In modeling change over time, developmental theories often emphasize meaningful quantities like peaks, inflections, timing, and tempo. However, longitudinal analyses typically rely on simple polynomial models that estimate powered terms of time in a linear, additive form which are disconnected from these meaningful quantities. While these linear parameterizations are computationally efficient and produce stable results, the quantities estimated in these models are difficult to directly connect to theoretical hypotheses. To address this disconnect between estimation and theory development, I propose several approaches for linear estimation with nonlinear inference (LENI), a framework that transforms results from stable, easily-estimated linear models into nonlinear estimates which align with theoretical quantities of interest through a set of principled transformation functions. I first outline derivations for the interpretable nonlinear parameters, and transform the results of the corresponding linear model—including fixed and random effects as well as conditional covariates effects —into the results we would have obtained by fitting a nonlinear version of the model. I conclude by summarizing a linearized structural equation model approach which can flexibly accommodate any known nonlinear target function within a linearly-estimable framework. I conclude with recommendations for applied researchers and directions for fruitful future work in this area.

The study of correlates of change is the investigation of systematic individual differences in growth. Our representation of systematic individual differences in growth is built up in two parts: (a) a model for individual growth and, (b) a model for the dependence of parameters in the individual growth models on individual characteristics. First, explicit representations of correlates of change are constructed for various models of individual growth. Second, for the special case of initial status as a correlate of change, properties of collections of growth curves provide new results on the relation between change and initial status. Third, the shortcomings of previous approaches to the assessment of correlates of change are demonstrated. In particular, correlations of residual change measures with exogenous individual characteristics are shown to be poor indicators of systematic individual differences in growth.

As a method for representing development, latent trait theory is presented in terms of a statistical model containing individual parameters and a structure on both the first and second moments of the random variables reflecting growth. Maximum likelihood parameter estimates and associated asymptotic tests follow directly. These procedures may be viewed as an alternative to standard repeated measures ANOVA and to first-order auto-regressive methods. As formulated, the model encompasses cohort sequential designs and allow for period or practice effects. A numerical illustration using data initially collected by Nesselroade and Baltes is presented.

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.

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.

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.

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.

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.