A Monte Carlo Study of the Robustness and Power Associated with Selected Tests of Variance Equality when Distributions are Non-Normal and Dissimilar in Form

Thumbnail image of item number 1 in: 'A Monte Carlo Study of the Robustness and Power Associated with Selected Tests of Variance Equality when Distributions are Non-Normal and Dissimilar in Form'.
Thumbnail image of item number 2 in: 'A Monte Carlo Study of the Robustness and Power Associated with Selected Tests of Variance Equality when Distributions are Non-Normal and Dissimilar in Form'.
Thumbnail image of item number 3 in: 'A Monte Carlo Study of the Robustness and Power Associated with Selected Tests of Variance Equality when Distributions are Non-Normal and Dissimilar in Form'.
Thumbnail image of item number 4 in: 'A Monte Carlo Study of the Robustness and Power Associated with Selected Tests of Variance Equality when Distributions are Non-Normal and Dissimilar in Form'.
Showing 1-4 of 97 pages in this dissertation.

PDF Version Also Available for Download.

Description

When selecting a method for testing variance equality, a researcher should select a method which is robust to distribution non-normality and dissimilarity. The method should also possess sufficient power to ascertain departures from the equal variance hypothesis. This Monte Carlo study examined the robustness and power of five tests of variance equality under specific conditions. The tests examined included one procedure proposed by O'Brien (1978), two by O'Brien (1979), and two by Conover, Johnson, and Johnson (1981). Specific conditions included assorted combinations of the following factors: k=2 and k=3 groups, normal and non-normal distributional forms, similar and dissimilar distributional forms, … continued below

Physical Description

vi, 90 leaves:ill.

Creation Information

Hardy, James C. (James Clifford) August 1990.

Context

This dissertation is part of the collection entitled: UNT Theses and Dissertations and was provided by the UNT Libraries to the UNT Digital Library, a digital repository hosted by the UNT Libraries. It has been viewed 48 times, with 8 in the last month. More information about this dissertation can be viewed below.

Who

People and organizations associated with either the creation of this dissertation or its content.

Author

Chair

Committee Members

Publisher

Rights Holder

For guidance see Citations, Rights, Re-Use.

  • Hardy, James C. (James Clifford)

Provided By

UNT Libraries

The UNT Libraries serve the university and community by providing access to physical and online collections, fostering information literacy, supporting academic research, and much, much more.

About | Browse this Partner

Contact Us

Corrections Questions

What

Descriptive information to help identify this dissertation. Follow the links below to find similar items on the Digital Library.

Degree Information

Description

When selecting a method for testing variance equality, a researcher should select a method which is robust to distribution non-normality and dissimilarity. The method should also possess sufficient power to ascertain departures from the equal variance hypothesis. This Monte Carlo study examined the robustness and power of five tests of variance equality under specific conditions. The tests examined included one procedure proposed by O'Brien (1978), two by O'Brien (1979), and two by Conover, Johnson, and Johnson (1981). Specific conditions included assorted combinations of the following factors: k=2 and k=3 groups, normal and non-normal distributional forms, similar and dissimilar distributional forms, and equal and unequal sample sizes. Under the k=2 group condition, a total of 180 combinations were examined. A total of 54 combinations were examined under the k=3 group condition. The Type I error rates and statistical power estimates were based upon 1000 replications in each combination examined. Results of this study suggest that when sample sizes are relatively large, all five procedures are robust to distribution non-normality and dissimilarity, as well as being sufficiently powerful.

Physical Description

vi, 90 leaves:ill.

Language

Item Type

Identifier

Unique identifying numbers for this dissertation in the Digital Library or other systems.

Collections

This dissertation is part of the following collection of related materials.

UNT Theses and Dissertations

Theses and dissertations represent a wealth of scholarly and artistic content created by masters and doctoral students in the degree-seeking process. Some ETDs in this collection are restricted to use by the UNT community.

About | Browse this Collection

What responsibilities do I have when using this dissertation?

Digital Files

When

Dates and time periods associated with this dissertation.

Creation Date

  • August 1990

Added to The UNT Digital Library

  • Aug. 22, 2014, 6 p.m.

Description Last Updated

  • May 12, 2016, 9:21 a.m.

Usage Statistics

When was this dissertation last used?

Yesterday: 0
Past 30 days: 8
Total Uses: 48
More Statistics

Interact With This Dissertation

Here are some suggestions for what to do next.

Top Search Results

We found eight places within this dissertation that matched your search. View Now

Start Reading

Thumbnail image of item number 1 in: 'A Monte Carlo Study of the Robustness and Power Associated with Selected Tests of Variance Equality when Distributions are Non-Normal and Dissimilar in Form'.
Thumbnail image of item number 2 in: 'A Monte Carlo Study of the Robustness and Power Associated with Selected Tests of Variance Equality when Distributions are Non-Normal and Dissimilar in Form'.
Thumbnail image of item number 3 in: 'A Monte Carlo Study of the Robustness and Power Associated with Selected Tests of Variance Equality when Distributions are Non-Normal and Dissimilar in Form'.
Thumbnail image of item number 4 in: 'A Monte Carlo Study of the Robustness and Power Associated with Selected Tests of Variance Equality when Distributions are Non-Normal and Dissimilar in Form'.

PDF Version Also Available for Download.

Citations, Rights, Re-Use

International Image Interoperability Framework

IIF Logo

We support the IIIF Presentation API

Links for Robots

Helpful links in machine-readable formats.

Archival Resource Key (ARK)

International Image Interoperability Framework (IIIF)

Metadata Formats

Images

URLs

Stats

Hardy, James C. (James Clifford). A Monte Carlo Study of the Robustness and Power Associated with Selected Tests of Variance Equality when Distributions are Non-Normal and Dissimilar in Form, dissertation, August 1990; Denton, Texas. (https://digital.library.unt.edu/ark:/67531/metadc332130/: accessed April 24, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; .

Back to Top of Screen