Advancing our understanding of cognitive development and motor vehicle crash risk: A multiverse representation analysis

Mirman, Jessica Hafetz; Murray, Aja Louise; Mirman, Daniel; Adams, Stephanie

doi:10.1016/j.cortex.2021.01.017

Cited by 5 publications

(3 citation statements)

References 60 publications

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“…A tree diagram can serve as a descriptive graphical approach to visualizing multiverse analysis results: the decision points are shown as branches and the results are shown as nodes (leaves), color-coded to show the outcome of the key statistical test. As example data we use a subset of models from our recent multiverse analysis of the relation between working memory development and motor vehicle crash risk among adolescent drivers (Mirman, Murray, Mirman, & Adams, 2021). The critical hypothesis was that the rate (slope) of working memory development during adolescence (11-20 years old) would be shallower for drivers who report having been in a crash compared to drivers who never crashed.…”

Section: Multiverse Analysismentioning

confidence: 99%

Graphing common stats tables

Mirman¹,

Al²,

Jh³

2021

Preprint

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Tables are effective as a compact and precise record, but they are less conducive to holistic communication or discovery of patterns. Researchers often resort to tables for reporting relationships among several variables or groups, or when reporting the results of statistical analyses. This tutorial discusses and demonstrates approaches to visualizing results in three situations that are relatively common in psychological science and where tables are often used: (1) factor analysis, (2) comparisons among multiple groups on multiple dimensions, and (3) multiverse analysis. The visualization strategies are implemented in R software (with ggplot2 and DiagrammeR packages), though they can also be implemented in other software. These visualizations trade off the compact precision of tables in favor a visual representation strategy that makes complex patterns more intuitive, both for exploratory purposes and for communicating results.

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Section: Multiverse Analysismentioning

confidence: 99%

Graphing common stats tables

Mirman¹,

Al²,

Jh³

2021

Preprint

Self Cite

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“…Multiverse analysis represents an invaluable step toward a transparent science. The method has become increasingly popular since it has been developed, and it has been applied in several experimental contexts, ranging from cognitive development and risk perception (Mirman et al, 2021), assessment of parental behavior (Modecki et al, 2020), and memory task (Wessel et al, 2020). While parts of the applications remain confined to exploratory purposes with the scope to define brief guidelines for conducting a multiverse analysis (Dragicevic et al, 2019;Liu et al, 2020), other studies aim to stimulate interest in this method as a robustness assessment for mediation analysis (Rijnhart et al, 2021), or an exhaustive modelling approach (Frey et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Post-selection Inference in Multiverse Analysis (PIMA): an inferential framework based on the sign flipping score test

Girardi¹,

Vesely²,

Lakens³

et al. 2022

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When analyzing data researchers make some decisions that are either arbitrary, based on subjective beliefs about the data generating process, or for which equally justifiable alternative choices could have been made. This wide range of data-analytic choices can be abused, and has been one of the underlying causes of the replication crisis in several fields. Recently, the introduction of multiverse analysis provides researchers with a method to evaluate the stability of the results across reasonable choices that could be made when analyzing data. Multiverse analysis is confined to a descriptive role, lacking a proper and comprehensive inferential procedure. Recently, specification curve analysis adds an inferential procedure to multiverse analysis, but this approach is limited to simple cases related to the linear model, and only allows researchers to infer whether at least one specification rejects the null hypothesis, but not which specifications should be selected. In this paper we present a Post-selection Inference approach to Multiverse Analysis (PIMA) which is a flexible and general inferential approach that accounts for all possible models, i.e., the multiverse of reasonable analyses. The approach allows for a wide range of data specifications (i.e. pre-processing) and any generalized linear model; it allows testing the null hypothesis of a given predictor not being associated with the outcome, by merging information from all reasonable models of multiverse analysis, and provides strong control of the family-wise error rate such that it allows researchers to claim that the null-hypothesis can be rejected for each specification that shows a significant effect. The inferential proposal is based on a conditional resampling procedure. We formally prove that the Psychometrika SubmissionOctober 7, 2022 4Type I error rate is controlled, and compute the statistical power of the test through a simulation study. Finally, we apply the PIMA procedure to the analysis of a real data set about coronavirus disease 2019 (COVID-19) vaccine hesitancy before and after the 2020 lockdown in Italy. We end with practical recommendations to consider when performing the proposed procedure.

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“…This second category is considered to be the most influential [ 7 ]. To date, neurobiological and cognitive maturation models are considered the gold standard for understanding problem behaviors in adolescence, especially in the area of at-risk behaviors, such as driving [ 8 ]. However, in line with a multidisciplinary approach typical of the developmental psychopathology framework [ 9 ], considering this phenomenon as a result of the complex interplay of neurobiological, cognitive and individual psychological factors is of cardinal importance.…”

Section: Introductionmentioning

confidence: 99%

The Role of Emotional Dysregulation, Impulsivity Traits and Aggressive Behaviors in Adolescents Who Sustain Multiple Motor-Vehicle Crashes

2022

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Adolescents tend to experience multiple motor-vehicle crashes (MVCs). Recent literature has thoroughly explored the psychological consequences following an MVC, but prior psychological functioning, the ability to regulate one’s emotions and tendencies to impulsivity and aggressive behaviors have been far less explored. This study aims to explore the emotional–behavioral functioning, measured with the Youth Self-Report (YSR); emotional dysregulation, measured with the Difficulties in Emotional Regulation Scale (DERS); impulsivity traits, measured with the Barratt Impulsiveness Scale (BIS-11); and the capacity to be mindful, measured with the Mindful Attention Awareness Scale (MAAS), in adolescents that have experienced one or more MVCs in a year. N = 295 adolescents who visited an emergency department for MVCs were divided in two groups based on the number of MVCs they had sustained over the course of a year. The adolescents in Group 1 sustained more than three MVCs, and adolescents in Group 2 sustained only one. Participants were assessed through self-report questionnaires. Adolescents showed difficulties in emotional regulation, impulsivity traits, aggressive behaviors and a low capacity to be mindful. These results may be useful in the creation of intervention and prevention programs focused on emotional awareness targets for adolescents.

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Advancing our understanding of cognitive development and motor vehicle crash risk: A multiverse representation analysis

Cited by 5 publications

References 60 publications

Graphing common stats tables

Graphing common stats tables

Post-selection Inference in Multiverse Analysis (PIMA): an inferential framework based on the sign flipping score test

The Role of Emotional Dysregulation, Impulsivity Traits and Aggressive Behaviors in Adolescents Who Sustain Multiple Motor-Vehicle Crashes

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