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From educational research to psychometrics? A citation update

Wed, 16 Sep 2026 22:30:00 +0200

In an earlier post, I looked at which topics of my work are cited on Google Scholar. At the time, I expected that the share of citations from educational research would decrease and that newer topics such as machine learning would show up in the citation profile at some point. Almost five years later, it is time to check whether these expectations hold.

Method

The Google Scholar profile was retrieved with the R package scholar. As before, all publications with at least 10 citations (i10-index) were included (2021: n = 50 publications with 2,268 citations; 2026: n = 79 publications with 5,691 citations). In contrast to the first round, I revised the tags using an LLM: each publication received on average 2.7 tags (range: 1–5) from a set of 34 tags. For example, the article The influence of item sampling on sex differences in knowledge tests is tagged ACO, gc, and gender. To make the two years comparable, the 2021 publications were retrospectively recoded using the same tagging scheme as in 2026, which is why the figures slightly differ.

Do the citations align with my research interests?

Mon, 21 Mar 2022 17:26:02 +0000

When citations are traded as the currency of science, it is difficult to estimate the price of a publication in advance or to understand it afterwards. One has the impression that precisely the topics that are frequently cited are those not in focus of one’s interest. In contrast, the work that one finds most interesting, might receive little attention. But maybe this perception is biased. To get a better understanding which articles are cited, I would like to give a short bibliometric evaluation of my google scholar citations in this post.

Pitfalls in measurement invariance testing

Fri, 04 Jan 2019 09:26:02 +0000

In a new paper in the European Journal of Psychological Assessment, Timo Gnambs and I examined the soundness of reporting measurement invariance (MI) testing in the context of multigroup confirmatory factor analysis (MGCFA). Of course, there are several good primers on MI testing (e.g., Cheung & Rensvold, 2002; Wicherts & Dolan, 2010) and textbooks that elaborate on the theoretical base (e.g., Millsap, 2011), but a clearly written tutorial with example syntax how to implement MI practically was still missing. In the first part of the paper, we demonstrate that a sobering large amount of reported degrees of freedom (df) do not match with the df recalculated based on information given in the articles. More specifically, we both reviewed 128 studies including 302 measurement invariance MGCFA testing procedures from six leading peer-reviewed journals that focus on psychological assessment and on a regular base. Overall, about a quarter of all articles included at least one discrepancy with some systematic differences between the journals. However, it was interesting to see that the metric and scalar step of invariance testing were more frequently affected.

Commitment to research transparency and open science

Sun, 08 Jan 2017 23:53:01 +0000

I signed the Commitment to Research Transparency and Open Science, which was initially worded by Felix Schönbrodt, Markus Maier, Moritz Heene, and Michael Zehetleitner from the LMU Munich. The first paragraph of this commitment summarizes the overall aim:

We embrace the values of openness and transparency in science. We believe that such research practices increase the informational value and impact of our research, as the data can be reanalyzed and synthesized in future studies. Furthermore, they increase the credibility of the results, as independent verification of the findings is possible.