Two-stage path analysis with definition variables: An alternative framework to account for measurement error

The reciprocal relationships between economic status and mental health: Investigating the between-person and within-person effects in a three-wave longitudinal study

BACKGROUND

Social drift/selection (i.e., mental health symptoms cause low economic status) and social causation theories (i.e., low economic status causes mental health symptoms) specify reciprocal relationships between economic status and mental health. Little is known regarding the disaggregation of within-person and between-person effects in the relationship between economic status and mental health in the long run. The current study sought to examine the reciprocal relationships between economic status and mental health over 20 years of adulthood, disaggregating within-person and between-person effects.

METHODS

Data were from three waves (7108 participants) of the Midlife Development in the United States study. Participants reported information about objective and subjective measures of economic status as well as a wide range of indicators of mental health on the positive dimension, including subjective, social, and psychological well-being, and on the negative side, depression, anxiety, panic attack, anhedonia, somatic amplification, alcohol abuse, and negative affect. Cross-lagged panel models were estimated.

RESULTS

At between-person levels, both social drift/selection and social causation hypotheses were confirmed when considering subjective measures of economic status. When using objective measures of economic status (i.e., income), the results showed decreased support for social drift/selection and social causation hypotheses. At within-person levels, social drift/selection and social causation hypotheses were generally not supported, with some notable exceptions.

LIMITATIONS

Restriction of the sample to one country limits the generalizability of the findings.

CONCLUSIONS

Social causation and social drift/selection processes act simultaneously mainly at a population level, but much less when considering individual changes. Policy and programs should be targeted at addressing inequality in income and mental health within a nation or a community.

Estimating nonlinear effects of random slopes: A comparison of multilevel structural equation modeling with a two-step, a single-indicator, and a plausible values approach

Multilevel structural equation modeling (MSEM) is a statistical framework of major relevance for research concerned with people's intrapersonal dynamics. An application domain that is rapidly gaining relevance is the study of individual differences in the within-person association (WPA) of variables that fluctuate over time. For instance, an individual's social reactivity - their emotional response to social situations - can be represented as the association between repeated measurements of the individual's social interaction quantity and momentary well-being. MSEM allows researchers to investigate the associations between WPAs and person-level outcome variables (e.g., life satisfaction) by specifying the WPAs as random slopes in the structural equation on level 1 and using the latent representations of the slopes to predict outcomes on level 2. Here, we are concerned with the case in which a researcher is interested in nonlinear effects of WPAs on person-level outcomes - a U-shaped effect of a WPA, a moderation effect of two WPAs, or an effect of congruence between two WPAs - such that the corresponding MSEM includes latent interactions between random slopes. We evaluate the nonlinear MSEM approach for the three classes of nonlinear effects (U-shaped, moderation, congruence) and compare it with three simpler approaches: a simple two-step approach, a single-indicator approach, and a plausible values approach. We use a simulation study to compare the approaches on accuracy of parameter estimates and inference. We derive recommendations for practice and provide code templates and an illustrative example to help researchers implement the approaches.

Assessing and accounting for measurement in intensive longitudinal studies: current practices, considerations, and avenues for improvement

PURPOSE

Intensive longitudinal studies, in which participants complete questionnaires multiple times a day over an extended period, are increasingly popular in the social sciences in general and quality-of-life research in particular. The intensive longitudinal methods allow for studying the dynamics of constructs (e.g., how much patient-reported outcomes vary across time). These methods promise higher ecological validity and lower recall bias than traditional methods that question participants only once, since the high frequency means that participants complete questionnaires in their everyday lives and do not have to retrospectively report about a large time interval. However, to ensure the validity of the results obtained from analyzing the intensive longitudinal data (ILD), greater awareness and understanding of appropriate measurement practices are needed.

METHOD

We surveyed 42 researchers experienced with ILD regarding their measurement practices and reasons for suboptimal practices.

RESULTS

Results showed that researchers typically do not use measures validated specifically for ILD. Participants assessing the psychometric properties and invariance of measures in their current studies was even less common, as was accounting for these properties when analyzing dynamics. This was mainly because participants did not have the necessary knowledge to conduct these assessments or were unaware of their importance for drawing valid inferences. Open science practices, in contrast, appear reasonably well ingrained in ILD studies.

CONCLUSION

Measurement practices in ILD still need improvement in some key areas; we provide recommendations in order to create a solid foundation for measuring and analyzing psychological constructs.

Perspectives in the Development of Tools to Assess Vaccine Literacy

Vaccine literacy (VL) is the ability to find, understand, and evaluate vaccination-related information to make appropriate decisions about immunization. The tools developed so far for its evaluation have produced consistent results. However, some dimensions may be underestimated due to the complexity of factors influencing VL. Moreover, the heterogeneity of methods used in studies employing these tools hinders a comprehensive understanding of its role even more. To overcome these limitations, a path has been sought to propose new instruments. This has necessitated updating earlier literature reviews on VL and related tools, exploring its relationship with vaccine hesitancy (VH), and examining associated variables like beliefs, attitudes, and behaviors towards immunization. Based on the current literature, and supported by the re-analysis of a dataset from an earlier study, we propose a theoretical framework to serve as the foundation for creating future assessment tools. These instruments should not only evaluate the psychological factors underlying the motivational aspect of VL, but also encompass knowledge and competencies. The positioning of VL in the framework at the intersection between sociodemographic antecedents and attitudes, leading to behaviors and outcomes, explains why and how VL can directly or indirectly influence vaccination decisions by countering VH and operating at personal, as well as at organizational and community levels.