Multiple Indicators Multiple Causes Model Research Articles

Validation of the Japanese version of the Patient Health Questionnaire-4 (PHQ-4-J) to screen for depression and anxiety

ObjectiveWe aimed to validate the Japanese version of the Patient Health Questionnaire-4 (PHQ-4-J). People in Japan, especially healthcare workers (HCWs) suffer from high rates of mental health symptoms. The PHQ-4 is an established ultra-brief mental health measure used in various settings, populations and languages. The Japanese version of the PHQ-4 has not been validated.MethodsTwo hundred eighty people in Japan (142 HCWs and 138 from the general public) responded to the PHQ-4-J. Internal consistency, and factorial validity were assessed using confirmatory factor analysis (CFA) and Multiple Indicators Multiple Causes (MIMIC) models.ResultsInternal consistency was high (α = 0.70–0.86). CFA yielded very good fit indices for a two-factor solution (RMSEA = 0.04, 95% CI 0.00–0.17) and MIMIC models indicated the performance differed between HCWs and the general population.ConclusionsThe PHQ-4-J is a reliable ultra-brief scale for depression and anxiety in Japanese, which can be used to meet current needs in mental health research and practice in Japan. Disaster research and gerontology research can benefit from this scale, enabling mental health assessment with little participant burden. In practice, early detection and personalised care can be facilitated by using the scale. Future research should target specific populations in Japan during a non-emergency time.

Genetic Risk for Alcohol Use Disorder in Relation to Individual Symptom Criteria: Do Polygenic Indices Provide Unique Information for Understanding Severity and Heterogeneity?

Alcohol Use Disorder (AUD) is a heterogenous category with many unique configurations of symptoms. Previous investigations of AUD heterogeneity using molecular genetics methods studied the association between genetic liability and individual AUD symptoms at the latent level or focusing on a small number of genetic variants. Notably, these studies did not investigate potential severity differences between symptoms in their genetic analyses. Therefore, the current study aimed to examine the genetic risk for individual AUD symptom criteria by using a polygenic risk score (PRS) approach to assess the relative severity of each AUD symptom and test for associates with AUD symptoms above and beyond a unidimensional AUD construct. An AUD PRS was created using summary statistics obtained from published genome-wide association studies (GWAS), and Multiple Indicators Multiple Causes (MIMIC) models were employed to examine the effect of the PRS on overall AUD severity as well as on individual symptoms after accounting for this overall effect. The phenotypic severity of AUD symptoms was highly correlated with the genetic severity of AUD symptoms (r = 0.78). Results of MIMIC models indicated that the AUD PRS significantly predicted the AUD factor. Regression paths testing the unique, direct effects of the PRS on individual AUD symptoms, independent of the latent AUD factor, were not significant. These results imply that PRSs derived from GWAS of AUD influence symptom expression through a single genetic factor that is highly correlated with the relative severity of individual symptoms when measured at the phenotypic level. Item-level GWAS of AUD symptoms are needed to further parse heterogeneous symptom expression and allow for more nuanced tests of these conclusions.

Can the digital economy development limit the size of the informal economy? A nonlinear analysis based on China's provincial panel data

This paper investigates whether and how digital economy (especially digital platforms and digital finance) restrains the informal economic activities in China measured by the MIMIC (multiple indicators multiple causes) model. Using Chinese provincial panel data from 1995 to 2020, we find that the average size of China's provincial informal economy displays a five-stage fluctuation, ranging from 13.63 % to 17.53 %. More importantly, we uncover a robust “inverted U-shaped” nonlinear relationship between the digital platform development and the informal economy, with a turning point of 0.4189. Initially, the development of digital platforms increases informal economic activities by disrupting traditional sectors, causing new types of tax evasion, challenging regulators and raising digital crime. However, beyond a certain threshold, digital platforms can overcome these problems through more job creations and improved regulation. Meanwhile, the digital financial development is proved to have a negative impact on the informal economy because it can offer much more advantages in addressing informality such as making payments transparent, easing credit constraints, raising total income, helping governments reach people and businesses. Our study provides convincing evidence and valuable advice for policymakers when developing digital economy to curb the informal economy.

A Bayesian Moderated Nonlinear Factor Analysis Approach for DIF Detection under Violation of the Equal Variance Assumption

AbstractResearch has shown that multiple‐indicator multiple‐cause (MIMIC) models can result in inflated Type I error rates in detecting differential item functioning (DIF) when the assumption of equal latent variance is violated. This study explains how the violation of the equal variance assumption adversely impacts the detection of nonuniform DIF and how it can be addressed through moderated nonlinear factor analysis (MNLFA) model via Bayesian estimation approach to overcome limitations from the restrictive assumption. The Bayesian MNLFA approach suggested in this study better control Type I errors by freely estimating latent factor variances across different groups. Our experimentation with simulated data demonstrates that the BMNFA models outperform the existing MIMIC models, in terms of Type I error control as well as parameter recovery. The results suggest that the MNLFA models have the potential to be a superior choice to the existing MIMIC models, especially in situations where the assumption of equal latent variance assumption is not likely to hold.

Exploring Factors Affecting People’s Acceptance of Connected Vehicle Technology in China

Connected vehicles (CVs) leverage the vehicle‐to‐everything (V2X) function to interact with various mobility systems. In China, regulations mandate that CVs must possess automatic recording capabilities as stipulated by the “Management Specification for Road Tests of Intelligent CVs.” Yet, public perception of these functionalities and their impact on the acceptance of V2X technology remains unclear. This study explores the acceptance of V2X in China by augmenting the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. Utilizing a survey of 567 Chinese drivers, we employed structural equation modeling (SEM) and Multiple Indicators Multiple Causes (MIMIC) analysis to dissect the factors influencing the behavioral intention (BI) to use V2X. Our findings reveal that social influence (SI), facilitating conditions (FC), and effort expectancy (EE) significantly predict the intention to adopt V2X. Interestingly, while trust (T) does not exert a direct influence, its overall effect on BI surpasses those of the previously mentioned factors. Moreover, the MIMIC models highlight that individuals’ understanding of V2X significantly shapes their acceptance attitudes. These insights underscore the importance of enhancing T, particularly in the data security aspects of V2X, to bolster its acceptance in China. By addressing these concerns, stakeholders can pave the way for wider adoption of this pivotal technology.

Uncovering Differential Item Functioning effects using MIMIC and mediated MIMIC models.

The aim of this study was twofold: first, to examine the presence of bias across gender in a scholastic achievement test named the Academic Achievement Test (AAT) for the Science Track. Second, to understand the underlying mechanism that causes these bias effects by examining the effect of general cognitive ability as a mediator. The sample consisted of 1,300 Saudi high school students randomly selected from a larger pool of 173,133 participants to reduce the effects of excessive power. To examine both goals, the Multiple Indicators Multiple Causes (MIMIC) approach for detecting Differential Item Functioning (DIF) items was used. The results showed that 13 AAT items exhibited DIF effects for different gender groups. In most of these items, male participants were more likely to answer them correctly than their female counterparts. Next, the mediated MIMIC approach was applied to explore possible underlying mechanisms that explain these DIF effects. The results from this study showed that general cognitive ability (i.e., General Aptitude Test - GAT) seems to be a factor that could explain why an AAT item exhibits DIF across gender. It was found that GAT scores fully explain the DIF effect in two AAT items (full mediation). In most other cases, GAT helps account for only a proportion of the DIF effect (partial mediation). The results from this study will help experts improve the quality of their instruments by identifying DIF items and deciding how to revise them, considering the mediator's effect on participants' responses.

Validation and measurement invariance of a Korean version of the Body Appreciation Scale-2 in cisgender sexual minorities and heterosexual adults

The Body Appreciation Scale-2 (BAS-2) has been validated in various languages across samples with different characteristics. However, its validation and invariance based on sexual orientation in Korean samples have yet to be examined. The current study validated the psychometric properties of the BAS-2 in Korean sexual minorities, and examined measurement invariance between sexual minority and heterosexual individuals and between sexual minority cisgender women and men. Responses from 449 sexual minority and 400 heterosexual individuals were analyzed. In the sexual minority sample, a confirmatory factor analysis supported the scale’s unidimensional structure, exhibiting strong factorial validity; further, a correlation analysis revealed positive correlations with body satisfaction, positive sexual minority identity, life satisfaction, and self-esteem, supporting the convergent and criterion-related validity. A hierarchical regression analysis confirmed the incremental validity of the BAS-2 on life satisfaction and self-esteem. The BAS-2 was invariant across women and men at the scalar level in the sexual minority sample, and across sexual minorities and heterosexuals at the partial scalar level in the total sample. Latent mean comparisons indicated no significant differences in BAS-2 scores between sexual minority women and men, while sexual minorities scored significantly lower than heterosexuals. The multiple-indicators multiple-causes model provided evidence of differential item functioning.

Bayesian regularization in multiple-indicators multiple-causes models.

Integrating regularization methods into structural equation modeling is gaining increasing popularity. The purpose of regularization is to improve variable selection, model estimation, and prediction accuracy. In this study, we aim to: (a) compare Bayesian regularization methods for exploring covariate effects in multiple-indicators multiple-causes models, (b) examine the sensitivity of results to hyperparameter settings of penalty priors, and (c) investigate prediction accuracy through cross-validation. The Bayesian regularization methods examined included: ridge, lasso, adaptive lasso, spike-and-slab prior (SSP) and its variants, and horseshoe and its variants. Sparse solutions were developed for the structural coefficient matrix that contained only a small portion of nonzero path coefficients characterizing the effects of selected covariates on the latent variable. Results from the simulation study showed that compared to diffuse priors, penalty priors were advantageous in handling small sample sizes and collinearity among covariates. Priors with only the global penalty (ridge and lasso) yielded higher model convergence rates and power, whereas priors with both the global and local penalties (horseshoe and SSP) provided more accurate parameter estimates for medium and large covariate effects. The horseshoe and SSP improved accuracy in predicting factor scores, while achieving more parsimonious models. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Supplemental Material for Bayesian Regularization in Multiple-Indicators Multiple-Causes Models

Supplemental Material for Bayesian Regularization in Multiple-Indicators Multiple-Causes Models

Investigation of the Factor Structure and Differential Item Functioning of the Child and Adolescent Mindfulness Measure (CAMM): Analysis of Data from a School-Based Cluster Randomised Controlled Trial

ObjectivesThis study used data from a randomised controlled trial of a school-based mindfulness programme in the UK to investigate the structure and performance of the 10-item Child and Adolescent Mindfulness Measure (CAMM).MethodThe study included 7924 children and adolescents aged 11 to 14 years. Participants provided CAMM data at pre-intervention, 7 months (post-intervention) and 1 year. Exploratory factor analysis (EFA) of pre-intervention data was undertaken. Multiple indicators multiple causes (MIMIC) models were fitted to pre-intervention responses to investigate differential item functioning across groups defined by gender, year group and ethnicity. Response shift resulting from receiving the mindfulness programme was investigated by fitting MIMIC models to compare item functioning between the intervention and control arms.ResultsEFA results indicated that the 2-factor model was a good fit. Eight items were associated with the first factor, while the remaining two items, which specifically addressed avoiding unwanted thoughts and feelings, were associated with the second factor. MIMIC model findings indicated that girls scored lower (ostensibly less mindful) on 4 items than boys that had the same latent level of mindfulness; as a result of receiving the mindfulness programme, participants scored lower on one item (“At school, I walk from class to class without noticing what I’m doing”) after holding latent level of mindfulness constant.ConclusionsFindings indicate that the CAMM has a 2-factor structure in the UK in late childhood and early adolescence. While we did observe some differences in how individual items performed across groups, these differences were small compared to the overall variability in the CAMM scores.PreregistrationCurrent controlled trials ISRCTN86619085.

Shifting Mobility Behaviors in Unprecedented Times: A Multigroup MIMIC Model Investigating Intentions to Use On-Demand Ride Services During the COVID-19 Pandemic.

The spread of COVID-19 has been a major disruptive force in people's everyday lives and mobility behavior. The demand for on-demand ride services, such as taxis and ridehailing, has been specifically affected given both restrictions in service operations and users' concerns about virus transmission in shared vehicles. In the early months of the pandemic, demand for these modes decreased by as much as 80%. This study examines intentions to use on-demand ride services in the early lockdown stage of the pandemic in the United States, a period of unprecedented mobility reductions, changing household routines and transforming travel behaviors. Using data from a survey disseminated in June 2020 to 700 U.S. respondents, we use multigroup MIMIC (Multiple Indicator Multiple Cause) models to investigate the stated shift in intentions to use on-demand modes of travel. By using group-based segmentation we control for variation in ridership intentions according to personal, household, attitudinal factors, and pandemic experiences. The results point to a reduction across the board in the likelihood of using on-demand mobility associated with a significant COVID-19 effect. Beyond this general decrease, several groups are found to have more positive intentions, including younger adults, urban residents, graduate-degree holders, and people of Hispanic, Latino, Asian, and Pacific Islander ethnicities/races. The attitudinal effect of "tech-savviness" drives higher user intentions, revealing indirect effects of gender, education, and age. Multigroup analysis provides further evidence of potential COVID-triggered shifts in on-demand ridership intentions. The most significant drops in likelihood are observed for younger respondents (below 45), Black compared with all other racial/ethnic status, and for past users of on-demand mobility. This latter result is somewhat surprising, as riders who are younger and more experienced with on-demand travel are more likely to have been users in the past, but also more likely to reduce use during the pandemic. To conclude, we discuss the need to investigate pandemic experiences, risk attitudes, and circumstances to understand evolving mobility behavior and specific service model impacts.

Measuring a cross-sectional sample of students’ intentions to engage with science and modelling associations according to two theoretical perspectives

ABSTRACT Social cognitive career theory (SCCT) and the theories of reasoned action and planned behaviour (TRAPB) have both guided studies seeking to explain students’ intentions and decisions related to future science engagement. The purpose of this study was to simultaneously validate measures for these two social psychological models and closely examine these constructs and their associations. Students were sampled in two groups, across grades 5 through 10, from rural schools in the United States (N = 538). Each group was administered two sets of measures, and each set of measures was associated with a theoretical model, and included corresponding sub-scales suitable for self-report. Respondents also supplied information about a common set of predictor variables based on a review of the literature. Structural equation modelling was used to examine the constructs more closely. Results from confirmatory factor analysis were generally consistent with the theoretical structures. Multiple indicators multiple causes (MIMIC) models were generated to compare SCCT and TRAPB, including associations between factors and variables. Associations between common predictor variables like race and gender differed between the MIMIC models. Frequency of talk with family members, perceived science ability, gender, and race were all significantly associated with multiple factors in both the SCCT- and TRAPB-aligned models.

Is digital transformation equally attractive to all manufacturers? Contextualizing the operational and customer benefits of smart manufacturing

PurposeThe implementation of Smart Manufacturing (SM) is deemed a key enabler in the enhancement of manufacturing competitiveness and performance. Nevertheless, SM's repercussion on consumer perceptions and the contextualization of SM's performance-enhancement effects remain undetermined and have yet to be clarified. This study analyzes the effect of SM on operational and customer performance. Moreover, this study explores how these relationships change depending on a firm's geography of production (i.e. national/local vs transnational operations) and the relational arrangement adopted (i.e. service-oriented vs transaction-oriented manufacturers).Design/methodology/approachThis research surveys 351 Spanish manufacturing firms operating in an SM environment. The theoretical framework comprises a Multiple-Indicators Multiple-Causes (MIMIC) model and is tested using a Generalized Structural Equations Model.FindingsThe results obtained substantiate the positive effect of SM implementation on both of the performance measures analyzed (i.e. operational and customer focused). Moreover, the study reveals that while geography of production moderates the effect on a firm's operational performance, relational arrangement also does so in terms of customer performance.Originality/valueThis research clearly differentiates the benefits of SM depending on business context. In this regard, transnational production firms tend to gain in operational performance while service-oriented manufacturers gain in customer performance.

The Positive Aspects of Caregiving Scale can be shortened to six items: application of item response theory

Objective For nearly two decades, with one exception, researchers have used the Positive Aspects of Caregiving Scale (PACS) without a close examination of how the items behave. This study examined PACS’ measurement model and item response characteristics, including differential item functioning (DIF) by race. Methods Item response theory was used to analyze the baseline data of the Resources for Enhancing Alzheimer’s Caregiver Health (REACH) I trial (N = 1229). Bifactor confirmatory models were estimated to assess dimensionality, and whether the items behaved differently in African Americans and Hispanics, with Whites serving as the comparison, were examined using the multiple-indicators multiple-causes model. Results For both 9- and 11-item versions, 1-factor and bifactor solutions supported a unidimensional structure. However, the items performed generally poorly in differentiating levels of the latent construct, especially between moderate and high true scores. Additionally, five items were found to have DIF, which were unrelated to relationship differences among racial groups. While African Americans and Hispanics reported higher positive gains than Whites, their differences were accentuated by the DIF items as a whole. A 6-item version called PACS-II was created by removing the DIF items. Besides reducing estimated racial differences, PACS-II removed race-based heterogeneity of variance that was evidenced in the original, longer versions. Conclusions Being free of racial bias, PACS-II is preferred over the original versions, though the ceiling effect of measurement (prevalence of extreme scores on the high end) remains to be an issue for improvement.

The effects of CNS atrophy and ICVD on tests of executive function and functional status are mediated by intelligence

BackgroundImpairments in executive function (EF) are often attributed to ischemic cerebrovascular disease (ICVD) and frontal circuit pathology. However, EF can be distinguished from general intelligence and the latter is likely to manifest in “executive” measures. We aimed to distinguish the effects of imaging biomarkers on these constructs. MethodsWe tested neuroimaging biomarkers as independent predictors of observed 12 month-prospective cognitive performance by a Multiple Indicators Multiple Causes (MIMIC) model in the Alzheimer's Disease Neuroimaging Initiative (ADNI) (N ≅ 1750). ResultsICVD was associated with ‘’Organization” (ORG) and “Planning” (PLAN) domain scores from the test of Every Day Cognition. Left anterior cingulate (LAC) atrophy was independently associated with Trail-Making part B and Animal Naming. The MIMIC model had excellent fit and tests additional latent variables i.e., EF and dEF (a latent δ homolog derived from Spearman's general intelligence factor, g). Only dEF was associated with instrumental activities of daily living (IADL). ICVD and LAC were both associated with observed executive measures through dEF. ICVD was independently associated with those same measures through EF. ConclusionsObserved EF is independently determined by multiple factors. The effects of EF-associated MRI biomarkers can be related to disability and dementia only via their effects on g. Because g /δ are unlikely to be located within the frontal lobes, the dementia-specific variance in executive measures may have little to do with either frontal structure or function. Conversely, domain-specific variance in EF may have little to do with either IADL-impairment or dementia.

Measuring the Level of the Youth Informal Economy in Lithuania in 2004–2020

This paper investigates the development of the youth informal economy in Lithuania in 2004–2020. Specific youth-tailored multiple indicators multiple causes (MIMIC) model has been derived in order to estimate the level of the youth informal economy. In total, 173 direct phone interviews with company managers were conducted in order to acquire information for the MIMIC model calibration. It was revealed that during the investigated period, the youth informal economy fluctuated between 38.7% and 46.1% and was significantly higher compared with the overall informal economy in Lithuania. Although showing a strong correlation level (0.742) the overall informal economy within the country and the youth informal economy show slightly different developmental paths, the latter being more prone to higher increases during a crisis period.

Synergies vs. Clustering Only of Depressive Symptoms in Diabetes and Co-Occurring Conditions: Symmetric Indicators with Asymmetric, Bidirectional Influences in MIMIC Models

I extend multiple indicators multiple causes (MIMIC) models to unveil unbiased, asymmetric, bidirectional influences using indicators of the same items within variable-defined subgroups. The strategy discerns (1) item-variation in interaction (and derivative) terms that capture synergies and cluster together (formative or causal indicators) from (2) item-variation in duplicate terms when items lack synergy and cluster together only (reflective or effect indicators). An item may reveal either or both influences. These symmetric indicators yield estimates of (1) the unique variation and synergy of each formative indicator within the structural model portion of the MIMIC model (based on moderated regression) and (2) the remaining shared variation in the reflective indicator within the measurement model portion (based on confirmatory factor analysis). I reveal two patterns of comorbidity in disease subgroups of a specific co-occurring condition across a community sample of older adults and in age and gender subsamples. First, as structural model indicators, depressive symptoms may display different synergies as they cluster within a disease subgroup of diabetes and a specific co-occurring condition. As measurement model indicators, depressive symptoms capture non-synergistic clustering within the disease subgroup. Second, diabetes may mediate the co-occurring condition when depressive symptoms lack synergies but cluster within the disease subgroup. Researchers should distinguish both comorbidity patterns, which have different implications. I offer insights for adaptive modeling, conceptualizing and screening symptom clusters, metabolomics, and economic or social monitoring.

A differential item functioning (DIF) analysis of the mobile phone problem use scale in Indonesian schools with and without smartphone banned policy

This study aimed to validate an Indonesian version of the Mobile Phone Problem Use Scale (MPPUS) in a sample of high school students from South Tangerang City, Banten Province,, Indonesia. Throughout this process, we evaluated the scale’s psychometric properties and examined the differential item functioning (DIF) across schools with and without a smartphone ban policy. The study used a cross-sectional survey design with a sample of 622 high school students (Mage = 16.10; SDage = 1.41). There were 312 respondents from schools with a smartphone ban and 310 from schools with no smartphone ban. We used confirmatory factor analysis (CFA), multiple indicators multiple causes (MIMIC) model, and Rasch Rating Scale Model (RSM) to analyze the data. The CFA and MIMIC model results indicated a good fit for the overall model using the pooled sample. Based on MIMIC model, one item was flagged as DIF. Furthermore, RSM analysis demonstrated that the Indonesian MPPUS has adequate psychometric properties. RSM-based DIF analysis identified four DIF items. Therefore, 23 of 27 items on the scale have the same meaning across schools with two different policies. Our findings provide empirical evidence that MPPUS performed well across students from schools with and without smartphone restrictions, despite the fact that the MPPUS was originally designed for the general population. The findings of this study can be used to develop an evidence-based policy for implementing smartphone policies in Indonesian schools.

Psychometric property and measurement invariance of internet addiction test: the effect of socio-demographic and internet use variables

BackgroundAccording to the validation literature on items of Young’s Internet Addiction Test (IAT), this study rephrased disputable items to improve the psychometric properties of this Chinese version of IAT and identify the presence of differential item function (DIF) among demographic and Internet use factors; detect the effect of demographic and Internet use factors on IAT after adjusting for DIF.MethodsAn online questionnaire was distributed to college students in Zhe Jiang province in two stage. The 1st phase study collected 384 valid responses to examine the quality of IAT items by using Rasch Model analysis and exploring factor analysis (EFA). The online questionnaire was modified according to the 1st phase study and distributed online for the 2nd phase study which collected a total of 1131 valid responses. The 2nd phase study applied confirmatory factor analysis (CFA) and a multiple indicator multiple causes (MIMIC) model to verify the construct of IAT, potential effect of covariates on IAT latent factors, as well as the effect of differential item functioning (DIF).ResultsRasch model analysis in the 1st phase study indicated a 5-point rating scale was performed better, no sever misfit was found on item. The overall property of Chinese version IAT with the 5-point scale was good to excellent person and item separation (2.66 and 6.86). A three-factor model was identified by EFA. In the 2nd phase study, IAT 13 were detected with DIF for gender in MIMIC model. After correcting DIF effect, the significant demographic and Internet use factors on IAT were time spent online per day, year 3, year 2, general users.ConclusionItem improvement was efficient that the problematic items found in literature was performed good in this study. The overall psychometric property of this Chinese version IAT was good with limited DIF effect in one item. Item improvement on IAT13 was encouraged in the future study to avoid gender bias and benefit for epidemiology on PIU.

The Quality of Life for Children with Autism Spectrum Disorder Scale: Factor Analysis, MIMIC Modeling, and Cut-Off Score Analysis.

Our purpose in this study was to further examine the psychometric properties of the Quality of Life for Children with Autism Spectrum Disorder (QOLASD-C) scale. We first investigated the factor structure and the internal consistency of the scale. The bifactor model showed good fit and strong reliability. Second, we used multiple-indicators multiple-causes (MIMIC) modeling to examine the associations between demographic variables and the QOLASD-C dimensions. Results showed differences on overall QOL based on age, race/ethnicity, and autism spectrum disorder severity, but no relationships with gender. All demographic variables were associated with one or all three subscales (i.e., interpersonal relationships, self-determination, emotional well-being) of the QOLASD-C. Third, an optimal cut-off score of 37 was identified. Implications for research and practice are discussed.