View in EDS

Zooming out on Education: Making Valid Psychological Inferences from Large-Scale Assessment Data

Saved in:
Bibliographic Details
Title: Zooming out on Education: Making Valid Psychological Inferences from Large-Scale Assessment Data
Language: English
Authors: Denis Dumas (ORCID 0000-0002-8446-4720), Benjamin Goecke, Sofiia Kagan, Selina Weiss
Source: Educational Psychology Review. 2026 38(1).
Availability: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Peer Reviewed: Y
Page Count: 15
Publication Date: 2026
Document Type: Journal Articles
Reports - Research
Descriptors: Educational Psychology, Measurement, Evaluation Methods, Group Testing, Data Interpretation, Data Use, Inferences, Population Distribution, Research Problems
DOI: 10.1007/s10648-025-10110-7
ISSN: 1040-726X
1573-336X
Abstract: Openly available datasets from large-scale educational assessments like PISA, PIRLS, TIMSS, or NAEP, among others, are some of the most valuable public resources in the education sciences. Understandably, educational psychologists are interested in analyzing these datasets to advance their research. But, in contrast to the kinds of psychoeducational assessments about which educational psychologists are typically trained, large-scale assessments are not designed to make inferences about the mental attributes of students themselves, but about the "population distributions" of those attributes. This seemingly subtle distinction leads to a host of analytic, epistemological, and interpretative challenges that can cause confusion and dissuade educational psychologists from using these interesting datasets. In this theoretical paper, we seek to clarify the kinds of inferences that can be validly made with large-scale assessment data, and justify those inferences based on the psychometric and score-generation procedures that underpin them. This paper is not intended to be a technical or methodological guide to analyzing large-scale assessment data but instead serves as an epistemic and conceptual introduction to the topic. After appropriately accounting for various sources of error in large-scale assessment proficiency estimates, researchers can make interesting inferences about education and psychology, but those inferences can only be validly made at the population level, not about individual students.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1501830
Database: ERIC
FullText Text:
  Availability: 0
Header DbId: eric
DbLabel: ERIC
An: EJ1501830
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Zooming out on Education: Making Valid Psychological Inferences from Large-Scale Assessment Data
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Denis+Dumas%22">Denis Dumas</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0002-8446-4720">0000-0002-8446-4720</externalLink>)<br /><searchLink fieldCode="AR" term="%22Benjamin+Goecke%22">Benjamin Goecke</searchLink><br /><searchLink fieldCode="AR" term="%22Sofiia+Kagan%22">Sofiia Kagan</searchLink><br /><searchLink fieldCode="AR" term="%22Selina+Weiss%22">Selina Weiss</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Educational+Psychology+Review%22"><i>Educational Psychology Review</i></searchLink>. 2026 38(1).
– Name: Avail
  Label: Availability
  Group: Avail
  Data: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 15
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2026
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Educational+Psychology%22">Educational Psychology</searchLink><br /><searchLink fieldCode="DE" term="%22Measurement%22">Measurement</searchLink><br /><searchLink fieldCode="DE" term="%22Evaluation+Methods%22">Evaluation Methods</searchLink><br /><searchLink fieldCode="DE" term="%22Group+Testing%22">Group Testing</searchLink><br /><searchLink fieldCode="DE" term="%22Data+Interpretation%22">Data Interpretation</searchLink><br /><searchLink fieldCode="DE" term="%22Data+Use%22">Data Use</searchLink><br /><searchLink fieldCode="DE" term="%22Inferences%22">Inferences</searchLink><br /><searchLink fieldCode="DE" term="%22Population+Distribution%22">Population Distribution</searchLink><br /><searchLink fieldCode="DE" term="%22Research+Problems%22">Research Problems</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1007/s10648-025-10110-7
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 1040-726X<br />1573-336X
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Openly available datasets from large-scale educational assessments like PISA, PIRLS, TIMSS, or NAEP, among others, are some of the most valuable public resources in the education sciences. Understandably, educational psychologists are interested in analyzing these datasets to advance their research. But, in contrast to the kinds of psychoeducational assessments about which educational psychologists are typically trained, large-scale assessments are not designed to make inferences about the mental attributes of students themselves, but about the "population distributions" of those attributes. This seemingly subtle distinction leads to a host of analytic, epistemological, and interpretative challenges that can cause confusion and dissuade educational psychologists from using these interesting datasets. In this theoretical paper, we seek to clarify the kinds of inferences that can be validly made with large-scale assessment data, and justify those inferences based on the psychometric and score-generation procedures that underpin them. This paper is not intended to be a technical or methodological guide to analyzing large-scale assessment data but instead serves as an epistemic and conceptual introduction to the topic. After appropriately accounting for various sources of error in large-scale assessment proficiency estimates, researchers can make interesting inferences about education and psychology, but those inferences can only be validly made at the population level, not about individual students.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2026
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1501830
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1501830
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s10648-025-10110-7
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 15
    Subjects:
      – SubjectFull: Educational Psychology
        Type: general
      – SubjectFull: Measurement
        Type: general
      – SubjectFull: Evaluation Methods
        Type: general
      – SubjectFull: Group Testing
        Type: general
      – SubjectFull: Data Interpretation
        Type: general
      – SubjectFull: Data Use
        Type: general
      – SubjectFull: Inferences
        Type: general
      – SubjectFull: Population Distribution
        Type: general
      – SubjectFull: Research Problems
        Type: general
    Titles:
      – TitleFull: Zooming out on Education: Making Valid Psychological Inferences from Large-Scale Assessment Data
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Denis Dumas
      – PersonEntity:
          Name:
            NameFull: Benjamin Goecke
      – PersonEntity:
          Name:
            NameFull: Sofiia Kagan
      – PersonEntity:
          Name:
            NameFull: Selina Weiss
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 12
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 1040-726X
            – Type: issn-electronic
              Value: 1573-336X
          Numbering:
            – Type: volume
              Value: 38
            – Type: issue
              Value: 1
          Titles:
            – TitleFull: Educational Psychology Review
              Type: main
ResultId 1