Ant Colony Optimization script

Ant Colony Optimization (ACO) mimics the foraging behavior of ants and is a popular optimization algorithm in computational sciences. Ants use pheromone tracks to find the shortest route from the nest to the food source, which generally accumulate faster on shorter routes which in turn attract more ants. Routes are constantly optimized until an efficient route is found. Due to its great flexibility the same principles can also be applied to psychological settings, for example, in the construction of short scales.

We have used ACO to construct measures that balance between reliability and validity (Steger et al., 2023; Schroeders et al., 2016), to compile short scales in combination with meta-analysis (Schroeders, Morgenstern, et al., 2023), to assemble parallel test versions (Zimny et al., 2023), to construct measurement invariant scales across cultures (Jankowsky et al., 2020) or age groups (Olaru et al., 2018), but also to amplify differences between groups to learn more about the influence of item sampling on the construct, specifically age differences in personality (Olaru et al., 2019), and gender differences in declarative knowledge (Schroeders et al., 2016).

A recent version of the syntax can be found here, which is described in more detail in Olaru et al. (2019).

Ant Colony Optimization script

Bee Swarm Optimization script

Bee Swarm Optimization (BSO) mimics the foraging search and complex communication of honey bees. In nature, scout bees open up new food sources, whereas onlooker bees search for food in the vicinity of previously explored, promising food sources. These principles can also be used to find the optimal factor structure of measure, which constitutes a prevalent optimization problem in test or questionnaire development. The main idea in model specification search is that scout bees initiate rather global model revisions (e.g., removing a factor), whereas onlooker bees investigate alternative models at a more fine-grained level (e.g., re-assigning a single item).

We have newly developed this BSO algorithm and described its functionality in a proof-of-concept study: Schroeders, Scharf, et al. (2023).

Bee Swarm Optimization script

Multigroup measurement invariance testing (in R und Mplus)

Measurement invariance (MI) is a key concept in psychological assessment and a fundamental prerequisite for meaningful comparisons across groups. In the prevalent approach, multi-group confirmatory factor analysis (MGCFA), specific measurement parameters are constrained to equality across groups, to test for (a) configural MI, (b) metric MI, (c) scalar MI, and (d) strict MI. In the online supplement to Schroeders & Gnambs (2018), we provide example syntax for all steps of MI in lavaan and Mplus for different ways of scaling latent variables: Identification by (a) marker variable, (b) reference group, and (c) effects coding.

Measurement Invariance Syntax

Online tool to check degrees of freedom in MGCFA

We encourage authors and reviewers to routinely use our online tool where you can enter the number of indicators, latent variables, groups, etc. to double-check the degrees of freedom of your reported models. In this context, we welcome the recent effort of journals in psychology to include soundness checks on manuscript submission such as statcheck to improve the accuracy of statistical reporting.

Online tool to check df in MGCFA