Best Laid Plans: A Guide to Reporting Preregistration Deviations

Willroth, Emily C.; Atherton, Olivia E.

doi:10.1177/25152459231213802

Cited by 21 publications

(5 citation statements)

References 33 publications

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“…Using guidance from these studies as well as Wysocki et al (2022), the current study does not distinguish between predictors and covariates and treats all factors as potential moderators of loneliness trajectories. This deviates from the preregistration and is disclosed for transparency in the deviations table (see Table S1 in the Supplemental Material available with the online version of the article; Willroth & Atherton, 2024). Thus, we present fully adjusted models here but also provide unadjusted results and exploratory analyses with covariate-by-slope interactions (see online supplementary materials at https:// emoriebeck.shinyapps.io/loneliness-trajectories/).…”

Section: Methodsmentioning

confidence: 99%

Do We Become More Lonely With Age? A Coordinated Data Analysis of Nine Longitudinal Studies

Graham,

Beck,

Jackson

et al. 2024

Psychol Sci

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Loneliness is a pervasive experience with adverse impacts on health and well-being. Despite its significance, notable gaps impede a full understanding of how loneliness changes across the adult life span and what factors influence these changes. To address this, we conducted a coordinated data analysis of nine longitudinal studies encompassing 128,118 participants ages 13 to 103 from over 20 countries. Using harmonized variables and models, we examined loneliness trajectories and predictors. Analyses revealed that loneliness follows a U-shaped curve, decreasing from young adulthood to midlife and increasing in older adulthood. These patterns were consistent across studies. Several baseline factors (i.e., sex, marital status, physical function, education) were linked to loneliness levels, but few moderated the loneliness trajectories. These findings highlight the dynamic nature of loneliness and underscore the need for targeted interventions to reduce social disparities throughout adulthood.

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Section: Methodsmentioning

confidence: 99%

Do We Become More Lonely With Age? A Coordinated Data Analysis of Nine Longitudinal Studies

Graham,

Beck,

Jackson

et al. 2024

Psychol Sci

Self Cite

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“…We preregistered our rationale, hypotheses, provisional variable coding scheme, and analysis plan for this study on OSF (https://osf.io/hqtga/?view_only=7c48ea0a10e940378abd5c03f922ee63) on July 14, 2023, prior to data access (granted July 19, 2023). We list our deviations from this preregistration (all reported via amendments and made to increase the validity of our analyses; Willroth & Atherton, 2024) in Supplement 1 (https://osf.io/6h9ad?view_only=6f27e8a6ddfb4dd19503ac50dcda7f53). We provide online supplementary materials, study materials, as well as R and Python code necessary to reproduce analyses, figures, and tables at https://osf.io/h6n3a/?view_only=6f27e8a6ddfb4dd19503ac50dcda7f53.…”

Section: Preregistration and Other Open Sciences Practicesmentioning

confidence: 99%

Minority stress and structural stigma predict well-being in European LGBTQ+ parents

Siegel,

Steyrl,

Goldberg

et al. 2024

Preprint

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Objective: This study tested whether exposure to minority stress and structural stigma across multiple levels of the family system were associated with two indicators of well-being (life satisfaction and depressive symptoms) in LGBTQ+ parents across 19 European countries.Background: Minority stress (i.e., identity-based stress resulting from systemic oppression) and structural stigma (i.e., hostile legal environments, prejudicial social attitudes) are heterogeneous, yet well-documented risk factors of reduced well-being within LGBTQ+ populations. However, a comprehensive, intersectionality-informed assessment that stratifies both concepts across multiple levels of the family system (i.e., the individual, couple, and family level) is lacking for LGBTQ+ parents.Method: Using data from the EU LGBTI Survey 2019, a sample of 3,808 LGBTQ+ parents from 19 European countries was analyzed. Associations between self-reported minority stress indicators, objective structural stigma indicators, sociodemographic predictors, and well-being were tested using non-linear, machine learning-based techniques (gradient boosted decision tree models). Results: Supporting preregistered hypotheses, exposure to individual-level minority stress and individual- and family-level structural stigma predicted life satisfaction and depressive symptoms. Couple-level minority stress predicted life satisfaction, but not depressive symptoms, and family-level minority stress predicted neither. Economic burdens and being trans emerged as sociodemographic risk factors.Conclusions: Exposure to minority stress and structural stigma, particularly within highly stigmatizing regions, are risk factors for LGBTQ+ parents’ well-being. Future research should examine the role of family-level minority stress further using validated measures.

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“…Data are publicly available or available by application (see online materials for details on accessing specific samples). There were a few minor deviations from the pre-registration, and these are described in Table S1 (Willroth & Atherton, 2024). Finally, rendered results are available as a standalone web page on GitHub (https://emoriebeck.github.io/data-synthesistutorial) and in an online R Shiny webapp (https://emoriebeck.shinyapps.io/data-synth-tutorial/).…”

Section: Transparency and Opennessmentioning

confidence: 99%

A Taxonomy of Data Synthesis: A Tutorial

Beck,

Willroth,

Delius

et al. 2024

Preprint

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As more data is shared and concerns over the replicability, reproducibility, and generalizability of psychological and other social sciences continue, more researchers aim to conduct multi-study or multi-sample research and synthesize findings via data synthesis, using different parameterizations of individual participant meta-analysis. However, there is no overarching framework organizing different parameterizations and a relatively small number of simulation-based or empirical examples testing or comparing these parameterizations. Thus, this tutorial paper has three main goals. First, we provide an overview of six parameterizations of individual participant meta-analysis, which we organize into a taxonomy based on different features of each parameterization (e.g., sample-specific parameters, meta-analytic parameters, number of models required). Second, using empirical data from 26,205 participants across 11 longitudinal studies, we provide a tutorial estimating each parameterization by investigating prospective meta-analytic and sample-specific associations between the Big Five personality traits and crystallized abilities along with four moderators of these associations. Finally, we compare convergence and divergence of findings across methods. We found that Openness is a robust predictor of crystallized abilities across samples and methods and that there were few moderators of personality trait-crystallized ability associations. Across methods, we largely see convergence in model estimates, with some exceptions. We conclude by making recommendations and providing a flow chart for choosing the most appropriate parameterization of data synthesis given a particular team’s research goals, questions, data availability, and model features.

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Best Laid Plans: A Guide to Reporting Preregistration Deviations

Cited by 21 publications

References 33 publications

Do We Become More Lonely With Age? A Coordinated Data Analysis of Nine Longitudinal Studies

Do We Become More Lonely With Age? A Coordinated Data Analysis of Nine Longitudinal Studies

Minority stress and structural stigma predict well-being in European LGBTQ+ parents

A Taxonomy of Data Synthesis: A Tutorial

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