Tagged "psychometrics"

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.

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.

“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.

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.

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.

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.

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.

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.