Students Social Lifestyle and First Year Average Exam Grade sample essay
The Relationship between a Students Social Lifestyle and First Year Average Exam Grade Alternate Hypothesis: There is a relationship between a Students social lifestyle and their first year average exam grades Null Hypothesis: There is no relationship between a Students social lifestyle and first year average exam grades Introduction
The aim of my investigation is to find a significant relationship between a student’s first year academic grades and his or her social lifestyle. I can relate myself to this study as I am in the second year so it would be interesting to see if my first year grades were influenced by my social lifestyle. In this study I will consider one dependant variable, the exam grades and three independent variables which I believe are the main components of a student’s social lifestyle. These are alcohol consumption, the amount of hours on Facebook and the amount of hours of television watched. The more units of alcohol consumed, the lower a student’s grade would be I myself believe that this hypothesis is significant because alcohol is consumed normally before and during a night out. Nights out normally end at 3am in the morning. This means that the next morning the student will wake up tired with a possible hangover which leads to decreased concentration during lectures and the effects may also reduce motivation to even attend the lecture.
Therefore as a result of this, the student may not work sufficiently enough to obtain a high grade. A study by Wood et al found that “A longitudinal study of 444 freshmen attending the University of Missouri found a significant cross-sectional association between heavy or problem drinking and academic problems.” (Wood et al., 1997) The more hours online on Facebook, the lower a student’s grade would be Facebook is a popular social networking website which is used by a large majority of students. Students that spend an excess amount of time on Facebook are missing out on time to study and revise. Therefore they have less time to prepare for their exams which will lead to them attaining lower grades. Kirschner and Karpinski carried out an experiment and “Results show that Facebook® users reported having lower GPAs (Grade Point Averages) and spend fewer hours per week studying than nonusers.” (Paul A. Kirschner, Aryn C. Karpinski, 2010)
The more hours of Television Watched, the lower a student’s grade would be We can presume that there is a link between the hours of television watched and a student’s grade. The reasons to this are as followed, if a student spends more time watching television they are dedicating less time to learning and revising. The students that watch less television may spend more time learning and therefore could perform better in exams obtaining a higher grade. Currently there has been no research in support of hours of television watched and a university student’s grade so this research would be a first in its field.
Data and methodology
The data collected has been obtained by using a survey. It contains answers from 82 second year undergraduate students from the Aston Business School. The questions were based on personal information, life style, family background, academic related information and other information. The answers that related to a student’s social lifestyle were then chosen to become variables for the research. In order to process the data, a programme called Stata which is an integrated statistical package for data analysis, will be used. It will enable the data to be viewed easily in the form of graphs, tables and numerical values. A problem that may occur in the study is the presence of heteroskedasticity. This only occurs in cross-sectional studies. We will carry out a heteroskedasticity test and if the probability shows a value quite far from 0 then heteroskedasticity is present. We will use the following methods to interpret the information:
* Mean: The average number for the data
* Standard Deviation: This creates a value that shows how dispersed the values are from their mean. The lower the standard deviation, the less dispersed the values are. * Min: shows the lowest value scored for the variable examined. * Max: Shows the highest value scored for the variable examined. * Correlation: Shows how strong the relationship between the two variables are. A positive correlation means that as one variable increases, so does the other. A negative correlation means as one variable decreases, the other increases. Zero correlation means that the variables have no effect on each other. I am looking for negative correlations in my study which will be portrayed as a negative coefficient value * Regression analysis – Shows how the value of the dependant variable changes as one independent variable is varied and the others remain fixed. * P value – A means to test the hypothesis, at the significance level 5%, if the P value is equal to or lower than 0.05, the null hypothesis is rejected * R2 – Value used to see how much percentage the dependent variable is explained by the independent variable.
The more units of alcohol consumed, the lower a student’s first year average grade would be Table 2 shows that there is a positive correlation of 0.0947 between the students grade and alcohol consumption. This suggests that the students that consumed more alcohol scored higher grades. However, this data could be inaccurate because of possible anomalies. Graph 1 Graph 1 shows a scatter graph of average score for first year against units of alcohol consumed per week. A linear trend line has been added to display the positive correlation that exists between the two variables. It can be argued that the line is only slightly angled which portrays a weak positive correlation. It can also be seen that there is a large concentration where there has been little alcohol consumed but the grades vary enormously. The highest grade was 85. One of the students that achieved this drank 0 units a week, however the other that achieved it drank 14 units a week which is much larger than the mean of 6.71 units a week.
A regression analysis was then carried out. Table 3 shows the statistical results obtained. The coefficient is 0.1057496, holding all other variables constant, means for every increase in unit of alcohol, there was a 0.1057496 increase in grades. The R2 shows a value of 0.009 which means only 0.9% of the students grades are explained by the units of alcohol consumption. Further the P-Value is larger than 0.05 at the 5% significance level which means the null hypothesis is accepted. Finally a heteroskedasticity test showed the value of 0.9975 which is larger than 0 suggesting that the relationship is heteroskedastic.
It is generally believed that an increase in alcohol consumption would have led to reduced grades because of the effects alcohol has on your body such as reduced concentration. However, the results show a weak positive correlation which suggests that as alcohol consumption increased, grades also increased. These results could be conclusive to Aston Business Students as they all have a similar amount of work and follow similar timetables. In other courses, drinking the same amounts of alcohol may have a different effect on grades depending on how difficult the course is whether the course is exam dominated or coursework dominated.
The more hours online on Facebook, the lower a student’s average grade would be Referring back to table 2, the correlation between the hours online on Facebook and a student grade is 0.1291. This also doesn’t conform to the hypothesis as it suggests that the more hours online on Facebook, the higher a student’s average grade would be.
Here we can see clearly a positive correlation between the two variables. As hours online on Facebook rises, so do the students grades. However the anomaly which is at (75,80) could have an effect on the trend line causing it to become inaccurate. There is a large concentration of grades around 0 hours. However the grades vary a lot at this area.
Table 4 shows the results of the regression analysis carried out on Stata. The coefficient was 0.1333656, holding all other variables constant. This implies that for every hour online on Facebook, there is a 0.1333656 increase in the average score of a student’s first year grades. The R2 value shows that 1.67% of the student’s grades are explained by the hours they spend online on Facebook. This again is small figure so there is a possibility that there is no real relationship between these two variables. The P-Value of 0.248 is larger than 0.05 at a 5% significance value which implies the null hypothesis should be accepted. The heteroskedasticity test showed a value of 0.4790 which is further than two showing that the relationship is heteroskedastic. In theory, the more hours online on Facebook would lead to reduced grades as it provides a distraction for revision and learning. However some students may use Facebook for work purposes such as an online study group.
Roblyer et al stated that Facebook has “the potential to become a valuable resource to support their (students) educational communications and collaborations with faculty.” (Roblyer et al 2010). This shows that students are able to communicate with lectures via Facebook so may be able to get help on topics they are struggling with. This will show an increase in grades. Another reason for the results found is that Facebook allows students to take a break from their work leaving them feeling motivated to return to their work after they have rested so maybe a couple of hours on Facebook is beneficial. Overall the theory and the results do not conform. The null hypothesis will have to be accepted, however on a larger population, theory and results may conform.
The more hours of Television Watched, the lower a student’s grade would be Referring back to table 2 shows that the coefficient between these two variables is -0.0569. This portrays a negative relationship meaning that as the hours of television watched are increased, the student’s average grade decreases. Graph 3
Graph 3 shows the relationship between the two variables and the trend line confirms a downward trend. It could be said that there are anomalies in this data at 20 hours of television watched, however 20 hours is possible and three people have reported to have watched 20 hours so in this case, it is justified as valid data. The samples are very spread out thought it can be viewed that a lot of students watch zero hours of television yet their grades varied from 44% to 85%. Similar results can be seen at 10 hours of television viewed per week. This evidence implies that there is no difference between hours of Television watched and the student’s first year average score.
Regression analysis results for the two variables are displayed on table 5. The correlation coefficient shows a value of -0.1073295. This shows that there is a 0.1073295 decrease in a student’s average grade for every hour of television watched holding all other variables constant. The P-Value and R2 both show that there is little relationship between the variables. The P-Value is 0.612 which is much larger than 0.05 at the 5% significance level. This means that the null hypothesis is accepted. The R2 is 0.0032 which means that only 0.32% of students grade are explained by hours of television watched.
Watching television in my view will reduce a student’s grades as, like Facebook, it provides a distraction towards learning and revision. Also most programmes are an hour long and this is a significant amount of time not to learn or revise particularly if the student watches 2 or 3 programmes in a row. However, MacLean and Roderick (1968) believe that television is “ a new facility through which the teacher can better achieve traditional educational aims.” In university, lecturers can record their lectures and put them on the internet to be viewed anytime, if this is counted as a form of online TV then watching these type of programmes will be educational and may help the student to attain better grades.
With all the results obtained and analysed and after considering the theory behind the relationship between the variables, it can be concluded that only one of the three sub hypotheses could be confirmed. The first sub hypothesis aimed to find a relationship between a student’s average first year grade and the amount of alcohol consumed, however, due to a positive correlation it was concluded that, in this study, as alcohol consumption increased, so did the students grade. The next sub hypothesis aimed to find a relationship between a student’s average first year grade and the amount of hours on Facebook per week a student used.
Again a positive correlation showed that, in this study, the more hours on Facebook, the higher a student’s average first year grade was. The last sub hypothesis aimed to find a relationship between hours of television watched per week and a student’s average grade. The positive correlation confirmed the relationship that the more hours of television watched, the lower a student’s average first year grade was. It must be taken into account that there may have been external factors also affecting the student’s grade that weren’t examined in this research.
There were some problems in the data provided that may have caused inaccuracies in the studies. Firstly, the data was collected by means of a survey with multiple questions asked regarding many topics of the student’s life. A problem with using surveys is that people may lack the motivation to fill out the survey correctly and truthfully. Further, if a student doesn’t understand the question, they may just guess the answer which leads to inaccurate results. It is highly doubtful that a student would accurately measure his/hers unit of alcohol consumption nor would they time the hours they are on Facebook or hours watching television. It could be argued that all the data collected is based on approximation rather than accuracy and this reduces the validity of the research. Next, the sample size which consists of 82 people is too small to represent an entire population of students. Also, all the students used were business students.
This might have had an effect on the grades achieved for the year and the amount of time the student could use for socializing. For other courses, it could be easier to achieve a higher grade with the same amount of alcohol drunk, time on Facebook and television watched. Finally, it was only students from Aston University who were surveyed which means students from other universities were not taken into account of. This questions the validity of this study and whether we can generalize it to an entire population. In my opinion, a much larger sample is needed to show a more accurate result. Too add to this, another survey should be conducted with a larger population and the only questions that should be asked are the ones that relate specifically to the variables in the hypothesis.
This study allowed me to apply the statistical and analytical skills learnt in the econometrics module. I learnt how to use software to help provide statistics to achieve a conclusion to my hypothesis and sub-hypothesis. The study in my opinion was not a success due to the lack of data received. In this study, my hypothesis was not proven so I must accept the null hypothesis: There is no relationship between a Students social lifestyle and first year exam grades. However I believe if I could redo the study with a much larger sample size and only specific questions related to my variables, then the results would confirm my hypothesis.
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Roblyer, M.D et al, 2010. Findings on Facebook in higher education: A comparison of college faculty and student uses and perceptions of social networking sites. The Internet and Higher Education, [Online]. 13/3, 134/140. Available at: http://www.sciencedirect.com/science/article/pii/S1096751610000278#aff1 [Accessed 03 December 2011].
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