Authors :
Isaac Oluwaseyi Ajao ; Aladesuyi Alademomi
Volume/Issue :
Volume 8 - 2023, Issue 5 - May
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://t.ly/_Xlg
DOI :
https://doi.org/10.5281/zenodo.8058486
Abstract :
Handling simple binary response data with
logistic regression has solved many problems
encountered in data analysis across various walks of life.
However, dealing with ordinal responses, especially
when they are more than two levels has remained a big
challenge to researchers. This paper therefore focuses
attention on the application of cumulative logit response
function with proportional odds in order to show its
robustness over chi-square, t-tests, percentages and so
on, in analyzing likert scale data which is common
among users of statistics in social sciences,
environmental, and medical sciences. To implement this,
500 random observations on five socio-demographic
variables were simulated.In order to justify the use of
proportional odds, score test was carried out on the data,
and the assumption was not rejected at 5% level (p-value
= 0.4222), this justifies the use of the method. Also, the
Deviance and Pearson goodness of fit statisticsshow pvalue = 0.7326 and 0.8130 respectively, this reveals that
the model fits the data adequately. Moreover, the
proportional odds model is fitted and reliable predictions
are made. The method is robust for analyzing ordinal
response data such as likert scale.
Keywords :
Ordinal data, likert scale, proportional odds model, logistic regression.
Handling simple binary response data with
logistic regression has solved many problems
encountered in data analysis across various walks of life.
However, dealing with ordinal responses, especially
when they are more than two levels has remained a big
challenge to researchers. This paper therefore focuses
attention on the application of cumulative logit response
function with proportional odds in order to show its
robustness over chi-square, t-tests, percentages and so
on, in analyzing likert scale data which is common
among users of statistics in social sciences,
environmental, and medical sciences. To implement this,
500 random observations on five socio-demographic
variables were simulated.In order to justify the use of
proportional odds, score test was carried out on the data,
and the assumption was not rejected at 5% level (p-value
= 0.4222), this justifies the use of the method. Also, the
Deviance and Pearson goodness of fit statisticsshow pvalue = 0.7326 and 0.8130 respectively, this reveals that
the model fits the data adequately. Moreover, the
proportional odds model is fitted and reliable predictions
are made. The method is robust for analyzing ordinal
response data such as likert scale.
Keywords :
Ordinal data, likert scale, proportional odds model, logistic regression.
