No abstract

Emotion regulation (ER) plays a vital role in individuals’ well-being and successful functioning. In this study, we attempted to develop a computerized adaptive testing (CAT) to efficiently evaluate ER, namely the CAT-ER. The initial CAT-ER item bank comprised 154 items from six commonly used ER scales, which were completed by 887 participants recruited in China. We conducted unidimensionality testing, item response theory (IRT) model comparison and selection, and IRT item analysis including local independence, item fit, differential item functioning, and item discrimination. Sixty-three items with good psychometric properties were retained in the final CAT-ER. Then, two CAT simulation studies were implemented to assess the CAT-ER, which revealed that the CAT-ER developed in this study performed reasonably well, considering that it greatly lessened the test items and time without losing measurement accuracy.

The Circumplex Scales of Interpersonal Values (CSIV) is a 64-item self-report measure of goals from each octant of the interpersonal circumplex. We used item response theory methods to compare whether dominance models or ideal point models best described how people respond to CSIV items. Specifically, we fit a polytomous dominance model called the generalized partial credit model and an ideal point model of similar complexity called the generalized graded unfolding model to the responses of 1,893 college students. The results of both graphical comparisons of item characteristic curves and statistical comparisons of model fit suggested that an ideal point model best describes the process of responding to CSIV items. The different models produced different rank orderings of high-scoring respondents, but overall the models did not differ in their prediction of criterion variables (agentic and communal interpersonal traits and implicit motives).

This research aims to explore how the COVID-19 pandemic has affected college students’ entrepreneurial intention (EI), as well as whether the well-studied link between entrepreneurial alertness (EA) and EI is involved. Data were collected from 612 respondents, and using the stepwise regression method we examined the moderating role of college students’ perceived risk of COVID-19 on the connection between EA and EI. The results show that students’ perceived risk of COVID-19 reduces their EI. Furthermore, the perceived risk of COVID-19 attenuates the relationship between EA and EI. Specifically, those who perceived a greater risk tended to show lower EI. This article contributes to a better understanding of how the relationship between EA and EI has changed during the pandemic.

Traditional IRT characteristic curve linking methods ignore parameter estimation errors, which may undermine the accuracy of estimated linking constants. Two new linking methods are proposed that take into account parameter estimation errors. The item-(IWCC) and test-information-weighted characteristic curve (TWCC) methods employ weighting components in the loss function from traditional methods by their corresponding item and test information, respectively. Monte Carlo simulation was conducted to evaluate the performances of the new linking methods and compare them with traditional ones. Ability difference between linking groups, sample size, and test length were manipulated under the common-item nonequivalent groups design. Results showed that the two information-weighted characteristic curve methods outperformed traditional methods, in general. TWCC was found to be more accurate and stable than IWCC. A pseudo-form pseudo-group analysis was also performed, and similar results were observed. Finally, guidelines for practice and future directions are discussed.