Commentary: Causal Effects in Mediation Modeling: An Introduction with Applications to Latent Variables

BMI mediates the association of family medical history with self-reported hypertension and diabetes among older adults: Evidence from baseline wave of the longitudinal aging study in India

Background

This study explored the association between family history of hypertension and diabetes with their diagnosis among older Indian adults. The study further examined the role of body mass index (BMI) as a potential mediator in these associations.

Methods

Data from the Longitudinal Ageing Study in India (LASI, 2017-18), wave-1 were used. The sample for the study included 31,464 older adults aged 60 years and above. Descriptive statistics and bivariate analysis has been conducted to assess the prevalence of self-reported hypertension and diabetes. Further, multivariable logistic regression models were used to test the research hypotheses of this study. The Karlson-Holm-Breen (KHB) mediation analysis was conducted to recover the direct and indirect effects of BMI in the association of family medical history and diagnosis of hypertension and diabetes.

Results

A proportion of 32.70% of older adults were diagnosed with hypertension and 14.23% of older adults were diagnosed with diabetes. A proportion of 19.48% and 14.69% of older adults had a family history of hypertension and diabetes, respectively. Also, 16.57% and 5.53% of older adults were overweight and obese, respectively in the current study. Older adults who had family history of hypertension had higher odds of being diagnosed with hypertension [aOR: 2.23, CI: 2.07-2.39] than those who had no such family history. This association was mediated by BMI (percent mediated: 6.31%). Similarly, older adults who had family history of diabetes had higher odds of being diagnosed with diabetes [aOR: 2.63, CI: 2.41-2.88] than those who had no such family history. This association was mediated by BMI (percent effect mediated: 6.66%).

Conclusion

The study highlights the relevance of using family medical history data along with information on BMI as potential source for the control and management of hypertension and diabetes among older population.

Finding the key to the black box of board diversity and firm performance: A mediating effect analysis of technological innovation

A growing body of research has focused on the relationship between board diversity and firm performance. A series of empirical literatures have also examined a significant positive correlation between the two. But these results only demonstrate the relationship between the input of ‘diversity’ and the output of ‘firm performance’. Such research is more of a black box because board diversity must act on certain strategies or decisions to affect firm performance. Some scholars try theoretical analysis with the purpose of opening the black box. In order to verify the relevant theoretical analysis results, this study uses the mediating effect analysis model in the field of psychology, through multiple regression, impulse analysis, variance decomposition and other methods, to thus empirically test the mediating effect of technological innovation in the process of board diversification promoting corporate performance. We found that board diversity can improve firm performance by enhancing the level of technological innovation. Among them, technological innovation has played a complete mediating role in the diversity of board members’ functional and occupational background, and played a partial mediating role in the diversification of directors’ part-time jobs. Technological innovation is a key indicator bridging board diversity and firm performance. This study can explore and explain the inner workings of the significant relationship between board diversity and firm performance, and link research findings on similar phenomena. The research results may make the existing board governance theories more systematic, expand the extension of theoretical research, and provide some empirical research references for academics and practitioners.

Mitigating Effect of Psychological Capital on Employees' Withdrawal Behavior in the Presence of Job Attitudes: Evidence From Five-Star Hotels in Malaysia

High turnover rate is one of the striking features of the hotel industry and one of the most significant challenges. High turnover rate causes substantial costs for recruitment, selection and training in hotels, on the other hand, it also leads to negative consequences such as the decline of organizational performance and service quality. Thus, it is necessary to search for the root causes of turnover and put forward solutions. This study was designed to examine the impact of psychological capital (PsyCap), organizational commitment (OC), and job satisfaction (JS) on turnover intention among hotel employees. Additionally, it aimed to test the mediating roles of job satisfaction (JS) and organizational commitment (OC). The data were obtained from 228 hotel customer-contact employees with a time lag of two weeks in three waves in Kuala Lumpur based on convenience sampling. A series of structural equation modeling analyses were utilized to investigate hypothesized relationships. The results reveal that there exists a significant and negative impact of PsyCap on employees' turnover intention and this correlation is partially mediated through two job attitudes. That is to say, to retain hotel talents, five-star hotel management should take proper measures to help employees obtain and maintain positive psychological resources such as PsyCap, on the other hand, how to cultivate positive job attitudes and strengthen their sense of identification and belonging for their organizations is supposed to be more focused on.

Temporal effects of maternal psychological distress on child mental health problems at ages 3, 5, 7 and 11: analysis from the UK Millennium Cohort Study

BACKGROUND

Psychological distress is common among women of childbearing age, and limited longitudinal research suggests prolonged exposure to maternal distress is linked to child mental health problems. Estimating effects of maternal distress over time is difficult due to potential influences of child mental health problems on maternal distress and time-varying confounding by family circumstances.

METHODS

We analysed the UK Millennium Cohort Study, a nationally representative sample with data collected throughout childhood. Adopting a marginal structural modelling framework, we investigated effects of exposure to medium/high levels of maternal psychological distress (Kessler-6 score 8+) on child mental health problems (Strengths and Difficulties Questionnaire borderline/abnormal behaviour cut-off) using maternal and child mental health data at 3, 5, 7 and 11 years, accounting for the influence of child mental health on subsequent maternal distress, and baseline and time-varying confounding.

RESULTS

Prior and concurrent exposures to maternal distress were associated with higher levels of child mental health problems at ages 3, 5, 7 and 11 years. For example, elevated risks of child mental health problems at 11 years were associated with exposure to maternal distress from 3 years [risk ratio (RR) 1.27 (95% confidence interval (CI) 1.08-1.49)] to 11 years [RR 2.15 (95% CI 1.89-2.45)]. Prolonged exposure to maternal distress at ages 3, 5, 7 and 11 resulted in an almost fivefold increased risk of child mental health problems.

CONCLUSIONS

Prior, concurrent and, particularly, prolonged exposure to maternal distress raises risks for child mental health problems. Greater support for mothers experiencing distress is likely to benefit the mental health of their children.

Extensions of Auxiliary Variable Approaches for the Investigation of Mediation, Moderation, and Conditional Effects in Mixture Models

Person-centered analyses and mixture models, such as latent profile analyses (LPA), are becoming increasingly common in the organizational literature. However, common usage of LPA rarely extends to the estimation of moderation, conditional effects, and mediation within a single model. This can affect the accuracy of parameter estimates, and it interferes with development and investigation of complex theories. The current study provides an overview of systematic approaches that allows researchers to investigate models involving moderation, conditional effects on outcomes, and mediation. Using M plus, we offer an accessible method of testing complex statistical models that are auxiliary to the focal mixture model. We provide syntax for typical moderation, conditional effects, and mediation hypotheses, and we provide a detailed explanation of the procedures. We demonstrate these procedures with applications involving the five-factor model (FFM) of personality and several additional variables that comprise complex auxiliary statistical models. The pedagogical approach offered by this research will facilitate future theoretical developments and empirical advancements in the use of person-centered analyses.

A Tutorial in Bayesian Potential Outcomes Mediation Analysis

Statistical mediation analysis is used to investigate intermediate variables in the relation between independent and dependent variables. Causal interpretation of mediation analyses is challenging because randomization of subjects to levels of the independent variable does not rule out the possibility of unmeasured confounders of the mediator to outcome relation. Furthermore, commonly used frequentist methods for mediation analysis compute the probability of the data given the null hypothesis, which is not the probability of a hypothesis given the data as in Bayesian analysis. Under certain assumptions, applying the potential outcomes framework to mediation analysis allows for the computation of causal effects, and statistical mediation in the Bayesian framework gives indirect effects probabilistic interpretations. This tutorial combines causal inference and Bayesian methods for mediation analysis so the indirect and direct effects have both causal and probabilistic interpretations. Steps in Bayesian causal mediation analysis are shown in the application to an empirical example.