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Maybe you have seen my recent Tweet:

Please share this call and contribute to a new Special Issue on “New Methods and Assessment Approaches in Intelligence Research” in the Journal of Intelligence, we are guest-editing together with Hülür, Hildebrandt, and Doebler.

— @Navajoc0d3, 11 November 2018

And this is the complete Call for the Special Issue in the Journal of Intelligence

Dear Colleagues,
Our understanding of intelligence has been—and still is—significantly influenced by the development and application of new computational and statistical methods, as well as novel testing procedures. In science, methodological developments typically follow new theoretical ideas. In contrast, great breakthroughs in intelligence research followed the reverse order. For instance, the once-novel factor analytic tools preceded and facilitated new theoretical ideas such as the theory of multiple group factors of intelligence. Therefore, the way we assess and analyze intelligent behavior also shapes the way we think about intelligence.
We want to summarize recent and ongoing methodological advances inspiring intelligence research and facilitating thinking about new theoretical perspectives. This Special Issue will include contributions that:

Our meta-analysis – Steger, Schroeders, & Gnambs (2018) – comparing test-scores of proctored vs. unproctored assessment is now available as online first publication and sometime in the future to be published in the European Journal of Psychological Assessment. In more detail, we examined mean score differences and correlations between both assessment contexts with a three-level random-effects meta-analysis based on 49 studies with 109 effect sizes. We think this is a timely topic since web-based assessments are frequently compromised by a lack of control over the participants’ test-taking behavior, but researchers are nevertheless in the need to compare the data obtained through unproctored test conditions with data from controlled settings. The inevitable question is to what extent such a comparison is feasible.

In a recent paper – Edossa, Schroeders, Weinert, & Artelt, 2018 – we came across the issue of longitudinal measurement invariance testing with categorical data. There are quite good primers and textbooks on longitudinal measurement invariance testing with continuous data (e.g., Geiser, 2013). However, at the time of writing the manuscript there wasn’t an application of measurement invariance testing in the longitudinal run with categorical data. In case your are interest in using such an invariance testing procedure, we uploaded the R syntax for all measurement invariance steps.

I had the great chance to co-author two recent publications of Timo Gnambs, both dealing with the Rosenberg Self-Esteem Scale (RSES; Rosenberg, 1965). As a reminder, the RSES is a popular ten item self-report instrument measuring a respondent’s global self-worth and self-respect. But basically both papers are not about the RSES per se, rather they are applications of two recently introduced powerful and flexible extensions of the Structural Equation Modeling (SEM) Framework: Meta-Analytic Structural Equation Modeling (MASEM) and Local Weighted Structural Equation Modeling (LSEM), which will be described in more detail later on.

After several years running this website on WordPress, it’s time for a change. WordPress has become overloaded, sometimes the back-end is not responsive, and writing a blog post is too tedious—in a nutshell, WordPress isn’t right for me. Hugo is an open-source static site generator built around Google’s Go programming language, which is renowned for its speed. In contrast to dynamic websites that heavily rely on php-scripting and MySQL-databases that are used to store all the content, static websites consist of html, css, and js. Making static websites sounds retro, but is in fact up-to-date and comes with a lot of benefits—in short, it’s right for me.