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Mediation analysis practices in social and personality psychology would benefit from the integration of practices from statistical mediation analysis, which is currently commonly implemented in social and personality psychology, and causal mediation analysis, which is not frequently used in psychology. In this chapter, I briefly describe each method on its own, then provide recommendations for how to integrate practices from each method to simultaneously evaluate statistical inference and causal inference as part of a single analysis. At the end of the chapter, I describe additional areas of recent development in mediation analysis that that social and personality psychologists should also consider adopting I order to improve the quality of inference in their mediation analysis: latent variables and longitudinal models. Ultimately, this chapter is meant to be a kind introduction to causal inference in the context of mediation with very practical recommendations for how one can implement these practices in one’s own research.
Over-time, repeated measures, or longitudinal data are terms referring to repeated measurements of the same variables within the same unit (e.g., person, family, team, company). Longitudinal data come from many sources, including self-reports, behaviors, observations, and physiology. Researchers collect repeated measures for a variety of reasons, such as wanting to model change in a process over time or wanting to increase measurement reliability. Whatever the reason for data collection, longitudinal methods pose unique challenges and opportunities. This chapter has three main goals: (1) to help researchers consider design decisions when developing a longitudinal study, (2) to describe the different decisions researchers have to make when analyzing longitudinal data, and (3) to consider the unique properties of longitudinal designs that researchers should be aware of when designing and analyzing longitudinal studies. We aim to provide a comprehensive overview of the major issues that researchers should consider, and we also point to more extensive resources.
Neglect remains understudied compared to other forms of maltreatment. While studies have shown that neglect has negative effects on mental health in adolescence, yet unresolved is whether these impacts result from critical period or cumulative effects. In the present article, we use a novel approach to compare these two hypotheses from the impact of two types of neglect, failure to provide (FTP) and lack of supervision (LOS), on adolescent depression and internalizing symptoms. Data derive from the LONGSCAN consortium, a diverse, multi-site, prospective study of children from approximately age 2–16. Despite our hypothesis that the critical period of early childhood would have the greatest impact on adolescent internalizing mental health, exposure to neglect during the critical period of adolescence (ages 12–16) was the best-fitting model for the effects of FTP neglect on depression, and the effects of LOS neglect on both depression and internalizing symptoms. The cumulative model (exposure across all time periods) best explained the effects of FTP neglect on internalizing symptoms. Results were robust to the addition of control variables, including other forms of maltreatment. These findings demonstrate that responding to neglect into adolescence must be considered as urgent for child welfare systems.
Executive control over low-level information processing is impaired proximal to psychosis onset with evidence of recovery over the first year of illness. However, previous studies demonstrating diminished perceptual modulation via attention are complicated by simultaneously impaired perceptual responses. The present study examined the early auditory gamma-band response (EAGBR), a marker of early cortical processing that appears preserved in first-episode psychosis (FEP), and its modulation by attention in a longitudinal FEP sample.
Methods
Magnetoencephalography was recorded from 25 FEP and 32 healthy controls (HC) during active and passive listening conditions in an auditory oddball task at baseline and follow-up (4–12 months) sessions. EAGBR inter-trial phase coherence (ITPC) and evoked power were measured from responses to standard tones. Symptoms were assessed using the Positive and Negative Syndrome Scale (PANSS).
Results
There was no group difference in EAGBR power or ITPC. While EAGBR ITPC increased with attention in HC, this modulation was impaired among FEP. Diminished EAGBR modulation in FEP persisted at longitudinal follow-up. However, among FEP, recovery of EAGBR modulation was associated with reduced PANSS negative scores.
Conclusion
FEP exhibit impaired executive control over the flow of information at the earliest stages of sensory processing within auditory cortex. In contrast to previous work, this deficit was observed despite an intact measure of sensory processing, mitigating potential confounds. Recovery of sensory gain modulation over time was associated with reductions in negative symptoms, highlighting a source of potential resiliency against some of the most debilitating and treatment refractory symptoms in early psychosis.
Chapter 12 explores the relationship between cognition and interaction. The longitudinal study, spanning over two years, utilises Conversation Analysis (CA) to investigate the cognitive and interactional abilities of a person with Alzheimer’s disease, ‘May’, through 70 audio recordings of telephone conversations with family members. The chapter acknowledges a close relationship between language and cognition by examining how memory and memory loss are displayed in verbal conduct over time. Furthermore, the chapter sets out to challenge the deficit-focused perspective pervasive in dementia literature, showcasing how May employs sophisticated communicative strategies and transacts routinised practices of interaction even with more advancing dementia. The findings suggest a nuanced understanding of cognitive abilities in dementia, questioning the binary framework of competence versus incompetence in analysing complex cognitive issues and interactional events. The findings contribute to understanding the complexities of Alzheimer’s disease, emphasising the need for tailored communication strategies to enhance the quality of interactions for individuals and their family’s facing dementia. The chapter underscores the significance of using interaction as a window to cognition, offering insights into the degenerative consequences of Alzheimer’s and paving the way for a more nuanced understanding of cognitive decline in the context of family communication.
Chapter 5 rigorously tests the observable implications of the argument with longitudinal analysis. I conduct two panel analyses that isolate the effects of educational expansion from the “contaminating” effects of affirmative action policies. First, drawing on annual household surveys conducted by the census bureau, I construct a synthetic panel of birth cohorts to test the hypothesis that better-educated Brazilians situated in the lower classes are mostly likely to self-darken over time. The analysis supports this hypothesis and finds that this relationship holds across diverse cultural regions of Brazil. Next, I introduce an original panel dataset of Brazilian municipalities in 2000 and 2010 to explore whether spatial variation in educational expansion causes higher rates of reclassification within Brazil. Fixed-effects analysis again supports the hypothesis, showing that greater rates of high school and university attendance correlate with greater black identification. Additional analysis indicates that the hypothesized patterns are clearest in urban centers, and are not conditional on the presence of state-level affirmative action policies.
Childhood adversities have been linked to psychosocial outcomes, but it remains uncertain whether subtypes of adversity exert different effects on outcomes. Research is also needed to explore the dynamic interplay between adversity and psychosocial outcomes from childhood to mid-adolescence. This study aimed to investigate these relationships and their role in shaping adolescent wellbeing. Data were extracted from three timepoints of the UK Household Longitudinal Survey when participants (n = 646) were aged 10–15. Cross-lagged panel models were used to explore the relationship between cumulative adversities, and separately non-household (i.e., bullying victimization and adverse neighborhood) and household (i.e., sibling victimization, quarrelsome relationship with parents, financial struggles, and maternal psychological distress) adversities, and psychosocial outcomes (i.e., internalizing and externalizing problems, delinquency, and life satisfaction). Our results revealed that heightened cumulative adversity predicted psychosocial outcomes from childhood to mid-adolescence. Increased levels of household adversity predicted psychosocial outcomes throughout early to mid-adolescence, while non-household adversity only predicted psychosocial outcomes in early adolescence. Furthermore, worse psychosocial outcomes predicted higher levels of adversities during adolescence, highlighting bidirectionality between adversity and psychosocial outcomes. These findings underscore the varying impacts of adversity subtypes and the mutually reinforcing effects of adversities and psychosocial functioning from childhood to mid-adolescence.
There is heterogeneity in the long-term trajectories of depressive symptoms among patients. To date, there has been little effort to inform the long-term trajectory of symptom change and the factors associated with different trajectories. Such knowledge is key to treatment decision-making in primary care, where depression is a common reason for consultation. We aimed to identify distinct long-term trajectories of depressive symptoms and explore pre-treatment characteristics associated with them.
Methods
A total of 483 patients from the PsicAP clinical trial were included. Growth mixture modeling was used to identify long-term distinct trajectories of depressive symptoms, and multinomial logistic regression models to explore associations between pre-treatment characteristics and trajectories.
Results
Four trajectories were identified that best explained the observed response patterns: “recovery” (64.18%), “late recovery” (10.15%), “relapse” (13.67%), and “chronicity” (12%). There was a higher likelihood of following the recovery trajectory for patients who had received psychological treatment in addition to the treatment as usual. Chronicity was associated with higher depressive severity, comorbidity (generalized anxiety, panic, and somatic symptoms), taking antidepressants, higher emotional suppression, lower levels on life quality, and being older. Relapse was associated with higher depressive severity, somatic symptoms, and having basic education, and late recovery was associated with higher depressive severity, generalized anxiety symptoms, greater disability, and rumination.
Conclusions
There were different trajectories of depressive course and related prognostic factors among the patients. However, further research is needed before these findings can significantly influence care decisions.
Negative symptoms remain poorly understood and treated despite their huge impact on patients’ lives and clinical outcomes. This is partly because of ongoing debates about the clinical constructs underlying negative symptoms. A longitudinal analysis of the structure of negative symptoms presented in BJPsych Open reports striking temporal stability of symptom structure, which behaves as a few independent domains. This further underscores the need to address specific symptom domains when considering interventions or pathophysiology studies.
In studies that contain repeated measures of variables, longitudinal analysis accounting for time-varying covariates is one of the options. We aimed to explore longitudinal association between diet quality (DQ) and non-communicable diseases (NCDs). Participants from the 1973–1978 cohort of the Australian Longitudinal Study on Women’s Health (ALSWH) were included, if they; responded to survey 3 (S3, 2003, aged 25–30 years) and at least one survey between survey 4 (S4, 2006) and survey 8 (S8, 2018), were free of NCDs at or before S3, and provided dietary data at S3 or S5. Outcomes were coronary heart disease (CHD), hypertension (HT), asthma, cancer (except skin cancer), diabetes mellitus (DM), depression and/or anxiety, and multimorbidity (MM). Longitudinal modelling using generalised estimation equation (GEE) approach with time-invariant (S4), time-varying (S4–S8) and lagged (S3–S7) covariates were performed. The mean (± standard deviation) of Alternative Healthy Eating Index-2010 (AHEI-2010) of participants (n = 8022) was 51·6 ± 11·0 (range: 19–91). Compared to women with the lowest DQ (AHEI-2010 quintile 1), those in quintile 5 had reduced odds of NCDs in time-invariant model (asthma: OR (95 % CI): 0·77 (0·62–0·96), time-varying model (HT: 0·71 (0·50–0·99); asthma: 0·62 (0·51–0·76); and MM: 0·75 (0·58–0·97) and lagged model (HT: 0·67 (0·49–0·91); and asthma: 0·70 (0·57–0·85). Temporal associations between diet and some NCDs were more prominent in lagged GEE analyses. Evidence of diet as NCD prevention in women aged 25–45 years is evolving, and more studies that consider different longitudinal analyses are needed.
This paper assesses trends in food environment and market concentration and racial and ethnic inequities in food environment exposure and food retail market concentration at the US census tract level from 2000 to 2019.
Design:
Establishment-level data from the National Establishment Time Series were used to measure food environment exposure and food retail market concentration. We linked that dataset to race, ethnicity and social vulnerability information from the American Community Survey and the Agency for Toxic Substances and Disease Registry. A geospatial hot-spot analysis was conducted to identify relatively low and high healthy food access clusters based on the modified Retail Food Environment Index (mRFEI). The associations were assessed using two-way fixed effects regression models.
Setting:
Census tracts spanning all US states.
Participants:
69 904 US census tracts.
Results:
The geospatial analysis revealed clear patterns of areas with high and low mRFEI values. Our empirical findings point to disparities in food environment exposure and market concentration by race. The analysis shows that Asian Americans are likelier to live in neighbourhoods with a low food environment exposure and low retail market concentration. These adverse effects are more pronounced in metro areas. The robustness analysis for the social vulnerability index confirms these results.
Conclusion:
US food policies must address disparities in neighbourhood food environments and foster a healthy, profitable, equitable and sustainable food system. Our findings may inform equity-oriented neighbourhood, land use and food systems planning. Identifying priority areas for investment and policy interventions is essential for equity-oriented neighbourhood planning.
Existing research on housing cost burden focuses on its evolution over time. Few empirical studies, meanwhile, investigate changes in housing cost burden as a function of age. Literature is also scarce on how people's housing cost burden is affected by the act of retiring. In order to fill this research gap, we examine how the burden of housing costs tends to change after retirement and how the impact of retirement on housing cost burden differs for tenants as compared to homeowners. Taking advantage of the longitudinal data provided by the German Socio-Economic Panel (1993–2019), we estimate fixed effects regressions and model impact functions to estimate how people's housing cost burdens change after they retire. In addition, we interact the retirement event with tenure status. Our results show that retirement is associated with an increase in housing cost burden and that this association is stronger among tenants than among homeowners. We contribute to the literature on housing cost burden by taking a longitudinal perspective and showing that critical life events such as retirement do have an impact on the financial pressures exerted on households by housing costs and can even exacerbate the existing inequality in terms of housing cost burden between tenants and homeowners. We also demonstrate the importance for policy makers and future research of identifying social groups that may be particularly prone to financial overburden as a result of elevated housing costs in old age in order to implement policies that avoid such overburden and prevent the increase in social inequality after retirement.
The aim of our study was to examine the longitudinal associations between two forms of second language (L2) knowledge (i.e., explicit and implicit knowledge) and the activity types that facilitate different processing mechanisms (i.e., form- and meaning-focused processing). L2 English speakers completed two tests of explicit knowledge (untimed written grammaticality judgment test and metalinguistic knowledge test) and three tests of implicit knowledge (timed written grammaticality judgment test, oral production, and elicited imitation) at the beginning and the end of a semester of university-level study. To track engagement in the activity types, participants completed self-reported language exposure logs across five days throughout the semester. The results from an autoregressive cross-lag analysis suggest L2 explicit and implicit knowledge influenced each other reciprocally over time. Neither activity type predicted knowledge development. We conclude that language acquisition is a developmental process typified by a dynamic, synergistic interface between explicit and implicit knowledge.
A growing body of research indicates that forecasting skill is a unique and stable trait: forecasters with a track record of high accuracy tend to maintain this record. But how does one identify skilled forecasters effectively? We address this question using data collected during two seasons of a longitudinal geopolitical forecasting tournament. Our first analysis, which compares psychometric traits assessed prior to forecasting, indicates intelligence consistently predicts accuracy. Next, using methods adapted from classical test theory and item response theory, we model latent forecasting skill based on the forecasters’ past accuracy, while accounting for the timing of their forecasts relative to question resolution. Our results suggest these methods perform better at assessing forecasting skill than simpler methods employed by many previous studies. By parsing the data at different time points during the competitions, we assess the relative importance of each information source over time. When past performance information is limited, psychometric traits are useful predictors of future performance, but, as more information becomes available, past performance becomes the stronger predictor of future accuracy. Finally, we demonstrate the predictive validity of these results on out-of-sample data, and their utility in producing performance weights for wisdom-of-crowds aggregations.
Considerable heterogeneity exists in treatment response to first-line posttraumatic stress disorder (PTSD) treatments, such as Cognitive Processing Therapy (CPT). Relatively little is known about the timing of when during a course of care the treatment response becomes apparent. Novel machine learning methods, especially continuously updating prediction models, have the potential to address these gaps in our understanding of response and optimize PTSD treatment.
Methods
Using data from a 3-week (n = 362) CPT-based intensive PTSD treatment program (ITP), we explored three methods for generating continuously updating prediction models to predict endpoint PTSD severity. These included Mixed Effects Bayesian Additive Regression Trees (MixedBART), Mixed Effects Random Forest (MERF) machine learning models, and Linear Mixed Effects models (LMM). Models used baseline and self-reported PTSD symptom severity data collected every other day during treatment. We then validated our findings by examining model performances in a separate, equally established, 2-week CPT-based ITP (n = 108).
Results
Results across approaches were very similar and indicated modest prediction accuracy at baseline (R2 ~ 0.18), with increasing accuracy of predictions of final PTSD severity across program timepoints (e.g. mid-program R2 ~ 0.62). Similar findings were obtained when the models were applied to the 2-week ITP. Neither the MERF nor the MixedBART machine learning approach outperformed LMM prediction, though benefits of each may differ based on the application.
Conclusions
Utilizing continuously updating models in PTSD treatments may be beneficial for clinicians in determining whether an individual is responding, and when this determination can be made.
Child protection systems monitoring is key to ensuring children’s wellbeing. In England, monitoring is rooted in onsite inspection, culminating in judgements ranging from ‘outstanding’ to ‘inadequate’. But inspection may carry unintended consequences where child protection systems are weak. One potential consequence is increased child welfare intervention rates. In this longitudinal ecological study of local authorities in England, we used Poisson mixed-effects regression models to assess whether child welfare intervention rates are higher in an inspection year, whether this is driven by inspection judgement, and whether more deprived areas experience different rates for a given inspection judgement. We investigated the impact of inspection on care entry, Child Protection Plan-initiation, and child-in-need status. We found that inspection was associated with a rise in rates across the spectrum of interventions. Worse judgements yielded higher rates. Inspection may also exacerbate existing inequalities. Unlike less deprived areas, more deprived areas judged inadequate did not experience an increase in the less intrusive ‘child-in-need’ interventions. Our findings suggest that a narrow focus on social work practice is unlikely to address weaknesses in the child protection system. Child protection systems monitoring should be guided by a holistic model of systems improvement, encompassing the socioeconomic determinants of quality.
Chapter 3 discusses methodological issues such as the need for a particular explanatory style to investigate multi-dimensional longitudinal change processes in large-scale heterogenous entities such as socio-technical systems. Chapter 3 also provides an operational analytical template for the empirical analyses in subsequent chapters and discusses the data-sources we used.
Little is known about the effects of informal care-giving on employees' absenteeism due to illness. This paper therefore provides a longitudinal analysis of the consequences of taking on informal care-giving for men's and women's working hours and workplace absenteeism due to illness. Data were taken from the Dutch Labour Supply Panel (waves 2004–2018); 495 of the 6,452 male observations in this panel and 696 of the 5,961 female observations had taken on informal care-giving. It was tested whether respondents who became (intensive) informal carers were more likely than respondents who remained non-care-givers to reduce their work hours or stop working between waves t and t1, or to be absent from work due to illness in wave t1. (Multinomial) logistic regression analyses showed that taking on informal care reduced women's working hours when the care they provided was intensive, but not men's. The predicted probability of women reducing their work hours was 12 per cent if they had remained non-care-givers between waves t and t1, 15 per cent if they had started giving non-intensive care and 19 per cent if they had begun providing intensive help. In addition, starting to provide (non-intensive) informal care increased the risk of workplace absenteeism among both women and men. The study highlights the need for workplace policies that prevent female carers from reducing their work hours, and enable male and female carers to continue working in a healthy way.
It is understood that ensuring equation balance is a necessary condition for a valid model of times series data. Yet, the definition of balance provided so far has been incomplete and there has not been a consistent understanding of exactly why balance is important or how it can be applied. The discussion to date has focused on the estimates produced by the general error correction model (GECM). In this paper, we go beyond the GECM and beyond model estimates. We treat equation balance as a theoretical matter, not merely an empirical one, and describe how to use the concept of balance to test theoretical propositions before longitudinal data have been gathered. We explain how equation balance can be used to check if your theoretical or empirical model is either wrong or incomplete in a way that will prevent a meaningful interpretation of the model. We also raise the issue of “
$I(0)$
balance” and its importance.
The effects of transgenic corn use and federal biofuel policies on state-level cropping patterns in the US Corn Belt region are investigated using state-level data from 2000 to 2019. During this time, producers moved away from diverse cropping patterns and toward simpler rotational practices. Empirical evidence indicates that the intensification of corn acres planted was positively impacted by the spread of genetically modified (GM) soybeans—used as a proxy for GM corn for biofuel usage—but the effects of biotech advancements on producer planting decisions vary across states. This suggests that future policy changes affecting corn production decisions at the farm level will also be heterogeneous across states.