View in EDS

Exploring Number of Response Categories in Factor Analysis: Implications for Sample Size

Saved in:
Bibliographic Details
Title: Exploring Number of Response Categories in Factor Analysis: Implications for Sample Size
Language: English
Authors: Fatih Orçan (ORCID 0000-0003-1727-0456)
Source: International Journal of Assessment Tools in Education. 2025 12(2):341-352.
Availability: International Journal of Assessment Tools in Education. Pamukkale University, Faculty of Education, Kinikli Campus, Denizli 20070, Turkey. e-mail: ijate.editor@gmail.com; Web site: https://dergipark.org.tr/en/pub/ijate
Peer Reviewed: Y
Page Count: 12
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Descriptors: Factor Analysis, Factor Structure, Item Response Theory, Sample Size, Test Items, Accuracy, Error of Measurement, Monte Carlo Methods, Structural Equation Models, Test Length, Goodness of Fit
ISSN: 2148-7456
Abstract: Factor analysis is a statistical method to explore the relationships among observed variables and identify latent structures. It is crucial in scale development and validity analysis. Key factors affecting the accuracy of factor analysis results include the type of data, sample size, and the number of response categories. While some studies suggest that reliability improves with more response categories, others find no significant relationship between the number of response categories and reliability. A key consideration is that increasing the number of response categories can introduce measurement errors, especially when there are too many categories for participants to respond accurately. The study examines how different numbers of response categories affect sample size requirements in factor analysis, particularly under misspecified and correctly specified models. MonteCarloSEM package in R was used to simulate data sets based on sample size, number of response categories, model specification, and test length. Results show that a higher number of categories helps reduce bias and improve model fit, especially in smaller samples. However, when sample sizes are small or when fewer categories are used, increasing the number of items or the number of categories can improve parameter estimation. The findings suggest that for optimal results, researchers should carefully balance sample size, number of items, and response categories, particularly in studies with categorical data.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1476455
Database: ERIC
FullText Text:
  Availability: 0
CustomLinks:
  – Url: https://eric.ed.gov/contentdelivery/servlet/ERICServlet?accno=EJ1476455
    Name: ERIC Full Text
    Category: fullText
    Text: Full Text from ERIC
Header DbId: eric
DbLabel: ERIC
An: EJ1476455
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Exploring Number of Response Categories in Factor Analysis: Implications for Sample Size
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Fatih+Orçan%22">Fatih Orçan</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-1727-0456">0000-0003-1727-0456</externalLink>)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22International+Journal+of+Assessment+Tools+in+Education%22"><i>International Journal of Assessment Tools in Education</i></searchLink>. 2025 12(2):341-352.
– Name: Avail
  Label: Availability
  Group: Avail
  Data: International Journal of Assessment Tools in Education. Pamukkale University, Faculty of Education, Kinikli Campus, Denizli 20070, Turkey. e-mail: ijate.editor@gmail.com; Web site: https://dergipark.org.tr/en/pub/ijate
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 12
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2025
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Factor+Analysis%22">Factor Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Factor+Structure%22">Factor Structure</searchLink><br /><searchLink fieldCode="DE" term="%22Item+Response+Theory%22">Item Response Theory</searchLink><br /><searchLink fieldCode="DE" term="%22Sample+Size%22">Sample Size</searchLink><br /><searchLink fieldCode="DE" term="%22Test+Items%22">Test Items</searchLink><br /><searchLink fieldCode="DE" term="%22Accuracy%22">Accuracy</searchLink><br /><searchLink fieldCode="DE" term="%22Error+of+Measurement%22">Error of Measurement</searchLink><br /><searchLink fieldCode="DE" term="%22Monte+Carlo+Methods%22">Monte Carlo Methods</searchLink><br /><searchLink fieldCode="DE" term="%22Structural+Equation+Models%22">Structural Equation Models</searchLink><br /><searchLink fieldCode="DE" term="%22Test+Length%22">Test Length</searchLink><br /><searchLink fieldCode="DE" term="%22Goodness+of+Fit%22">Goodness of Fit</searchLink>
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 2148-7456
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Factor analysis is a statistical method to explore the relationships among observed variables and identify latent structures. It is crucial in scale development and validity analysis. Key factors affecting the accuracy of factor analysis results include the type of data, sample size, and the number of response categories. While some studies suggest that reliability improves with more response categories, others find no significant relationship between the number of response categories and reliability. A key consideration is that increasing the number of response categories can introduce measurement errors, especially when there are too many categories for participants to respond accurately. The study examines how different numbers of response categories affect sample size requirements in factor analysis, particularly under misspecified and correctly specified models. MonteCarloSEM package in R was used to simulate data sets based on sample size, number of response categories, model specification, and test length. Results show that a higher number of categories helps reduce bias and improve model fit, especially in smaller samples. However, when sample sizes are small or when fewer categories are used, increasing the number of items or the number of categories can improve parameter estimation. The findings suggest that for optimal results, researchers should carefully balance sample size, number of items, and response categories, particularly in studies with categorical data.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2025
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1476455
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1476455
RecordInfo BibRecord:
  BibEntity:
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 12
        StartPage: 341
    Subjects:
      – SubjectFull: Factor Analysis
        Type: general
      – SubjectFull: Factor Structure
        Type: general
      – SubjectFull: Item Response Theory
        Type: general
      – SubjectFull: Sample Size
        Type: general
      – SubjectFull: Test Items
        Type: general
      – SubjectFull: Accuracy
        Type: general
      – SubjectFull: Error of Measurement
        Type: general
      – SubjectFull: Monte Carlo Methods
        Type: general
      – SubjectFull: Structural Equation Models
        Type: general
      – SubjectFull: Test Length
        Type: general
      – SubjectFull: Goodness of Fit
        Type: general
    Titles:
      – TitleFull: Exploring Number of Response Categories in Factor Analysis: Implications for Sample Size
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Fatih Orçan
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-electronic
              Value: 2148-7456
          Numbering:
            – Type: volume
              Value: 12
            – Type: issue
              Value: 2
          Titles:
            – TitleFull: International Journal of Assessment Tools in Education
              Type: main
ResultId 1