Using Residual Estimators to Detect Outliers and Potential Controlling Observations in Structural Equation Modelling: QQ Plot Approach ()
Affiliation(s)
1Department of Mathematics & Statistics, Kumasi Technical University (KsTU), Kumasi, Ghana.
2Department of Statistics, Faculty of Mathematical Sciences, C.K. Tedam University of Technology and Applied Sciences (CKT-UTAS), Navrongo, Ghana.
3Department of Statistical Sciences, Tamale Technical University (TaTU), Tamale, Ghana.
ABSTRACT
The structural equation
model (SEM) concept is generally influenced by the presence of outliers and
controlling variables. To a very large extent, this could have consequential
effects on the parameters and the model fitness. Though previous researches
have studied outliers and controlling observations from various perspectives
including the use of box plots, normal probability plots, among others, the use
of uniform horizontal QQ plot is yet to be explored. This study is, therefore,
aimed at applying uniform QQ plots to identifying outliers and possible
controlling observations in SEM. The results showed that all the three methods of estimators manifest the
ability to identify outliers and possible controlling observations in SEM. It
was noted that the Anderson-Rubin estimator of QQ
plot showed a more efficient or visual display of spotting outliers and
possible controlling observations as compared to the other methods of
estimators. Therefore, this paper provides an efficient way identifying outliers as it fragments the data
set.
Share and Cite:
Abdul-Aziz, A. , Luguterah, A. and Saeed, B. (2020) Using Residual Estimators to Detect Outliers and Potential Controlling Observations in Structural Equation Modelling: QQ Plot Approach.
Open Journal of Statistics,
10, 905-914. doi:
10.4236/ojs.2020.105053.
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