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.
Tagged "crystallized intelligence"
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.
120 item gc test This is a 120 item measure of crystallized intelligence (gc), more precisely, declarative knowledge. Based on previous findings concerning the dimensionality of gc (Steger et al., 2019), we sampled items from four broad knowledge areas - humanities, life sciences, natural sciences, and social sciences. Each knowledge area contained three domains with ten items each, resulting in a total of 120 items. Items were selected to have a wide range of difficulty and to broadly and deeply cover the content domain.
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.
Intelligence Research Our understanding of intelligence has been — and still is — significantly influenced by the development and application of new testing procedures as well as novel computational and statistical methods. In science, methodological developments typically follow new theoretical ideas. In intelligence research, however, great breakthroughs often 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.