因子分析与聚类分析确定学生录取和专业兴趣最重要影响因素的比较——以埃尔比勒萨拉哈丁大学为例

Mohammed Abdullah, R. Ahmed, Y. Altun
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引用次数: 1

摘要

本文的主要目的是运用多元分析方法,确定影响学生入学的最重要因素和学生对专业的兴趣。因此,本文着重于利用因子分析,对获得的一些因素进行识别,并将其分为五类进行聚类分析。并将因子分析和聚类分析结果进行对比。此外,本研究依赖于对伊拉克北部萨拉哈丁大学2018-2019学年三个不同学院(包括科学学院和人文学院)第一阶段学生的350份随机分层抽样调查问卷的分析。因此,使用IBM SPSS Statistics V: 25软件程序进行数据分析。此外,结果表明,信度是可以接受的,并且在因子分析中,总方差解释率为%62.157。此外,因子分析和聚类分析之间最常见的变量可以被认为是学生入学和他们选择专业兴趣的最重要和最具影响力的变量。因此,第一个因素和第一个集群有五个共同的显著变量;它们是V1, V2, V3, V4和V5(该系统有助于学生进入大学获得他们想要的专业)。第二个因素和第二个集群共有四个影响变量,分别是V24、V32、V35和V37(新系统可能会帮助硕士和博士研究生利用他们的账户进入大学并获得有竞争力的成绩)。在第四个因素和第四个集群中,有一个共同的变量,即V18(通过使用不能重新填写录取表格的毕业生来减少并行系统录取的学生数量)。最后,本文的结论显示了因子分析与聚类分析的一种方法和相似性。
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Comparison Between Factor Analysis and Cluster Analysis to Determine the Most Important Affecting Factors for Students' Admission and Their Interests in The Specializations: A Sample of Salahaddin University-Erbil
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.
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Purely semismall Compressible Modules Extended-Cyclic Operators Comparison Between Factor Analysis and Cluster Analysis to Determine the Most Important Affecting Factors for Students' Admission and Their Interests in The Specializations: A Sample of Salahaddin University-Erbil Integer-valued polynomials and binomially Noetherian rings Statistics department- College of Administration and Economics - Salahaddin University - Erbil
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