An extension of the mixed‐effects growth model that considers between‐person differences in the within‐subject variance and the autocorrelation

Nestler, Steffen

doi:10.1002/sim.9280

Cited by 15 publications

(25 citation statements)

References 31 publications

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“…3 More information about the FLUX and FLIP sample data can be found in Nestler and Hofmann (2020). The FLUX and FLIP data have also been used elsewhere (Nestler, 2020a(Nestler, , 2020b(Nestler, , 2020cNestler & Humberg, 2020). 4 To analyze the stability of interindividual differences in distribution parameters and in state contingencies, the data were split into four blocks of 14 days for Sample 1 and into two blocks of 42 days for Sample 2.…”

Section: Data Accessibility Statementmentioning

confidence: 99%

Daily Dynamics of Grandiose Narcissism: Distribution, Stability, and Trait Relations of Admiration and Rivalry States and State Contingencies

Mota¹,

Mielke²,

Kroencke³

et al. 2022

Preprint

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On the basis of the Narcissistic Admiration and Rivalry Concept and recent theories on narcissistic pursuit of status, we provide a differentiated analysis of individual differences in the within-person dynamics of grandiose narcissism. In two daily diary studies (Sample 1: 56 days; Sample 2: 82 days; total participants: N = 198; total observations: N = 12,404), we investigated the degree, stability, and trait correlates of individual differences in average narcissism-relevant states (perceived status success, perceived admiration and rejection, positive and negative affect, and assertive and hostile behavior) as well as individual differences in within-person contingencies between these states. The results indicated substantial and stable between-person differences in averaged states that were related to their corresponding narcissism trait self-reports. State contingencies showed substantial strength, significant interindividual differences, and stability across the 56 and 82 days, respectively. We only found weak support for associations between state contingencies and trait narcissism self-reports. These findings support a differentiated approach to the conceptualization and assessment of grandiose state narcissism and call for even more comprehensive and fine-grained investigations.

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Section: Data Accessibility Statementmentioning

confidence: 99%

Daily Dynamics of Grandiose Narcissism: Distribution, Stability, and Trait Relations of Admiration and Rivalry States and State Contingencies

Mota¹,

Mielke²,

Kroencke³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Survival analytical approaches that are widely adopted in medical and epidemiological research, such as Cox proportional-hazards regression models, which assume loglinearity in covariates, could also be used to examine the timevarying effects of covariates (55). Finally, Bayesian approaches could be applied to handle time-varying coefficient models with greater complexity (e.g., multiple random effects) (56).…”

Section: Discussionmentioning

confidence: 99%

Rethinking Autism Intervention Science: A Dynamic Perspective

2022

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Recent advances in longitudinal methodologies for observational studies have contributed to a better understanding of Autism as a neurodevelopmental condition characterized by within-person and between-person variability over time across behavioral domains. However, this finer-grained approach to the study of developmental variability has yet to be applied to Autism intervention science. The widely adopted experimental designs in the field—randomized control trials and quasi-experimental designs—hold value for inferring treatment effects; at the same time, they are limited in elucidating what works for whom, why, and when, given the idiosyncrasies of neurodevelopmental disorders where predictors and outcomes are often dynamic in nature. This perspective paper aims to serve as a primer for Autism intervention scientists to rethink the way we approach predictors of treatment response and treatment-related change using a dynamic lens. We discuss several empirical gaps, and potential methodological challenges and opportunities pertaining to: (1) capturing finer-grained treatment effects in specific behavioral domains as indexed by micro-level within-person changes during and beyond intervention; and (2) examining and modeling dynamic prediction of treatment response. Addressing these issues can contribute to enhanced study designs and methodologies that generate evidence to inform the development of more personalized interventions and stepped care approaches for individuals on the heterogeneous spectrum of Autism with changing needs across development.

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“…An application of a MELS model for ordinal data was discussed by Hedeker et al 13 Furthermore, future research might take into consideration the autocorrelation of observation‐level residuals. A recent development by Nestler 14 extends the MELS models to include AR(1) autocorrelation influenced by subject‐level covariates and a random subject effect for the autocorrelation.…”

Section: Discussionmentioning

confidence: 99%

Defining R‐squared measures for mixed‐effects location scale models

Zhang

Hedeker

2022

Statistics in Medicine

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Ecological momentary assessment and other modern data collection technologies facilitate research on both within-subject and between-subject variability of health outcomes and behaviors. For such intensively measured longitudinal data, Hedeker et al extended the usual two-level mixed-effects model to a two-level mixed-effects location scale (MELS) model to accommodate covariates' influence as well as random subject effects on both mean (location) and variability (scale) of the outcome. However, there is a lack of existing standardized effect size measures for the MELS model. To fill this gap, our study extends Rights and Sterba's framework of R 2 measures for multilevel models, which is based on model-implied variances, to MELS models. Our proposed framework applies to two different specifications of the random location effects, namely, through covariate-influenced random intercepts and through random intercepts combined with random slopes of observation-level covariates. We also provide an R function, R2MELS, that outputs summary tables and visualization for values of our R 2 measures. This framework is validated through a simulation study, and data from a health behaviors study and a depression study are used as examples to demonstrate this framework. These R 2 measures can help researchers provide greater interpretation of their findings using MELS models.

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An extension of the mixed‐effects growth model that considers between‐person differences in the within‐subject variance and the autocorrelation

Cited by 15 publications

References 31 publications

Daily Dynamics of Grandiose Narcissism: Distribution, Stability, and Trait Relations of Admiration and Rivalry States and State Contingencies

Daily Dynamics of Grandiose Narcissism: Distribution, Stability, and Trait Relations of Admiration and Rivalry States and State Contingencies

Rethinking Autism Intervention Science: A Dynamic Perspective

Defining R‐squared measures for mixed‐effects location scale models

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