Impact of missing data strategies in studies of parental employment and health: Missing items, missing waves, and missing mothers

Help-Seeking Tendencies and Subjective Well-Being: A Cross-Cultural Comparison of the United States and Japan

Help-seeking is commonly conceived as an instrumental behavior that improves people’s subjective well-being. However, most findings supporting a positive association between help-seeking and subjective well-being are observed in independence-preferring countries. Drawing from research demonstrating that the pathways to subjective well-being are culturally divergent, we posit that help-seeking tendencies may be detrimental to subjective well-being for members in interdependence-preferring countries where norms for preserving relational harmony and face concerns are prevalent. This study tested the moderating role of country in the relationship between help-seeking tendencies and subjective well-being using data from 5,068 American and Japanese participants. Results revealed that although help-seeking tendencies were associated with greater life satisfaction, higher positive affect, and lower negative affect among Americans, help-seeking tendencies were associated with poorer life satisfaction and lower positive affect among Japanese. We discuss the importance of adopting culturally sensitive perspectives when examining antecedents of subjective well-being.

Bayesian network-based missing mechanism identification (BN-MMI) method in medical research

Background

Traditional approaches to identify missing mechanisms are usually based on the hypothesis test and confronted with both theoretical and practical challenges. It has been proved that the Bayesian network is powerful in integrating, analyzing and visualizing information, and some previous researches have verified the promising features of Bayesian network to deal with the aforementioned challenges in missing mechanism identification. Based on the above reasons, this paper explores the application of Bayesian network to the identification of missing mechanisms for the first time, and proposes a new method, the Bayesian network-based missing mechanism identification (BN-MMI) method, to identify missing mechanism in medical research.

Methods

The procedure of BN-MMI method consists three easy-to-implement steps: estimating the missing data structure by the Bayesian network; assessing the credibility of the estimated missing data structure; and identifying the missing mechanism from the estimated missing data structure. The BN-MMI method is verified by simulation research and empirical research.

Results

The simulation study verified the validity, consistency and robustness of BN-MMI method, and indicated its outperformance in contrast to the traditional logistic regression method. In addition, the empirical study illustrated the applicability of BN-MMI method in the real world by an example of medical record data.

Conclusions

It was confirmed that the BN-MMI method itself, together with human knowledge and expertise, could identify the missing mechanisms according to the probabilistic dependence/independence relations among variables of interest. At the same time, our research shed light upon the potential application of BN-MMI method to a broader range of missing data issues in medical studies.

Missing Data in Research on Youth and Family Programs

BACKGROUND

Multilevel data can be missing at the individual level or at a nested level, such as family, classroom, or program site. Increased knowledge of higher-level missing data is necessary to develop evaluation design and statistical methods to address it.

METHODS

Participants included 9,514 individuals participating in 47 youth and family programs nationwide who completed multiple self-report measures before and after program participation. Data were marked as missing or not missing at the item, scale, and wave levels for both individuals and program sites.

RESULTS

Site-level missing data represented a substantial portion of missing data, ranging from 0-46% of missing data at pre-test and 35-71% of missing data at post-test. Youth were the most likely to be missing data, although site-level data did not differ by the age of participants served. In this dataset youth had the most surveys to complete, so their missing data could be due to survey fatigue.

CONCLUSIONS

Much of the missing data for individuals can be explained by the site not administering those questions or scales. These results suggest a need for statistical methods that account for site-level missing data, and for research design methods to reduce the prevalence of site-level missing data or reduce its impact. Researchers can generate buy-in with sites during the community collaboration stage, assessing problematic items for revision or removal and need for ongoing site support, particularly at post-test. We recommend that researchers conducting multilevel data report the amount and mechanism of missing data at each level.

Culture moderates the link between perceived obligation and biological health risk: Evidence of culturally distinct pathways for positive health outcomes

RATIONALE

Although perceived obligations to meet the expectations of family, friends, and society can be detrimental to physical health, much research in this area has thus far been conducted exclusively on Western samples. Cross-cultural research importantly suggests that positive health can be dependent on whether one engages in modes of being that are sanctioned by one's culture. Specifically, studies show that better health is predicted when people from cultures that value independence are able to exercise their personal autonomy and when people from cultures that value interdependence are able to maintain relational harmony (Kitayama et al., 2010).

OBJECTIVE

Based on these lines of research, as the fulfillment of perceived obligations can facilitate relational harmony but infringe on personal autonomy, we posit that culture will moderate the impact of perceived obligations on health outcomes. To gain further insight, we additionally examined people's goal disengagement tendency as an individual difference that may influence their likelihood of shunning perceived obligations in order to avoid associated stressors.

METHOD

Drawing from the parallel biomarker projects of Midlife in the United States and Midlife in Japan, we examined the interaction between perceived obligations and goal disengagement tendency on health among American and Japanese middle-aged adults. Health outcomes were indexed by biomarkers of inflammation (interleukin-6 and C-reactive protein levels) and cardiovascular risk (systolic blood pressure and total/high-density lipoprotein cholesterol).

RESULTS

We found that a higher tendency to disengage from stressful social obligations is associated with better health for Americans. In contrast, we found poorer health outcomes amongst Japanese participants who tend to disengage from their perceived obligations.

CONCLUSIONS

Our results highlight the importance of examining how perceived obligations influence physical health from a cultural perspective. The current study supports the hypothesis that culturally distinct pathways underlie health outcomes.

The treatment of incomplete data: Reporting, analysis, reproducibility, and replicability

Proper analysis and reporting of incomplete data continues to be a challenging task for practitioners from various research areas. Recently Nguyen, Strazdins, Nicholson and Cooklin (NSNC; 2018) evaluated the impact of complete case analysis and multiple imputation in studies of parental employment and health. Their work joins interdisciplinary efforts to educate and motivate scientists across the research community to use principled statistical methods when analyzing incomplete data. Although we fully support and encourage work in parallel to NSNC's, we also think that further actions should be taken by the research community to improve current practices. In this commentary, we discuss some aspects and misconceptions related to analysis of incomplete data, in particular multiple imputation. In our view, the missing data problem is part of a larger problem of research reproducibility and replicability today. Thus, we believe that improving analysis and reporting of incomplete data will make reproducibility and replicability efforts easier. We also provide a brief checklist of recommendations which could be used by members of the scientific community, including practitioners, journal editors, and reviewers to set higher publication standards.

Relationship transitions and change in health behavior: A four-phase, twelve-year longitudinal study

RATIONALE

Extensive scientific evidence shows an association between involvement in social relationships and healthy lifestyle. Prospective studies with many participants and long follow-ups are needed to study the dynamics and change in social factors within individuals over time.

OBJECTIVE

Our aim was to determine whether a change in relationship status (single, married, divorced, widow, cohabiting) is followed by a change in health behavior (smoking, alcohol consumption, physical activity, and body mass index).

METHODS

We used data from 81,925 healthy adults participating in the prospective longitudinal Finnish Public Sector Study in the period 2000-2013. We analyzed 327,700 person-observations from four data collection phases. Missing data were multiply imputed. A within-individual methodology was used to minimize the possibility of selection effects affecting the interpretation.

RESULTS

All four health behaviors showed associations with relationship status. The effects were very similar and in the same direction in women and men, although there were gender differences in the magnitudes of the effects. The end of a relationship was followed by a decrease in body mass index, increased odds of being a smoker, increase in physical activity, and increase in alcohol consumption (widowed men). The effects were reverse when forming a new relationship.

CONCLUSION

A change in relationship status is associated with a change in health behavior. The association is not explained by socioeconomic status, subjective health status, or anxiety level. People leaving or losing a relationship are at increased risk of unhealthy behavior (smoking and alcohol consumption), but at the same time they have a lower BMI and show higher physical activity compared to the time they were in a relationship. It is not clear if the cumulative health effect of these health behavior changes is positive or negative.