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Analysis of Fixed Effects ANOVA Models for Factorial Experiments with No Data in Some Cells

Analysis of Fixed Effects ANOVA Models for Factorial Experiments with No Data in Some Cells. Michael H. Kutner, Ph.D. Rollins Professor Department of Biostatistics and Bioinformatics Rollins School of Public Health Emory University. Abstract.

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Analysis of Fixed Effects ANOVA Models for Factorial Experiments with No Data in Some Cells

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  1. Analysis of Fixed Effects ANOVA Models for Factorial Experiments with No Data in Some Cells Michael H. Kutner, Ph.D. Rollins Professor Department of Biostatistics and Bioinformatics Rollins School of Public Health Emory University

  2. Abstract Most standard statistical software programs provide F-tests to test main effect hypotheses and interactions for fixed effects in factorial experimental models when some of the cells do not contain data. Simple examples using SAS will be given documenting the difficulties with properly dealing with cells without data. SAS claims that the results may not be meaningful. We will see that the tests for main effects are not invariant to simple permutations of the rows and columns using data from a two-factor 3X3 factorial experiment. The hypotheses that are actually being tested will be delineated. The balanced incomplete block (BIB) design will be shown to be a special case.

  3. Data: Example 1

  4. 2,1 and 1,2 missing

  5. 2,1 and 1,2 missing

  6. Data: Example 2

  7. 3,1 and 2,2 missing

  8. 3,1 and 2,2 missing

  9. Data: Example 3

  10. 1,3 and 2,3 and 3,2 missing

  11. 1,3 and 2,3 and 3,2 missing

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