Mohammed O ABDULLAH¹⁾, Rizgar M AHMED²⁾, Yener ALTUN³⁾

¹ STATISTICS DEPARTMENT, INSTITUTE OF NATURAL AND APPLIED SCIENCES, YUZUNCU YIL UNIVERSITY, Van,Turkey.

² STATISTICS DEPARTMENT, ADMINISTRATOR AND ECONOMICS,, SALAHADDIN UNIVERSITY- ERBIL, Iraq.

³ STATISTICS DEPARTMENT, INSTITUTE OF NATURAL AND APPLIED SCIENCES, YUZUNCU YIL UNIVERSITY, Van, Turkey.

Author Emails

^a)muhamad91m91@gmail.com

1. ^b) ahmed@su.edu.krd

^c)yeneraltun@yyu.edu.tr

The main goal of this thesis is to determine the most important effective factors for student admission and his/her interests in the specialization by using multivariate methods. Therefore, it focused on using factor analysis by identifying a number of the obtained factors and cluster analysis by classifying them into five clusters. Furthermore, the factor analysis and cluster analysis results will be compared to each other. Moreover, this study depends on the analysis of 350 questionnaire forms, distributed by random stratified sample method on students in the first stage of three different colleges, including Scientific colleges and Humanity colleges of Salahaddin University in Northern Iraq for the academic year 2018-2019. Thus, the IBM SPSS Statistics V: 25 software programs have been used in data analysis. Additionally, the results have demonstrated that Reliability is accepted, and also in factor analysis, the rate of the total variance interpretation is %62.157. Moreover, the most common variables between the factor analysis and cluster analysis can be considered the most important and influential variables for student admission and their interests in choosing a specialization. Consequently, the first factor and the first cluster have five significant variables in common; they are V1, V2, V3, V4 and V5 (the system is helpful for student admission to colleges to get their desired professions). The second factor and the second cluster have four influential variables in common they are V24, V32, V35 and V37 (the new system may help master’s and PhD students to be admitted to colleges and get competitive results by utilizing their accounts). In the fourth factor and the fourth cluster, there is one variable in common, which is V18 (decreasing the number of students admitted in the parallel system by using the graduated students who must not be able to refill admission forms). Ultimately, the conclusion has shown a kind of approach and similarity between factor analysis and cluster analysis.