This study investigates the internal structure and construct validity of Complex Problem Solving (CPS), which is measured by a Multiple-Item-Approach. It is tested, if (a) three facets of CPS -rule identification (adequateness of strategies), rule knowledge (generated knowledge) and rule application (ability to control a system) -can be empirically distinguished, how (b) reasoning is related to these CPS-facets and if (c) CPS shows incremental validity in predicting school grade point average (GPA) beyond reasoning. N = 222 university students completed Micro-DYN, a computer-based CPS test and Ravens Advanced Progressive Matrices. Analysis including structural equation models showed that a 2-dimensionsal model of CPS including rule knowledge and rule application fitted the data best. Furthermore, reasoning predicted performance in rule application only indirectly through its influence on rule knowledge indicating that learning during system exploration is a prerequisite for controlling a system successfully. Finally, CPS explained variance in GPA even beyond reasoning, showing incremental validity of CPS. Thus, CPS measures important aspects of academic performance not assessed by reasoning and should be considered when predicting real life criteria such as GPA.

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We argue that it is useful to distinguish between three key goals of personality science – description, prediction and explanation – and that attaining them often requires different priorities and methodological approaches. We put forward specific recommendations such as publishing findings with minimum a priori aggregation and exploring the limits of predictive models without being constrained by parsimony and intuitiveness but instead maximising out-of-sample predictive accuracy. We argue that naturally-occurring variance in many decontextualized and multi-determined constructs that interest personality scientists may not have individual causes, at least as this term is generally understood and in ways that are human-interpretable, never mind intervenable. If so, useful explanations are narratives that summarize many pieces of descriptive findings rather than models that target individual cause-effect associations. By meticulously studying specific and contextualized behaviours, thoughts, feelings and goals, however, individual causes of variance may ultimately be identifiable, although such causal explanations will likely be far more complex, phenomenon-specific and person-specific than anticipated thus far. Progress in all three areas – description, prediction, and explanation – requires higher-dimensional models than the currently-dominant “Big Few” and supplementing subjective trait-ratings with alternative sources of information such as informant-reports and behavioural measurements. Developing a new generation of psychometric tools thus provides many immediate research opportunities.

Collaborative problem solving (CPS) is an important 21st century skill that is increasingly recognized as being critical to efficiency, effectiveness, and innovation in the modern global economy (Fiore, Graesser, & Greiff, 2018; Organisation for Economic Cooperation and Development [OECD], 2017a). CPS has attracted interest in international assessments; national assessments of middle and high school students; and training in colleges, industry, and the military (Care,

Innovative assessments of cross-curricular competencies such as complex problem solving (CPS) have currently received considerable attention in large-scale educational studies. This study investigated the nature of CPS by applying a state-of-the-art approach to assess CPS in high school. We analyzed whether two processes derived from cognitive psychology, knowledge acquisition and knowledge application, could be measured equally well across grades and how these processes differed between grades. Further, relations between CPS, general mental ability (g), academic achievement, and parental education were explored. Hungarian high school students in Grades 5 to 11 (N = 855) completed MicroDYN, which is a computer-based CPS test, and the Culture Fair Test 20-R as a measure of g. Results based on structural equation models showed that empirical modeling of CPS was in line with theories from cognitive psychology such that the two dimensions identified above were found in all grades, and that there was some development of CPS in school, although the Grade 9 students deviated from the general pattern of development. Finally, path analysis showed that CPS was a relevant predictor of academic achievement over and above g. Overall, results of the current study provide support for an understanding of CPS as a cross-curricular skill that is accessible through computer-based assessment and that yields substantial relations to school performance. Thus, the increasing attention CPS has currently received on an international level seems warranted given its high relevance for educational psychologists.

This article is about Complex Problem Solving (CPS), its history in a variety of research domains (e.g., human problem solving, expertise, decision making, and intelligence), a formal definition and a process theory of CPS applicable to the interdisciplinary field. CPS is portrayed as (a) knowledge acquisition and (b) knowledge application concerning the goal-oriented control of systems that contain many highly interrelated elements (i.e., complex systems). The impact of implicit and explicit knowledge as well as systematic strategy selection on the solution process are discussed, emphasizing the importance of (1) information generation (due to the initial intransparency of the situation), (2) information reduction (due to the overcharging complexity of the problem's structure), (3) model building (due to the interconnectedness of the variables), (4) dynamic decision making (due to the eigendynamics of the system), and (5) evaluation (due to many, interfering and/or ill-defined goals).

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