The effect of changing scores for multi-way tables with open-ended ordered categories

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YILMAZ A. E., SARAÇBAŞI T.

Hacettepe Journal of Mathematics and Statistics, vol.45, no.6, pp.1881-1890, 2016 (SCI-Expanded) identifier identifier

Abstract

Log-linear models are used to analyze the contingency tables. If the
variables are ordinal or interval, because the score values aect both
the model signicance and parameter estimates, selection of score values
has importance. Sometimes an interval variable contains open-ended
categories as the rst or last category. While the variable has open-
ended classes, estimates of the lowermost and/or uppermost values of
distribution must be handled carefully. In that case, the unknown
values of the rst and last classes can be estimated rstly, and then
the score values can be calculated. In the previous studies, the un-
known boundaries were estimated by using interquartile range (IQR). In
this study, we suggested interdecile range (IDR), interpercentile range
(IPR), and mid-distance range (MDR) as alternative to IQR to detect
the eects of score values on model parameters.

Log-linear models are used to analyze the contingency tables. If the variables are ordinal or interval, because the score values affect both the model significance and parameter estimates, selection of score values has importance. Sometimes an interval variable contains open-ended categories as the first or last category. While the variable has open-ended classes, estimates of the lowermost and/or uppermost values of distribution must be handled carefully. In that case, the unknown values of the first and last classes can be estimated firstly, and then the score values can be calculated. In the previous studies, the unknown boundaries were estimated by using interquartile range (IQR). In this study, we suggested interdecile range (IDR), interpercentile range (IPR), and mid-distance range (MDR) as alternative to IQR to detect the effects of score values on model parameters.