Latent Constructs Research Articles

Study of Regularized Generalized Structured Component Analysis to Overcome Multicollinearity in Component-Based SEM

Multicollinearity is one of the issues that may arise in the analysis of Structural Equation Modeling (SEM). An indication of multicollinearity is the high correlation between latent variables and the correlation between indicators forming the latent construct. Multicollinearity causes the interpretation of SEM analysis to be inappropriate. In this study, Regularized generalized structured component analysis (RGSCA) is used as a solution to overcome multicollinearity in component-based SEM. The research aims to apply RGSCA to East Java poverty data, which contains multicollinearity. The first step is analyze data using GSCA, however the weights of and indicators are not significant, and the three estimated path coefficients are also not significant at the 95% confidence interval. The high correlation value between the indicators further indicates the presence of multicollinearity. Futhermore, the data are analyzed using RGSCA with ridge parameters namely which provides minimum prediction error (CV). The results of the analysis reveal that all estimation of loading factors, weights and path coefficients are significant at 95% confidence intervals. The interpretation of the path coefficient results suggests that education, health, and economy significantly influence poverty, while health and economy also have a significant effect on education, and health additionally exhibits a significant effect on economy. The overall model evaluation results obtained a FIT value of 0.662, indicating that the model can explain about 66.2% of the data variation.

Can We Compare Attitudes Towards Crime Around the World? Assessing Measurement Invariance of the Morally Debatable Behavior Scale Across 44 Countries

ObjectivesWe aim to encourage scholars who conduct cross-national criminological studies to routinely assess measurement invariance (MI), that is, verify if multi-item instruments that capture latent constructs are conceptualized and understood similarily across different populations. To promote the adoption of MI tests, we present an analytical protocol, including an annotated R script and output file. We implement the protocol and, doing so, document the first test of configural, metric, and scalar invariance of the three-factor Morally Debatable Behavior Scale (MDBS).MethodsWe worked with data from wave seven of the World Values Survey (WVS). Applying multi-group confirmatory factor analyses, we, first, explored invariance of the MDBS in 44 countries (N = 59,482). Next, we conducted analyses separately for seven South-american, six South-east Asian, six East-asian, two North American and Australasian, and all four Anglophone countries.ResultsThe MDBS displays an overall lack of invariance. However, we confirmed configural invariance of the MDBS for the South-east Asian sample, metric invariance in the sample of Anglophone countries, and scalar invariance for the Australasian and North American countries.ConclusionsWave seven of the WVS can be used for latent mean score comparisons of the MDBS between the Australasian and North American countries. Associative relationships can be compared in the larger Anglophone sub-sample. Taken together, MI must be tested, and cannot be assumed, even when analyzing data from countries for which previous research has established cultural similarities. Our protocol and practical recommendations guide researchers in this process.

Uniformity in Dress:A Worldwide Cross-Cultural Comparison.

Focusing on clothing and adornment (dress), this worldwide cross-cultural comparison asks why people in some societies appear to dress in uniform or standardized ways, whereas in other societies individuals display considerable variability in dress. The broader research question is why some societies have more within-group variation than others. Hypotheses are tested on 80 societies drawn from the Standard Cross-Cultural Sample (SCCS). The central hypotheses consider the impact of general societal tightness or looseness, degree of egalitarianism as well as other aspects of societal complexity, and the role of resource stress on dress standardization. Exploratory methods identify four latent constructs of dress from newly coded variables, one latent construct for tightness/looseness, and one latent construct for resource stress. As expected, (1) increased societal tightness was positively related to increased standardization and rules regarding dress and (2) increased resource stress is generally related to more standardization of dress and rules regarding adornment. However, contrary to theoretical expectations, the predictors of tightness-looseness differ from the predictors of dress. Most importantly, resource stress negatively predicts tightness but positively predicts three of the latent dress constructs. The relationship between dress standardization and societal complexity may be curvilinear, with mid-range societies having more standardization. Although some of the theorized relationships are supported (including that standardization of dress is predicted by societal tightness and more resource stress), at the end of paper we discuss some puzzling findings, speculate about possible explanations, and suggest further lines of research.

Cognitive reserve and other determinants of cognitive function in older adults: Insights from a community-based cross-sectional study.

India will be the home of 323 million elderly persons by 2050. This means a surge in the dependent population primarily due to age-related cognitive decline. Evidence suggests that life course factors may have a modulatory role on cognitive function. The present study explores such potential influence by investigating the effect of cognitive reserve (a latent construct using education and occupation) and physical, psychological, and social determinants on cognitive function in community dwelling elderly. A community-based cross-sectional study was conducted in urban areas of West Bengal (India) among elderly aged ≥60 years. Data was collected by personal interviews for socio-demographic and medical profile. Cognitive function was assessed using Bangla Adaptation of Mini-Mental State Examination (BAMSE). Educational level and occupational complexity were used as proxy indicators for calculating cognitive reserve. Of the 370 elderlies interviewed (mean age = 68.9 years), cognitive function was abnormal in 13.5%. The cognitive function had a significant inverse relationship with depression symptoms, loneliness, hypertension, anemia, and basic activities of daily living. There was a significant difference in the cognitive reserve of the elderly with normal and abnormal cognitive function (mean 33.7 and 26.8, respectively). In the presence of covariates like sleep quality, depression, hypertension, and hemoglobin levels, the effect of age on cognitive function had a significant mediation influence of cognitive reserve - total effect = -0.2349; 95% CI = (-0.2972 to -0.1725) and direct effect = -0.2583; 95% CI = (-0.3172 to -0.1994). The quantum of effect of the age on cognitive function decreases with good cognitive reserve as a cognitive reserve has a significant mediation effect on the relationship between age and cognitive function.

Further Validation of the Social Efficacy and Social Outcome Expectations Scale

The current study sought out to advance the Social Self-Efficacy and Social Outcome Expectations scale using multiple approaches to scale development. Data from 583 undergraduate students were used in two scale development approaches: Classic Test Theory (CTT) and Item Response Theory (IRT). Confirmatory factor analysis suggested a 2-factor structure that aligns with the theoretically based domains for SEOES items and supports previously proposed models of this scale from CTT and psychometric analyses. The IRT analysis indicated that the SEOES items have greater measurement precision at measuring lower levels of the latent constructs. Future research directions are provided and practice implications are discussed.

Assessing concept mapping competence using item expansion‐based diagnostic classification analysis

AbstractConcept mapping is widely used as a tool for assessing students' understanding of science. To fully realize the diagnostic potential of concept mapping, a scoring method that not only provides an objective and accurate assessment of students' drawn concept maps but also provides a detailed understanding of students' proficiency and deficiencies in knowledge is necessary. However, few of the existing scoring methods focus on the latent constructs (e.g., knowledge, skills, and cognitive processes) that guide the creation of concept maps. Instead, they focus on the completeness of the concept map by assigning a composite score, which makes it difficult to generate targeted diagnostic feedback information for advancing students' learning. To apply the diagnostic classification model to the quantitative analysis of concept maps, this study introduced the novel application of the item expansion‐based diagnostic classification analysis (IE‐DCA) for this purpose. The IE‐DCA can not only assess students' concept mapping abilities along a continuum but also classify students according to their concept mapping attributes when constructing the concept maps. The application and benefits of this approach were illustrated using a physics concept‐mapping item related to particle and rigid body. Results showed that the estimated attribute profiles via the IE‐DCA provided more detailed information about students' latent constructs than the composite score. Overall, this study illustrates the feasibility and potential of applying IE‐DCA to analyze concept maps. Future applications of IE‐DCS in other assessments in science education are discussed.

Promoting Strategic Flexibility and Business Performance through Organizational Ambidexterity

The purpose of this paper is to develop a comprehensive structural model to understand the outcomes of organizational ambidexterity (OA) within the manufacturing and service sector. It focuses on evaluating the effects of OA on business performance and strategic flexibility while also exploring the under-researched relationship between strategic flexibility and business performance. To accomplish this objective, an empirical survey was carried out among a sample of 370 Greek manufacturing and service firms. Exploratory and confirmatory factor analyses were utilized to extract and validate the latent constructs examined. Finally, the structural relationships among these latent constructs were determined using structural equation modeling (SEM). The study’s findings reveal the significant contributions of OA to both firm performance and strategic flexibility. Additionally, the results demonstrate the positive influence of strategic flexibility on business performance. This research sheds light on the multifaceted impacts of ambidexterity, offering valuable implications for managers, decision-makers, and practitioners. The outcomes underscore the importance of ambidexterity in various dimensions of business performance and highlight its role in fostering strategic flexibility. This study stands out by offering a holistic model that explicates the outcomes of OA within the manufacturing and service sector. It places particular emphasis on the interplay between business performance and strategic flexibility, an area that has received limited attention in prior research. By empirically examining these relationships and providing practical guidelines, the proposed model enriches our understanding of ambidexterity’s significance and supports further research in this domain within organizations.

Patterns of Womenʼs exposure to psychological violence: A global examination of low- and middle-income countries

IntroductionUnder Sustainable Development Goal 5, prevalence of intimate partner violence (IPV) is a globally reportable indicator. There is a lack of consensus on how to measure and report psychological IPV, affecting prevalence estimates and cross-country comparability. We examine similarities and differences in the patterning of women's experiences of psychological abuse in low- and middle-income countries (LMICs) to inform common cut points. MethodsData include 13,452 ever-partnered women from six LMICs participating in the WHO multi-country study on women's health and domestic violence against women and 306,101 from 47 LMICs participating in the Demographic and Health Surveys. A confirmatory latent class analysis (LCA) approach was applied to identify the optimal class structure using the 3 DHS and 4 WHO psychological IPV items, assessed the impact of physical and sexual IPV on class structure, and tested class generalizability across countries. We validated the three-class solution by regressing the classes on physical IPV, sexual IPV, controlling behaviors, and injury due to domestic violence. We used item response theory (IRT) methods to assess item-level characteristics of the items. ResultsAnalysis confirmed the three-class structure in most countries. Addition of physical and sexual IPV did not change overall class structure or improve discrimination or homogeneity of the items. The three-class structure was invariant within most WHO-classified regions. Operationalized classes informed by the LCA resulted in prevalences of roughly 90% low-to-no class, 7% moderate-intensity class, and 3% high-intensity class. Classes showed convergent validity with all outcomes tested. IRT analysis revealed good discriminations but substantial information overlaps over a narrow range of the latent psychological violence construct. ConclusionsThis study confirms the three-class pattern but suggests some differences across countries. and regions. We suggest cut points distinguishing violent from non-violent acts and demarcating levels of severity for future study. Findings offer evidence-based guidance to rectify challenges.

Cross-Classified Item Response Theory Modeling With an Application to Student Evaluation of Teaching

The cross-classified data structure is ubiquitous in education, psychology, and health outcome sciences. In these areas, assessment instruments that are made up of multiple items are frequently used to measure latent constructs. The presence of both the cross-classified structure and multivariate categorical outcomes leads to the so-called item-level data with cross-classified structure. An example of such data structure is the routinely collected student evaluation of teaching (SET) data. Motivated by the lack of research on multilevel IRT modeling with crossed random effects and the need of an approach that can properly handle SET data, this study proposed a cross-classified IRT model, which takes into account both the cross-classified data structure and properties of multiple items in an assessment instrument. A new variant of the Metropolis–Hastings Robbins–Monro (MH-RM) algorithm was introduced to address the computational complexities in estimating the proposed model. A preliminary simulation study was conducted to evaluate the performance of the algorithm for fitting the proposed model to data. The results indicated that model parameters were well recovered. The proposed model was also applied to SET data collected at a large public university to answer empirical research questions. Limitations and future research directions were discussed.

Development of freight trip generation model using observed and unobserved information of supply chain characteristics for a sustainable urban transformation

Supply Chain Characteristics (SCC) drive the city logistics to control the economic activities that support urban life and transformation. The dynamics of freight trip activity and SCC in city logistics are less understood by city planners, disrupting the prediction of urban Freight Trip Generation (FTG) models. To develop robust urban FTG models, the present study introduces the concept of estimating the freight trip activity using observed and unobserved (subjective) information of SCC for sustainable urban transport movement. Traditionally, the objective variable excludes the unobserved information of SCC while developing the FTG model; this gap can be addressed by incorporating subjective variables to measure both observed and unobserved information of SCC. A unique framework was designed by a combination of objective and subjective variables through Exploratory Factor Analysis (EFA) and Structural Equation Modelling (SEM) to estimate the freight trip activity. An Establishment Based Freight Survey (EBFS) collected from an Indian smart city (1793 samples) comprising various SCC was used to test the developed framework. Deployed EFA can identify the subjective variables using latent constructs to explain the observed and unobserved information in the SCC. Then, sequential execution of SEM with hypothesis path structure for estimating the freight trip activity. The direct, indirect, and total effects of the FTG model revealed added advantage of causal inter-relationship of urban freight trip activity and SCC between intermediate and pure receiver establishment. The developed FTG models using observed and unobserved information of SCC predicted freight trip activity with an accuracy of 57% for intermediate and 63% for pure receiver establishment. The effect of SSC on freight trip activity aids in creating sustainable urban freight transportation that could be an agile, responsive, and flexible adaptive system for the global market demand.

Usefulness of QoS in Multidimensional QoE Prediction for Haptic–Audiovisual Communications

This article investigates prediction of QoE (Quality of Experience) by comparing borrowing–from–neighbor situations and isolated ones. We demonstrate that joint utilization of multiple QoE measures enhances the accuracy of QoE prediction compared to that by a collection of individual QoE measures each regressed on QoS (Quality of Service) parameters, while the accuracy improvement with additional usage of QoS information in the former is limited. As an example of system which needs multidimensional QoE representation, the article gives haptic audiovisual interactive communications. We employ QoE and QoS data taken previously in an experiment, where 13 QoE measures (a five–point score each) and 12 QoS parameters (nonnegative continuous values each) are available at three average rates of load traffic. We build two kinds of Bayesian models for QoE prediction; one is a logistic regression model of a single QoE measure as the response variable and QoS parameters as predictors, which is a typical traditional method of discrete QoE prediction and isolated in a sense. The other is a structural equation model (SEM) with latent constructs (i.e., factors) of audiovisual quality, haptic quality and user experience quality; the original SEM, which contains only QoE indicators of three constructs (AVQ, HQ and UXQ), was proposed in one of the author’s previous studies. This article extends the SEM in order to accommodate QoS parameters. We develop two kinds of new SEMs with QoS parameters: One has three extended constructs referred to as eAVQ, eHQ and eUXQ, each of which has both QoE and QoS indicators, and the other has separate constructs for QoE and QoS, which lead to totally six constructs (AVqoe, Hqoe, UXqoe, AVqos, Hqos and UXqos). We performed Markov chain Monte Carlo (MCMC) simulation of the Bayesian models with the JAGS software in an R environment. For comparison of QoE prediction accuracy, we adopt the 10–fold cross validation method and in part WAIC (widely applicable information criterion). We then found that the three–construct models outperform the logistic regression models with respect to all subjective QoE measures and that the two kinds of the models are comparable as for the objective measure. The six–construct model exhibits almost the same accuracy as that of the three-construct one unless the number of QoE measures ( n qoe ) in the model is small. When the number n qoe is small, single–construct models may be a better choice. We have thus learned that multiple QoE measures should be utilized jointly (i.e., borrowing from neighbor) in QoE prediction rather than resorting to QoS information only.

The Role of Halal Awareness on the Relationship between Religiosity and Halal Purchase Intention

This study aims to investigate the effect of religiosity and halal awareness on halal purchase intention and analyze the mediating effect of halal awareness on the relationship between religiosity and halal purchase intention. The cluster sampling method was employed to select the sample size, and the total sample was 400 respondents from three regions of Aceh Province. This study used Structure Equation Modeling (SEM) AMOS to predict a set of indicators of latent variables constructed by several dimensions and indicators, i.e., religiosity was measured by ritual, ideology, intellectual, passion, and consequential dimensions, while halal awareness was determined by five indicators, as well as halal purchase intention. The validity and reliability were tested, and all the latent constructs had a good discriminant validity value. Additionally, the full model after modification was utilized to raise the Goodness of Fit (GOF). The results demonstrated that religiosity positively and significantly affected halal awareness. Similarly, religiosity and halal awareness positively and significantly affected halal purchase intention. It indicates that increasing religiosity and halal awareness enhance interest in buying halal food. It can be concluded that religiosity and awareness are vital indicators in explaining the increasing interest in buying halal products. Moreover, the mediating effect results uncovered a partial mediation of halal awareness on the relationship between religiosity and halal purchase intention. It implies that halal awareness has an essential role in mediating this relationship.

Analyzing the passengers’ satisfaction with the services in long-distance bus stations – The case of Mien Dong station in Ho Chi Minh City, Vietnam

Customer’s satisfaction plays the most important role in shaping continuance usage intention (i.e. loyalty). Therefore, much scientific effort has been invested in exploring the factors contributing to the satisfaction of passengers using the bus, air, intercity rail, and metro services. Notwithstanding, little is known about the determinants of customers’ satisfaction with the services offered by long-distance bus stations. Aiming at filling this research gap, the current study analyzed the passengers’ satisfaction in the case of Mien Dong station in Ho Chi Minh City, Vietnam. The used data from 300 passengers were collected in August 2022 through face-to-face interviews. As regards the analytical methods, the Exploratory Factor Analysis was applied to the responses to the attitudinal items in order to extract underlying constructs, which were then used together with demographical variables to fit an ordinal logit model. The findings show that most demographics were irrelevant, but habit was a significant predictor. Specifically, the higher usage frequency was involved in the lower level of satisfaction. All constructs derived from EFA (Information availability, Customer services, Reliability, Comfort, Safety and Security) were positively associated with the satisfaction level. Among latent constructs, security & safety and comfort were the strongest while information availability was the weakest. Based on the results of influential factors, some practical implications were suggested. Since Ho Chi Minh City is a typical megacity in developing countries, our findings are expected to be useful for other research in settings beyond Vietnamese cities/provinces.

Item response theory approach to ethnocentrism

IntroductionAlthough ethnocentrism is one of the fundamental concepts in the social sciences, its study has been impeded by a diversity of conceptualizations and measures. In recent years, a growing number of political scientists and psychologists have undertaken in-depth research into ethnocentrism. In addition, researchers have recently proposed a comprehensive reconceptualization of ethnocentrism and developed a new Ethnocentrism scale. There is strong evidence for this scale's reliability and validity in indexing ethnocentrism, but like most measures in psychology and political science, this scale is based on classical test theory. Item response theory (IRT) is a powerful psychometric technique that can provide a much more sophisticated test of test performance and is currently under-utilized in research.MethodsWe performed IRT to assess the psychometric properties of the Ethnocentrism scale on a sample of 4,187 participants.ResultsThe scale's items had strong psychometric properties to capture the ethnocentrism latent construct, particularly in the below average to above average range. Men required marginally lower levels of ethnocentrism to endorse less socially acceptable items than women (items relating to superiority, purity, or exploitativeness). When compared to liberals, conservatives responded more readily to nearly all ethnocentrism items. Given this variation, the IRT approach highlighted that future measurements must adjust for differential item functioning, albeit more for political orientation than gender identity.DiscussionThe findings detail how IRT can enhance measurement in political science and demonstrate the implications for how gender and political ideology may affect the differential performance of items.

Influencing Travelers’ Behavior in Thailand Comparing Situations of during and Post COVID-19

Tourism is the primary source of income for many countries, particularly developing ones. However, due to the impact of the 2019 Coronavirus epidemic, the tourism sector has been significantly affected. This study aims to identify factors that influence motivation and travel frequency. Two scenarios were compared: during COVID-19 and post-COVID-19. The questionnaire was developed based on the Health Belief model. The data collection process involved distributing a comprehensive questionnaire throughout Thailand, with the aim of achieving a fair and balanced representation of respondents from six distinct regions: northern, central, eastern, western, north-eastern, and southern. The study included a total of 2100 participants. Twenty hypotheses were formulated to analyze the relationship between the latent constructs. Factor analysis and Structural Equation Modeling were utilized to analyze the data from the questionnaires. The results from SEM found that the model was consistent with the empirical data. The model of the during COVID-19 pandemic had three supported hypotheses, namely H2d, H5d, and H9d, which were the correlation between tourism motivation and perceived severity, self-efficacy, and outcome expectations, respectively. In the post-COVID-19 pandemic period, seven hypotheses, including H1p, H2p, H3p, H4p, H5p, H9p, and H10p, supported the correlation between intention and perceived susceptibility, perceived severity, perceived benefits, perceived barriers, self-efficacy, outcome expectations, and frequency. The support for H10p indicated that stimulated motivation could lead to behavioral changes and increase people’s travel frequency. This study proposes policy recommendations and public relations guidelines to encourage more frequent travel.

Electric Vehicle Adoption Behavior and Vehicle Transaction Decision: Estimating an Integrated Choice Model with Latent Variables on a Retrospective Vehicle Survey

Electric vehicles (EVs) promise a sustainable solution to mitigating negative emission externalities of transportation systems caused by fossil-fueled conventional vehicles (CVs). While recent developments in battery technology and charging infrastructure can help evolve the niche market of EVs into the mass market, EVs are yet to be widely adopted by the public. This calls for an in-depth understanding of public adoption behavior of EVs as one dimension of vehicle decision making, which itself may be intertwined with other vehicle decision-making dimensions, especially vehicle transaction. This study presents an integrated choice model with latent variables (ICLV) to investigate households’—as a decision-making unit—decisions on vehicle transaction type (i.e., no transaction, sell, add, and trade) and vehicle fuel type (i.e., CVs and all EV types, including hybrid EV, plug-in hybrid EV, and battery EV) choice. To analyze the ICLV model empirically, one of the first revealed preferences national vehicle survey involving CVs and all EV types was conducted, which retrospectively inquired about 1,691 American households’ dynamics of vehicle decision making and demographic attributes over a 10-year period as well as their attitudes/preferences. The model estimation results highlight that EV adoption and vehicle transaction choice is influenced mainly by (1) the dynamics of household demographic attributes and (2) four latent constructs explaining attentiveness to vehicle attributes, social influence, environmental consciousness, and technology savviness. Notably, EV adoption promotion policies are found to be likely most effective on socially influenced individuals, who tend to consider advertisement and social trend more when making vehicle decisions.

Teleworking behavior pre-, during, and expected post-COVID: Identification and empirical description of trajectory types

Teleworking is a major lifestyle change whose adoption expanded substantially during the COVID-19 pandemic, and which might permanently change the working habits of some. To understand how much during-COVID teleworking behavior will “stick” in the post-pandemic world, this study explores pre-, during-, and expected post-COVID teleworking frequency trajectories based on during-COVID survey responses, which were weighted to represent the population shares of non–/non-usual/usual teleworkers (TWers). Taking respondents’ self-reported expectations at face value, post-COVID teleworking frequency will be 2.6–3.3 times that of pre-COVID, but we suspect that those expectations are over-optimistic. We developed a typology based on the teleworking trajectories, and identified eight mutually exclusive and collectively exhaustive worker segments: Climbers (13.6%), Plateauers (8.4%), Cappers (12.6%), Stable unables (43.1%), Stable non-choosers (13.5%), Stable non-usual TWers (1.5%), Stable usual TWers (3.2%), and Reducers (4.0%). We conducted three exploratory factor analyses and identified latent attitudinal constructs including 11 general, 7 work-related, and 7 telework-related attitudes. We profiled the eight segments regarding various attitudes and sociodemographic traits to reveal their potential connections to teleworking behavior, and to suggest further explorations with respect to residential location, vehicle ownership, and other travel patterns. Specifically, we see research needs pertaining to residential relocation and household vehicle ownership changes for segments expecting net increases in teleworking frequency between pre- and post-COVID. We also observed heterogeneity of teleworking adoption/frequency choices, attitudes, and travel behaviors among (tele)worker segments. Overall, this study deepens our understanding of post-COVID teleworking behavior changes, and provides a starting point for understanding the heterogeneity among (tele)workers.

Random item slope regression: An alternative measurement model that accounts for both similarities and differences in association with individual items.

In psychology, researchers often predict a dependent variable (DV) consisting of multiple measurements (e.g., scale items measuring a concept). To analyze the data, researchers typically aggregate (sum/average) scores across items and use this as a DV. Alternatively, they may define the DV as a common factor using structural equation modeling. However, both approaches neglect the possibility that an independent variable (IV) may have different relationships to individual items. This variance in individual item slopes arises because items are randomly sampled from an infinite pool of items reflecting the construct that the scale purports to measure. Here, we offer a mixed-effects model called random item slope regression, which accounts for both similarities and differences of individual item associations. Critically, we argue that random item slope regression poses an alternative measurement model to common factor models prevalent in psychology. Unlike these models, the proposed model supposes no latent constructs and instead assumes that individual items have direct causal relationships with the IV. Such operationalization is especially useful when researchers want to assess a broad construct with heterogeneous items. Using mathematical proof and simulation, we demonstrate that random item slopes cause inflation of Type I error when not accounted for, particularly when the sample size (number of participants) is large. In real-world data (n = 564 participants) using commonly used surveys and two reaction time tasks, we demonstrate that random item slopes are present at problematic levels. We further demonstrate that common statistical indices are not sufficient to diagnose the presence of random item slopes. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

The Dimensions of Spirituality Inventory

The Dimensions of Spirituality Inventory (DSI) is a 50-item quantitative assessment of spirituality. Whereas “spirituality” has seemed to some to be too vague for research purposes, the DSI follows earlier qualitative research in showing that usage of the word points to an intelligible conceptual structure. Instead of defining spirituality and then operationalizing it, as most extant instruments do, the DSI defines and operationalizes 21 relatively uncontroversial elemental components of spirituality, so the overall interpretation of spirituality can only emerge after factor analysis. Just as an alphabet flexibly expresses words and sentences, so the 21 DSI dimensions permit the discovery of latent constructs corresponding to cultural packages of spirituality at the present time. The DSI avoids culturally parochial formulations of items, thanks to intense multidisciplinary engagement among social scientists, scholars of religion, and psychologists of religion, and thus is suitable for cross-cultural application. This paper reports on a study based on a participant pool recruited for ExploringMyReligion.org , in part from Prolific (N = 820, after culling unreliable responses). The DSI is interpreted and validated in relation to several other relevant measures, and the factor analysis is strongly resonant with prior qualitative studies. The psychometric properties of the DSI make it usable as an overall spirituality scale as well as a sensitive inventory of types and configurations of spirituality.

Understanding Blame in the Context of Childhood Obesity

ABSTRACT This paper explicates blame as a psychological construct in communication processes, with a focus on its underlying structure and its mediating role between message characteristics and the public’s issue engagement. Data were collected from Amazon Mechanical Turk (N = 373) via a Web-based experiment, where we manipulated a news story about a child suffering an obesity-related health risk (asthma attack vs. heart attack) with different levels of preventability (high vs. low) and severity (high vs. low). Findings showed that blame should best be conceptualized and operationalized as a latent construct comprising both cognitive and affective components. Blame mediated message effects on social responses related to obesity prevention, including punitive attitudes, policy support, and intentions toward interpersonal communication and civic participatory behaviors.