Multinomial Logistic Modelling of Socio-Economic Factors Influencing Spending Behavior of University Students

Main Article Content

Gogo Jacqueline Akelo
Stephen Muteti Mbunzi
Cyrus Gitonga Ngari


This study aims at determining the use of Multinomial Logistic Regression (MLR) model which is one of the important methods for categorical data analysis. This model particularly deals with one nominal or ordinal response variable that has more than two categories. Despite the fact that many researchers have applied this model in data analysis in many areas, for instance behavioral, social, health, and educational, a study on spending habits of University students have never been done. To identify the model by practical way, we conducted a survey research among students from University of Embu. Segment of the population of students in undergraduate level, a sample of 376 was selected. We employed the use stratified random sampling and simple random sampling without replacement in each stratum. The response variable consisted of five categories. Four of explanatory variables were used for building the primary (MLR) model. The model was tested through a set of statistical tests to ensure its appropriateness for the data. From the results, the study reveals that year of study, family financial level, gender and school are significant factors in explaining spending habits of students. Despite the fact that gender is one of the deterministic factors of financial behavior of student, this model identified family level of income as a major deterministic factor. Conclusively, using MLR model accurately defines the relationship between the group of explanatory variables and the response variable. It also identifies the effect of each of the variables, and we can predict the classification of any individual case. The researchers recommend that, the Universities peer counselling department, should hold trainings on the basis of major determinant of financial spending behavior i.e. family financial level.

Multinomial logistic regression model, categorical data, Undergraduate University students, spending behavior

Article Details

How to Cite
Akelo, G., Mbunzi, S., & Ngari, C. (2019). Multinomial Logistic Modelling of Socio-Economic Factors Influencing Spending Behavior of University Students. Asian Journal of Probability and Statistics, 3(4), 1-23.
Original Research Article


Gutter MS, Garrison S, Capur Z. Social learning opportunities and the financial behaviors of college students. Family and Consumer Sciences Research Journal. 2010;38(4):387-404.

Lyons AC. The credit practices and financial education needs of community college students. Journal of Financial Counselling and Planning. 2004;14(2).

Furnham A. The savings and spending habits of young people. Journal of Economic Psychology. 1999; 20(6):677-697.

Norvitilis J. Personality factors, money attitudes, financial knowledge and credit card debt in college students. Applied Social Psychology. 2006;36:1395-1413.

Roberts JA, Jones. Consuming in a consumer culture: College students, materialism, status consumption and compulsive buying. The Marketing Management Journal. 2000;76-91.

Villanueva S. An analysis of the factors affecting the spending and saving habits of college students. Skidmore College Creative Matter; 2017.

Haiyang Chen R. An analysis of financial literacy among college students. Financial Service Review; 2008.

Sabri MF, MacDonald M. Saving behavior and financial problems among college students. Cross-Cultural Communication. 2010;6(3):103-110.

University of Embu. The Flashlight Magazine. September. Retrieved from University of Embu; 2018.


Xiangqin Cui, Gary A. Churchill. Statistical test for differential expressions in DNA. Genome Biology. 2003;1-10.

Yamane T. Statistics: An introductory analysis. New York: Harper and Row; 1967.

Mark Saunders, Philip Lewis, Adrian Thornhill. Research methods for business students (Illustrated Ed.). Financial Times/Prentice Hall; 2003.

McNabb DE. Research methods in public administration and nonprofit management: Qualitative and quantitative approaches. (Ed.) USA: Sharpe ME, Incorporated; 2008.

Edward G. Carmines, Richard A. Zeller. Reliability and validity assessment: Volume 17 of quantitative applications in the social sciences. SAGE Publications. 1979;17.

Allan S. Willmott, Desmond L. Nuttal. The reliability of examination 16+: Schools council of research studies: Research studies, Schools Council (Great Britain). Macmillan; 1975.

Emanuel J. Mason, William J. Bramble. Understanding and conducting research: Application in education and the behavioral sciences (2, Illustrated Ed.). McGraw-Hill Book Company; 1989.

Bartlett P, Mendelson S. Rademacher and gaussian complexities: Risk bounds and structural results. J. Machine Learning Research. 2002;3:463-482.

Akaike H. On entropy maximization principle. In: Proc. Symp. On Applications of Statistics. The Netherland. 1997;27-47.

Barnett V, Lewis T. Outliers in statistical data. New York, USA: John Willey; 1978.

Madhu B, Ashok NC, Balasubramanian S. A multinomial logistic regression analysis to study the influence of residence and socio-economic status on breast cancer incidences in Southern Karnataka. International Journal of Mathematics and Statistics Invention (IJMSI). 2014;2(5):01-08.

O’Halloran PS. Logistic regression ii: Multinomial data. New York: Colombia University; 2005.

Williams R. Multicollinearity. University of Notre Dame: [Retrieved from online]; 2015.

El-Habil AM. An application on multinomial logistic regression model. Pak. J. Stat. Oper. Res. 2012; 271-291.

Borooah VK. Logit and probit order and multinomial models. London New Delhi: International, professional and Educational Publisher; 2012.