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Well-being enhancement is an essential goal of urban transportation. As an emerging and popular mode of urban transportation, the impact of the ride-hailing service on people’s well-being has not been well examined, especially in developing countries. In order to study the influencing factors of ride-hailing passenger satisfaction and the relationship between passenger satisfaction and subjective well-being, a conceptual framework of the relationships between ride-hailing passengers’ characteristics, the service quality of ride-hailing (service perception, operation service, external influence, and safety perception), passenger satisfaction, and life satisfaction is developed and verified with data collected in Dalian city, China. A comparative analysis between express and hitch in the ride-hailing service is conducted by a multiple indicators multiple causes model. The result shows that service perception, safety perception, external influence, and operation service have significantly positive effects on passenger satisfaction in both express and hitch, but they play diverse roles. Passenger satisfaction in express and hitch positively and differently affects their respective life satisfaction with the consideration of individual heterogeneity in terms of socio-economic characteristics. These findings complement the interaction mechanism of service quality, passenger satisfaction, and life satisfaction in the field of ride-hailing; they provide critical insights for ride-hailing platforms and policymakers to satisfy the diversified travel needs and the well-being improvement of the public.

We examined differential item functioning (DIF) of the Center for Epidemiologic Studies Depression Scale (CES-D) items by country and statistically harmonized common cross-national factor scores for the CES-D to aid further cross-national research. Data were from Harmonized Cognitive Assessment Protocol (HCAP) studies in China (N = 9639), England (N = 1262), India (N = 4048), Mexico (N = 1918), South Africa (N = 631), and the United States (N = 3321). Multiple indicators, multiple causes models were estimated to test DIF in the CES-D items by country. DIF items were defined as having an odds ratio (OR) outside the range of 0.75–1.25 in multiple indicators, multiple causes models. We evaluated DIF impact and identified salient DIF by examining whether the difference between DIF-adjusted factor scores and non-DIF–adjusted factor scores exceeded a threshold of 0.30 standard deviation (SD) units. Confirmatory factor analysis was used to create DIF-adjusted, cross-nationally harmonized CES-D factor scores. Controlling for underlying depressive symptoms, HCAP participants in India had higher odds of reporting being not hopeful about future (OR = 1.38, 95% confidence interval [CI]: 1.34–1.42), not enjoying life (OR = 1.43, 95% CI: 1.38–1.48), and being unhappy (OR = 1.29, 95% CI: 1.25–1.34), compared to HCAP participants in the United States. These identified DIF items artificially increased mean harmonized CES-D factor scores by 0.48 SD units in the India HCAP, with over 50% of the factor scores increased by over 0.30 SD units, indicating salient DIF in the India HCAP. Our findings demonstrate cross-national heterogeneity in the expression of depressive symptoms. We provide DIF-adjusted CES-D factor scores to improve the quality of cross-national comparisons in aging research. •Inconsistent CES-D items challenge cross-national comparisons of depression.•We detected items with salient DIF in LASI-DAD, relative to other HCAP studies.•Items with DIF increased mean CES-D factor scores by 0.48 SD units in LASI-DAD.•DIF items in LASI-DAD should be adjusted for further cross-national comparisons.

The objective of the study was to determine the size of Ghana's \"underground economy\" and the extent of tax evasion in Ghana. The underground economy in most countries is vital because it serves as a survival place for most people. However, their activities are mostly related to tax evasion because their economic activities are mostly concealed from government tax authority agencies. The study used the Multiple Indicator Multiple Cause (MIMIC) model to estimate the size of Ghana's \"underground economy\". The data was obtained from the World Bank country indicators, Economic Freedom and Bank of Ghana and its spans from 1990 to 2020. The study is one of the premier to estimate the size of Ghana's \"underground economy\" using the MIMIC model. The study found that the average size of Ghana's underground economy is about 44% of the official GDP of the economy and is primarily caused by tax burden, government integrity, unemployment, government spending, self-employment, inflation and the agricultural sector employment. The estimated tax evasion due to the presence of the \"underground economy\" is, on average, about 6.28% of GDP. Other findings from the study were that, while tax evasion negatively affects economic growth, the underground economy's size positively affects economic growth in Ghana. We recommend that since the underground economy, to some extent, provides job security to some individuals within the country, their activities must be formalized by ensuring proper documentation and registration. Furthermore, the government should improve the ways of detecting tax evasion through intensive tax audit.

Screening for depression can be challenging in Multiple Sclerosis (MS) patients due to the overlap of depressive symptoms with other symptoms, such as fatigue, cognitive impairment and functional impairment, for MS patients. The aim of this study was to understand these overlapping symptoms and subsequently develop an adjusted depression screening tool for better clinical assessment of depressive symptoms in MS patients. We evaluated 3,507 MS patients with a self-reported depression screening (PHQ-9) score using a multiple indicator multiple cause modeling approach. Our models showed significant differential item functioning effects denoting significant overlap of depressive symptoms with all MS symptoms under study and good model fit. The magnitude of the overlap was especially large for fatigue. Adjusted depression screening scales were formed based on factor scores and loadings that will allow clinicians to understand the depressive symptoms separate from other symptoms for MS patients for improved patient care.

Most of the common models to examine depression are one-factor models; however, previous studies provided several-factor structure models on each depressive symptom using the Patient Health Questionnaire-9 (PHQ-9). The Nine-Questions Depression-Rating Scale (9Q) is an alternative assessment tool that was developed for assessing the severity of depressive symptoms in Thai adults. This study aimed to examine the factor structure of this tool based on the factor structure models for the PHQ-9 provided in previous studies using confirmatory factor analysis (CFA). We also examined the association of chronic diseases and depressive symptoms using the Multiple Indicators Multiple Causes model among 1346 participants aged 19 years old or more without psychiatric disorders. The results show that the two-factor CFA model with six items in the cognitive-affective domain and three items in the somatic domain provided the best fit for depressive symptoms in the study population (RMSEA = 0.077, CFI = 0.953, TLI = 0.936). Dyslipidemia was positively associated with both cognitive-affective symptoms (β = 0.120) and somatic depressive symptoms (β = 0.080). Allergies were associated with a higher level of cognitive-affective depressive symptoms (β = 0.087), while migraine (β = 0.114) and peptic ulcer disease (β = 0.062) were associated with a higher level of somatic symptoms. Increased age was associated with a lower level of somatic symptoms (β = −0.088). Our findings suggested that considering depressive symptoms as two dimensions yields a better fit for depressive symptoms. The co-occurrence of chronic diseases associated with depressive symptoms should be monitored.

Learner satisfaction is an important element in online learning because it impacts achievement motivation, success, and retention. It was the purpose of this study to develop an instrument that measures the satisfaction of learners in online learning environments. Data were collected from 335 students at higher education institutions. The Online Learner Satisfaction Instrument (OLSI) includes 46 Likert-type scale items and four subscales: (a) learner, (b) instructor, (c) course, (d) program and organization. It also has one item measuring overall learner satisfaction. Three models were evaluated and a modified bi-factor model was selected as a final model because it aligned with the factor structure of the OLSI. All items on the OLSI revealed means greater than 3.0 (on a scale ranging from 1-very dissatisfied to 4-very satisfied). General course satisfaction and the comfort level with online courses were a significant predictor of the latent constructs of the OLSI.

Abstract Red-light running (RLR) is a crucial violation that causes traffic accidents and injuries. Understanding factors that affect RLR is very significant to reduce the potential of this violation. Current studies have paid considerable attention to the observable factors, but not to unobservable factors. This study aims to examine the effects of observable and unobservable factors on RLR. This study uses a latent class model (LCM) to assign individuals into two classes—red-light-respectful and red-light-disrespectful road users—by surveying 751 respondents who use private transportation modes. This study incorporates psychological determinants into the LCM to account for unobservable factors. The contribution of this study is the in-depth investigation into law-respectful and law-disrespectful behaviours and intentional and unintentional violators. Such a study has not yet been conducted in the existing literature. In addition, a comprehensive comparison of the LCM and a traditional ordered probit model was conducted. Overall, the results suggest that the LCM is superior to the model that does not consider latent classes. Our estimation results are in alignment with previous studies on RLR: males, younger drivers/riders, less educated road users and motorcyclists are more likely to run red lights. An analysis of the latent variables shows that surrounding conditions—the behaviour of other violators, the absence of traffic police, and long waiting times—increase the possibility of violations. Based on these results, we provide suggestions to policymakers and traffic engineers: the implementation of enforcement cameras and penalties for violators are critical countermeasures to minimize the potential of RLR.

We adopt a long-run perspective to investigate the size of the shadow economy and explore the trends in this area. The analysis is based on a panel of peripheral EMU countries over the period 1965-2015. Our empirical approach relies on a multiple indicators and multiple causes (MIMIC) framework. This approach is a variant of structural equation modelling (SEM). We used two sets of variables, (i.e. potential determinants and indicator variables) to estimate an underlying (unobserved) index that measures the evolution of the shadow economy. Ascertaining the relative importance of the shadow economy enabled analysis of its relationship with other institutional and social issues (e.g. corruption, productivity and economic growth), and helped identify the channels through which the shadow economy might negatively influence the performance of different economies. In the sampled countries, shadow activity increased over the study period. It also seemed to be affected by the economic cycle.

In the current times, knowledge work and knowledge worker play an important role in organizational development. The purpose of this paper is to examine the associations between perceptions of the work environment and job burnout among 679 knowledge workers with a publicly available data. Based on the exploratory factor analysis, five multiple indicators multiple causes (MIMIC) models are acceptable and confirm, including socioeconomic factors→perceptions of the work environment model, socioeconomic factors→job burnout model, perceptions of the work environment←socioeconomic factors→job burnout model, perceptions of the work environment→job burnout model, and job burnout→perceptions of the work environment model. The results from MIMIC models indicated job burnout has significant associations with perceptions of the work environment. The implications of these results for well-beings among the knowledge workers are discussed.