Author(s)

Anthony Joe Turkson^*, John Awuah Addor, Douglas Yenwon Kharib

Mathematics, Statistics and Actuarial Science Department, Takoradi Technical University, Sekondi-Takoradi, Ghana.

Statistics is a powerful tool for data measurement. Statistical techniques properly planned and executed give meaning to meaningless data. The difficulty some practitioners encounter hinges on the fact that though there are numerous statistical methods available for use in analysis, the extent of their understanding and ease of using these tools for analysis is limited. This study has twofold purpose: firstly, literature on categorical data commonly used in research was reviewed; next, we reported the results of a survey we designed and executed. Categorical data was collected via questionnaire and analyzed to serve as a backbone of the robustness of categorical data. Several conjectures about the independence of the socio-economic variables and e-commence were tested. Some of the factors influencing patronage of e-commerce were identified. It is clear from the literature that as one’s academic qualification improves, there is an associated improvement in their preference for e-commerce, but the results revealed otherwise. Size of family was found to influence e-commerce. Both income and social status positively affected patronage in e-commerce. Gender also appeared to affect patronage in e-commerce. 62.3% of staff had patronized e-commerce. This shows that e-commerce patronage was gradually increasing. It is therefore our considered view that policy documents regulating and monitoring the use of e-commerce be developed to increase e-commerce participation across the globe. It is also recommended that the bottlenecks which obstruct patronage in e-commence be addressed so that a lot more staff will develop a positive attitude towards e-commerce.

Categorical Data, Chi-Square, E-Commerce, Ordinal Data, Nominal Data

Turkson, A. , Addor, J. and Kharib, D. (2021) Validating Intrinsic Factors Informing E-Commerce: Categorical Data Analysis Demo. Open Journal of Statistics, 11, 737-758. doi: 10.4236/ojs.2021.115044.