Evaluation of the performance of a random ...
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Evaluation of the performance of a random coefficient regression model cumulative summation control chart under varying model conditions: with human services applications
Evaluation of the performance of a random coefficient regression model cumulative summation control chart under varying model conditions: with human services applications
Name:Personal
McKinney, Christopher John Role :Text(marcrelator)
creator
McKinney, Christopher John Role :Text(marcrelator)
creator
Name:Personal
Schaffer, Jay Role :Text(marcrelator)
thesis advisor
Schaffer, Jay Role :Text(marcrelator)
thesis advisor
Name:Personal
Mundfrom, Daniel Role :Text
committee member
Mundfrom, Daniel Role :Text
committee member
Name:Personal
Shafie, Khalil Role :Text
committee member
Shafie, Khalil Role :Text
committee member
Name:Personal
Allen, Michael T Role :Text
committee member
Allen, Michael T 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)
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Thesis
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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
92 pages
born digital
92 pages
born digital
abstract
The use of quality control charts with metrics within the educational, behavioral, and other human services field has typically been considered very limited due to the complications imposed by nested structures, conditional relationships, and complex variance-covariance structures. The current study evaluated the performance of the random coefficient regression model control chart (RCRMCC) under conditions found commonly in the human services fields. Derived from the Regression Control Chart (RCC), the RCRMCC utilizes the residuals of the random coefficient regression model as inputs for the quality control charts. Using Monte Carlo simulations, the RCRMCC and RCC are compared, in regards to their average run lengths (ARL), under varying in-control and out-of-control population conditions, within the Cumulative Summation (CUSUM) Control Chart framework. It was found that the RCRMCC is advantageous under conditions similar to those found in the educational, behavioral, and other human services industries. Applications of the findings and future directions with the RCRMCC are discussed. note
Subject
Statistics
Operations Research
Cumulative Summation Control Chart
Human Services
Quality Control
Random Coefficient Regression Model
Statistics
Operations Research
Cumulative Summation Control Chart
Human Services
Quality Control
Random Coefficient Regression Model
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Ph.D.
Ph.D.
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doctoral
doctoral
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McKinney_unco_0161D_10059.pdf
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http://hdl.handle.net/10176/cogru:1333
http://hdl.handle.net/10176/cogru:1333
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eng
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eng
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PhD note:thesis(displayLabel="Degree Name")
doctoral
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