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Factors Affecting GPA BUS 302 Professor Fang Camille Legaspi Raphielle Perez Allison Remigio July Sengsourya 1 INTRODUCTION Research Question Our research question is: What factors affect GPA? One of our friends was the inspiration for choosing the related question of what factors affect GPA. We wanted to know what exactly affected GPA. We went about forming research questions that pertained to that. We wanted to see what really affects a person’s grades in school. This question has value because it encompasses all the different factors that would affect someone going to school. It helps students prioritize their own schedule to know their limits in school and work. It can help new students by showing them that it is important to balance out your school schedule with work, family and other important things. Research Plan In our research plan, we plan to survey 100 college students at Cal State San Marcos. We want our surveys to be short and concise and include questions about factors that affect a student’s GPA. As a group, we will brainstorm factors that are important such as: work hours, units taken, study hours, living arrangements, motivation of getting a higher GPA, plans after graduation, and the importance of getting a 3.0 or greater. The theory we are going to use would be the hypothesis theory. The hypothesis theory provides us with a structural analytical method for making decisions of this type. To formulate the hypothesis we used the null and alternative hypotheses. The null hypothesis is a statement about the sample values that we tested. The null hypothesis will be rejected only if the data provided has contradictory evidence. The alternative hypothesis includes all the values not covered by the null hypothesis. The alternative hypothesis is to be true if the null hypothesis is rejected. 2 Predictions Before conducting the survey we believe that there will be strong and positive correlations between working hours and the decrease in GPA, study hours and the increase in GPA, unemployed students will have a higher GPA verses students who are employed. The factor with the weakest correlation we predict will be commute time and it’s affect on GPA. The more hours students spend working takes away from time that can be put towards their studies. This is why we believe working hours and employed students will have a decrease affect on GPA. Commute time affects each and every student, but the amount of effect we believe is minimal. METHODOLOGY Research Procedure Once we agreed on a research question, we decided that the best way to answer our question was to conduct a survey. We felt that a survey would be the simplest and easiest research method. Due to time constraints, we agreed to personally hand out surveys instead of sending them to people through the Internet or mail. By handing out the surveys, we could get responses immediately because we would not have to wait until respondents mailed the surveys back to us or respond electronically. Also, we were guaranteed that for every survey we handed out, we would get a completed survey back since surveys sent through mail or email have a higher chance of getting ignored. It was also convenient to hand out the surveys personally because we could just give them to classmates and acquaintances. Therefore, our sampling technique was based on convenience sampling. 3 After we made the decision to conduct a survey, we began to brainstorm the factors that would most likely affect GPA. With these factors in mind, each of us separately came up with questions that relate to factors that affect GPA. We then combined our questions and removed duplicate and irrelevant questions because we wanted to keep the ones that were significant. After submitting the rough draft of our survey to the teacher, we further revised our questions so that they would be more focused on the research question. During this process, we reworded the questions so that respondents would know exactly how to respond. After revising the survey, we were left with the 16 questions that we felt were the most relevant. It was really important that our questionnaire be short (15-20 questions) and fit one page because we did not want to overwhelm and discourage our respondents. After we created our survey, we began discussion on how we were going to execute our research. We decided to have a sample size of 100 respondents because we felt that it would be feasible, considering our time constraints. To further expedite the surveying process, each member of the group was to hand 25 surveys. This way, each person could hand the surveys out to their respective classmates and acquaintances. After each person had their surveys filled out, he/she inputted the results into Microsoft Excel. It was easier to input the results of 25 surveys individually than inputting 100 surveys at once. We then combined our results to begin analyzing. Outcome There were many advantages and disadvantages of our research methodology. It was advantageous because it was efficient and quick. It was convenient because we handed out the surveys to whomever we knew and was near. The convenience factor and the fact that it took less than one minute to fill out the survey enabled us to get results immediately. However, 4 because the respondents were put on the spot, many answered our questions with estimations. For instance, some did not know their GPA at the top of their head. Had they been on a computer, they could have looked up more accurate information regarding their GPA on SMART Web. Some answered the question hastily, possibly because of bad timing or because they wanted to rush through the survey process. Finally, the fact that we kept the survey short helped because everyone was willing to participate and, with exception of seven people, completed the entire survey. We could have reworded our questions to be clearer because some answered our questions incorrectly. For instance, with our question, “What motivates you most in getting/maintaining a high GPA (3.0 or greater)?” we wanted them to answer by choosing one of the responses. However, some disregarded the word “most” and chose more than one answer. This problem could have been avoided had we been clearer, by specifying that they choose one. Also, since we passed out the surveys to classmates and acquaintances, the respondents were characteristically too similar; our results were not as spread out. Therefore, our sample is definitely not representative of the population in CSUSM. The survey that was handed out to CSUSM students is shown on the next page. 5 Age __________ Do you have children? Gender __________ YES Marital status __________ NO What is your commute time to school? _____ minutes Do you participate in extracurricular (school-related) activities? YES NO If yes, how many hours do you spend on those activities a week? _____ Do you have a job? YES NO If yes, how many hours do you work a week? _____ What is your cumulative GPA? _____ How many units are you taking this semester? _____ How many hours do you spend studying a week?____ Who do you live with? a) Parents b) Significant other and/or children c) Roommates d) Alone What motivates you the most in getting/maintaining a high GPA (3.0 or greater)? a) Acceptance to graduate school b) Attention of future employer(s) c) Outside motivation (i.e. pressure from parents, friends, etc.) d) Competition with fellow students e) Self-recognition f) Other ____________________________________________________ What are your plans after graduating from CSUSM? a) Enroll in graduate school b) Start career c) Focus on family How important is it for you to get/maintain a high GPA (3.0 or greater)? Not Important Very Important 1 2 3 4 5 6 RESULTS The following are the results of our survey. The questions of the survey can be broken up into three categories: demographics, school, and outside school. We felt that all the questions pertaining to these categories are relevant to our research. We included demographic questions because we wanted to know who our respondents were and to determine their validity. The school and outside school related questions were intended to help us find a commonality between students who have GPA’s of 3.0 or higher versus those who have GPA’s lower than 3.0. Demographics The mean, median, and mode age of our respondents is 23. Although the mean, median, and mode of age are equal, the frequency distribution is right-skewed. Out of pure luck, the amount of females and males that we surveyed were almost equal with 50 female respondents and 43 male respondents. As expected—those surveyed were college students—the majority of students was not married and did not have children. Only 13% were married and 11% had children. According to the right-skewed frequency distribution of commute time, most students’ drive to school takes 10 to 20 minutes. Lastly, almost half of the students we survey live with their parents. We asked questions regarding marital status, children, commute time, and living situation because we wanted to know if there was a correlation between these factors and GPA. We believe that differing priorities, distractions, responsibilities, and time constraints would affect GPA. 7 Respondent's Age 40 35 30 25 20 15 10 5 0 18 < 21 21 < 24 24 < 27 27 < 30 30 < 33 33 < 36 36 < 39 39 < 42 Age Range Gender 52 50 48 46 44 42 40 38 Female Male 8 Marital Status 90 80 70 60 50 40 30 20 10 0 Married Single Children 90 80 70 60 50 40 30 20 10 0 Yes No 9 Commute Time to School 35 30 25 20 15 10 5 0 0 < 10 10 < 20 20 < 30 30 < 40 40 < 50 50 < 60 Minutes Living Situation Significant Other 25% Alone 3% Parent s Roommates 23% 10 60 < 70 70 < 80 School The mean GPA of students is 2.99, which is close to the mode of 3.00 and the frequency distribution is almost symmetrical. Most students are full-time students, with the majority taking 13-16 units. Among those we surveyed, most spend 6-11 hours studying for school a week. Many believe that getting a GPA of 3.0 or higher is very important and most are motivated to maintain a high GPA because they want to get noticed by future employers. Finally, 82% plan to start their career after graduating from CSUSM. These questions were asked because we wanted to know whether students’ GPA is affected by the amount of units they are taking, the amount of time they spend studying, their motivation to have a high GPA, and their future plans. We figured that students with high GPA’s are those that are taking the right amount of units (12-14 units), spend a lot of time studying, and are highly motivated. Students with low GPA’s are the ones taking too many units, spend little time studying, and have low motivation. Respondent's Cumulative GPA 35 30 25 20 15 10 5 0 2.00 < 2.25 2.25 < 2.50 2.50 < 2.75 2.75 < 3.00 3.00 < 3.25 Cumulative GPA 11 3.25 < 3.50 3.50 < 3.75 3.75 < 4.00 Units Students are Taking 40 35 30 25 20 15 10 5 0 4<7 7 < 10 10 < 13 13 < 16 16 < 19 Units Hours Spent Studying per Week 35 30 25 20 15 10 5 0 1<6 6 < 11 11 < 16 16 < 21 21 < 26 Hours 12 26 < 31 31 < 36 36 < 41 Motivation Other 2% Gradute School 14% Self 30% Competition 0% Future Employer s Outside 13% Future Plans Focus on Family 5% Start Career 82% 13 Enroll in Graduate School 13% Importance of GPA > 3.0 35 30 25 20 15 10 5 0 Not Very Important Not Imporant Neutral Imporant Level of Importance 14 Very Important Outside School We believe that the busier students are, the greater the chance that their GPA’s were going to be lower than those that mostly focuses on school. So we felt it was important to ask questions regarding students’ lives outside of school. The majority of students do not participate in extracurricular activities. But for those students that do participate in school-related (CSUSM clubs, fraternities, sports, etc.) extracurricular activities, most spent 1-9 hours on those activities while one person spent 40 hours on these activities. In addition, most students are currently unemployed. However, for those that do work, the majority works part-time (30 hours or less). Participation in Extracurricular Activities 80 70 60 50 40 30 20 10 0 Yes No 15 Hours Spent on Extracurricular Activities per Week 16 14 12 10 8 6 4 2 0 1<9 9 < 17 17 < 25 25 < 33 Hours Employment 90 80 70 60 50 40 30 20 10 0 Yes No 16 33 < 41 Hours Spent Working per Week 25 20 15 10 5 0 4 < 11 11 < 18 18 < 25 25 < 32 Hours 17 32 < 39 39 < 46 46 < 53 ANALYSIS GPA AGE COMM TIME WORK HRS EA HRS GPA AGE COMM TIME EA HRS WORK HRS 1 0.04967062 1 -0.0382282 -0.0523229 -0.0378705 -0.123538 1 0.04036569 1 -0.1320484 0.12682592 -0.0267086 -0.0258425 UNITS 0.18181975 -0.2420766 0.05111876 0.18664791 STUDY 0.02034314 0.26456598 0.01320398 0.07834934 1 0.2636274 0.1096085 UNITS STUDY 1 0.37468962 1 Lower 95.0% Upper 95.0% SUMMARY OUTPUT Regression Statistics Multiple R 0.2621 R Square 0.0687 Adjusted R Square 0.0037 Standard Error 0.4177 Observations 93 ANOVA df Regression SS MS F 1.0577 6 1.1071 0.1845 Residual 86 15.0038 0.1745 Total 92 16.1110 Coefficients Standard Error Intercept 2.3277 Age 0.4280 5.4385 0.0000 1.4769 3.1786 1.4769 3.1786 0.0160 0.0132 1.2109 0.4197 0.6853 0.9251 0.2293 -0.0102 0.0422 -0.0102 0.0422 0.6758 -0.0075 0.0049 -0.0075 0.0049 0.4950 -0.0257 0.0125 -0.0257 0.0125 0.3575 -0.0103 0.0038 -0.0103 0.0038 0.0494 0.0001 0.0739 0.0001 0.0739 0.3488 -0.0195 0.0069 -0.0195 0.0069 0.0031 -0.0066 0.0096 Work Hours -0.0033 0.0035 0.0370 0.0186 -0.0063 0.0066 1.9930 0.9421 18 Lower 95% Upper 95% P-value -0.0013 Study Time 0.3944 t Stat Commute Time Extracurricular Activities Semester Units Significance F According to the multiple regression analysis we ran through Microsoft Excel, our model is not significant because the Significance F of 0.3944. Even though we knew that our model is not significant we decided to examine each of the factors separately to see if they correlate. We first analyzed whether there was a correlation between the number of units a person is taking and the student’s cumulative GPA. Our hypothesis was that the more units students are taking, the lower their GPA is going to be. We made this conclusion because overloading on units can lead students to work less effectively because they have to worry about more classes. Although the relationship is not significant, there apparently is a negative correlation between the two factors. Therefore, our claim is somewhat. 19 To further support our hypothesis we ran a t-test. H 0: r = 0 H A: r ≠ 0 t Test for Differences in Two Means Data Hypothesized Difference Level of Significance Population 1 Sample Sample Size Sample Mean Sample Standard Deviation Population 2 Sample Sample Size Sample Mean Sample Standard Deviation Intermediate Calculations Population 1 Sample Degrees of Freedom Population 2 Sample Degrees of Freedom Total Degrees of Freedom Pooled Variance Difference in Sample Means t Test Statistic 0 0.05 93 2.99 0.418 93 12.67 2.814 92 92 184 4.04666 -9.68 32.813569 Two-Tail Test Lower Critical Value Upper Critical Value p-Value Reject the null hypothesis 1.9729405 1.9729405 8.106E-79 This t-test indicates that, however insignificant the correlation, there still is one. 20 We then did a two tail test on one sample with the following results. This t test was for hours worked. We hypothesized that the more hours worked means the less time for students to study and it shows in the correlation. It shows that we should reject the null hypothesis, the upper and lower critical values. There was no correlation, and therefore we should reject the null hypothesis. 21 t Test for Differences in Two Means Data Hypothesized Difference Level of Significance Population 1 Sample Sample Size Sample Mean Sample Standard Deviation Population 2 Sample Sample Size Sample Mean Sample Standard Deviation Intermediate Calculations Population 1 Sample Degrees of Freedom Population 2 Sample Degrees of Freedom Total Degrees of Freedom Pooled Variance Difference in Sample Means t Test Statistic 0 0.05 93 18.92 12.79 93 2.99 0.418 92 92 184 81.879412 15.93 12.004799 Two-Tail Test Lower Critical Value Upper Critical Value p-Value Reject the null hypothesis 22 1.9729405 1.9729405 6.815E-25 The null and alternative hypotheses are: H0 : r = 0 HA :r ≠ 0 Correlation = -0.0382282 23 t Test for Hypothesis of the t Test for Hypothesis of the Mean Mean Data Data Null Hypothesis µ= Null Hypothesis 0 Level of Significance 0.05 Sample Size Sample Mean µ= 0 Level of Significance 94 0.05 Sample Size 23.215054 94 Sample Mean 2.99 Sample Standard Deviation 14.0103538 Sample Standard Deviation Intermediate Calculations Standard Error of the Mean Degrees of Freedom t Test Statistic 0.418 Intermediate Calculations 1.445057658 Standard Error of the Mean 93 0.043113408 Degrees of Freedom 16.06514029 t Test Statistic Two-Tail Test 93 69.35197499 Two-Tail Test - - Lower Critical Value 1.985801768 Lower Critical Value 1.985801768 Upper Critical Value 1.985801768 Upper Critical Value 1.985801768 p-Value 1.42657E-28 p-Value Reject the null hypothesis 7.0767E-82 Reject the null hypothesis In this analysis between commute time vs. GPA, we conducted a two tailed test. In this two tailed test, we looked at the rejection region where it is split into the two tails of the sampling distribution. The alpha level is spilt evenly among the two tails. We chose our level of significance to be ∝= .05 . In our case since the null hypothesis will be rejected, that means that 24 the sample mean is extremely larger or extremely smaller. From our data, the p-value of in GPA is significantly smaller than commute time. This tells us that the smaller the p-value than the probability is in the rejection region, then the null hypothesis is rejected. In both of these cases, however are both rejected because they fall outside of the region of given hypothesis. We believe there will be a strong correlation between the hours that a student spends studying and their GPA. Below is the graph of sample data we collected. To test out our sample of interest we formed the hypothesis : Ho : r < 0 H A:r > 0 α=0.05 With a sample size of 93 our degree of freedom is 91. Below is a t-test that was performed for these to sample means. By looking at the result of the PHStat we can see that the critical value 1.65; therefore our decision rule was: If t > 1.65 reject the null hypothesis, otherwise do not reject. 25 t Test for Differences in Two Means Data Hypothesized Difference Level of Significance Population 1 Sample Sample Size Sample Mean Sample Standard Deviation Population 2 Sample Sample Size Sample Mean Sample Standard Deviation Intermediate Calculations Population 1 Sample Degrees of Freedom Population 2 Sample Degrees of Freedom Total Degrees of Freedom Pooled Variance Difference in Sample Means t Test Statistic Upper-Tail Test Upper Critical Value p-Value Reject the null hypothesis 0 0.05 93 12.65 7.71 93 2.99 0.418 92 92 184 29.809412 9.66 12.064988 1.6531771 2.266E-25 t= 12.065, thus 12.065 > 1.65, therefore we reject the null hypothesis. Because the null hypothesis was rejected the sample showed there was a correlation between study hours and GPA. The following are the results to our qualitative data. Because we were working with nonquantitative data, we decided to analyze these factors differently. We decided to find the mean GPA of each category to determine any trends. 26 In this study, we predicted that living with parents would increase a student’s GPA. We predicted this beacause living with parents, the student would not have to work as much to generate enough income for rent. The student will likely spend more time on studying and or extracircular activities rather than spending more time working. However, in our survey that we conducted, we found out that our prediction was false. Living alone would result in a GPA of a 3.08. Followed by living with parents with a GPA of 3.01 , roomates with a GPA of 2.98 , and living with a significant other with a GPA of 2.95. Living alone would increase a student’s GPA due to the limited distrations avaliable in the environment. We would like to believe that if there is motivation behind one’s action or goal, the better the chances they are to obtain and achieve their goal. This is the reason why we choose this question. In this case, the motivation for the student would be their future plans after graduating. Having a plan will help one stay motivated and helps provide needed momentum throughout the course of study. The distribution of average GPA for each individual future plan is shown below. 27 As you can see by the graph students whose future plan is to focus on their family had the highest GPA average, followed by future plans of starting a career and then enrolling in Graduate School. This was not the result we as a group thought we would see. Our assumption was that the students who’s main goal was to focus on family would have had the lowest average GPA and the students who wanted to start a career or enroll in Graduate School would be neck and neck for the highest average GPA. We thought the future plan of focusing on family would have the lowest average GPA because after graduation these individuals are not needing to compete to get accepted elsewhere or nor will the be competing with future students for career posistions. A reason for this outcome could be that people who are enrolling in Graduate School are taking on more units and stress therefore taking a toll on their GPA or maybe it is something as simple as the distribution on sample. Next we examined a person’s motivation to get a high GPA and how it affects there overall GPA. 28 Our hypothesis was that the more motivation a student had he/she would have a higher GPA then students that did not have any or little motivation. Above is the bar graph showing the outcomes from graphing motivation vs. GPA. With motivation consisting of 5 different options, most students with any form of motivation had a higher GPA then other students with less motivation. Therefore we can conclude from the above results that any form of outside motivation can help increase Student’s overall grades. We then looked at how the level of importance affects GPA. 29 We wanted to determine whether a person's GPA is dependent on how important getting/maintaining high GPA is to them. Our conclusion is that people who view having a high GPA will most likely have higher GPA's because they are more likely put more effort into studying hard to get better grades. On the other hand, people that do not consider a high GPA to be important will most likely take the attitude of just wanting to get by and pass their classes. When students have this attitude, they will mostly likely have lower GPA’s. Surprisingly, the highest mean GPA belonged to the group of people that do not consider a high GPA of being important. However, as the level of importance increased, the more the mean GPA increased. This further supports our theory that the more important it is for someone to have a high GPA, the more likely their GPA will be high. CONCLUSION In this research, we surveyed 100 college students at Cal State San Marcos. We surrounded our questions on motivational factors on getting a high GPA. We considered important factors such as the amount of works hours in a given week, units taken during the semester, study hours on an average week, living arrangements, motivation of getting a higher GPA, and the importance of achieving a 3.0 or greater to the student. From our data, we determined that the most of our factors had no significant correlation except for study hours vs. GPA. This comparison looked to have a stronger correlation because the more hours spent studying the higher the student’s GPA will be. The weakest correlation that we determined would be commute time because it did not have any significance due to the miles driven verses the student’s GPA. From our study, we can conclude that many factors can affect a student’s GPA. The amount of study hours is most significant because the amount of hours a student 30 studies, determines the grade received in each class and then the outcome at the end of semester is to receive a high GPA. 31