Statistical Study: Comparing Fast Food Consumption to Body Mass Index

Transcription

1 Statistical Study: Comparing Fast Food Consumption to Body Mass Index Janet Hiestan Biology major Jennifer Sanchez Biology major Jason Majirsky Biology major Introduction: Does fast food consumption influence body mass index? This is interesting to biology majors because being overweight is a problem in today s society and eating fast food especially for college students is nearly unavoidable. Originally we were to study a sample of 100 people age 18 to 60, but given our limited resources the study became 41 subjects age 18 to 23. These subjects were all students at Youngstown State University. Our null hypothesis is that fast food does not influence body mass index. Our alternative hypothesis is fast food increases the likelihood of a high BMI. This data comes from a survey, we sampled group a people with our survey forms and recorded the responses. The data collected is a representative sample of our target population, which means we will be able to apply inferential statistics to make estimations and predictions about the target population. Data collection Techniques: The data was collected by the means of a survey. The surveys were passed out to one in every five students who were passing by Ward Beecher Hall. This was done to try to achieve the closest thing to a random sample that was possible in our case. Some limitations that had to be overcome were time, money, and availability of willing volunteers. Time because a sample of 100 was the original plan, but due to available time, only 41 samples were taken. Money was a limitation because in order to have a true random sample it would be necessary to have rosters of all the colleges in the United States and to use a random number generator to choose a list of college students. This is so that each student would have an equally likely chance to be chosen. Finally, the last limitation was the availability of willing volunteers. Of every one and five people who were asked to complete the survey, about 25% were in a hurry and unwilling to participate, this may have caused volunteer bias, while it also further limited our sample size. In addition to these limitations, the validity of the survey method is questionable because people may lie. After the survey was collected the data was entered into SPSS for analysis. Then in order to make a connection between weight and height, information on the Body Mass Index was looked up on the Internet and compared to three other similar sources to insure accuracy. A chart as well as an equation was given which resulted in the value that is called the Body Mass Index or BMI. The body mass index (BMI) calculator is one of

2 the best tools for assessing whether a person is overweight because it allows for variances in body size. It applies to both men and women and is an excellent way of calculating the ideal weight according to What s your ideal weight? Check your Body Mass Index, an article from the Internet. Unfortunately it too has limitations. It fails to take the frame size into account, so people with larger body frames may be considered overweight even if their body fat is low. Also, tests and tools that directly measure body fat are more accurate. Finally the BMI is a poor predictor in children and teens, because the ranges are based on adult heights in athletes. Due to the high muscle weight in these athletes, pregnant or nursing women due to higher fat content, and people over the age of 65 will not have accurate BMI reading off these charts. For this reason, the sample was limited to traditional college students, which just happened to fall between the ages of Pregnant women were not included in the survey. Summary Information: The body mass index range was given in the same Internet article that the formula for body mass index was found. : BMI Range Freque Percent Valid Cumulative ncy Percent Percent Valid underweight Valid underweight normal normal overweight overweight obese obese obese obese Total Total The underweight group for this experiment was pretty much neglected due to our interest in being overweight. The remaining four groups then were compared to each other by how much fast food per week was eaten. For SPSS use, these groups were recoded. Underweight was given a numeric value of 0, normal: 1, overweight: 2, obese 1: 3, and obese 2 was given a value of 4. These are the numbers that will be shown in later charts.

3 Below is a pie graph demonstrating these results: obese 2 7.3% obese 1 underweight 4.9% 9.8% overweight 24.4% normal 53.7% distribution for the fast food data Frequen Percent ValidCumulativ cy Percent e Percent Valid Valid Total Total

4 The following histograms show the distribution for each of the BMI categories: 7 Histogram For BMIRANGE= Std. Dev = 1.52 Mean = 2.7 N = Histogram For BMIRANGE= Std. Dev =.79 Mean = 2.80 N = 10 Histogram For BMIRANGE= Std. Dev = 2.36 Mean = 3.8 N = 0

5 Histogram 2.5 For BMIRANGE= Std. Dev = 1.15 Mean = 4.3 N = 0 These Histograms show the distribution of how many times fast food is eaten for each BMI range. More importantly, however, is the mean associated with each of these ranges. The average weight BMI group shows the averages times fast food is eaten per week at 2.7, the overweight at 2.8, the obese 1 at 3.8, and the obese 2 group at 4.3 times per week. This shows an increase in the BMI as the number of times fast food is eaten per week is increased. This is however not truly accurate since only a few people are in these categories. The number of times that fast food was eaten ranged from 0-7 to make some analyses easier, this group was divided into two groups: (1) Infrequent fast food eaters: those with values between 0 and 3 were given a value of 0 (2) Frequent fast food eaters: those with values between 4 and 7 were given a value of 1 The results are displayed in a table below: Report BMI Range FFRAN GE Mean N Std. Deviatio n Total Total As is seen in the table, the mean for infrequent fast food eaters was , which is lower than the mean for frequent fast food eater 909. This shows that those who eat fast food more frequently have higher BMIs.

6 Analysis: Null Hypothesis: Fast food consumption has no influence on body mass index. Alternative Hypothesis: Fast food consumption increases body mass index. The Chi-squared test however proves that fast food consumption and an increased BMI are not significantly related. The following charts show the value that was received using SPSS. FFRANGE * NEWBMI Crosstabulation Count NEWBMI Total FFRANGE FFRANGE Total Total Chi-Square Tests Value df Asymp. Sig. (2- sided) Exact Sig. (2-sided) Exact Sig. (1-sided) Pearson Chi- Square Pearson Chi Square Continuity Correction Continuity Correction Likelihood Ratio Likelihood Ratio Fisher's Exact Test Fisher's Exact Test Linear-by-Linear Association Linear-by-Linear Association N of Valid Cases 41 N of Valid Cases 41 a Computed only for a 2x2 table b 1 cells (2%) have expected count less than 5. The minimum expected count is Conclusion: Based on these numbers, the null hypothesis is not rejected. This is because the Chi-square value of is very large, and shows that fast food consumption has no significant influence on body mass index. There were many biases that were discussed

7 earlier. Also, the limited number of responses of people that were in the overweight, obese 1, and obese 2 categories may have affected the results of the experiment. Only 7 of the 41 people in the survey registered as actually being obese. Perhaps a more widespread research would obtain more obese people and show whether there is a correlation with the amount of fast food consumed. As for this experiment, the BMI showed no increase in relation to the amount of fast food eaten.