Publications

Sat, 16 May 2026 11:10:00 +0200

Preprints (under review)

Schroeders, U., Breuer, J., Einsiedler, J., Frank, M., Gnambs, T., Haim, M., Hommel, B., Jankowsky, K., Knöpfle, P., & Schönbrodt, F. (2026, May 16). A framework for reproducible AI-assisted research in the social and behavioral sciences. PsyArXiv.
preprint
Goecke, B., Golle, J., Zettler, I., & Schroeders, U. (2026, May 10). Boys and things, girls and people? Gender-related interest patterns in elementary school. PsyArXiv.
preprint syntax / data
Trautwein, T., Steger, D., Wilhelm, O., & Schroeders, U. (2026, April 21). Predicting item-level characteristics of knowledge tests using large-language models. PsyArXiv.
preprint syntax / data
Achaa-Amankwaa, P., Walter, J., & Schroeders, U. (2026, April 6). Wordle - A game-based assessment of verbal ability? PsyArXiv.
preprint reg. report
Jankowsky, K., Speck, K., & Schroeders, U. (2026, March 24). Taming the Beast: Why and How Applied Machine Learning Benefits From Registered Reports. PsyArXiv.
preprint
Achaa-Amankwaa, P., Hommel, B., Robitzsch, A., Schipolowski, S., & Schroeders, U. (2026, March 20). Predicting item difficulties in C-tests using linguistic features and transformer-based language models. PsyArXiv.
preprint syntax / data
Schroeders, U., Achaa-Amankwaa, P., & Wilhelm, O. (2026, March 11). Echoes of Division: Public-Events Knowledge Reveals Cohort-Dependent East-West Socialization in Germany.
Schroeders, U.* & Walter, J.* (2026, January 14). Developing BOLT - A matrix test using Boolean Operations to assess Logical Thinking. PsyArXiv. [* Shared 1st authorship]
preprint syntax / data
Breit, M., Brunner, M., Preuß, J., Daseking, M., Esser, G., Grob, A., Freudenstein, J.-P., Kieschke, U., Kreuzpointner, L., Pauls, F., Perleth, C., Ricken, G., Schipolowski, S., Schroeders, U., Walter, F., Wilhelm, O., Wyschkon, A., & Preckel, F. (2025, November 25). General intelligence contributions to cognitive performance across ability levels and age: An IPD meta-analysis of child and adolescent norming data. PsyArXiv.
preprint
Achaa-Amankwaa, P., Walter, J., Hübner, V., Hübner, P., & Schroeders, U. (2025, November 5). Modeling item difficulty in large-scale game-based language assessment PsyArXiv.
preprint syntax / data
Schroeders, U. (2025, September 6). Crystallized intelligence: Exploring the dark matter of intelligence. PsyArXiv.
preprint
Schroeders, U., Achaa-Amankwaa, P., Walter, J., Endlich, D., Hasselhorn, M., Golle, J., & Goecke, B. (2025, June 10). PINGUIN – Assessing elementary students’ initial competencies. PsyArXiv.
preprint syntax / data

Peer-reviewed articles

Advance online publications

Speck, K.-L., Jankowsky, K., Scharf, F., & Schroeders, U. (2026). Beyond the hype: A simulation study evaluating the predictive performance of machine learning models in psychology. Accepted for publication in Psychological Methods. https://doi.org/10.1037/met0000832
preprint syntax
Trautwein, T., Scharf, F., & Schroeders, U. (2026). Model specification search in correlated factor models using Bee Swarm Optimization. Structural Equation Modeling: A Multidisciplinary Journal, 1–16. https://doi.org/10.1080/10705511.2025.2612170
open access syntax / data BSO algo
Schroeders, U., & Achaa-Amankwaa, P. (2026). Developing NOVA: A Next-Generation Open Vocabulary Assessment. European Journal of Psychological Assessment. Advance online publication. https://doi.org/10.1027/1015-5759/a000937
open access syntax / data Shiny app
Achaa-Amankwaa, P., Trautwein, T., Lenhard, W., & Schroeders, U. (2025). Balancing between categorical and dimensional assessment in short-scale construction using Ant Colony Optimization. European Journal of Psychological Assessment. Advance online publication. https://doi.org/10.1027/1015-5759/a000892
open access syntax / data prereg
Schroeders, U., Loos, A., Wiedemann, S., & Jankowsky, K. (2024). Is it just a game? Development and validation of a deductive version of mastermind as measure of reasoning ability. European Journal of Psychological Assessment. Advance online publication. https://doi.org/10.1027/1015-5759/a000855
open access reg. report syntax / data

2026

Altgassen, E.*, Hartung, J.*, Steger, D., Schroeders, U. & Wilhelm, O. (2026). From school lessons to life lessons: School knowledge, life knowledge and their relation to biographical experiences. Intelligence, 116, Article 102007. https://doi.org/10.1016/j.intell.2026.102007 [* Shared 1st authorship]

Research

Sat, 29 Nov 2025 21:10:00 +0200

Technology-Based Assessment

In the last decades, the digitalization of educational content, the integration of computers in different educational settings and the opportunity to connect knowledge and people via the Internet has led to fundamental changes in the way we gather, process, and evaluate information. Also, more and more tablet PCs or notebooks are used in schools and—in comparison to traditional sources of information such as text books—the Internet seems to be more appealing, versatile, and accessible. Technology-based assessment has been concerned with questions of comparability of test scores across test media, transferring already existing measurement instruments to digital devices. Nowadays, researchers are more interested in enriching the assessment by using interactive tasks and video material or make the testing more efficient using digital behavior traces.

Method-Toolbox

Wed, 05 Nov 2025 17:32:00 +0200

Sample size estimation in Item Response Theory

Although Item Response Theory (IRT) models offer well-established psychometric advantages over traditional scoring methods, they have been largely confined to specific areas of psychology, such as educational assessment and personnel selection, while their broader potential remains underutilized in practice. One reason for this is the challenge of meeting the (presumed) larger sample size requirements, especially in complex measurement designs. Accurate a priori sample size estimation is essential for obtaining accurate estimates of item/person parameters, effects, and model fit. As such, it serves as an essential tool for effective study planning, especially in pre-registration and registered reports.

Curriculum vitae

Mon, 27 Oct 2025 22:30:00 +0200

Prof. Dr. Ulrich Schroeders

Professor of Psychological Assessment
Institute of Psychology, University of Kassel
Holländische Str. 36-38, Raum 3304, 34127 Kassel
tel +49 (0) 561 804-7529

Education

10/2017 Full Professor of Psychological Assessment, University of Kassel
04/2017 Positive intermediate evaluation of Junior professorship
04/2014 Junior Professor of Educational Research, Member of the Bamberg Graduate School of Social Sciences (BAGSS), University of Bamberg
07/2010 PhD, Humboldt-Universität zu Berlin, Title of Thesis: “Measurement of Cognitive Abilities Using Modern Technologies: Artifacts, Equivalence, and New Constructs”, Humboldt-Universität zu Berlin
05/2004 Diploma in Psychology, Julius-Maximilians-University Würzburg, Title of Thesis: “TeDDy – Test zur Diagnose von Dyskalkulie in der 1. Jahrgangsstufe”, Julius-Maximilians-University Würzburg

Professional Experience

Since 10/2017 Full Professor of Psychological Assessment, University of Kassel
04/2017-09/2017 Interim Professor of Educational Research (W3, 75%) and Junior Professor (W1, 25%), 2nd phase, University of Bamberg
04/2014-09/2017 Junior Professor of Educational Research, Bamberg Graduate School of Social Sciences (BAGSS), University of Bamberg
05/2011-03/2014 Research scientist (PostDoc), Institute for Educational Quality Improvement (Prof. Stanat, Prof. Pant), Humboldt-Universität zu Berlin
04/2010-03/2011 Research scientist (PostDoc), Department of Psychology, Section Educational and Psychological Assessment (Prof. Wilhelm), University of Duisburg-Essen
04/2006-03/2010 Research scientist (PhD student), Institute for Educational Quality Improvement (IQB, Prof. Wilhelm, Prof. Köller), Humboldt-Universität zu Berlin
01/2005-01/2006 Research scientist, Department of Psychology, Section Educational Psychology (Prof. Schneider), Julius-Maximilians-University Würzburg

Current Memberships

Deutsche Gesellschaft für Psychologie (DGPs), http://www.dgps.de/

Editorial Board

Intelligence
Zeitschrift für Pädagogische Psychologie

Ad-hoc reviews

Advances in Methods and Practices in Psychological Science (AMPPS)
Applied Psychological Measurement (APM)
Assessment (ASMNT)
Behaviour & Information Technology (BIT)
Behavior Research Methods (BRM)
British Journal of Educational Psychology
British Journal of Mathematical and Statistical Psychology
Computers in Human Behavior (CHB)
Diagnostica
Educational and Psychological Measurement (EPM)
Educational Psychology
Educational Psychology Review (EDPR)
European Journal of Personality (EJP)
European Journal of Psychological Assessment (EJPA)
Frontiers in Psychology
High Ability Studies
Intelligence
Interacting with Computers
International Journal of Selection and Assessment (IJSA)
International Journal of Testing (IJT)
Journal for Educational Research Online (JERO)
Journal of Economic Psychology
Journal of Educational Measurement (JEM)
Journal of Educational Psychology (EDU)
Journal of Experimental Education
Journal of Genetic Psychology
Journal of Individual Differences (JID)
Journal of Intelligence (JoI)
Journal of Personality Assessment (JPA)
Journal of Speech, Language, and Hearing Research
Large-scale Assessments in Education (LSAE)
Learning and Individual Differences (L&ID)
Learning and Instruction
Personality and Individual Differences (PAID)
PLOS ONE
Psicológica
Psychiatrische Praxis
Psychological Review (REV)
Psychologie in Erziehung und Unterricht
Psychology, Crime and Law (GPCL)
Psychology of Aesthetics, Creativity, and the Arts (ACA)
Scandinavian Journal of Psychology (SJOP)
Social and Personality Psychology Compass (SPCO)
Social Science Research (SSR)
Studies in Educational Evaluation
Structural Equation Modeling (SEM)
Survey Research Methods (SRM)
Thinking Skills and Creativity (TSC)
Zeitschrift für Entwicklungspsychologie und Pädagogische Psychologie (ZfEP)
Zeitschrift für Erziehungswissenschaft (ZfE)
Zeitschrift für Pädagogische Psychologie (ZfPP)
Zeitschrift für Psychologie/ Journal of Psychology (ZfP)

Other reviews

Bundesministerium für Bildung und Forschung (BMBF)
Deutscher Akademischer Austauschdienst (DAAD)
Deutsche Forschungsgemeinschaft (DFG)
Deutsches Institut für Internationale Pädagogische Forschung (DIPF)
Institut zur Qualitätsentwicklung im Bildungswesen (IQB)
Netherlands Organisation for Scientific Research (NWO)
Schweizerischer Nationalfonds zur Förderung der wissenschaftlichen Forschung (SNF)

Tests-Questionnaires

Sun, 20 Jul 2025 08:32:00 +0200

NOVA - Next-Generation Open Vocabulary Assessment

NOVA (= Next-Generation Open Vocabulary Assessment) are two openly available, parallel vocabulary tests designed to measure the receptive vocabulary of German-speaking adults. Given the scarcity of modern, non-proprietary instruments, NOVA was developed to fill this gap, using Ant Colony Optimization to ensure high reliability, appropriate item difficulty and discrimination, and close parallelism across forms. The tests showed high conditional reliability in the lower ability range, making them well suited for individual assessment in neuropsychological contexts, and correlated strongly with a test of declarative knowledge. The test development, including the construction rationale, and the psychometric prorperties are described in detail in Schroeders and Achaa-Amankwaa (2026). The norms are based on a large, heterogeneous sample of adults (N = 1,052). A Shiny app is available for scoring, allowing users to compute IRT-based norm scores and percentile ranks from individual response patterns. The items are available in the OSF project.

Projects

Thu, 29 May 2025 15:31:00 +0200

German translation of this site

↗ PINGUIN
↗ Replication Crisis in Machine Learning

Potential Identification in Elementary School

In the project “Potential Identification in Elementary School for Individual Support” (PINGUIN), we are developing a screening tool to objectively and reliably assess students’ cognitive potential and initial learning conditions at school entry. The computer-based assessment of the PINGUIN project consists of four modules: (1) cognitive potential, (2) language skills, (3) early literacy, and (4) basic mathematical competencies. For each module, tasks are selected adaptively from a comprehensive item bank. The study is conducted in small groups at school using tablets. PINGUIN is designed to help identify children’s potential at an early stage, to provide an objective and fair evaluation of their initial learning conditions, and provide individual support. Teachers can use the knowledge of each child’s individual strengths and weaknesses to tailor their teaching.

Projects

Thu, 29 May 2025 15:31:00 +0200

↗ PINGUIN
↗ Replikationskrise im Machine Learning

Potenzialidentifikation in der Grundschule zur individuellen Förderung

Im Projekt “Potenzialidentifikation IN der GrUndschule zur INdividuellen Förderung”, kurz PINGUIN, entwickeln wir in einem großem Team ein Screening zur objektiven und zuverlässigen Erfassung des kognitiven Potenzials sowie der Lernausgangslage von Schülerinnen und Schülern in der Schuleingangsphase. Das computerbasierte Messinstrument des PINGUIN-Projekts besteht aus vier Modulen: (1) kognitives Potenzial, (2) sprachliche Leistungen, (3) schriftsprachliche und (4) mathematische Basiskompetenzen. Für jedes Modul werden die Aufgaben adaptiv aus einer umfangreichen Aufgabendatenbank gezogen. Die Untersuchung wird mittels Tablets in Kleingruppen in der Schule durchgeführt. PINGUIN soll dazu beitragen, die Potenziale der Kinder frühzeitig zu erkennen und eine faire, datenbasierte Förderung zu ermöglichen. Das Wissen über die individuellen Stärken und Schwächen der einzelnen Kinder kann von Lehrkräften für ihre Unterrichtsgestaltung herangezogen werden.

Example 1: Piloting Testing with a Linked Test Design

Sat, 21 Dec 2024 09:12:01 +0000

Objective

The example demonstrates how to determine the sample size required to estimate the item difficulties of a one-parametric item response model with a given precision. In the study, two test versions, A and B, are administered, each containing 18 items. Twelve items are unique to each test version, while six items are common to both test versions. The parameter of interest is the Mean Squared Error (MSE) of the item difficulty parameters in a one-parametric item response model.

Example 2: Test Validation With Randomized Item Sampling

Sat, 21 Dec 2024 09:12:01 +0000

Objective

The example describes the validation of a newly developed computerized personality test with a forced-choice response format comparable to the Eysenck Personality Inventory (e.g., “Do you prefer reading to going out?”, yes/no). The test is assumed to contain 30 items. A random sample of the personality test items is drawn and the correlation with an external metric criterion (e.g., number of Facebook friends) is estimated. The parameter of interest is the standard error of the correlation between the latent trait and the criterion, which depends on both the sample size and the amount of missingness.

Example 3: Conditional Reliabilities of Three Measures

Sat, 21 Dec 2024 09:12:01 +0000

Objective

The example demonstrates how to identify the sample size required to estimate the conditional reliability of a test using the graded response model (GRM; Samejima, 1969) with a given precision. It is assumed that respondents are randomly administered two out of three depression instruments, that is, the 21-item Beck’s Depression Inventory-II (BDI-II), the 20-item Center for Epidemiological Studies Depression Scale (CES-D), and 9-item the Patient Health Questionnaire (PHQ). These instruments are intended to screen patients for clinically relevant levels of depression. Therefore, the focus is on the measurement precision, that is conditional reliability, at two standard deviations above the mean.

Bee Swarm Optimization (BSO)

Mon, 05 Dec 2022 17:26:02 +0000

“Bees are amazing, little creatures” (Richardson, 2017) – I agree. Bees have fascinated people since time immemorial, and yet even today there are still novel and fascinating discoveries (see the PLOS collection for some mind-boggling facts). Although bees as an insect species might seem as the prime example of state-building insects, highly social forms of community are the exception among bees. The large majority of all bee species are solitary bees or cuckoo bees that do not form insect states.

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.

Best practice self commitment

Mon, 20 Sep 2021 12:45:02 +0000

Many standards in psychological research concerning the study design, data analysis, reporting and publishing have changed in the light of the so-called replication crisis. The following overview attempts to take this development into account to foster transparency, robustness, and integrity of our own research. My research team and I took the form of a voluntary commitment. The aim is to commit ourselves publicly and then work accordingly for all upcoming projects, starting Oct-21.

Age-related nuances in knowledge assessment - Much ado about machine learning

Sat, 17 Jul 2021 17:40:02 +0000

This is the third post in a series on a paper — “Age-related nuances in knowledge assessment” — we recently published in Intelligence. The first post reflected on how knowledge is organized, the second post dealt with psychometric issues. This post is going to be more mathematical (yes, there will be some formulae) and it will be a cautionary note on the use of machine learning algorithms. Machine learning algorithms have positively influenced research in various scientific disciplines such as astrophysics, genetics, or medicine. Also, subdisciplines in psychology such as personality science (e.g., Stachl et al., 2020) or clinical research (Cearns et al., 2019) are adapting the new statistical tools. However, as pointed out in my research statement, every new method initially bears the risk of applying new techniques without the necessary background knowledge. I mainly blame statistical and methodological courses in psychology studies for this. We really have to teach math, stats, and methods more rigorously in university teaching, especially in structured PhD programs.

Age-related nuances in knowledge assessment - A modeling perspective

Mon, 24 May 2021 05:26:02 +0000

This is the second post in a series on a recent paper entitled “Age-related nuances in knowledge assessment” that we wrote with Luc Watrin and Oliver Wilhelm. The first post dealt with the way how we conceptualize the organization of knowledge in a hierarchy in a multidimensional knowledge space. The second post reflects on the way we measure or model knowledge. In textbooks knowledge assessments have a special standing, because they can be modeled both from a reflective and a formative perspective.

Age-related nuances in knowledge assessment - A hierarchy of knowledge

Sat, 08 May 2021 22:26:02 +0000

We published a new paper entitled “Age-related nuances in knowledge assessment” in Intelligence. I really like this paper because it deals with on the way we assess, model, and understand knowledge. And, btw, it employs machine learning methods. Thus, both in terms of content and methodology it hopefully sets a stage for future research avenues that are promising to follow up on. I would like to cover some of the key findings in a series of blog posts. This first post deals with knowledge at different levels of granularity, how they relate to age, and the recurring finding that item sampling plays an important role in test compilation.

Imprint

Wed, 30 Dec 2020 17:32:01 +0000

Disclaimer

Responsible for journalistic editorial content according to § 55 II RstV:

Prof. Dr. Ulrich Schroeders
Professor of Psychological Assessment
Institute of Psychology, University of Kassel
Holländische Str. 36-38, Raum 3304, 34127 Kassel
tel +49 (0) 561 804-7529

Science self-concept – More than the sum of its parts?

Sat, 21 Mar 2020 22:26:02 +0000

The article “Science Self-Concept – More Than the Sum of its Parts?” has now been published in “The Journal of Experimental Education” (btw in existence since 1932). The first 50 copies are free, in case you are interested.

My first preprint. 😀Is a general science self-concept equivalent to an aggregated subject-specific science concept? It's about different modeling approaches, measurement invariance and concepts of equivalence. Check it out! Comment if you like: https://t.co/3STwiTV0Up pic.twitter.com/SfbYxuHfse
— Ulrich Schroeders (@Navajoc0d3) November 6, 2019

In comparison to the preprint version, some substantial changes have been made to the final version of the manuscript, especially in the research questions and in the presentation of the results. Due to word restriction, we also removed a section from the discussion, in which we summarized differences and commonalities of the bifactor vs. higher-order models. We also speculated about why the type of modeling may also depend on the study’s subject, that is, on conceptual differences in intelligence vs. self-concept research. The argumentation may be a bit wonky, but at least I find the idea so persuasive that I want to reproduce it in the following. If you have any comments, please feel free to drop me a line.

Testing for equivalence of test data across media

Sun, 28 Apr 2019 19:26:02 +0000

In 2009, I wrote a small chapter that was part of an EU conference book on the transition to computer-based assessment. Now and then I’m coming back to this piece of work - in my teaching and my publications (e.g., the EJPA paper on testing reasoning ability across different devices). Now I want to make it publically available. Hopefully, it will be interesting to some of you. The chapter is the (unaltered) preprint version of the book chapter, so if you want to cite it, please use the following citation:

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.

New methods and assessment approaches in intelligence research

Sun, 11 Nov 2018 09:26:02 +0000

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 @Jintelligence1, we are guest-editing together with Hülür, @HildePsych, and @pdoebler. More information: https://t.co/PevdPeyRgm pic.twitter.com/Y6hRllQa8m
— Ulrich Schroeders (@Navajoc0d3) November 11, 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:

Meta-analysis proctored vs. unproctored assessment

Mon, 08 Oct 2018 12:26:02 +0000

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.

Longitudinal measurement invariance testing with categorical data

Sun, 30 Sep 2018 12:26:02 +0000

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.

Recalculating df in MGCFA testing

Sun, 10 Jun 2018 12:26:02 +0000

Plase cite as follows:
Schroeders, U., & Gnambs, T. (2020). Degrees of freedom in multigroup confirmatory factor analyses: Are models of measurement invariance testing correctly specified? European Journal of Psychological Assessment, 36(1), 105–113. https://doi.org/10.1027/1015-5759/a000500

A. Number of indicators	C. Number of cross-loadings	E. Number of resid. covar.
B. Number of factors	D. Number of ortho. factors	F. Number of groups

MI testing	constraints	df	comparison	delta(df)
config.	(item:factor)	0	-	-
metric	(loadings)	2	metric-config	2
scalar	(loadings+intercepts)	4	scalar-metric	2
residual	(loadings+residuals)	5	residual-metric	3
strict	(loadings+intercepts+residuals)	7	strict-scalar	3

Additional information

A	Indicates the number of indicators or items.
B	Indicates the number of latent variables or factors.
C	Indicates the number of cross-loadings. For example, in case of a bifactor model the number equals twice the number of indicators (A).
D	Indicates the number of orthogonal factors. For example, in case of a nested factor model with six indicators loading on a common factor and three items additionally loading on a nested factors, you have to specify 2 factors (B) and 1 orthogonal factor (D).
E	Indicates the number of residual covariances.
F	Indicates the number of groups.

The Rosenberg Self-Esteem Scale - A drosophila melanogaster of psychological assessment

Tue, 09 Jan 2018 12:26:02 +0000

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.

Less is more - A WordPress blog goes Hugo

Mon, 01 Jan 2018 10:40:02 +0000

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.

Equivalence of screen versus print reading comprehension depends on task complexity and proficiency

Sat, 12 Aug 2017 07:01:14 +0000

Reference. Lenhard, W., Schroeders, U., & Lenhard, A. (2017). Equivalence of screen versus print reading comprehension depends on task complexity and proficiency. Discourse Processes, 54(5-6), 427–445. doi: https://doi.org/10.1080/0163853X.2017.1319653

Abstract. As reading and reading assessment become increasingly implemented on electronic devices, the question arises whether reading on screen is comparable with reading on paper. To examine potential differences, we studied reading processes on different proficiency and complexity levels. Specifically, we used data from the standardization sample of the German reading comprehension test ELFE II (n = 2,807), which assesses reading at word, sentence, and text level with separate speeded subtests. Children from grades 1 to 6 completed either a test version on paper or via computer under time constraints. In general, children in the screen condition worked faster but at the expense of accuracy. This difference was more pronounced for younger children and at the word level. Based on our results, we suggest that remedial education and interventions for younger children using computer-based approaches should likewise foster speed and accuracy in a balanced way.

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.

Meta-heuristics in short scale construction

Sat, 07 Jan 2017 23:13:02 +0000

Reference. Schroeders, U., Wilhelm, O., & Olaru, G. (2016). Meta-heuristics in short scale construction: Ant Colony Optimization and Genetic Algorithm. PLOS ONE, 11, e0167110. doi:10.1371/journal.pone.0167110

Abstract. The advent of large-scale assessment, but also the more frequent use of longitudinal and multivariate approaches to measurement in psychological, educational, and sociological research, caused an increased demand for psychometrically sound short scales. Shortening scales economizes on valuable administration time, but might result in inadequate measures because reducing an item set could: a) change the internal structure of the measure, b) result in poorer reliability and measurement precision, c) deliver measures that cannot effectively discriminate between persons on the intended ability spectrum, and d) reduce test-criterion relations. Different approaches to abbreviate measures fare differently with respect to the above-mentioned problems. Therefore, we compare the quality and efficiency of three item selection strategies to derive short scales from an existing long version: a Stepwise COnfirmatory Factor Analytical approach (SCOFA) that maximizes factor loadings and two metaheuristics, specifically an Ant Colony Optimization (ACO) with a tailored userdefined optimization function and a Genetic Algorithm (GA) with an unspecific cost-reduction function. SCOFA compiled short versions were highly reliable, but had poor validity. In contrast, both metaheuristics outperformed SCOFA and produced efficient and psychometrically sound short versions (unidimensional, reliable, sensitive, and valid). We discuss under which circumstances ACO and GA produce equivalent results and provide recommendations for conditions in which it is advisable to use a metaheuristic with an unspecific out-of-the-box optimization function.

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