Different types of tasks exist, including tasks for research purposes or exams assessing knowledge. According to expectation-value theory, tests are related to different levels of effort and importance within a test taker. Test-taking effort and importance in students decreased over the course of high-stakes tests or low-stakes-tests in research on test-taking motivation. However, whether test-order changes affect effort, importance, and response processes of education students have seldomly been experimentally examined. We aimed to examine changes in effort and importance resulting from (...) variations in test battery order and their relations to response processes. We employed an experimental design assessing N = 320 education students’ test-taking effort and importance three times as well as their performance on cognitive ability tasks and a mock exam. Further relevant covariates were assessed once such as expectancies, test anxiety, and concentration. We randomly varied the order of the cognitive ability test and mock exam. The assumption of intraindividual changes in education students’ effort and importance over the course of test taking was tested by one latent growth curve that separated data for each condition. In contrast to previous studies, responses and test response times were included in diffusion models for examining education students’ response processes within the test-taking context. The results indicated intraindividual changes in education students’ effort or importance depending on test order but similar mock-exam response processes. In particular effort did not decrease, when the cognitive ability test came first and the mock exam subsequently but significantly decreased, when the mock exam came first and the cognitive ability test subsequently. Diffusion modeling suggested differences in response processes on cognitive ability tasks suggesting higher motivational levels when the cognitive ability test came first than vice versa. The response processes on the mock exam tasks did not relate to condition. (shrink)

Discrete choice experiments—selecting the best and/or worst from a set of options—are increasingly used to provide more efficient and valid measurement of attitudes or preferences than conventional methods such as Likert scales. Discrete choice data have traditionally been analyzed with random utility models that have good measurement properties but provide limited insight into cognitive processes. We extend a well-established cognitive model, which has successfully explained both choices and response times for simple decision tasks, to complex, multi-attribute discrete choice data. The (...) fits, and parameters, of the extended model for two sets of choice data (involving patient preferences for dermatology appointments, and consumer attitudes toward mobile phones) agree with those of standard choice models. The extended model also accounts for choice and response time data in a perceptual judgment task designed in a manner analogous to best–worst discrete choice experiments. We conclude that several research fields might benefit from discrete choice experiments, and that the particular accumulator-based models of decision making used in response time research can also provide process-level instantiations for random utility models. (shrink)

Network Analysis is considered as a new method that challenges Latent Variable models in inferring psychological attributes. With Network Analysis, psychological attributes are derived from a complex system of components without the need to call on any latent variables. But the ontological status of psychological attributes is not adequately defined with Network Analysis, because a psychological attribute is both a complex system and a property emerging from this complex system. The aim of this article is to reappraise the legitimacy of (...) latent variable models by engaging in an ontological and epistemological discussion on psychological attributes. Psychological attributes relate to the mental equilibrium of individuals embedded in their social interactions, as robust attractors within complex dynamic processes with emergent properties, distinct from physical entities located in precise areas of the brain. Latent variables thus possess legitimacy, because the emergent properties can be conceptualized and analyzed on the sole basis of their manifestations, without exploring the upstream complex system. However, in opposition with the usual Latent Variable models, this article is in favor of the integration of a dynamic system of manifestations. Latent Variables models and Network Analysis thus appear as complementary approaches. New approaches combining Latent Network Models and Network Residuals are certainly a promising new way to infer psychological attributes, placing psychological attributes in an inter-subjective dynamic approach. Pragmatism-realism appears as the epistemological framework required if we are to use latent variables as representations of psychological attributes. (shrink)