- IDEALS Home
- →
- iSchools
- →
- iConferences
- →
- iConference 2010
- →
- iConference 2010 Posters
- →
- View Item
Files in this item
Files | Description | Format |
---|---|---|
application/pdf ![]() | (no description provided) |
Description
Title: | Commonality Analysis: Demonstration of an SPSS Solution for Regression Analysis |
Author(s): | Nimon, Kim; Gavrilova, Mariya |
Subject(s): | Commonality analysis
multicollinearity suppression |
Abstract: | Multiple regression is a widely used technique to study complex interrelationships among people, information, and technology. In the face of multicollinearity, researchers encounter challenges when interpreting multiple linear regression results. Although standardized function and structure coefficients provide insight into the latent variable ( ) produced, they fall short when researchers want to fully report regression effects. Regression commonality analysis provides a level of interpretation of regression effects that cannot be revealed by only examining function and structure coefficients. Importantly, commonality analysis provides a full accounting of regression effects which identifies the loci and effects of suppression and multicollinearity. Conducting regression commonality analysis without the aid of software is laborious and may be untenable, depending on the number of predictor variables. A software solution in SPSS is presented for the multiple regression case and demonstrated for use in evaluating predictor importance. |
Issue Date: | 2010-02-03 |
Genre: | Conference Poster |
Type: | Text |
URI: | http://hdl.handle.net/2142/15062 |
Date Available in IDEALS: | 2010-03-02 |
This item appears in the following Collection(s)
-
iConference 2010 Posters
iConference 2010 Posters