Perspectives on Bayesian inference and their implications for data analysis.

Levy, Roy; McNeish, Daniel

doi:10.1037/met0000443

Cited by 18 publications

(9 citation statements)

References 115 publications

(249 reference statements)

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“…As Zyphur et al (2021) noted, maximum likelihood estimation can have difficulty "when estimating timevarying unit effects and multiple lagged effects" (p. 2), whereas Bayesian estimation can fit the models. Such a use of Bayesian analysis is consistent with computational frequentism (Levy and McNeish, 2023), which involves "turn [ing] to Bayesian methods to bypass complexities posed by frequentist methods" (p. 721). Zyphur et al (2021) further noted that choosing the parameterization for the unit effect factor loadings is arbitrary, with alternatives necessary to obtain model identification.…”

Section: Resultsmentioning

confidence: 81%

Longitudinal processes among humility, social justice activism, transcendence, and well-being

Jankowski,

Sandage,

Wang

et al. 2024

Front. Psychol.

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IntroductionExisting research shows positive associations between humility and well-being, and between civic engagement and well-being. Rarely have humility, civic engagement, and well-being been examined together. We build off of previous cross-sectional findings and a prior longitudinal study that used three waves of data and found significant positive bivariate correlations between humility and the presence of life purpose across time points.MethodsExtending these previous findings, we used six waves of data obtained from graduate students at 18 seminaries across North America (N = 574; Mage = 31.54; 46.7% female; 65.3% White) to explore the dynamic associations among humility and life purpose, along with horizontal transcendence (an indicator of the attitudinal dimension of civic engagement) and social justice activism (an indicator for the behavioral dimension). We explored reciprocal short-run processes and dynamic long-run effects using a general cross-lagged panel model.Results and discussionWe found robust evidence for a reciprocal influence between the presence of life purpose and horizontal transcendence, and long-run effects for initial levels of life purpose to influence later levels of horizontal transcendence. We also found long-run effects for the influence of initial levels of life purpose on later levels of humility, and initial levels of social justice activism on later levels of horizontal transcendence. Implications center on the use of the findings for planning future one-time life purpose and social justice interventions to affect changes in humility and horizontal transcendence.

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Section: Resultsmentioning

confidence: 81%

Longitudinal processes among humility, social justice activism, transcendence, and well-being

Jankowski,

Sandage,

Wang

et al. 2024

Front. Psychol.

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“…Bayesian estimation is preferred with DSEM for computational purposes because maximum likelihood and other frequentist methods often encounter convergence issues or are intractable with many latent variables (Asparouhov et al, 2018). This reflects a "Bayes as Computational Frequentism" approach whereby computational advantages of Markov Chain Monte Carlo motivate Bayesian methods rather than a philosophical Bayesian approach where subjective beliefs are built into the model (Levy & McNeish, 2022). Because the motivation for Bayesian methods is computational rather than philosophical, the interpretation of the results minimally deviates from a model estimated with frequentist methods and the Mplus output changes little when the estimation Bayesian compared to when the estimation is frequentist.…”

Section: Model Fitting and Resultsmentioning

confidence: 99%

Intensive Longitudinal Mediation in Mplus

McNeish¹,

MacKinnon²

2022

Preprint

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Much of the existing longitudinal mediation literature focuses on panel data where relatively few repeated measures are collected over a relatively broad timespan. However, technological advances in data collection (e.g., smartphones, wearables) have led to a proliferation of short duration, densely collected longitudinal data in behavioral research. These intensive longitudinal data differ in structure and focus relative to traditionally collected panel data. As a result, existing methodological resources do not necessarily extend to nuances present in the recent influx of intensive longitudinal data and designs. In this tutorial, we first cover potential limitations of traditional longitudinal mediation models to accommodate unique characteristics of intensive longitudinal data. Then, we discuss how recently developed dynamic structural equation models (DSEM) may be well-suited for mediation modeling with intensive longitudinal data and can overcome some of the limitations associated with traditional approaches. We describe four increasingly complex intensive longitudinal mediation models: (1) stationary models where the indirect effect is constant over time and people, (2) person-specific models where the indirect effect varies across people, (3) dynamic models where the indirect effect varies across time, and (4) cross-classified models where the indirect effect varies across time and people. We apply each model to a running example featuring a mobile health intervention designed to improve health behavior of individuals with binge eating disorder. In each example, we provide annotated Mplus code and interpretation of the output to guide empirical researchers through mediation modeling with this increasingly popular type of longitudinal data.

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“…Many perspectives exist that underlie the scientific rationale for using Bayesian methods (Levy & McNeish, 2021). For example, a model may be too complex for frequentist estimation methods, such as maximum likelihood.…”

Section: Bayesian Framework Overviewmentioning

confidence: 99%

Bilingual oral language development among dual language immersion students: Use of a Bayesian approach with language learning progressions

Ryan,

Botter,

Luna

et al. 2024

Applied Psycholinguistics

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The dual language development of dual language immersion (DLI) students, although often examined at the domain level (e.g., listening or reading), remains understudied for more specific skills (e.g., word, sentence, or discourse). This study examines the eleven-month progression of oral language skills in a picture description task in two languages (French and English) for early-elementary (Transitional Kindergarten through first grade) DLI students (N = 42). Using Bayesian methods, which estimate parameters using both the data and prior information, we describe French and English growth patterns as measured by learning progressions whose focus is on language features at the word, sentence, and discourse levels. For French oral language, we found evidence of meaningful positive linear growth for all language features, whereas for English oral language, meaningful linear positive growth was only detected for sophistication of topic vocabulary. Overall, coming from a French-speaking household was associated with steeper French oral language trajectories, but coming from an English-only household did not specifically impact English oral language trajectories. In both languages, grade level influenced the trajectories of some—but not all—features. We conclude with theoretical and practical implications, advocating for a language progression approach in instruction and research on bilingualism.

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Perspectives on Bayesian inference and their implications for data analysis.

Cited by 18 publications

References 115 publications

Longitudinal processes among humility, social justice activism, transcendence, and well-being

Longitudinal processes among humility, social justice activism, transcendence, and well-being

Intensive Longitudinal Mediation in Mplus

Bilingual oral language development among dual language immersion students: Use of a Bayesian approach with language learning progressions

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