Statistics

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Application of ordinal logistic regression model to occupation data
(Duncan Science Company, 2009) Adepoju, A. A.; Adegbite, M.
People's occupational choices might be influenced by their parents' occupation, gender, previous experiences, ages, and their own education level. We can study the relationship of one's occupation choice with education level and father's occupation. The occupational choices will be the outcome variable which consists of categories of occupations. The regression methods are capable of allowing researchers to identify explanatory variables related to organizational programs and services that contribute to the overall staff status. These methods also permit researchers to estimate the magnitude of the effect of the explanatory variables on the outcome variable. Therefore, regression methods seem to be superior in studying the relationship between the explanatory and outcome variables. This study used ordinal logistic regression method to examine the relationship between the ordinal outcome variable, different levels of staff status in the Lagos State Civil Service of Nigeria, the explanatory variables are Gender, Indigenous status, Educational Qualification, Previous Experience and Age. The outcome variable was measured on an ordered, categorical, and three-point Likert scale as Junior staff Middle Management staff, and Senior Management staff. Within the complete models, the legit link was the better choice because of its satisfying parallel lines assumption and larger model- fitting statistics. The study revealed that two explanatory variables namely, Education Qualification and Previous Working Experience significantly predicted the probability of an individual staff being a member of any of the three levels of staff status
Performances of the full information estimators in a two-equation structural model with correlated disturbances
(Bachudo Science, 2009) Adepoju, A. A.
The performances of two full information techniques, Three Stage Least Squares (3SLS) and Full Information Maximum Likelihood (FIML) of simultaneous equation models with correlated disturbance terms are compared with the Ordinary Least Squares (OLS) method in small samples. Comparative performance evaluation of the estimators was done using Average of Estimates, Total Absolute Bias (TAB) of Estimates, Root Mean Squared Error (RMSE) and Sum of Squared Residuals (RSS) of parameter estimates. The results of the Monte Carlo experiment showed that OLS is best with large negative or positive correlation, while 3SLS is best with feebly correlated error terms in the case of replication-based averages. The total absolute biases increase consistently as the sample size increases for OLS while FIML estimates reveal no distinct pattern. The magnitudes of the estimates yielded by two estimators, OLS and 3SLS, exhibited fairly consistent reaction to changes in magnitudes and direction of correlations of error terms
The effect of students' pre-admission performance on post-admission performance
(2008) Olayiwola, O. M.; Adepoju, A. A.; Okunlade, A.; Akomolafe, A.
This study uses canonical correlation analysis to investigate the effect of pre-admission performance (performance in SSCE/GCE and JAMB) on the post-admission performance (performance in 1OOlevel to 400 Level). The study population comprised of a set of students that were admitted in the same year in the department of computer science. University of Ibadan, Nigeria. The students’ SSCE/GC, JAMB. 100 Level to 400 level results were studied. The result shows that students ' pre-admission performance are highly correlated with their 100 level to 300 level, but uncorrelated with 400 level result. This may due to: complexity of the course as they are moving higher, effect of the project work. Strike, riot, lack of relevant textbooks, social activities. etc. This study then recommends that the University should introduce or assign level advisers to advice the students; they should ensure that they provide relevant textbooks to the library for the students.
Comparative performance of the limited information techniques in a two equation structural model
(2007) Adepoju, A. A.
The samples with which we deal in practice are rather small. seldom exceeding 80 observations and frequently much smaller. 'Thus, it is of great interest to inquire into the properties of estimators for the typical sample sizes encountered in practice. The performances of three simultaneous estimation method using a model consisting of a mixture of an identified and over identified equations with correlated error terms and compared. The result of the Monte Carlo study revealed that the Two Stage least Squares (2SLS) and the Limited Information Maximum Likelihood (LIML) estimates are similar and in most cases identical in respect of the just-identified equation. The Total Absolute Biases (TAB) of 2SLS and LIML revealed asymptotic behavior under (upper triangular matrix) P1 while those of Ordinary Least Squares (OLS) exhibited no such behavior. For both upper and lower triangular matrices (P, and P2), 2SI.S estimates showed asymptotic behavior in the middle interval. The OI.S is the only stable estimator with a stable behavior of Root Mean Square Error (RM3F.) as its estimates increase (decrease) consistently for equation 1(equation 2) for P, (for P2).

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