ReviewPsychometrika2023
Comparing Bayesian Variable Selection to Lasso Approaches for Applications in Psychology.
Review in Psychometrika, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
15 citing papers in PubMed, 17 citations in OpenAlex.
- The Signature Emotions of a Happy Life, a Meaningful Life, and a Psychologically Rich Life.Affective science · 2026Article
- The influence of anti-involution training on the critical thinking of young healthcare professionals in dental outpatient clinics: a pre-post quasi-experimental study.BMC medical education · 2026Article
- How to apply Bayesian stochastic search variable selection with multiply imputed data.Psychological methods · 2026Article
- A clinically interpretable prediction model for acute mortality in patients with pneumonia requiring mechanical ventilation.Respiratory research · 2026Article
- Deciphering N7-methylguanosine-driven immune dysregulation in unexplained recurrent spontaneous abortion based on transcriptome data and experimental validation.European journal of medical research · 2025Article
- A Two-Step Variable Selection Strategy for Multiply Imputed Survival Data Using Penalized Cox Models.Bioengineering (Basel, Switzerland) · 2025Article
- Body mass index mediates the association between heavy metal exposure and overactive bladder risk: insights from a nationally representative cross-sectional study using explainable machine learning.Translational andrology and urology · 2025Article
- Polyanhydride-Based Microparticles for Programmable Pulsatile Release of Diphtheria Toxoid (DT) for Single-Injection Self-Boosting Vaccines.Advanced materials (Deerfield Beach, Fla.) · 2025Article
- Predicting Physical Aggression among Schizophrenia Patients in Rural Communities of Southwestern China.Alpha psychiatry · 2025Article
- Using Machine Learning to Predict Treatment Outcome in a Concatenated Dataset of Youth Anxiety Treatments.Child psychiatry and human development · 2025Article
- An introduction to Sequential Monte Carlo for Bayesian inference and model comparison-with examples for psychology and behavioral science.Behavior research methods · 2025Article
- Estimating the Minimum Sample Size for Neural Network Model Fitting-A Monte Carlo Simulation Study.Behavioral sciences (Basel, Switzerland) · 2025Article
- Integrated multi-omics analysis and experimental investigation of mitochondrial dynamics-related genes: molecular subtypes, immune landscape, and prognostic implications in lung adenocarcinoma.Frontiers in immunology · 2025Article
- PreBP: an interpretable, optimized ensemble framework using routine complete blood count for rapid pathogen identification in bacterial pneumonia.Frontiers in bioinformatics · 2025Article
- Establishment and validation of apnea risk prediction models in preterm infants: a retrospective case control study.BMC pediatrics · 2024Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
In the current paper, we review existing tools for solving variable selection problems in psychology. Modern regularization methods such as lasso regression have recently been introduced in the field and are incorporated into popular methodologies, such as network analysis. However, several recognized limitations of lasso regularization may limit its suitability for psychological research. In this paper, we compare the properties of lasso approaches used for variable selection to Bayesian variable selection approaches. In particular we highlight advantages of stochastic search variable selection (SSVS), that make it well suited for variable selection applications in psychology. We demonstrate these advantages and contrast SSVS with lasso type penalization in an application to predict depression symptoms in a large sample and an accompanying simulation study. We investigate the effects of sample size, effect size, and patterns of correlation among predictors on rates of correct and false inclusion and bias in the estimates. SSVS as investigated here is reasonably computationally efficient and powerful to detect moderate effects in small sample sizes (or small effects in moderate sample sizes), while protecting against false inclusion and without over-penalizing true effects. We recommend SSVS as a flexible framework that is well-suited for the field, discuss limitations, and suggest directions for future development.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.