{"title":"Solving variables with Monte Carlo simulation experiments: A stochastic root-solving approach.","authors":"R Philip Chalmers","doi":"10.1037/met0000689","DOIUrl":null,"url":null,"abstract":"<p><p>Despite their popularity and flexibility, questions remain regarding how to optimally solve particular unknown variables of interest using Monte Carlo simulation experiments. This article reviews two common approaches based on either performing deterministic iterative searches with noisy objective functions or by constructing interpolation estimates given fitted surrogate functions, highlighting the inefficiencies and inferential concerns of both methods. To address these limitations, and to fill a gap in existing Monte Carlo experimental methodology, a novel algorithm termed the probabilistic bisection algorithm with bolstering and interpolations (ProBABLI) is presented with the goal providing efficient, consistent, and unbiased estimates (with associated confidence intervals) for the stochastic root equations found in Monte Carlo simulation research. Properties of the ProBABLI approach are demonstrated using practical sample size planning applications for independent samples <i>t</i> tests and structural equation models given target power rates, precision criteria, and expected power functions that incorporate prior beliefs. (PsycInfo Database Record (c) 2024 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":null,"pages":null},"PeriodicalIF":7.6000,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Psychological methods","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1037/met0000689","RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHOLOGY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Despite their popularity and flexibility, questions remain regarding how to optimally solve particular unknown variables of interest using Monte Carlo simulation experiments. This article reviews two common approaches based on either performing deterministic iterative searches with noisy objective functions or by constructing interpolation estimates given fitted surrogate functions, highlighting the inefficiencies and inferential concerns of both methods. To address these limitations, and to fill a gap in existing Monte Carlo experimental methodology, a novel algorithm termed the probabilistic bisection algorithm with bolstering and interpolations (ProBABLI) is presented with the goal providing efficient, consistent, and unbiased estimates (with associated confidence intervals) for the stochastic root equations found in Monte Carlo simulation research. Properties of the ProBABLI approach are demonstrated using practical sample size planning applications for independent samples t tests and structural equation models given target power rates, precision criteria, and expected power functions that incorporate prior beliefs. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
期刊介绍:
Psychological Methods is devoted to the development and dissemination of methods for collecting, analyzing, understanding, and interpreting psychological data. Its purpose is the dissemination of innovations in research design, measurement, methodology, and quantitative and qualitative analysis to the psychological community; its further purpose is to promote effective communication about related substantive and methodological issues. The audience is expected to be diverse and to include those who develop new procedures, those who are responsible for undergraduate and graduate training in design, measurement, and statistics, as well as those who employ those procedures in research.