Stress has a significant influence when it comes to the eating habits of college students. The question I am attempting to answer is "How does stress relate to eating habits in college students?" The independent variable is stress, and the dependent variable is food choices. My alternative hypothesis is there is significance between stress and eating habits in college students. While my null hypothesis states, there is no significance between stress and eating habits in college students. This hypothesis is critical to test to see the effect that stress has on food choices in college students. I find this to be an exciting topic to study because it has been proven that stress is correlated to unhealthy food choices.
When college students are stressed, they often turn to food like chips, soda, and candy as a motivator to complete their assignments. My hypothesis is plausible because it measures stress levels amongst college students and the probability that they will resort to unhealthy food choices to cope (Morgan, 2017). This hypothesis relates to my observations because I can understand how difficult it is to make healthy eating choices on a college campus. Most of the food around you is quick, easy, cheap food that is meant for the student on-the-go and student food services fail to promote healthy food choices. I have noticed that the more vigorous food selections are often pricier or not convenient for the college student who has a tight class schedule.
Field Experiment to Test the Hypothesis
Null hypothesis: there is no significance between stress and eating habits in college students.
Alternative hypothesis: there is significance between stress and eating habits in college students.
A field experiment to subject the null and alternative hypothesis, in this case, would be linked to a survey. The survey will be conducted on many college students say for instance n=30. A sample population of thirty students will make it easy to experiment and is also recommended for a t-test.
Assumptions for the Experimental Test
The experiment will apply all the premises of a t-test for measurability, choice of the sample size, adequacy, data distribution, as well as assume normality. The assumptions would help to reduce incidences of inaccuracies and precision. The data collected in this case will be subject to the standard distribution of data just as the t-test suggests. Data collection for the survey would not be limited to questionnaires and interviews. But preference will be given to content analysis.
The crude data collected from the field in comparison with that obtained from secondary sources will be then be exposed to graduation methods, for example, the use of statistical methods and if necessary, the use of the computer. Graduated data will then be presented to data analysis. Data analysis tools will be chosen in accordance to reliability as well as accessibility. The use of statistical tools like SPSS, R-programming will be of help in this case (Carriero & Todesco, 2017). The emphasis, however, will be on SPSS courtesy of its ease of use and accessibility and ability to manipulate simple and few data samples for this case n=30.
I will construct treatments in sections and call them treatment effects based on the independence of the variables. Among the procedures, I will incorporate the placebo to be used as a reference in cases of impact for the question in force. The treatments will be appropriate especially through making them fit in the ANOVA model for calculations of the required ratios for instance MSSE and the F-ratio which in this case are references for the difference the variance amongst treatment effects and other factors that are included that may act as regression variables (Mutz, Pemantle & Pham, 2017). This hypothesis relates to my observations because I can understand how difficult it is to make healthy eating choices on a college campus.
Most of the food around you is quick, easy, cheap food that is meant for the student on-the-go and student food services fail to promote healthy food choices. This in effect would minimize standardized errors allowing for higher confidence interval with a relatively lower significant level. In that respect, the experiment would have met the essential requirement for minimization of bias as well as qualifying for randomness (Creswell & Poth, 2017). I have noticed that the healthier food selections are often pricier or not convenient for the college student who has a tight class schedule.
Outliers in this context are also catered for through proper manipulation of every treatment level in consideration of the inputs for production of reliable results for analysis. Focus at the end of it all is to come out with a viable conclusion of either a rejecting or do not rejecting the null hypothesis in favor or out of support of the alternative hypothesis which forms the right guesses that the experiment may take the side.
Measurement of the dependent variable in the case food choices amongst college students will be by use of an independent sample t-test. It will compare the correlation between the unrelated groups over the same continuous variable. Important is to help highlight how the variable has differences in itself when applied to different groups of students who will be party to the experiment and more so those involved in the research. The categories of students will be divided based on their entry behavior in the case college. Gender will also form part of the factors for consideration. In an attempt to satisfy randomization effect, students will be picked right from first-year students to post-graduates. The point here is to bring randomization effect in full measure as well as avoid biases. The result is to minimize the mean squared errors as a result of preferences. This, in fact, would mitigate standardized mistakes allowing for higher confidence interval with a relatively lower significant level. In that respect, the experiment would have met the essential requirement for minimization of bias as well as qualifying for randomness. The degree of freedom for the F-ratio will also be reduced accordingly resulting to a smaller regression parameter as a figure for reference.
Critique of the Experiment
The experiment presented cannot be purely perfect and as such is prone to correction and critique by third parties in cases that call for the same. It may not be perfect because it is singly presented and hence it is prone to one person opinion which in logical instances should be subject to criticism.
The experiment has advantages in many ways since it is laying the ground for similar operations that may be conducted in future. It will be used for reference as a secondary source of data as well as a virgin piece of information for record keeping (Boggiano et al., 2017). The experiment in the same wavelength forms a background for a scientific approach to the social issues coupled with economic parameters in as far as student eating habits is concerned. It is also a fertile feasibility study and resource for investors seeking to provide food services to students in colleges. The research will also serve as a yardstick for those interested in the venture.
The experiment is breaking ground for further research, and so it is subject to many errors. It is faced with many challenges for example unavailability of resources for credibility. This will make the analysis ineffective
Boggiano, M. M., Wenger, L. E., Burgess, E. E., Tatum, M. M., Sylvester, M. D., Morgan, P. R., & Morse, K. E. (2017). Eating tasty foods to cope, enhance reward, socialize or conform: What other psychological characteristics describe each of these motives?. Journal of health psychology, 22(3), 280-289.
Carriero, R., & Todesco, L. (2017). The Interplay between Equity and Gender Ideology in Perceived Housework Fairness: Evidence from an Experimental Vignette Design. Sociological Inquiry.
Creswell, J. W., & Poth, C. N. (2017). Qualitative inquiry and research design: Choosing among five approaches. Sage publications.
Morgan, B. M. (2017). Stress Management for College Students: An Experiential Multi-Modal Approach. Journal of Creativity in Mental Health, 1-13.
Mutz, D. C., Pemantle, R., & Pham, P. (2017). Forthcoming in The American Statistician The perils of balance testing in experimental design: Messy analyses of clean data.
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