The effectiveness of stepwise discriminant analysis ...
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The effectiveness of stepwise discriminant analysis as a follow up procedure to a significant MANOVA using both the F-statistic and partial R-square criterion
The effectiveness of stepwise discriminant analysis as a follow up procedure to a significant MANOVA using both the F-statistic and partial R-square criterion
Name:Personal
Chandran, Raj K. Role :Text(marcrelator)
creator
Chandran, Raj K. Role :Text(marcrelator)
creator
Name:Personal
Mundfrom, Dan J. Role :Text(marcrelator)
thesis advisor
Mundfrom, Dan J. Role :Text(marcrelator)
thesis advisor
Name:Personal
Perrett, Jamis J. Role :Text
committee member
Perrett, Jamis J. Role :Text
committee member
Name:Personal
Schaffer, Jay R, Role :Text
committee member
Schaffer, Jay R, Role :Text
committee member
Name:Personal
Heiny, Robert L. Role :Text
committee member
Heiny, Robert L. Role :Text
committee member
Name:Corporate
Applied Statistics & Research Methods Role :Text(marcrelator)
sponsor
Applied Statistics & Research Methods Role :Text(marcrelator)
sponsor
Name:Corporate
University of Northern Colorado Role :Text(marcrelator)
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University of Northern Colorado Role :Text(marcrelator)
degree grantor
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University of Northern Colorado (keyDate="yes")
2009-05 Place :Text
Greeley (Colo.)
2009-05
University of Northern Colorado (keyDate="yes")
2009-05 Place :Text
Greeley (Colo.)
2009-05
Language
:Text
English
English
Physical Description
199 pages
born digital
199 pages
born digital
abstract
This study examined the effectiveness of stepwise discriminant analysis (SWDA) using the F-statistic and Partial R-square criterion as a follow up analysis to a significant MANOVA. Monte Carlo simulations were conducted, and 7,128 scenarios were examined using different combinations of levels of number of MANOVA dependent variables, sample size, population correlation matrices, effect sizes, alpha significance levels and Partial R-square correlations. The two group case of MANOVA was considered, and simulations were run under the assumptions of multivariate normality, homogeneity of variance-covariance matrices, and linearity among all pairs of predictors within each group. This study has shown that SWDA is a viable option as a follow up analysis to a significant MANOVA if the correct conditions are met. It was found that SWDA performs well when the number of dependent variables with significantly differing means in each group is held low. Based on the results SWDA performs best when the number of significant dependent variables is three or less. Additionally, SWDA only works well when correlations between dependent variables are quite low. If correlations between dependent variables are held low, then SWDA can be used in situations where there are three dependent variables or less. SWDA can be used in situations where there are more than three dependent variables, but the number of significant dependent variables must be below four in order for SWDA to perform well. Another procedure could be used to gauge what that may be, then SWDA could be employed if the correct conditions are met. Because SWDA only works well when low correlations between dependent variables are present, it could be combined with another procedure, perhaps descriptive discriminant analysis to supplement situations when higher correlations are found. This dissertation has shown however, that using several univariate F-tests, also known as the "protected" F-test, should not be used after a significant MANOVA and SWDA should be used instead if the correct conditions are met. note
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Chandran_unco_0161N_10000.pdf
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http://hdl.handle.net/10176/cogru:120
http://hdl.handle.net/10176/cogru:120
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eng
English :Code(ISO639-2B)
eng
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note:bibliography
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PhD note:thesis(displayLabel="Degree Name")
doctoral
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Title Information:Alternative
Subject
Subject
Stepwise Discriminant Analysis
Stepwise Discriminant Analysis
Subject
Statistics
Statistics
Subject
MANOVA
MANOVA
Subject
Follow-up Analysis
Follow-up Analysis
Subject
Post-hoc Analysis
Post-hoc Analysis