View in EDS

Effect size comparison for populations with an application in psychology.

Saved in:
Bibliographic Details
Title: Effect size comparison for populations with an application in psychology.
Authors: Chattopadhyay, Bhargab (AUTHOR), Bapat, Sudeep R. (AUTHOR)
Source: British Journal of Mathematical & Statistical Psychology. Feb2026, Vol. 79 Issue 1, p146-172. 27p.
Subjects: Effect sizes (Statistics), Statistical power analysis, Simulation methods & models, Resource allocation, Sequential machine theory, Standard deviations, Statistics
Abstract: Effect size estimates are now widely reported in various behavioural studies. In precise estimation or power analysis studies, sample size planning revolves around the standard error (or variance) of the effect size. Note these studies are carried out under sampling‐budget constraints. Hence, the optimum allocation of resources to populations with different inherent population variances is paramount as this affects the effect size variance. In this paper, a general effect size meant to compare two population characteristics is defined, and under budget constraints, we aim to optimize the variance of the general effect size. In the process, we use sequential theory to arrive at optimum sample sizes of the corresponding populations to achieve minimum variance. The sequential method we developed is a distribution‐free method and does not need knowledge of population parameters. Mathematical justification of the characteristics enjoyed by our sequential method is laid out along with simulation studies. Thus, our work has wide applicability in the effect size comparison context. [ABSTRACT FROM AUTHOR]
Copyright of British Journal of Mathematical & Statistical Psychology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
FullText Text:
  Availability: 0
Header DbId: pbh
DbLabel: Psychology and Behavioral Sciences Collection
An: 190789399
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Effect size comparison for populations with an application in psychology.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Chattopadhyay%2C+Bhargab%22">Chattopadhyay, Bhargab</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bapat%2C+Sudeep+R%2E%22">Bapat, Sudeep R.</searchLink> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22British+Journal+of+Mathematical+%26+Statistical+Psychology%22">British Journal of Mathematical & Statistical Psychology</searchLink>. Feb2026, Vol. 79 Issue 1, p146-172. 27p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Effect+sizes+%28Statistics%29%22">Effect sizes (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+power+analysis%22">Statistical power analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Simulation+methods+%26+models%22">Simulation methods & models</searchLink><br /><searchLink fieldCode="DE" term="%22Resource+allocation%22">Resource allocation</searchLink><br /><searchLink fieldCode="DE" term="%22Sequential+machine+theory%22">Sequential machine theory</searchLink><br /><searchLink fieldCode="DE" term="%22Standard+deviations%22">Standard deviations</searchLink><br /><searchLink fieldCode="DE" term="%22Statistics%22">Statistics</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Effect size estimates are now widely reported in various behavioural studies. In precise estimation or power analysis studies, sample size planning revolves around the standard error (or variance) of the effect size. Note these studies are carried out under sampling‐budget constraints. Hence, the optimum allocation of resources to populations with different inherent population variances is paramount as this affects the effect size variance. In this paper, a general effect size meant to compare two population characteristics is defined, and under budget constraints, we aim to optimize the variance of the general effect size. In the process, we use sequential theory to arrive at optimum sample sizes of the corresponding populations to achieve minimum variance. The sequential method we developed is a distribution‐free method and does not need knowledge of population parameters. Mathematical justification of the characteristics enjoyed by our sequential method is laid out along with simulation studies. Thus, our work has wide applicability in the effect size comparison context. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of British Journal of Mathematical & Statistical Psychology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=pbh&AN=190789399
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1111/bmsp.70001
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 27
        StartPage: 146
    Subjects:
      – SubjectFull: Effect sizes (Statistics)
        Type: general
      – SubjectFull: Statistical power analysis
        Type: general
      – SubjectFull: Simulation methods & models
        Type: general
      – SubjectFull: Resource allocation
        Type: general
      – SubjectFull: Sequential machine theory
        Type: general
      – SubjectFull: Standard deviations
        Type: general
      – SubjectFull: Statistics
        Type: general
    Titles:
      – TitleFull: Effect size comparison for populations with an application in psychology.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Chattopadhyay, Bhargab
      – PersonEntity:
          Name:
            NameFull: Bapat, Sudeep R.
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 02
              Text: Feb2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 00071102
          Numbering:
            – Type: volume
              Value: 79
            – Type: issue
              Value: 1
          Titles:
            – TitleFull: British Journal of Mathematical & Statistical Psychology
              Type: main
ResultId 1