Linear Possibility Model for Ordered Categorical Data: A Similar Way of Analysis to Regression Analysis
Abstract
This research is concerned with introducing the linear possibility model for ordered categorical data, a model which is extended from the idea of the probit model. In linear possibility model the response variable for ordered categorical data is having a wider range of values, and consequently, the conditional expected values for this response given a number of regressors will be outside the range of 0-1. This research shows how the problems surrounding the linear possibility model can be solved, to a large extent, allowing the model to give simple and straight forward interpretations for the relationships between the categorical variables similar to regression analysis. The study gives empirical estimates of the effects between the variables and explains estimates corresponding to the ordering nature of the categorical variables under concerned. The application data for this research are collected from a random sample of students at the Omdurman Islamic University. The ordered response categorical variable for the study is the academic performance of students, which is assumed to be associated with three categorical variables: the specialization of the students, whether the students live with their families or not, and the educational level of their guardians. The result showed that the conditional possibility of the academic performance of the students is lower by almost a third for all the students whose specialization is social science. Likewise, the conditional possibility of the academic performance of the students is higher by almost a third for all the students whose the educational level of their guardian is secondary. AS for those, whose specialization is natural science and the educational level of their guardian is primary or lower, the conditional possibility of their academic performance is 3.791, which is "good". The data of the study is analysed with the aid of SPSS, (Statistical Package for the Social Sciences) and Minitab.
References
2. Adam, Amin. I. (1996). Analysis of Categorical Data from a Case Study of Child Safety. Unpublished PhD thesis: University of Keele, Dept. of Mathematics, England.
3. Adam, Amin. I. (2010). Concepts on the Chi-square Test of Independence for Analyzing Categorical data. Journal of the Faulty of Economics & Political Science, Omdurman I. University, Sudan, Vol.4:131-143.
4. Adam, Amin. I. (2010). Local-Local, Local-Global, Global-Local and Global-Global Odds Ratios for Categorical data. J. of Economics and Political and Statistical Sciences, Omdurman I. University, Vol. 5:165-186.
5. Adam, Amin. I. (2010). Measures of Associations for Ordered Categorical Data: Different Measures but Similar Conclusions. J. of Economics and Political and Statistical Sciences, Omdurman I. University, Vol. 6:180-198, December 2010.
6. Agresti, A. (2002). Categorical Data Analysis, 2nd ed. Wiley, New York.
7. Agresti, A. (2007). An Introduction to Categorical Data Analysis. Wiley, New York.
8. Agresti, A. (2010). Analysis of Ordinal Categorical Data. Wiley, New York.
9. Baglivo, J., Oliver, D. & Pagano, M. (1992). Methods for Exact Goodness-of-Fit Tests. Journal of the American Statistical Association 87:464-469.
10. Becker, M. P. & Clogg, C. C. (1989). Analysis of Sets of Two-Way Contingency Tables Using Association Models. Journal of the American Statistical Association 84:142-151.
11. Bilder, C. & Loughin, T. M. (2007). Modeling Association Between Two or More Categorical Variables that Allow for Multiple Categorical Choices. Communications in Statistics 36:433-451.
12. Bower, K.M. (2000), “Analysis of Variance (ANOVA) Using MINITAB” Scientific Computing & Instrumentation.
13. Draper, N. R. & Smith, H. (1998). Applied Regression Analysis, 3rd ed. Wiley, New York.
14. Everitt, B. S. (1977). The Analysis of Contingency Tables. Chapman & Hall, London.
Gezira J. Econ. & Soci. Scie. VOL(6) .No (2. 2015-1436
15. Eye, A. V. & Bogat, G. A. (2009). Analysis of Intensive Categorical Longitudinal Data. Springer, New York.
16. Fan, Y. (2008). Strategic Groups and cluster Analysis. Henry Stewart, London.
17. Fienberg, S. E. (2007). The Analysis of Cross-classified Categorical Data. Springer, New York.
18. Freeman, D.H. (1987). Applied Categorical Data Analysis. Marcel Dekker, New York.
19. Greenland, S. (1991). On the Logical Justification of Conditional Tests for Two-by-Two Contingency Tables. American Statistician 45:248-251.
20. Gujarati, D. N. (2004). Basic Econometrics, 4th ed. McGraw-Hill, New York.
21. Hjorth, J. S. U. (1994). Computer Intensive Statistical Methods: Validation Model Selection and Bootstrap. Chapman, London.
22. Imrey, P. B. & Koch, G. G. (2005). Categorical Data Analysis. Wiley, New York.
23. Johnston, J. (1984). Econometric Methods, 3rd ed. McGraw-Hill, New York.
24. Johnston, j. & DiNardo, J. (2001). Econometric Methods, 4th ed. McGraw-Hill, New York.
25. Liu, I. & Agresti, A. (2005). The Analysis of Ordinal Categorical Data: An Overview and a Survey of Recent Development. Sociedad de Estadistica e Investigacion OperativaTe Vol.14 No. 1:1-73.
26. Maddala, G. S. & Lahiri, K.(2009). Introduction to Econometrics. Wiley, New York.
27. McCullagh, P. (1980). Regression Models for Ordinal Data. J. Roy. Statist. Soc. B 42:109-142.
28. Ott, R. L. & Longnecker M. (2008). An Introduction to Statistical Methods and Data Analysis, 6th ed. Brooks/Cole, Bolmont, U.S.A.
29. Powers, D. A. (2008). Statistical Methods for Categorical Data Analysis, 2nd ed. Emerald, Bingley, U.K.
30. Simono, J.(2003). Analyzing Categorical Data. Springer, New York.