Determining Factors of GPA Natalie Arndt Allison Mucha MA /6/07

Objectives Determine important factors related to a Stevens student’s GPA Make use of methods and analytic techniques discussed in class Observe differences between (or lack thereof) engineering and science students

Initial Variable Ideas Years at school Hours work / week Hours sleep / night Cleanliness rating Which SAT score was higher Number of siblings Expected graduation year

Final Variable Ideas Gender (Primary) major # Semesters # Credits / semester GPA each semester Cumulative # credits Cumulative GPA Gender: ____________Major: ____________ SemesterCreditsGPA for Semester Total credits earned: ______Cumulative GPA: ____

Data Collection Method Voluntary Survey Anonymous Sent out to several subsets of general student body Only full-time (≥12 credits), undergraduate Stevens students considered Alumni who satisfied these conditions during their time at Stevens also considered

Lurking Variables Influence of extracurricular activities Changes in curriculum from year to year certainly a factor Personal issues, medical problems, stressful situations unaccounted for Differences between same course as time passes (professor, size, textbook, etc.) Large variability to begin with

Data Collected 28 students participated in the survey Combined 154 semesters worth of data 18 males, 10 females 19 engineering, 8 science, 1 art GPA ranged from to Credits ranged from 12.0 (imposed) to 25.5 Cumulative credits ranged from 33.0 to 177.0

After Data Was Collected … All names removed, obs category created for relating information for one individual Semester 0 refers to cumulative data Primary major used to create categorical school column Number of credits per semester used to create load category

Data Compilation obsgendermajorschoolsemcreditsloadGPA 2MaleEngineering ManagementE117.0b MaleEngineering ManagementE417.5b MaleEngineering ManagementE218.0c MaleEngineering ManagementE318.5c MaleEngineering ManagementE520.0c MaleEngineering ManagementE0101.0N/A3.947 … 20MaleComputer ScienceS313.0a MaleComputer ScienceS413.0a MaleComputer ScienceS115.0b MaleComputer ScienceS219.0c MaleComputer ScienceS069.0N/A3.884 … 26FemaleElectrical EngineeringE115.0b FemaleElectrical EngineeringE214.0a FemaleElectrical EngineeringE320.0c FemaleElectrical EngineeringE420.0c FemaleElectrical EngineeringE069.0N/A3.592

Preliminary Analysis somewhat normalskewed, left-tailed (by semester)

Initial Regressions GPA = *credits R 2 = GPA = *credits R 2 = semester datacumulative data

Residual Plots semester datacumulative data

Comparisons by Gender semester data cumulative data MaleFemale MaleFemale

Comparisons by School semester datacumulative data EngineeringScience Engineering

Comparisons by Load Load ALoad BLoad CLoad DLoad E

Stepwise Regression > stepwise = step(lm(gpa~credits+school+gender+sem),direction="both") Start: AIC= gpa ~ credits + school + gender + sem Df Sum of Sq RSS AIC - gender sem credits school Step: AIC= gpa ~ credits + school + sem Df Sum of Sq RSS AIC - sem credits school gender Step: AIC= gpa ~ credits + school Df Sum of Sq RSS AIC sem school credits gender Call: lm(formula = gpa ~ credits + school) Coefficients: (Intercept) credits schoolE schoolS > summary(stepwise) Call: lm(formula = gpa ~ credits + school) Residuals: Min 1Q Median 3Q Max Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) <2e-16 *** credits schoolE schoolS Signif. codes: 0 '***' '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: on 122 degrees of freedom Multiple R-Squared: , Adjusted R-squared: F-statistic: on 3 and 122 DF, p-value: > anova(stepwise) Analysis of Variance Table Response: gpa Df Sum Sq Mean Sq F value Pr(>F) credits school Residuals Signif. codes: 0 '***' '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Important Variables Both forward and stepwise regression return credits and school as most important variables Gender and semester deemed insignificant using AIC Summary returns that credits is marginally significant (10%) Anova returns that school is marginally significant (10%)

Observations & Conclusions Intercept: 2.96 Engineering majors: add 0.09 Science majors: add 0.27 Add 0.02 to GPA per credit Allows us to conclude that the science majors represented by our study average a GPA 0.18 points higher than engineering majors.

Recommendations Create a more refined study that allows us to focus on a specific area, rather than manipulating several variables at once Draw data from a significantly larger sample Find appropriate methodology to remove effect of lurking variables

Questions?