Open AccessHow a variable’s partial correlation with other variable(s) can make a good predictor: the suppressor variable case
Author(s) -
Akinwande Michael Olusegun,
Aminu Muktar,
Kaile Nasiru Kabir,
Ibrahim Abubakar Adamu,
Umar Adamu Abubakar
Publication year - 2015
Publication title -
international journal of advanced statistics and probability
Language(s) - English
Resource type - Journals
ISSN - 2307-9045
DOI - 10.14419/ijasp.v3i2.5400
Subject(s) - suppressor , variable (mathematics) , regression analysis , variables , regression , set (abstract data type) , notation , computer science , econometrics , mathematics , statistics , biology , arithmetic , mathematical analysis , genetics , cancer , programming language
Suppression effect is one of the most elusive and difficult to understand dynamics in multiple regression analysis. Suppressor variable(s) and their dynamics in multiple regression analyses are important in reporting accurate research outcomes. However, quite a number of researchers are unfamiliar with the possible advantages and importance of these variables. Suppressor variables tend to appear useless as separate predictors, but have the potential to change the predictive ability of other variables and completely influence research outcomes. This research describes the role suppressor variables play in a multiple regression analysis and provides practical examples that further explain how suppressor effects can alter research outcomes. Finally, we employed mathematical set notation to demonstrate the concepts of suppressor effects.