Kernel Smoothing Item Response Theory in R: A Didactic
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| Title: | Kernel Smoothing Item Response Theory in R: A Didactic |
|---|---|
| Language: | English |
| Authors: | Effatpanah, Farshad (ORCID |
| Source: | Practical Assessment, Research & Evaluation. May 2023 28. |
| Availability: | Center for Educational Assessment. 813 North Pleasant Street, Amherst, MA 01002. e-mail: pare@umass.edu; Tel: 413-577-2180; Web site: https://scholarworks.umass.edu/pare |
| Peer Reviewed: | Y |
| Page Count: | 28 |
| Publication Date: | 2023 |
| Intended Audience: | Researchers |
| Document Type: | Journal Articles Reports - Evaluative |
| Descriptors: | Item Response Theory, Feedback (Response), Mathematical Models, Item Analysis, Psychological Testing, Educational Assessment, Test Anxiety, Children, Measures (Individuals), Factor Analysis |
| ISSN: | 1531-7714 |
| Abstract: | Item response theory (IRT) refers to a family of mathematical models which describe the relationship between latent continuous variables (attributes or characteristics) and their manifestations (dichotomous/polytomous observed outcomes or responses) with regard to a set of item characteristics. Researchers typically use parametric IRT (PIRT) models to measure educational and psychological latent variables. However, PIRT models are based on a set of strong assumptions that often are not satisfied. For this reason, non-parametric IRT (NIRT) models can be more desirable. An exploratory NIRT approach is kernel smoothing IRT (KS-IRT; Ramsay, 1991) which estimates option characteristic curves by non-parametric kernel smoothing technique. This approach only gives graphical representations of item characteristics in a measure and provides preliminary feedback about the performance of items and measures. Although KS-IRT is not a new approach, its application is far from widespread, and it has limited applications in psychological and educational testing. The purpose of the present paper is to give a reader-friendly introduction to the KS-IRT, and then use the KernSmoothIRT package (Mazza et al., 2014, 2022) in R to straightforwardly demonstrate the application of the approach using data of Children's Test Anxiety scale. |
| Abstractor: | As Provided |
| Entry Date: | 2023 |
| Accession Number: | EJ1392871 |
| Database: | ERIC |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://eric.ed.gov/contentdelivery/servlet/ERICServlet?accno=EJ1392871 Name: ERIC Full Text Category: fullText Text: Full Text from ERIC |
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| Header | DbId: eric DbLabel: ERIC An: EJ1392871 AccessLevel: 3 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Kernel Smoothing Item Response Theory in R: A Didactic – Name: Language Label: Language Group: Lang Data: English – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Effatpanah%2C+Farshad%22">Effatpanah, Farshad</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-3970-5588">0000-0003-3970-5588</externalLink>)<br /><searchLink fieldCode="AR" term="%22Baghaei%2C+Purya%22">Baghaei, Purya</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-5765-0413">0000-0002-5765-0413</externalLink>) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="SO" term="%22Practical+Assessment%2C+Research+%26+Evaluation%22"><i>Practical Assessment, Research & Evaluation</i></searchLink>. May 2023 28. – Name: Avail Label: Availability Group: Avail Data: Center for Educational Assessment. 813 North Pleasant Street, Amherst, MA 01002. e-mail: pare@umass.edu; Tel: 413-577-2180; Web site: https://scholarworks.umass.edu/pare – Name: PeerReviewed Label: Peer Reviewed Group: SrcInfo Data: Y – Name: Pages Label: Page Count Group: Src Data: 28 – Name: DatePubCY Label: Publication Date Group: Date Data: 2023 – Name: Audience Label: Intended Audience Group: Audnce Data: Researchers – Name: TypeDocument Label: Document Type Group: TypDoc Data: Journal Articles<br />Reports - Evaluative – Name: Subject Label: Descriptors Group: Su Data: <searchLink fieldCode="DE" term="%22Item+Response+Theory%22">Item Response Theory</searchLink><br /><searchLink fieldCode="DE" term="%22Feedback+%28Response%29%22">Feedback (Response)</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+Models%22">Mathematical Models</searchLink><br /><searchLink fieldCode="DE" term="%22Item+Analysis%22">Item Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Psychological+Testing%22">Psychological Testing</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Assessment%22">Educational Assessment</searchLink><br /><searchLink fieldCode="DE" term="%22Test+Anxiety%22">Test Anxiety</searchLink><br /><searchLink fieldCode="DE" term="%22Children%22">Children</searchLink><br /><searchLink fieldCode="DE" term="%22Measures+%28Individuals%29%22">Measures (Individuals)</searchLink><br /><searchLink fieldCode="DE" term="%22Factor+Analysis%22">Factor Analysis</searchLink> – Name: ISSN Label: ISSN Group: ISSN Data: 1531-7714 – Name: Abstract Label: Abstract Group: Ab Data: Item response theory (IRT) refers to a family of mathematical models which describe the relationship between latent continuous variables (attributes or characteristics) and their manifestations (dichotomous/polytomous observed outcomes or responses) with regard to a set of item characteristics. Researchers typically use parametric IRT (PIRT) models to measure educational and psychological latent variables. However, PIRT models are based on a set of strong assumptions that often are not satisfied. For this reason, non-parametric IRT (NIRT) models can be more desirable. An exploratory NIRT approach is kernel smoothing IRT (KS-IRT; Ramsay, 1991) which estimates option characteristic curves by non-parametric kernel smoothing technique. This approach only gives graphical representations of item characteristics in a measure and provides preliminary feedback about the performance of items and measures. Although KS-IRT is not a new approach, its application is far from widespread, and it has limited applications in psychological and educational testing. The purpose of the present paper is to give a reader-friendly introduction to the KS-IRT, and then use the KernSmoothIRT package (Mazza et al., 2014, 2022) in R to straightforwardly demonstrate the application of the approach using data of Children's Test Anxiety scale. – Name: AbstractInfo Label: Abstractor Group: Ab Data: As Provided – Name: DateEntry Label: Entry Date Group: Date Data: 2023 – Name: AN Label: Accession Number Group: ID Data: EJ1392871 |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1392871 |
| RecordInfo | BibRecord: BibEntity: Languages: – Text: English PhysicalDescription: Pagination: PageCount: 28 Subjects: – SubjectFull: Item Response Theory Type: general – SubjectFull: Feedback (Response) Type: general – SubjectFull: Mathematical Models Type: general – SubjectFull: Item Analysis Type: general – SubjectFull: Psychological Testing Type: general – SubjectFull: Educational Assessment Type: general – SubjectFull: Test Anxiety Type: general – SubjectFull: Children Type: general – SubjectFull: Measures (Individuals) Type: general – SubjectFull: Factor Analysis Type: general Titles: – TitleFull: Kernel Smoothing Item Response Theory in R: A Didactic Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Effatpanah, Farshad – PersonEntity: Name: NameFull: Baghaei, Purya IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Type: published Y: 2023 Identifiers: – Type: issn-electronic Value: 1531-7714 Numbering: – Type: volume Value: 28 Titles: – TitleFull: Practical Assessment, Research & Evaluation Type: main |
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