Development of a Regression Model for Estimating the Effects of Assumption Violations on Type I Error Rates in the Student's T-Test: Implications for Practitioners.
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| Title: | Development of a Regression Model for Estimating the Effects of Assumption Violations on Type I Error Rates in the Student's T-Test: Implications for Practitioners. |
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| Language: | English |
| Authors: | Newman, Isadore, Hall, Rosalie J., Fraas, John |
| Peer Reviewed: | N |
| Page Count: | 25 |
| Publication Date: | 2003 |
| Document Type: | Reports - Research Speeches/Meeting Papers |
| Descriptors: | Estimation (Mathematics), Monte Carlo Methods, Multiple Regression Analysis, Regression (Statistics) |
| Abstract: | Multiple linear regression is used to model the effects of violating statistical assumptions on the likelihood of making a Type I error. This procedure is illustrated for the student's t-test (for independent groups) using data from previous Monte Carlo studies in which the actual alpha levels associated with violations of the normality assumption, homogeneity of variance, or unbalanced designs were determined. The observed Type I error rates were recorded, along with information coding the type and extent of statistical assumption violation. The resulting linear models had R squared values of 0.88 to 0.91 and adjusted R squared values of 0.87 to 0.90. The results of the suggested methodological approach: (1) reveal the feasibility of developing multiple linear regression models to predict actual Type I error rates based on various assumption violation conditions for the independent groups-t-test; (2) suggest that alpha inflation is rarely larger than a factor of 2; and (3) provide a template for the development of assumption violation models for other types of statistical tests. (Contains 3 tables and 27 references.) (Author/SLD) |
| Entry Date: | 2004 |
| Accession Number: | ED476919 |
| Database: | ERIC |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://eric.ed.gov/contentdelivery/servlet/ERICServlet?accno=ED476919 Name: ERIC Full Text Category: fullText Text: Full Text from ERIC |
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| Header | DbId: eric DbLabel: ERIC An: ED476919 AccessLevel: 3 PubType: Report PubTypeId: report PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Development of a Regression Model for Estimating the Effects of Assumption Violations on Type I Error Rates in the Student's T-Test: Implications for Practitioners. – Name: Language Label: Language Group: Lang Data: English – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Newman%2C+Isadore%22">Newman, Isadore</searchLink><br /><searchLink fieldCode="AR" term="%22Hall%2C+Rosalie+J%2E%22">Hall, Rosalie J.</searchLink><br /><searchLink fieldCode="AR" term="%22Fraas%2C+John%22">Fraas, John</searchLink> – Name: PeerReviewed Label: Peer Reviewed Group: SrcInfo Data: N – Name: Pages Label: Page Count Group: Src Data: 25 – Name: DatePubCY Label: Publication Date Group: Date Data: 2003 – Name: TypeDocument Label: Document Type Group: TypDoc Data: Reports - Research<br />Speeches/Meeting Papers – Name: Subject Label: Descriptors Group: Su Data: <searchLink fieldCode="DE" term="%22Estimation+%28Mathematics%29%22">Estimation (Mathematics)</searchLink><br /><searchLink fieldCode="DE" term="%22Monte+Carlo+Methods%22">Monte Carlo Methods</searchLink><br /><searchLink fieldCode="DE" term="%22Multiple+Regression+Analysis%22">Multiple Regression Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Regression+%28Statistics%29%22">Regression (Statistics)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Multiple linear regression is used to model the effects of violating statistical assumptions on the likelihood of making a Type I error. This procedure is illustrated for the student's t-test (for independent groups) using data from previous Monte Carlo studies in which the actual alpha levels associated with violations of the normality assumption, homogeneity of variance, or unbalanced designs were determined. The observed Type I error rates were recorded, along with information coding the type and extent of statistical assumption violation. The resulting linear models had R squared values of 0.88 to 0.91 and adjusted R squared values of 0.87 to 0.90. The results of the suggested methodological approach: (1) reveal the feasibility of developing multiple linear regression models to predict actual Type I error rates based on various assumption violation conditions for the independent groups-t-test; (2) suggest that alpha inflation is rarely larger than a factor of 2; and (3) provide a template for the development of assumption violation models for other types of statistical tests. (Contains 3 tables and 27 references.) (Author/SLD) – Name: DateEntry Label: Entry Date Group: Date Data: 2004 – Name: AN Label: Accession Number Group: ID Data: ED476919 |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=ED476919 |
| RecordInfo | BibRecord: BibEntity: Languages: – Text: English PhysicalDescription: Pagination: PageCount: 25 Subjects: – SubjectFull: Estimation (Mathematics) Type: general – SubjectFull: Monte Carlo Methods Type: general – SubjectFull: Multiple Regression Analysis Type: general – SubjectFull: Regression (Statistics) Type: general Titles: – TitleFull: Development of a Regression Model for Estimating the Effects of Assumption Violations on Type I Error Rates in the Student's T-Test: Implications for Practitioners. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Newman, Isadore – PersonEntity: Name: NameFull: Hall, Rosalie J. – PersonEntity: Name: NameFull: Fraas, John IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Type: published Y: 2003 |
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