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IntroductionRecent technology has enabled researchers to collect ecological momentary assessments (EMA) to examine within-person correlates of suicidal thoughts. Prior studies examined generalized temporal dynamics of emotions and suicidal thinking over brief periods, but it is not yet known how variable these processes are across people.MethodWe use data EMA data delivered over two weeks with youth/young adults (N = 60) who reported past year self-injurious thoughts/behaviors. We used group iterative multiple model estimation (GIMME) to model group- and person-specific associations of negative emotions (i.e., fear, sadness, shame, guilt, and anger) and suicidal thoughts.Results29 participants (48.33%) reported at least one instance of a suicidal thought and were included in GIMME models. In group level models, we consistently observed autoregressive effects for suicidal thoughts (e.g., earlier thoughts predicting later thoughts), although the magnitude and direction of this link varied from person-to-person. Among emotions, sadness was most frequently associated with contemporaneous suicidal thoughts, but this was evident for less than half of the sample, while other emotional correlates of suicidal thoughts broadly differed across people. No emotion variable was linked to future suicidal thoughts in >14% of the sample, CONCLUSIONS: Emotion-based correlates of suicidal thoughts are heterogeneous across people. Better understanding of the individual-level pathways maintaining suicidal thoughts/behaviors may lead to more effective, personalized interventions.

In this article, we briefly clarify several points regarding immunopsychiatry. In particular, we argue that higher density data and a greater focus on temporal dynamics are both important, and that studies incorporating these features have the potential to greatly advance the field. We also respond to recent comments made on our original article on this topic (Moriarity and Slavich, 2023), including the contention that our perspective on immunopsychiatry is reductionistic. To the contrary, we believe that strong immunopsychiatry studies are highly integrative and include data from multiple major levels of analysis to form a more complete picture of how processes that are relevant for mental health and behavior emerge and dynamically change over time in relation to one another.

Self-report data are essential in health psychology research where an individual's perception is critical to understanding one's health and psychological status. Intensive data collection over time, including daily diary assessments, is necessary in understanding within- and between-person variability in health and psychological processes over time. An "initial elevation or latent decline" (IELD) effect, inherent of self-report data, is increasingly acknowledged in the social psychology literature, but awareness of this effect in health psychology research is lacking, particularly in studies that emphasize within- and between-person variability in self-reports. The IELD effect is a pattern in which responses tend to be more extreme at the initial interview relative to subsequent responses. This paper illustrates the impact of IELD in applications of mixed-effects models based on observational self-reports and concludes that researchers take such effects into account in data analysis or in the research designing phase to help mitigate such effects.

Research on real-world patterns of substance use increasingly involves intensive longitudinal data (ILD) collection, requiring long assessment windows. The present study extends limited prior research examining event- and person-level influences on compliance and response consistency by investigating how these behaviors are sustained over time in an ILD study of alcohol and cannabis co-use in college students. Participants (n = 316) completed two 28-day bursts of ILD comprising five daily surveys, which included a morning survey of prior-day drinking. We used linear mixed effects models in a multilevel interrupted time series framework to evaluate the associations of time and measurement burst with (a) noncompliance (count of missed surveys) and (b) response consistency (difference between same-day report of drinking and morning report of prior-day drinking). We observed that time was positively associated with noncompliance, with no discontinuity associated with measurement burst. The slope of time was more positive in the second burst. Neither time nor measurement burst were significantly associated with consistent reporting. However, survey nonresponse and consistency of responding appeared to be impacted by the same-day use of substances. Overall, compliance decreased while consistency was stable across the duration of a lengthy ILD protocol. Shorter assessment windows or adaptive prompting strategies may improve overall study compliance. Further work examining daily burden and context is needed to inform future ILD design. Public Significance Statement: This study examined compliance and consistency of reporting in a lengthy, intensive longitudinal data protocol spanning 56 days of data collection across two measurement bursts. Results showed that compliance decreased over time, with no change associated with the discontinuity between bursts, while consistency remained constant and of moderate magnitude. Findings inform the design and handling of missing data in future intensive longitudinal research. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

BackgroundMethodResultsConclusionsAnxiety disorders are prevalent among youth and adults. Increasing studies examined the dynamic associations between momentary fluctuations of anxiety and well-being, primarily focusing on the severity of general anxiety. Scant research has explored the co-fluctuations between different anxiety symptoms and mental health outcomes.The current study evaluated the multi-level factor structure and assessed the subclinical symptoms of generalized anxiety disorder (GAD), social phobia (SP), and panic disorder (PD) in a sample of non-clinical young adults (N = 271, Mage = 18 years, 72% female, 68% non-White) who participated in a 30-day daily diary study.Between persons, GAD, SP, and PD were positively correlated with depressive symptoms, stress, as well as emotional and peer problems. Within persons, both SP and PD were positively associated with stress, peer and emotional problems on the same day. Across days, there was positive reciprocal relation between PD and stress, whereas negative reciprocal link was observed between SP and emotional problems.Current findings showed dynamic and distinct patterns in the associations between different anxiety symptoms and several mental health outcomes, which emphasizes the need to disentangle between- and within-person variation of anxiety symptoms with intensive longitudinal designs. [ABSTRACT FROM AUTHOR]

Introduction: Psychosis often develops gradually along a continuum of severity. Little is known about the role of protective factors such as positive affect (PA) in the development of psychotic experiences (PEs). This study investigated i) the temporal (between-day) and contemporaneous (within-day) daily associations between PA and PEs in individuals at different early clinical stages for psychosis and ii) whether these associations differed per clinical stage. Methods: Daily diary data for 90 days came from 96 individuals at risk for psychosis, distributed over four subgroups defined according to the clinical staging model (stages 0-1b). We constructed multilevel models with PA as a predictor of PEs and vice versa. We investigated within- and between-person temporal and contemporaneous associations and tested whether these associations differed among early stages with multilevel moderation analyses. Results: We found no within-person temporal effects between PA and PEs in either direction. Contemporaneously, current-day PA predicted current-day PEs (B = -0.14, p < 0.001) and vice versa (B = -0.61, p < 0.001). Between persons, more 90-day PA predicted fewer PEs in the temporal model (B = -0.14, p = 0.03). In addition, more 90-day PEs predicted PA in the temporal (B = -0.26, p < 0.001) and contemporaneous (B = -0.36, p < 0.001) models. The contemporaneous association between PA and PEs was stronger in individuals at ultra-high risk (UHR) for psychosis than in earlier stages. Discussion: Our study supported a significant within-day, bidirectional relationship between PA and PEs. This suggests that a focus on PA and methods to improve PA may be an important addition to early intervention practices, particularly in those at UHR for psychosis. [ABSTRACT FROM AUTHOR]

Dynamic structural equation modeling (DSEM) is a useful technique for analyzing intensive longitudinal data. A challenge of applying DSEM is the missing data problem. The impact of missing data on DSEM, especially on widely applied DSEM such as the two-level vector autoregressive (VAR) cross-lagged models, however, is understudied. To fill the research gap, we evaluated how well the fixed effects and variance parameters in two-level bivariate VAR models are recovered under different missingness percentages, sample sizes, the number of time points, and heterogeneity in missingness distributions through two simulation studies. To facilitate the use of DSEM under customized data and model scenarios (different from those in our simulations), we provided illustrative examples of how to conduct Monte Carlo simulations in Mplus to determine whether a data configuration is sufficient to obtain accurate and precise results from a specific DSEM. [ABSTRACT FROM AUTHOR]

In public health research an increasing number of studies is conducted in which intensive longitudinal data is collected in an experience sampling or a daily diary design. Typically, the resulting data is analyzed with a mixed-effects model or mixed-effects location scale model because they allow one to examine a host of interesting longitudinal research questions. Here, we introduce an extension of the mixed-effects location scale model in which measurement error of the observed variables is considered by a latent factor model and in which-in addition to the mean-or location-related effects-the residual variance of the latent factor and the parameters of the autoregressive process of this latent factor can differ between persons. We show how to estimate the parameters of the model with a maximum likelihood approach, whose performance is also compared with a Bayesian approach in a small simulation study. We illustrate the models using a real data example and end with a discussion in which we suggest questions for future research.

Intensive longitudinal data (ILD) has been collected to capture the dynamic fluctuations of procrastination; however, researchers have typically measured daily procrastination by modifying trait measures (e.g., adding a time reference "today") without adequately testing their reliabilities. The main purpose of this study was to use an advanced approach, dynamic structural equation modeling, to assess the between- and within-person reliabilities of a widely used six-item measure of daily procrastination. A total of 252 participants completed retrospective measures of various types of trait procrastination and daily measures of procrastination over 34 consecutive days. The results showed that the entire scale for daily procrastination and five of its six items had high between- and within-person reliabilities, but one item had much lower reliabilities, suggesting that this item may be inappropriate in everyday contexts. Furthermore, we found moderate to strong associations between the latent trait factor of procrastination and trait measures of procrastination. In addition, we identified substantial between-person variation in person-specific reliabilities and explored its relevant factors. Overall, this study assessed the reliabilities of a daily measure of procrastination, which facilitated future studies to obtain more reliable and consistent results and to better estimate the reliability of ILD. [ABSTRACT FROM AUTHOR]

Background: There is widespread interest in the general factor of psychopathology or 'p factor', which has been proposed to reflect vulnerability to psychopathology. We examined to what extent this 'vulnerability' is associated with dysregulations in affect and behavior that occur in daily life. As such we hoped to provide an account of how this vulnerability may be maintained. Methods: We used data from the Tracking Adolescents' Individual Lives Survey (TRAILS; N = 2,772) collected at ages 11, 14, 16, 19, and 22 years to fit a bifactor model with a general psychopathology factor, alongside internalizing, externalizing (EXT), attention‐deficit/hyperactivity, and autism spectrum problem domains. Following the fifth TRAILS assessment, a subsample of participants (n = 133, age = 22.6, 43% women) with heightened risk for psychopathology completed a 6‐month daily diary protocol with one assessment each day. Using a dynamic structural equation approach, we examined to what extent mean intensity, variability, inertia, and within‐day co‐occurrence of EXT, anxious–tense, and depressed–withdrawn affects and behaviors were associated with general factor scores. Results: Unexpectedly, higher general factor scores were not associated with higher mean intensity of any of the three types of daily negative affects and behaviors, but were associated with higher variability and less carryover (inertia) EXT affects and behaviors. Conclusions: We showed that individual differences in general factor scores do not manifest as differences in average levels of daily affects and behaviors, but instead were related to a type of EXT reactivity to the environment. Future research is necessary to investigate whether reactive irritable moods may be involved in or signal vulnerability sustained psychopathology. [ABSTRACT FROM AUTHOR]

Up to now, almost all existing methods for joint modeling survival data and longitudinal data rely on parametric/semiparametric assumptions on longitudinal covariate process, and the resulting inferences critically depend on the validity of these assumptions that are difficult to verify in practice. The kernel method-based procedures rely on choices of kernel function and bandwidth, and none of the existing methods provides estimate for the baseline distribution in proportional hazards model. This article proposes a proportional hazards model for joint modeling right censored survival data and intensive longitudinal data taking into account of within-subject historic change patterns. Without any parametric/semiparametric assumptions or use of kernel method, we derive empirical likelihood-based maximum likelihood estimators and partial likelihood estimators for the regression parameter and the baseline distribution function. We develop stable computing algorithms and present some simulation results. Analyses of real dataset are conducted for smoking cessation data and liver disease data. [ABSTRACT FROM AUTHOR]

Mucosal immunity is a multifaceted system of immunological responses that provides a barrier against pathogenic invasion and can be regulated by psychosocial and neuroendocrine factors. The present study aims to elucidate the association between everyday emotional states, emotion regulation skills, and mucosal immunity by utilizing an ambulatory assessment approach. 30 healthy subjects (61% male; M = 30.18 years old) completed an emotion questionnaire (PANAS) and collected saliva samples via passive drool to determine salivary immunoglobulin‐A (S‐IgA) excretion rate three times a day over a period of 1 week. In a multi‐level model, the influence of emotions on S‐IgA, both on a within‐subject and between‐subject level, was estimated. We found that most of the variation in S‐IgA (74%) was accounted for by within‐subject changes rather than stable between‐subject differences. On a within‐subject level, negative emotions had a significant positive effect on S‐IgA levels (b = 1.87, p =.015), while positive emotions had no effect. This effect of negative emotions was moderated by the individual emotion regulation skills, with higher regulation skills corresponding to smaller effects (b = −2.67, p =.046). Furthermore, S‐IgA levels decreased over the course of a day, indicating circadian rhythmicity (b = −0.13, p =.034). These results highlight the possibilities of intensive longitudinal data to investigate the covariance between psychological and immunological states over time. This article elucidates the relationship between everyday emotional states and mucosal immune function, specifically demonstrating how negative emotions are associated with increased salivary immunoglobulin‐A concentrations. Furthermore, the study provides evidence for the modulatory influence of emotion regulation skills on this association. This work not only advances the empirical understanding of the emotion–immunity interconnections but also offers potential future directions for using intensive longitudinal data in psychoneuroimmunology research. [ABSTRACT FROM AUTHOR]

Currently, dynamic structural equation modeling (DSEM) and residual DSEM (RDSEM) are commonly used in testing intensive longitudinal data (ILD). Researchers are interested in ILD mediation models, but their analyses are challenging. The present paper mathematically derived, empirically compared, and step-by-step demonstrated three types (i.e., 1-1-1, 2-1-1, and 2-2-1) of intensive longitudinal mediation (ILM) analyses based on DSEM and RDSEM models. Specifically, each ILM model was demonstrated with a simulated example and illustrated with the corresponding annotated Mplus codes. We compared two types of detrending methods in mediation analyses and showed that RDSEM was superior to DSEM because the latter included the timetj variable as a Level 1 predictor. Lastly, we extended ILM analyses based on DSEM and RDSEM to multilevel autoregressive mediation models, cross-classified DSEM, and intensive longitudinal moderated mediation models. [ABSTRACT FROM AUTHOR]

Abstract Background Our study aims to investigate inter- and intraindividual effects of trainee teachers’ perceived opportunities of participation in vocational education and training teacher training on their emotional states. Additionally, it is examined whether the subjective reason for action as in intrinsic and extrinsic motivation affects the relationship between perceived participation and emotional states. In this respect, control-value theory constitutes the guiding conceptual framework in our study. Methods On the basis of a longitudinal design with a total of 75 trainee teachers in Germany and a maximum of 58 survey time points during 18-months of teacher training (in total 1790 individual measurements), we traced the perception of participatory opportunities, intrinsic and extrinsic motivation as well as weekly emotional states. Multilevel modeling allowed us to examine both between- as well as within-person effects of perceived participation. Results The results of our multilevel models indicate, among other findings, statistically significant within- and between-person effects of perceived participation opportunities on enjoyment, hope, pride, as well as anger, anxiety, shame, and hopelessness. Furthermore, the attributed subjective reason for action, i.e., the intrinsic or extrinsic motivation, moderates the relationship between perceived opportunities to participate and emotional states. Discussion The findings, which are consistent across different emotions of the same valence and for the two different types of motivation, indicate that those trainee teachers who report low values for intrinsic motivation (or high values for extrinsic motivation) in a given week react more responsively to changes with regard to opportunities for participatory influence.

BackgroundCo-use of tobacco and cannabis is highly prevalent among young US adults. Same-day co-use of tobacco and cannabis (ie, use of both substances on the same day) may increase the extent of use and negative health consequences among young adults. However, much remains unknown about same-day co-use of tobacco and cannabis, in part due to challenges in measuring this complex behavior. Nuanced understanding of tobacco and cannabis co-use in terms of specific products and intensity (ie, quantity of tobacco and cannabis use within a day) is critical to inform prevention and intervention efforts.ObjectiveWe used a daily-diary data collection method via smartphone to capture occurrence of tobacco and cannabis co-use within a day. We examined (1) whether the same route of administration would facilitate co-use of 2 substances on the same day and (2) whether participants would use more tobacco on a day when they use more cannabis.MethodsThis smartphone-based study collected 2891 daily assessments from 147 cigarette smokers (aged 18-26 years, n=76, 51.7% female) during 30 consecutive days. Daily assessments measured type (ie, cigarette, cigarillo, or e-cigarette) and intensity (ie, number of cigarettes or cigarillos smoked or number of times vaping e-cigarettes per day) of tobacco use and type (ie, combustible, vaporized, or edible) and intensity (ie, number of times used per day) of cannabis use. We estimated multilevel models to examine day-level associations between types of cannabis use and each type of tobacco use, as well as day-level associations between intensities of using cannabis and tobacco. All models controlled for demographic covariates, day-level alcohol use, and time effects (ie, study day and weekend vs weekday).ResultsSame-day co-use was reported in 989 of the total 2891 daily assessments (34.2%). Co-use of cigarettes and combustible cannabis (885 of the 2891 daily assessments; 30.6%) was most commonly reported. Participants had higher odds of using cigarettes (adjusted odds ratio [AOR] 1.92, 95% CI 1.31-2.81) and cigarillos (AOR 244.29, 95% CI 35.51-1680.62) on days when they used combustible cannabis. Notably, participants had higher odds of using e-cigarettes on days when they used vaporized cannabis (AOR 23.21, 95% CI 8.66-62.24). Participants reported a greater intensity of using cigarettes (AOR 1.35, 95% CI 1.23-1.48), cigarillos (AOR 2.04, 95% CI 1.70-2.46), and e-cigarettes (AOR 1.48, 95% CI 1.16-1.88) on days when they used more cannabis.ConclusionsTypes and intensities of tobacco and cannabis use within a day among young adult smokers were positively correlated, including co-use of vaporized products. Prevention and intervention efforts should address co-use and pay attention to all forms of use and timeframes of co-use (eg, within a day or at the same time), including co-use of e-cigarettes and vaporized cannabis, to reduce negative health outcomes.

Introduction Method Results Conclusion Existing research highlights the significance of prosocial behavior (voluntary, intentional behavior that results in benefits for another) to people's well‐being. Yet, the extent to which this expected positive relation operates at the within‐person level (e.g., is more prosocial behavior than usual related to a higher than usual level of well‐being?) while taking into account stable interindividual differences, remains a research question that deserves further investigation. In this study, we aimed to explore the relations between prosocial behavior and hedonic (HWB; subjective assessment of life satisfaction and happiness) and eudaimonic (EWB; actualization of human potential in alignment with personal goals, including concepts like meaning in life and closeness to others) well‐being in daily life.Using ecological momentary assessment for 4 weeks, data were collected from two British samples, comprising 82 adolescents and 166 adults.Dynamic Structural Equation Modeling revealed a positive relations between prosocial behavior and HWB/EWB at both between and within‐person levels across the samples.In summary, these findings further support the positive link between prosocial behavior and well‐being in everyday life. Notably, this association was consistent across different age groups (adolescent and adults) at both between and within‐person levels. [ABSTRACT FROM AUTHOR]

Background: Our study aims to investigate inter- and intraindividual effects of trainee teachers' perceived opportunities of participation in vocational education and training teacher training on their emotional states. Additionally, it is examined whether the subjective reason for action as in intrinsic and extrinsic motivation affects the relationship between perceived participation and emotional states. In this respect, control-value theory constitutes the guiding conceptual framework in our study. Methods: On the basis of a longitudinal design with a total of 75 trainee teachers in Germany and a maximum of 58 survey time points during 18-months of teacher training (in total 1790 individual measurements), we traced the perception of participatory opportunities, intrinsic and extrinsic motivation as well as weekly emotional states. Multilevel modeling allowed us to examine both between- as well as within-person effects of perceived participation. Results: The results of our multilevel models indicate, among other findings, statistically significant within- and between-person effects of perceived participation opportunities on enjoyment, hope, pride, as well as anger, anxiety, shame, and hopelessness. Furthermore, the attributed subjective reason for action, i.e., the intrinsic or extrinsic motivation, moderates the relationship between perceived opportunities to participate and emotional states. Discussion: The findings, which are consistent across different emotions of the same valence and for the two different types of motivation, indicate that those trainee teachers who report low values for intrinsic motivation (or high values for extrinsic motivation) in a given week react more responsively to changes with regard to opportunities for participatory influence. [ABSTRACT FROM AUTHOR]

According to environmental sensitivity models, children vary in responsivity to parenting. However, different models propose different patterns, with responsivity to primarily: (1) adverse parenting (adverse sensitive); or (2) supportive parenting (vantage sensitive); or (3) to both (differentially susceptible). This preregistered study tested whether these three responsivity patterns coexist. We used intensive longitudinal data of Dutch adolescents (N = 256, M age = 14.8, 72% female) who bi-weekly reported on adverse and supportive parenting and their psychological functioning (t mean = 17.7, t max = 26). Dynamic Structural Equation Models (DSEM) indeed revealed differential parenting effects. As hypothesized, we found that all three responsivity patterns coexisted in our sample: 5% were adverse sensitive, 3% vantage sensitive, and 26% differentially susceptible. No adolescent appeared unsusceptible, however. Instead, we labeled 28% as unperceptive, because they did not perceive any changes in parenting and scored lower on trait environmental sensitivity than others. Furthermore, unexpected patterns emerged, with 37% responding contrary to parenting theories (e.g., decreased psychological functioning after more parental support). Sensitivity analyses with concurrent effects and parent-reported parenting were performed. Overall, findings indicate that theorized responsivity-to-parenting patterns might coexist in the population, and that there are other, previously undetected patterns that go beyond environmental sensitivity models. [ABSTRACT FROM AUTHOR]

Food insecurity poses a serious threat to children's development, but the mechanisms through which food insecurity undermines child development are far less clear. Specifically, food insecurity may influence children through its effect on parents' psychological well-being and parent–child interactions as a result, but past research on the role of parents is correlational and undermined by omitted variable bias. Using a partially rural, low-income sample of parents living in Pennsylvania (N = 272, 90% mother, Mage = 35) and their school-aged children (ages 4–11, 50% female) alongside daily measures of parent-reported food insecurity and parent and child mood and behavior, we examine how daily changes in food insecurity predict daily changes in parent and child well-being, and the extent to which food insecurity operates through parents to affect children. This method not only explores families' daily, lived experiences of food insecurity, but improves upon the methodological issues undermining past research. Findings indicate that food insecurity influences parent and child well-being on a daily basis, but that associations are stronger and sustain longer for parents than children. Further, parent mood and behavior partially explain the association between daily food insecurity and child mood and behavior, but food insecurity is also independently associated with child well-being. This study is the first to examine daily associations between food insecurity and parent and child well-being. Its implications for food assistance programs, policies, and the future of food insecurity research are discussed. Public Significance Statement: Using daily observations, this study finds that food insecurity is associated with decreased well-being for parents and children on daily basis. Associations are stronger and last longer for parents than children, and daily food insecurity partially works through parent well-being to influence children. These results are an important building block in understanding how the well-documented, long-term effects of food insecurity on child outcomes develop. [ABSTRACT FROM AUTHOR]

AbstractBackground: Relations among psychological variables are assumed to be complex and to vary over time. Personalized networks can model multivariate complex interactions. The development of time-varying networks allows to model the variation of parameters over time. Objectives: We aimed to determine the value of time-varying networks for clinical practice. Methods: We applied time-varying mixed graphical models (TV-MGM) and time-varying vector autoregressive models (TV-VAR) to intensive longitudinal data of nine participants with depressive symptoms (n = 6) or anxiety (n = 3). Results: Most of the participants showed temporal changes in network topology within the assessment period of 30 days. Time-varying networks of participants with small, medium, and large time variability in edge parameters clearly show the different temporal evolvements of dynamic interactions between variables. The case example indicates clinical utility but also limitations to the application of time-varying networks in clinical practice. Conclusion: Time-varying network models provide a data-driven and exploratory approach that could complement current diagnostic standards by reflecting interacting, often mutually reinforcing processes of mental health problems and by accounting for variation over time. They can be used to generate hypotheses for further confirmatory and clinical testing. [ABSTRACT FROM AUTHOR]

Numerous theories suggest that parents and adolescents influence each other in diverse ways; however, whether these influences differ between subgroups or are unique to each family remains uncertain. Therefore, this study explored whether data-driven subgroups of families emerged that exhibited a similar daily interplay between parenting and adolescent affective well-being. To do so, Subgrouping Group Iterative Multiple Model Estimation (S-GIMME) was used to estimate family-specific dynamic network models, containing same- and next-day associations among five parenting practices (i.e., warmth, autonomy support, psychological control, strictness, monitoring) and adolescent positive and negative affect. These family-specific networks were estimated for 129 adolescents (Mage = 13.3, SDage = 1.2, 64% female, 87% Dutch), who reported each day on parenting and their affect for 100 consecutive days. The findings of S-GIMME did not identify data-driven subgroups sharing similar parenting-affect associations. Instead, each family displayed a unique pattern of temporal associations between the different practices and adolescent affect. Thus, the ways in which parenting practices were related to adolescents' affect in everyday life were family specific. [ABSTRACT FROM AUTHOR]

Bidirectional relations have long been of interest in psychology and other social behavioral sciences. In recent years, the widespread use of intensive longitudinal data has provided new opportunities to examine dynamic bidirectional relations between variables. However, most previous studies have focused on the effect of one variable on the other (i.e., cross-lagged effects) rather than the overall effect representing the dynamic interplay between two variables (i.e., feedback effects), which we believe may be due to a lack of relevant methodological guidance. To quantify bidirectional relations as a whole, this study attempted to provide guidance for the estimation and interpretation of feedback effects based on dynamic structural equation models. First, we illustrated the estimation procedure for the average and person-specific feedback effects. Then, to facilitate the interpretation of feedback effects, we established an empirical benchmark by quantitatively synthesizing the results of relevant empirical studies. Finally, we used a set of empirical data to demonstrate how feedback effects can help (a) test theories based on bidirectional relations and (b) reveal correlates of individual differences in bidirectional relations. We also discussed the broad application prospects of feedback effects from a dynamic systems perspective. This study provides guidance for applied researchers interested in further examining feedback effects in bidirectional relations, and the shift from focusing on cross-lagged effects only to a comprehensive consideration of feedback effects may provide new insights into the study of bidirectional relations. [ABSTRACT FROM AUTHOR]

Intensive longitudinal designs are increasingly popular, as are dynamic structural equation models (DSEM) to accommodate unique features of these designs. Many helpful resources on DSEM exist, though they focus on continuous outcomes while categorical outcomes are omitted, briefly mentioned, or considered as a straightforward extension. This viewpoint regarding categorical outcomes is not unwarranted for technical audiences, but there are non-trivial nuances in model building and interpretation with categorical outcomes that are not necessarily straightforward for empirical researchers. Furthermore, categorical outcomes are common given that binary behavioral indicators or Likert responses are frequently solicited as low-burden variables to discourage participant non-response. This tutorial paper is therefore dedicated to providing an accessible treatment of DSEM in Mplus exclusively for categorical outcomes. We cover the general probit model whereby the raw categorical responses are assumed to come from an underlying normal process. We cover probit DSEM and expound why existing treatments have considered categorical outcomes as a straightforward extension of the continuous case. Data from a motivating ecological momentary assessment study with a binary outcome are used to demonstrate an unconditional model, a model with disaggregated covariates, and a model for data with a time trend. We provide annotated Mplus code for these models and discuss interpretation of the results. We then discuss model specification and interpretation in the case of an ordinal outcome and provide an example to highlight differences between ordinal and binary outcomes. We conclude with a discussion of caveats and extensions. [ABSTRACT FROM AUTHOR]

IntroductionPsychosis often develops gradually along a continuum of severity. Little is known about the role of protective factors such as positive affect (PA) in the development of psychotic experiences (PEs). This study investigated i) the temporal (between-day) and contemporaneous (within-day) daily associations between PA and PEs in individuals at different early clinical stages for psychosis and ii) whether these associations differed per clinical stage.MethodsDaily diary data for 90 days came from 96 individuals at risk for psychosis, distributed over four subgroups defined according to the clinical staging model (stages 0–1b). We constructed multilevel models with PA as a predictor of PEs and vice versa. We investigated within- and between-person temporal and contemporaneous associations and tested whether these associations differed among early stages with multilevel moderation analyses.ResultsWe found no within-person temporal effects between PA and PEs in either direction. Contemporaneously, current-day PA predicted current-day PEs (B = −0.14, p < 0.001) and vice versa (B = −0.61, p < 0.001). Between persons, more 90-day PA predicted fewer PEs in the temporal model (B = −0.14, p = 0.03). In addition, more 90-day PEs predicted PA in the temporal (B = −0.26, p < 0.001) and contemporaneous (B = −0.36, p < 0.001) models. The contemporaneous association between PA and PEs was stronger in individuals at ultra-high risk (UHR) for psychosis than in earlier stages.DiscussionOur study supported a significant within-day, bidirectional relationship between PA and PEs. This suggests that a focus on PA and methods to improve PA may be an important addition to early intervention practices, particularly in those at UHR for psychosis.

Introduction: Recent technology has enabled researchers to collect ecological momentary assessments (EMA) to examine within-person correlates of suicidal thoughts. Prior studies examined generalized temporal dynamics of emotions and suicidal thinking over brief periods, but it is not yet known how variable these processes are across people. Method: We use data EMA data delivered over two weeks with youth/young adults (N = 60) who reported past year self-injurious thoughts/behaviors. We used group iterative multiple model estimation (GIMME) to model group- and person-specific associations of negative emotions (i.e., fear, sadness, shame, guilt, and anger) and suicidal thoughts. Results: 29 participants (48.33%) reported at least one instance of a suicidal thought and were included in GIMME models. In group level models, we consistently observed autoregressive effects for suicidal thoughts (e.g., earlier thoughts predicting later thoughts), although the magnitude and direction of this link varied from person-to-person. Among emotions, sadness was most frequently associated with contemporaneous suicidal thoughts, but this was evident for less than half of the sample, while other emotional correlates of suicidal thoughts broadly differed across people. No emotion variable was linked to future suicidal thoughts in >14% of the sample, Conclusions: Emotion-based correlates of suicidal thoughts are heterogeneous across people. Better understanding of the individual-level pathways maintaining suicidal thoughts/behaviors may lead to more effective, personalized interventions.

Just-in-time adaptive interventions (JITAIs) are time-varying adaptive interventions that use frequent opportunities for the intervention to be adapted-weekly, daily, or even many times a day. The microrandomized trial (MRT) has emerged for use in informing the construction of JITAIs. MRTs can be used to address research questions about whether and under what circumstances JITAI components are effective, with the ultimate objective of developing effective and efficient JITAI. The purpose of this article is to clarify why, when, and how to use MRTs; to highlight elements that must be considered when designing and implementing an MRT; and to review primary and secondary analyses methods for MRTs. We briefly review key elements of JITAIs and discuss a variety of considerations that go into planning and designing an MRT. We provide a definition of causal excursion effects suitable for use in primary and secondary analyses of MRT data to inform JITAI development. We review the weighted and centered least-squares (WCLS) estimator which provides consistent causal excursion effect estimators from MRT data. We describe how the WCLS estimator along with associated test statistics can be obtained using standard statistical software such as R (R Core Team, 2019). Throughout we illustrate the MRT design and analyses using the HeartSteps MRT, for developing a JITAI to increase physical activity among sedentary individuals. We supplement the HeartSteps MRT with two other MRTs, SARA and BariFit, each of which highlights different research questions that can be addressed using the MRT and experimental design considerations that might arise. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

In public health research an increasing number of studies is conducted in which intensive longitudinal data is collected in an experience sampling or a daily diary design. Typically, the resulting data is analyzed with a mixed‐effects model or mixed‐effects location scale model because they allow one to examine a host of interesting longitudinal research questions. Here, we introduce an extension of the mixed‐effects location scale model in which measurement error of the observed variables is considered by a latent factor model and in which—in addition to the mean‐or location‐related effects—the residual variance of the latent factor and the parameters of the autoregressive process of this latent factor can differ between persons. We show how to estimate the parameters of the model with a maximum likelihood approach, whose performance is also compared with a Bayesian approach in a small simulation study. We illustrate the models using a real data example and end with a discussion in which we suggest questions for future research. [ABSTRACT FROM AUTHOR]

We introduce a theoretical framework for conceptualizing Psychological Well-Being (PWB) as a process that unfolds over short and longer time-scales. We argue that this framework can be especially useful for studying the change mechanisms in PWB within the context of mobile Health (mHealth) interventions. Four lines of research are considered within this framework to inform the scientific exploration of PWB in the context of mHealth interventions. First, we explore key dynamic characteristics of change in PWB functioning. Second, we discuss PWB intervention response as a learning process (i.e., meaningful and transformational changes in state), reflected as change in key dynamic characteristics of PWB. Third, we explain mechanisms of change in PWB intervention practices through an underlying process of skill development. Fourth, we discuss intervention response heterogeneity within this framework. The approach we outline is intended to articulate currently unanswered, process-oriented research questions about PWB interventions, how they work, and a methodological path forward for exploring them. [ABSTRACT FROM AUTHOR]

Intensive longitudinal data has been widely used to examine reciprocal or causal relations between variables. However, these variables may not be temporally aligned. This study examined the consequences and solutions of the problem of temporal misalignment in intensive longitudinal data based on dynamic structural equation models. First the impact of temporal misalignment on parameter estimation were investigated in a simulation study, which showed that temporal misalignment led to incomparable cross-lagged effects between variables. Then, two solutions, model adjustment and data interpolation, were proposed, and their performance was compared with those of the naive estimation which blindly treating temporally misaligned data as aligned. The simulation results supported the effectiveness of the model adjustment method over the other two methods. Finally, all three methods were applied to two empirical data collected by daily diaries and empirical sampling method, and recommendations were made for collecting and analyzing intensive longitudinal data. [ABSTRACT FROM AUTHOR]

Item selection is a critical decision in modeling psychological networks. The current preregistered two-study research used random selections of 1,000 symptom networks to examine which eating disorder (ED) and co-occurring symptoms are most central in longitudinal networks among individuals with EDs (N = 71, total observations = 6,060) and tested whether centrality changed based on which items were included in the network. Participants completed 2 weeks of ecological momentary assessment (five surveys/day). In Study 1, we obtained initial strength centrality values by estimating an a priori network using eight items with the highest means. We then estimated 1,000 networks and their centrality from a random selection of unique eight-item symptom combinations. We compared the strength centrality from the a priori network to the distribution of strength centrality estimates from the random-item networks. In Study 2, we repeated this procedure in an independent longitudinal dataset (N = 41, total observations = 4,575) to determine if our results generalized across samples. Shame, guilt, worry, and fear of losing control were consistently central across networks, regardless of items included in the network or sample. Results suggest that these symptoms may be important to the structure of ED psychopathology and have implications for how we understand the structure of ED psychopathology. Existing methods for item inclusion in psychological networks may distort the structure of ED symptom networks by either under- or overestimating strength centrality, or by omitting consistently central symptoms that are nontraditional ED symptoms. Future research should consider including these symptoms in models of ED psychopathology. General Scientific Summary: The current studies examine central symptoms in eating disorders (EDs) by taking two approaches to item selection in two independent longitudinal samples. Our results suggest that many of the symptoms important to ED psychopathology that consistently replicate across models and samples are nontraditional ED symptoms (e.g., shame and guilt), supporting arguments that it is essential to extend scientific inquiry of psychological phenomena beyond diagnostic classification frameworks. [ABSTRACT FROM AUTHOR]

The multilevel hidden Markov model (MHMM) is a promising method to investigate intense longitudinal data obtained within the social and behavioral sciences. The MHMM quantifies information on the latent dynamics of behavior over time. In addition, heterogeneity between individuals is accommodated with the inclusion of individual-specific random effects, facilitating the study of individual differences in dynamics. However, the performance of the MHMM has not been sufficiently explored. We performed an extensive simulation to assess the effect of the number of dependent variables (1–8), number of individuals (5–90), and number of observations per individual (100–1600) on the estimation performance of a Bayesian MHMM with categorical data including various levels of state distinctiveness and separation. We found that using multivariate data generally alleviates the sample size needed and improves the stability of the results. Moreover, including variables only consisting of random noise was generally not detrimental to model performance. Regarding the estimation of group-level parameters, the number of individuals and observations largely compensate for each other. However, only the former drives the estimation of between-individual variability. We conclude with guidelines on the sample size necessary based on the level of state distinctiveness and separation and study objectives of the researcher. [ABSTRACT FROM AUTHOR]

Autonomous motivation for self-set goals (pursuing goals for more intrinsic and less extrinsic reasons) has been linked to affective well-being. Using intensive longitudinal data, the present article examines the link between university students' autonomous study motivation with affective well-being and targets fulfillment of the basic psychological needs as a potential mediating factor of this association on the within-person and the between-person level. University students in Germany (N = 488) completed an online questionnaire once a week over up to two semesters, indicating their weekly study motivation, need fulfillment, and affective well-being. Multilevel structural equation models were employed to target (a) the structure of autonomous study motivation and (b) the associations of autonomous study motivation with need fulfillment and affective well-being. Autonomous study motivation was associated with students' positive and negative affect on both levels. In line with predictions by self-determination theory, multilevel mediation models suggested indirect effects via need fulfillment on both levels. Results highlight the central role of autonomous study motivation and need fulfillment in university students' socioemotional adjustment. A better understanding of this socioemotional adjustment of university students may be an important step for increasing overall study satisfaction and developing interventions to reduce study dropout. Educational Impact and Implications Statement: The reasons why students pursue their studies have been suggested to directly affect students' fulfillment of their basic psychological needs which in turn affects well-being. Results of this study are in line with this assertion and highlight that the reasons for pursuing one's studies may be crucial to better understand students' socioemotional adjustment. This is an important step toward maintaining and improving student mental health and developing interventions to reduce dropout and promote educational success in higher education. [ABSTRACT FROM AUTHOR]

Externalizing psychopathology is highly prevalent in children and adolescents. Yet, current understanding of the externalizing psychopathology spectrum is predominantly founded on cross-sectional and conventional longitudinal designs. Compared to these designs, intensive longitudinal data have greater ecological validity and provide insight into within-person fluctuations and short-term developmental dynamics. In this Special Issue, we bring together a selection of 10 innovative and original empirical articles to demonstrate the benefits of intensive longitudinal data for understanding the development of the externalizing psychopathology spectrum during childhood and adolescence, as well as one thoughtful commentary from leaders in the externalizing psychopathology field. In this Introduction to the Special Issue, we describe the articles included in this Special Issue in relation to study designs, timescales, samples, and statistical modeling techniques. We conclude by considering the implications of intensive longitudinal data for informing and enhancing our understanding of externalizing psychopathology with child and adolescent samples, as well as critical future research directions. [ABSTRACT FROM AUTHOR]

This study revisits the association between coercive parent-adolescent interactions and adolescent externalizing behaviors. Specifically, we investigate the moment-to-moment coercive exchanges between parents and adolescents and how these dynamic processes map to the long-term development of substance use and antisocial behavior from middle adolescence to early adulthood. We collected videotaped observations with 794 adolescents (ages 16–17 years) and their parents during interactions and coded their real-time behavioral exchanges. State Space Grid analyses were used to measure the proportion of time in which each parent-adolescent dyad engaged in the Dyadic Coercion region as an indicator of rigidity in dyadic coercion. We also measured adolescents' substance use and antisocial behavior at ages 16–17, ages 18–19, and ages 21–22. The enduring impact of parent-adolescent coercive interaction on substance use and antisocial behavior was tested using categorical latent growth curve models and path models. Adolescents with more coercive interactions with parents showed higher rates of increase in alcohol use and higher levels of antisocial behavior through early adulthood. The findings highlight the unique contribution of using intensive data to understand coercive interactions on a micro-timescale and how these dynamics influence long-term development in externalizing behaviors. Implications for intervention studies are discussed. [ABSTRACT FROM AUTHOR]

Intensive longitudinal methods (e.g., daily diary) inform understanding of dynamic processes by parsing within-person state-like fluctuations from stable between-person trait-like differences. In this exploratory study, we investigated whether self-reported callous-unemotional (CU) traits (callousness, uncaring) demonstrated daily fluctuations, as well as whether daily CU traits were associated with multiple forms of daily emotional and behavioral functioning. A sample of 99 adolescents (55.8% female; Mage = 14.60 years) provided baseline information and completed a naturalistic 30-day diary reporting on CU traits, positive and negative affect, and emotional and conduct problems in their daily lives. Dynamic structural equation modeling revealed that many CU traits items showed within-person autoregressive and cross-lagged links; however, there was substantial between-person variation in within-person fluctuations across items. At the subscale level, cross-day associations were observed between callousness and uncaring, conduct problems and uncaring, positive affect and callousness, negative affect and emotional problems, and emotional problems and negative affect. By harnessing intensive longitudinal data, our findings provide preliminary state-level evidence of CU traits, as well as functional information with regards to CU traits and emotional and behavioral problems in daily life. We consider the implications of our findings in terms of informing future CU traits intensive longitudinal evaluations. [ABSTRACT FROM AUTHOR]

Extant research on physiological dysregulation in children has focused on point-in-time measures and absolute mean levels of physiology. However, these methods do not capture dynamic fluctuations in physiology that characterize dysregulation. In the present work, we aimed to assess whether physiological dysregulation as captured by fluctuations rather than mean levels would differentiate between children with and without clinically elevated levels of externalizing behavior. As an exploratory approach, we examined fluctuations in children's physiological responses (i.e., root mean square of successive differences [RMSSD] in beat-to-beat heart rate intervals) to social transgression scenarios across 15 short-term measurement occasions (5-second bins). Controlling for mean RMSSD, as well as emotional and cognitive correlates of externalizing behavior (i.e., sympathy and inhibitory control), children with externalizing difficulties exhibited greater within-person fluctuations in RMSSD (i.e., physiological dysregulation) compared to children without externalizing difficulties. The present findings provide preliminary support for using intensive longitudinal data comprised of short-term physiological measurements and point to the centrality of within-child physiological variability as a marker of dysregulation, particularly amongst children with externalizing disorders for whom self-regulation is a core challenge. [ABSTRACT FROM AUTHOR]

Longitudinal research demonstrates that child ADHD symptoms and behaviors exhibit reciprocal associations with parenting behaviors over time. However, minimal research has investigated these associations and their dynamic links at the daily level. Intensive longitudinal data can disentangle stable between-person differences from within-person fluctuations and reveal nuanced short-term family dynamics on a micro timescale. Using 30-day daily diary data from a community sample of 86 adolescents (Mage = 14.5, 55% female, 56% White, 22% Asian) and latent differential equation modeling, this study examined the links between perceived daily parental warmth and ADHD symptoms as coupled dynamical systems. The results show that the magnitude of fluctuations in perceived daily parental warmth generally remains stable, while elevated ADHD symptoms return to their normal level over time. Perceived parental warmth is sensitive to change in ADHD symptoms such that adolescents feel that their parents will fine-tune their warmth with gradual changes when adolescents demonstrate heightened symptoms. There are substantial between-family differences in these regulating system dynamics. Among families with more baseline parental non-harsh discipline, both perceived parental warmth and ADHD symptoms tend to be more stable and fluctuate less often. Intensive longitudinal data and dynamical systems approaches offer a new lens to uncover short-term family dynamics and adolescent adjustment at a refined micro level. Future research should explore antecedents and consequences of between-family differences in these short-term family dynamics on multiple timescales. [ABSTRACT FROM AUTHOR]

Introduction: Nonsuicidal self‐injury (NSSI) is a prevalent and concerning behavior and its risk pathways require a greater understanding, particularly in predicting short‐term risk. Although the literature has supported a between‐person link among NSSI and alcohol use, limited research has directly examined the nuances of this relationship at the within‐person level using intensive longitudinal data. Method: Utilizing two independent samples (total n = 85), the current study examined bidirectional, concurrent and prospective risk relationships between NSSI and alcohol, considering both urges and behavior engagement, via ecological momentary assessment. Results: Findings demonstrate concurrent, within‐person relationships between NSSI urges and alcohol urges, as well as alcohol use. Alternatively, prospective between‐person findings demonstrated negative relationships between NSSI urges and alcohol use, as well as alcohol urges and NSSI acts; however, this may represent suppression effects as associations were positive with the removal of autoregressive effects. Conclusions: Together, findings support proximal risk relationships between NSSI and alcohol experiences that, for urges in particular, is bidirectional. [ABSTRACT FROM AUTHOR]

Dynamic Structural Equation Model (DSEM) is a powerful statistical modeling approach that has recently gained popularity among researchers studying intensive longitudinal data. Despite its exciting potential, the stability and replicability of DSEM is yet to be closely examined. This study empirically investigates DSEM using recently published data to explore its strengths and potential limitations. The results show that while some of its parameter estimates are stable, others are characterized by substantial variation as a function of seemingly innocuous initial model estimation conditions. Indeed, some parameters fluctuate between significance and non-significance for the same model estimated using the same data. The instability of DSEM estimates poses a serious threat to the internal and external validity of conclusions drawn from its analyses, challenging the reproducibility of findings from applied research. Given the recent focus on the replication crisis in psychology, it is critical to address these issues as the popularity of DSEM in psychological research continues to rise. Several potential solutions are investigated to address this problem and recommendations of best practice are offered to applied researchers who plan to use DSEM in intensive longitudinal data analysis. [ABSTRACT FROM AUTHOR]

Abstract Background Collection of intensive longitudinal health outcomes allows joint modeling of their mean (location) and variability (scale). Focusing on the location of the outcome, measures to detect influential subjects in longitudinal data using standard mixed-effects regression models (MRMs) have been widely discussed. However, no existing approach enables the detection of subjects that heavily influence the scale of the outcome. Methods We propose applying mixed-effects location scale (MELS) modeling combined with commonly used influence measures such as Cook’s distance and DFBETAS to fill this gap. In this paper, we provide a framework for researchers to follow when trying to detect influential subjects for both the scale and location of the outcome. The framework allows detailed examination of each subject’s influence on model fit as well as point estimates and precision of coefficients in different components of a MELS model. Results We simulated two common scenarios in longitudinal healthcare studies and found that influence measures in our framework successfully capture influential subjects over 99% of the time. We also re-analyzed data from a health behavior study and found 4 particularly influential subjects, among which two cannot be detected by influence analyses via regular MRMs. Conclusion The proposed framework can help researchers detect influential subject(s) that will be otherwise overlooked by influential analysis using regular MRMs and analyze all data in one model despite influential subjects.

Time-varying effect modeling (TVEM), a statistical technique for modeling dynamic patterns of change, presents new opportunities to study biobehavioral health processes. TVEM is particularly useful when applied to intensive longitudinal data (ILD) because it permits highly flexible modeling of outcomes over continuous time, as well as of associations between variables and moderation effects. TVEM coupled with ILD is ideal for the study of addiction. This article provides a general overview of using TVEM, particularly when applied to ILD, to better enable addiction scientists to conduct novel analyses that are important to realizing the dynamics of addiction-related processes. It presents an empirical example using ecological momentary assessment data from participants throughout their first 90 days of addiction recovery to estimate the (1) associations between morning craving and same-day recovery outcomes, (2) association between morning positive and negative affect and same-day recovery outcomes and (3) time-varying moderation effects of affect on the association between morning craving and recovery outcomes.We provide a didactic overview in implementing and interpreting the aims and results, including equations, computer syntax and reference resources. Our results highlight how affect operates as both a time-varying risk and protective factor on recovery outcomes, particularly when considered in combination with experiences of craving (i.e. dynamic moderation). We conclude by discussing our results, recent innovations and future directions of TVEM for advancing addiction science, including how 'time' can be operationalized to probe new research questions. [ABSTRACT FROM AUTHOR]

Background: Collection of intensive longitudinal health outcomes allows joint modeling of their mean (location) and variability (scale). Focusing on the location of the outcome, measures to detect influential subjects in longitudinal data using standard mixed-effects regression models (MRMs) have been widely discussed. However, no existing approach enables the detection of subjects that heavily influence the scale of the outcome. Methods: We propose applying mixed-effects location scale (MELS) modeling combined with commonly used influence measures such as Cook's distance and DFBETAS to fill this gap. In this paper, we provide a framework for researchers to follow when trying to detect influential subjects for both the scale and location of the outcome. The framework allows detailed examination of each subject's influence on model fit as well as point estimates and precision of coefficients in different components of a MELS model. Results: We simulated two common scenarios in longitudinal healthcare studies and found that influence measures in our framework successfully capture influential subjects over 99% of the time. We also re-analyzed data from a health behavior study and found 4 particularly influential subjects, among which two cannot be detected by influence analyses via regular MRMs. Conclusion: The proposed framework can help researchers detect influential subject(s) that will be otherwise overlooked by influential analysis using regular MRMs and analyze all data in one model despite influential subjects. [ABSTRACT FROM AUTHOR]

Introduction: Both variable- and person-centered approaches identify grandiose and vulnerable themes in pathological narcissism (PN). However, person-centered results rely on cross-sectional data, preventing identification of subtypes of individuals through transitory self-states. Methods: We perform a cluster analysis on the joint trajectory of daily ratings of grandiose narcissism (GN) and vulnerable narcissism (VN), collected during a 28-day experience sampling (N = 196 participants). Results: The best partition—identified by multiple criteria—includes three clusters: a "low PN" cluster displays below-average levels of both daily GN and VN; a "high VN" cluster displays average daily GN and above-average levels of daily VN; a "high GN" cluster shows above-average levels of daily GN and below-average levels of daily VN. Significant inter-cluster differences emerge on both daily and trait measures of narcissism, and on trait measures of self-esteem and shame, but less sharply on impairment in personality structure. There is no inter-cluster difference on the variability and instability of daily narcissism. Discussion: We conclude that the constructs of daily GN and VN define corresponding groups of individuals with either high GN or VN, the latter group being more distressed. Yet, this group is also less "pure" in its narcissistic characterization, showing at least some levels of trait and state GN. [ABSTRACT FROM AUTHOR]

Psychology has witnessed a dramatic increase in the use of intensive longitudinal data (ILD) to study within-person processes, accompanied by a growing number of indices used to capture individual differences in within-person dynamics (WPD). The reliability of WPD indices is rarely investigated and reported in empirical studies. Unreliability in these indices can bias parameter estimates and yield erroneous conclusions. We propose an approach to (a) estimate the reliability and (b) correct for sampling error of WPD indices using "Level-1 variance-known" (V-known) multilevel models (Raudenbush & Bryk, 2002). When WPD indices are calculated for each individual, the sampling variance of the observed WPD scores is typically falsely assumed to be zero. V-known models replace this "zero" with an approximate sampling variance fixed at Level 1 to estimate the true variance of the index at Level 2, following random effects meta-analysis principles. We demonstrate how V-known models can be applied to a broad range of emotion dynamics commonly derived from ILD, including indices of the average level (mean), variability (intraindividual standard deviation), instability (probability of acute change), bipolarity (correlation), differentiation (intraclass correlation), inertia (autocorrelation), and relative variability (relative standard deviation) of emotions. A simulation study shows the usefulness of V-known models to recover the true reliability of these indices. Using a 21-day diary study, we illustrate the implementation of the proposed approach to obtain reliability estimates and to correct for unreliability of WPD indices in real data. The techniques may facilitate psychometrically sound inferences from WPD indices in this burgeoning research area. [ABSTRACT FROM AUTHOR]

Various theoretical accounts suggest that within-person effects relating to everyday experiences (assessed, e.g., via experience sampling studies or daily diary studies) are a central element for understanding between-person differences in future outcomes. In this regard, it is often assumed that the within-person effect of a time-varying predictor X on a time-varying mediator M contributes to the long-term development in an outcome variable Y. In the present work, we demonstrate that traditional multilevel mediation approaches fall short in capturing the proposed mechanism, however. We suggest that a model in which between-person differences in the strength of within-person effects predict the outcome Y mediated via mean levels in M more adequately aligns with the presumed theoretical account that within-person effects shape between-person differences. Using simulated data, we show that the central parameters of this multilevel structural equation model can be recovered well in most of the investigated scenarios. Our approach has important implications for whether or not to control for mean levels in models with within-person effects as predictors. We illustrate the model using empirical data targeting the question if the within-person association of occurrence of daily stressors (X) with daily experiences of negative affect (M) longitudinally predicts between-person differences in change in depressive symptoms (Y). Implications for other multilevel designs and intervention studies are discussed. [ABSTRACT FROM AUTHOR]

Serial dependence is present in most time series data sets collected in psychological research. This paper investigates the implications of various approaches for handling such serial dependence, when one is interested in the linear effect of a time-varying covariate on the time-varying criterion. Specifically, the serial dependence is either neglected, corrected for by specifying autocorrelated residuals, or modeled by including a lagged version of the criterion as an additional predictor. Using both empirical and simulated data, we showcase that the obtained results depend considerably on which approach is selected. We discuss how these differences can be explained by understanding the restrictions imposed under the various approaches. Based on the insight that all three approaches are restricted versions of an autoregressive distributed lag model, we demonstrate that accessible statistical tools, such as information criteria and likelihood-ratio tests can be used to justify a chosen approach empirically. [ABSTRACT FROM AUTHOR]

Although small sample sizes represent an important issue, few studies investigated the requirements in dynamic latent variable model frameworks (e.g., dynamic structural equation modeling, DSEM; dynamic latent class analysis, DLCA). We conduct a small sample performance study of Bayesian estimation for the non-linear dynamic latent class structural equation model which generalizes DSEM and DLCA to include time-dependent latent class transitions. We simulate data using a two-level (non-linear) dynamic latent class model with a varying number of subjects ( N = 10 , 25 , 50 , 75 ) and time points (T = 10, 25, 50) which are in our main focus among other simulation conditions. The results show that at least a sample size of N ≥ 50 with T ≥ 25 is required to ensure good estimates. Using diffuse priors on the between level, especially for the (co-)variance parameters and the factor loadings should be avoided. [ABSTRACT FROM AUTHOR]