Comparison of multivariate methods for measuring ...
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Comparison of multivariate methods for measuring change from pretest to posttest
Comparison of multivariate methods for measuring change from pretest to posttest
Name:Personal
Rogers, Justin Leslie Role :Text(marcrelator)
creator
Rogers, Justin Leslie Role :Text(marcrelator)
creator
Name:Personal
Mundfrom, Daniel Role :Text(marcrelator)
thesis advisor
Mundfrom, Daniel Role :Text(marcrelator)
thesis advisor
Name:Personal
Perrett, Jamis Role :Text(marcrelator)
thesis advisor
Perrett, Jamis Role :Text(marcrelator)
thesis advisor
Name:Personal
Schaffer, Jay Role :Text(marcrelator)
thesis advisor
Schaffer, Jay Role :Text(marcrelator)
thesis advisor
Name:Personal
Heiny, Robert Role :Text
committee member
Heiny, Robert 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)
degree grantor
University of Northern Colorado Role :Text(marcrelator)
degree grantor
typeOfResource
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Thesis
Origin Information
Place
:Text
Greeley (Colo.)
University of Northern Colorado (keyDate="yes")
2011-05
2011-05
Greeley (Colo.)
University of Northern Colorado (keyDate="yes")
2011-05
2011-05
Language
:Text
English
English
Physical Description
102 pages
born digital
102 pages
born digital
abstract
Three multivariate methods for measuring change from pretest to posttest are compared with respect to statistical power over various levels and combinations of effect size, alpha level, sample size, number of dependent variables, number of significantly different dependent variables, correlation between corresponding pretest and posttest scores, and correlation between unrelated pretest and posttest scores. The method utilizing posttests as the dependent variables and pretests as covariates was found to have superior statistical power in the majority of the scenarios examined. However, there were scenarios where the method utilizing change scores as dependent variables and the method utilizing only posttests as the dependent variables displayed greater power. Using results from the Monte Carlo simulations, comparisons are presented that reveal the conditions under which each of the three multivariate methods displayed greater statistical power than the other two. In addition to the immediate implications of the current study, suggested future avenues of research that could expand upon the current findings are discussed. note
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:series
Related Item
:thesis(displayLabel="Degree Type")
Ph.D.
Ph.D.
Related Item
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doctoral
doctoral
identifier:Local
Rogers_unco_0161D_10074.pdf
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http://hdl.handle.net/10176/cogru:1321
http://hdl.handle.net/10176/cogru:1321
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Record Information
languageOfCataloging
:Text(ISO639-2B)
English :Code(ISO639-2B)
eng
English :Code(ISO639-2B)
eng
note:admin
note:bibliography
note:thesis(displayLabel="Degree Type")
PhD note:thesis(displayLabel="Degree Name")
doctoral
Subject
Subject
Subject
Name:Personal
Subject
Name:Corporate
Subject
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Title Information:Alternative
Subject
Monte Carlo Simulation
Monte Carlo Simulation
Subject
Statistical Power
Statistical Power
Subject
Repeated Measures
Repeated Measures
Subject
Applied Mathematics
Applied Mathematics
Subject
MANCOVA
MANCOVA
Subject
MANOVA
MANOVA
Subject
Mathematics
Mathematics
Subject
Change Cores
Change Cores