointed out the field of systematic reviews and meta-analyses. After doing some research, I realized that meta-analyses are a great tool to help create a cumulative body of knowledge in the social sciences. So here I am, trying to contribute to that task.

What work do you do now that is related to systematic review and/or meta-analysis?

I am working on synthesizing information on digital divides. The COVID-19 accelerated the transformation of education towards digital education, so it is crucial to identify populations that can be harmed the most for lack of access to technology. Hopefully, the evidence will serve as a guide for developing targeted solutions to specific populations. I am also looking into more methodological research questions. For example, I am testing the robustness of meta-analytic structural equational models (MASEM). I hope we can inform how robust these methodologies are to variations in some assumptions.

What do you love about systematic review and/or meta-analysis?

I love that it provides a framework to test a theoretical hypothesis’s robustness. It is difficult to decide between contradictory evidence and understand the sources of differences. However, systematic reviews and meta-analyses enable researchers to compile multiple pieces of evidence, explore possible sources of the differences and build a take-home message for other researchers, practitioners, and policy-makers—all of this while striving for systematicity, reproducibility, and openness.

What advice do you have for graduate students and early career researchers about working in systematic review and meta-analysis research?

It is crucial to get yourself familiar with the software and statistical packages that can support you with searching, screening, coding, and analyzing the data. A few handy tools, for example, are the litsearchr R package that can help you to develop your search strategy. The reference manager Zotero can support you in saving and storing your searches. Covidence, Google Survey, or Excel can help you in the process of screening and coding your literature. Finally, the metafor, metaSEM, robumeta, and clubSandwich R packages can help you analyze your data.

If you want to know about new software and statistical packages, the Evidence Synthesis Hackaton is a great place to do that. Also, do not forget to get yourself a pair of sweatpants and a comfy couch; you will spend quite some time there while doing a systematic review.