Alcohol Consumption Among YSU Students Statistics 3717, CC:2290 Elia Crisucci, Major in Biology Tracy Hitesman. Major in Biology Melissa Phillipson, Major in Biology May 7, 2002
I. Executive Summary: Many substances affect the daily lives of college students, and since we are college students ourselves, we decided to examine one of those, alcohol, and the affects specific factors, both genetic and environmental have on them and the relationship among them. Some of the variables we included in our studies were the students majors, living situations, class rank, age and gender. For the most part, we examined the data from the students who did drink, since a majority of the people who participated in our study did drink. We did not get enough non-drinkers to make any subjective statements about that, so, for the rest of this project, we will ignore the 14% of the people in our study who do not drink and instead concentrate on the people who do drink, and see which kind of person will drink more, based on the questions we asked. We had a total number of 171 people participate in our study, which was the amount of surveys returned out of 200. With the amount of students registered at YSU, however, this is hardly representative of the entire population. Still, though, it can be used to make certain assumptions and test our hypotheses anyway, though we must be aware that it may not be accurate and therefore not make too many assumptions outside of the community. After comparing the data using bar charts and the Chi-square test we were able to prove and even disprove some of our hypotheses, details of which will follow in the report below. Now, without further ado, the questions and answers to alcohol use in the life of YSU students. II. Introduction: We had several hypotheses which were as follows: 1) Men drink more often, and consume greater quantities of alcohol when drinking, than females do, 2) Of the people who drink, those living with their parents, as opposed to those living alone or at the dorms, tend to consume less alcohol than the others, and finally 3) People with jobs drink more than those without, possibly for a variety of reasons, which we will recount later. Those are the questions we want to examine in order to improve the understanding of why youths turn to alcohol. Perhaps, after viewing these statistics, measures can be taken to change the way alcohol effects students. This is why we decided to review this subject. For each of our above simple hypotheses, we concocted a null and alternative hypothesis, which will now be stated here for clarity s sake, and presented again later with the actual numerical values that determine whether we can reject or must in fact accept the null hypothesis in each group. 1.) Null: Males consume less than or equal amounts of alcohol as females when they drink Alternative: Males consume greater amounts of alcohol than females when they drink. 2) Null: Frequency of drinking and living situation is not related.
Alternative: Frequency of drinking and living situation is related. 3) Null: People with jobs drink less alcohol when they drink than people without jobs. Alternative: People with jobs drink equal or greater amounts of alcohol than those who are unemployed when they drink Seeing as we observed the responses of the surveys, it was an observational experiment, because we also had dependent and independent variables. The independent variables were that they drank, and the dependent variables were gender, employment status and living situations. Besides the easily readable comparisons of the bar graphs, we will be getter more precise answers using the independent sample T-test, and the Chi-Square test. III. Data Collection: We distributed surveys to a wide variety of YSU students, and returns were 171 out of 200. The questions were all multiple choice, except for the one asking about their majors, and most of the information was either demographic or concerning behaviors. Some possible errors could have occurred in the fact that the sample might not really be representative of the YSU population, and even if it was, there no way to guarantee honesty on the surveys. Another similar drawback, we are all biology majors, so biology majors in our classes were more likely to participate in our research and skew the results, and thus we may have fewer majors represented. Summary of Information: Numeric Summary: (Visual Summaries- i.e. GRAPHS on other pages.) Below are the frequency results of some of our demographic data. Males made up 55.6% of our surveys, and females the other 44.4% (see figure 1). As I mentioned before, 14% of those surveyed did not drink, while the other 86% did, and so we only examined 86% of our population, and decided to disregard the remainder of the people, since they were not needed for the purpose of our studies (see figure 2). We also did the percentages of how much alcohol was consumed when drinking, since we will later use this data by cross-referencing it with gender, so that we may see the differences (see figure 3). People who have 1-2 drinks make up 35.1 % of the total population. People who have 3-4 drinks make up 18.1 % of the total population. People who have 5-6 drinks make up 23.4 % of the total population. People who have more than 6 drinks make up 9.4% of the total population. Analysis: Hypothesis 1: Null Rejected; p=0.045 Hypothesis 2: Null Accepted; x².05>x²; 15.5073>12.88 Hypothesis 3: Null Rejected; p=0.004 According to the graphs, it is very ostensible to see that our hypotheses are either true or false.
Now that we know that men do drink more than women and more often, that people living at home actually do drink less often but consume more when they do drink, and that people with a job drink more than people without one, what we want to know is why. So, first we will have to come up with several possible reasons, and later control the other variables so that we can test and see which of these reasons is most foretelling. But since this is not part of our study, we will simply names possible reasons, and hope that later studies will attempt to analyze this part of our interpretations. Men may drink more than women because they believe that it is a masculine trait that they be able to do so. Men and women, even raised the same as children, often come out very differently, and since psychologists still debate the reasons why this is, it is pretty near impossible for us to determine why this is, if in fact it is the case. Students living at home may drink less often that people who aren t out because they have that parental element of discipline to control them. But when they break free of it, they may consume greater quantities because it is their only chance to do so. Finally, people with jobs drink more than people without. There are plenty of reasons for this. One, they have the money to spend on it, two, they may have more a need to relieve stress. One other factor, though, that works in opposition to this: one would think that people with jobs would have more responsibility than those without, so you would think they would be less likely to drink. But then again, drinking isn t wrong, only drinking responsibly. And we didn t study that, so they may or may not be. Further study is needed to determine this. Conclusions: The first null hypothesis that men consume less or equal amounts of alcohol that women, was rejected. As you can see by looking at the bar graph under figure 4, men clearly drink more than women. This is probably because generally men can metabolize alcohol quicker and can handle more. The second null hypothesis that drinking and living situation is not related was accepted. The bar chart under figure 5 shows in red that students who live with their parents actually drink less than those who are independent. However, the Chi square test shows that there is no significant difference among the populations. This means that were a student lives has no relation to how often they drink. Finally, the third hypothesis that people with jobs drink less was also rejected. The bar chart under figure 6 shows in blue that people who have a job in addition to school actually drink more. This may be because people with jobs have more money to drink and more stress to relieve with such an activity. FIGURES AND GRAPHS: NEXT SECTION
Figure 1: Percentages of females and males involved in the study. females 76.00 / 44.4% males 95.00 / 55.6% Figure 2: Percent of the sample that drink. do not drink 24.00 / 14.0% drink 147.00 / 86.0%
Figure 3: Number of drinks consumed while drinking. 0 drinks more than 6 14.0% 28.7% 1-2 drinks 12.9% 5-6 drinks 18.1% 3-4 drinks 26.3%
Figure 4: Examining gender and how often they drink and the amount they drink. (Hypothesis 1) Group Statistics NUMBER FREQUENC GENDER male female male female N Std. Std. Error Mean Deviation Mean 95 4.1158 2.1970.2254 76 2.8289 1.9825.2274 95 2.0000 1.2548.1287 76 1.5263 1.1252.1291 NUMBEREqual variances Equal variances FREQUEEqual variances Equal variances Levene's Test fo uality of Varianc F Independent Samples Test Sig. t df t-test for Equality of Means 95% Confiden Interval of th Sig. Mean Std. Error Difference (2-tailed) Difference DifferenceLower Upp 3.768.054 3.973 169.000 1.2868.3239.6475 1.926 4.019 66.520.000 1.2868.3202.6547 1.919.587.445 2.567 169.011.4737.1845.1094.837 2.598 66.768.010.4737.1823.1138.833
4.5 4.0 3.5 3.0 2.5 2.0 Mean 1.5 1.0 male female FREQUENC NUMBER GENDER Figure 5: Examining living situation and drinking. (Hypothesis 2) FREQUENC * LIVE Crosstabulation Count FREQUENC Total none oncea month once a week 2-3 times a week more that 3 times a week LIVE with on independ parents campus ent Total 15 5 4 24 41 9 10 60 19 4 8 31 32 4 4 40 9 7 16 116 22 33 171
4.0 3.5 3.0 2.5 2.0 Mean 1.5 1.0 with parents on campus independent FREQUENC NUMBER LIVE Figure 6: Examining working a job and amount of drinking. (Hypothesis 3)
FREQUENC NUMBER JOB have job no job have job no job Group Statistics N Mean Std. Deviation Std. Error Mean 141 1.8652 1.1845 9.976E-02 30 1.4333 1.3309.2430 141 3.6738 2.0822.1754 30 2.9333 2.6121.4769 Independent Samples Test FREQUEEqual variances Equal variances NUMBEREqual variances Equal variances Levene's Test fo uality of Varianc F Sig. t df t-test for Equality of Means 95% Confiden Interval of th Sig. Mean Std. Error Difference (2-tailed) Difference DifferenceLower Upp.630.428 1.774 169.078.4319.2435 87E-02.912 1.644 39.368.108.4319.2627 92E-02.963 8.669.004 1.687 169.093.7404.4388 -.1258 1.606 1.457 37.231.153.7404.5081 -.2889 1.769
4.0 3.5 3.0 2.5 2.0 Mean 1.5 1.0 no job job FREQUENC NUMBER JOB