Structured mixture item response models (StrMixIRMs) are a special type of constrained confirmatory mixture item response theory (IRT) model for detecting latent performance differences in a measurement instrument by characteristic item groups, and classifying respondents according to these differences. In light of limited software options for estimating StrMixIRMs under existing frameworks, this paper proposes reparameterizing it as a confirmatory mixture IRT model using interaction effects between latent classes and item groups. The reparameterization allows for easier implementation of StrMixIRMs with multiple software programs that have mixture modeling capabilities, including open-source ones. This widens the accessibility to these models to a broad range of users and thus can facilitate research and applications of StrMixIRMs. This paper serves two main goals: First, we introduce StrMixIRMs, focusing on the proposed reparameterization based on interaction effects and its various extensions. Second, we illustrate use cases of this novel reparameterization within the mirt 1.41 package in R by employing two empirical datasets. Detailed R code with notes are provided for the applications along with an interpretation of the outputs.
Background: Personal emotions and affects have been identified and studied in the context of pandemics, as well as coping strategies centered on emotional regulation or the balance between positive and negative emotions. Objectives: The objectives of this paper are to identify an emotion and affect structure in our sample and analyze the relationship of these dimensions with resilient coping in the context of the COVID-19 pandemic. Method: This study employed a cross-sectional design that involved a non-probabilistic sample with 598 participants over the age of 18, with 51.1% being female, and an average age of 40.73 years. First, the emotional structure was identified through principal component analysis (PCA). Secondly, a linear regression analysis was performed to investigate emotional dimensions as predictors of coping. Results: A valid and reliable emotional structure with four dimensions was identified. The regression model revealed that coping is positively associated with the active and positive dimension and negatively correlated with the negative and moral dimensions. Conclusions: Emotional dimensions are predictors of coping, with moral and negative dimensions having a negative effect, while active and positive dimensions have a positive effect. When designing interventions for coping strategies, multiple dimensions of emotions and affective states in people who are in vulnerable situations must be considered.
The present article provides a detailed comparison of two psychological phenomena, the freezing effect and the bystander effect, across their neurobiological, cognitive, emotional, and behavioral dimensions. This study focuses on identifying and analyzing the similarities and differences between these two responses to stressful and traumatic events. While the freezing effect is characterized by an involuntary neurobiological response to immediate threats, resulting in temporary immobilization or paralysis, the bystander effect describes a cognitive and social phenomenon where individuals refrain from offering help in emergencies when others are present. The study explores affective aspects, including emotional responses and trauma-related impacts associated with both phenomena. Through a comparative analysis, this research unveils important understandings regarding the distinctions among these responses, including their triggers, underlying mechanisms, and observable behaviors. It also highlights overlapping aspects, particularly in how both phenomena can lead to inaction in critical moments. This comparative study contributes to a deeper understanding of the complex interaction between the brain, individual cognition, and social dynamics in the face of danger and stress. The findings of this research have significant implications for understanding human behavior in emergencies, offering valuable perspectives that can be applied in the domains of psychology, training for emergency response, and trauma therapy.
Drawing from the 2015 Gucciardi et al.’s mental toughness (MT) framework, this study examines the association between grit and MT in US college students, while considering the moderating role of at least 75 min of moderate-to-vigorous physical activity (MVPA) based on recommendations from the American College of Sports Medicine. We administered the Grit-S Scale and the Mental Toughness Index in two samples of a total of 340 US undergraduate student-athletes and graduate students. The Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies was employed to ensure internal validity, while statistical procedures including principal component analysis and regression models were utilized to analyze the collected data. A weighted component combining grit and the interaction between MVPA and grit significantly predicted MT, explaining 23% of its variability. Drawing from a specific conceptual framework, this study provides novel insights into the relationship between grit, engagement in at least 75 min of MVPA per week, and MT among US collegiate students. The findings support a positive association between grit, MVPA, and both MT and a specific component of MT, highlighting the significance of these factors in enhancing performance and suggesting potential implications for future research and practical applications in the field.
Likert scales are the most common psychometric response scales in the social and behavioral sciences. Likert items are typically used to measure individuals’ attitudes, perceptions, knowledge, and behavioral changes. To analyze the psychometric properties of individual Likert-type items and overall Likert scales, mostly methods based on classical test theory (CTT) are used, including corrected item–total correlations and reliability indices. CTT methods heavily rely on the total scale scores, making it challenging to directly examine the performance of items and response options across varying levels of the trait. In this study, Kernel Smoothing Item Response Theory (KS-IRT) is introduced as a graphical nonparametric IRT approach for the evaluation of Likert items. Unlike parametric IRT models, nonparametric IRT models do not involve strong assumptions regarding the form of item response functions (IRFs). KS-IRT provides graphics for detecting peculiar patterns in items across different levels of a latent trait. Differential item functioning (DIF) can also be examined by applying KS-IRT. Using empirical data, we illustrate the application of KS-IRT to the examination of Likert items on a psychological scale.
