A Hierarchical Linear Modeling Approach To Higher Education Research : The Influence Of Student And Institutional Characteristics
This research paper is basically written with the central idea of showing how multi level modeling is a more appropriate way of dealing with data that is of hierarchical or structured nature. Generally the ordinary least square method is used to analyze such data but it gives out results that are misleading and incorrect.
Multi level modeling ,also known as hierarchical linear modeling a type of modeling approach that uses linear models for dealing with structured data. In this type of approach, the target population is studied by forming different levels with in the population and studying the variation of characteristics between and within different levels. Data is nested into levels and groups for this purpose. Multilevel models extend to handle situations where there are multiple classifications arranged in nested, crossed and multiple membership relations
In this paper the area that has been targeted to be studied using hierarchical linear modeling is the educational research problems. A lot of research is being carried out now a days on studying the behaviors and patterns of students from different perspectives . various organizations collect massive data on different characteristics of students which is used in researches on students. Author suggests that the most appropriate way of dealing with such massive large scale secondary data ,in order to study and use it for desired investigation, is through the use of multi level linear modeling approach.
In this paper, the author is studying the relationship between the student satisfaction of faculty in his major/department and the influence of student and institutional level factors on it. Measuring student satisfaction is critical to the institute as it helps in their retention. Institutes want to arrange their resources in a way to keep students involved in academic...
Please join StudyMode to read the full document