Event Title

Using Logistic Regression Model to Analyze Student Satisfaction Data

Location

CSU 253/4/5

Start Date

4-4-2011 1:30 PM

End Date

4-4-2011 3:00 PM

Student's Major

Mathematics and Statistics

Student's College

Science, Engineering and Technology

Mentor's Name

Deepak Sanjel

Mentor's Department

Mathematics and Statistics

Mentor's College

Science, Engineering and Technology

Description

Measuring and analyzing customer‘s satisfaction has been an important element in the quality improvement of businesses and organizations. At colleges and universities, researches have attempted to gain a better understating of what short of factors influence college student‘s satisfactions through surveys. In literature, several methods have been used to measure and analyze student‘s satisfaction data. Most often chi-square test has been used but there are limitations on using this test. In this research, student satisfaction survey data have been analyzed using logistic regression model. Variables considered are Gender, Age Category, and Attendance, to measure the satisfaction of six categories. Model adequacy test shows the data are appropriate for logistic regression.

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Apr 4th, 1:30 PM Apr 4th, 3:00 PM

Using Logistic Regression Model to Analyze Student Satisfaction Data

CSU 253/4/5

Measuring and analyzing customer‘s satisfaction has been an important element in the quality improvement of businesses and organizations. At colleges and universities, researches have attempted to gain a better understating of what short of factors influence college student‘s satisfactions through surveys. In literature, several methods have been used to measure and analyze student‘s satisfaction data. Most often chi-square test has been used but there are limitations on using this test. In this research, student satisfaction survey data have been analyzed using logistic regression model. Variables considered are Gender, Age Category, and Attendance, to measure the satisfaction of six categories. Model adequacy test shows the data are appropriate for logistic regression.

Recommended Citation

Tsegaye, Amanuel. "Using Logistic Regression Model to Analyze Student Satisfaction Data." Undergraduate Research Symposium, Mankato, MN, April 4, 2011.
http://cornerstone.lib.mnsu.edu/urs/2011/poster-session-C/24