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The Target Population
Campus students can be categorized into long-distance learners, full-time and part-time students. In regard to on-campus food services, part-time and full-time students form the population sample required for an in-depth survey. In the recent past, the food and beverage industry has undergone significant changes, more specifically the shift from traditional hotel set-ups to mobile food trucks (Middha & Lewis, 2021). This change has significantly shaped eating practices, instilling people with new perspectives of healthy eating and convenience. Since the food truck is mobile and can be operated at different times of the day, the survey will target both full-time and part-time students and staff.
Although fixed food joints in Australian universities have been effective in delivering quality food to students, significant attention has not been paid to the cost and health aspects of the meals prepared. Research conducted by Roy et al. (2019) revealed that healthy meals were rare and more costly in urban campuses than unhealthy junk food. In addition, research has shown that staff members are more likely to purchase healthy food compared to students. Following these results, the survey should comprise a well-constituted sample of staff and students to gain a deeper insight into varying expectations between the two groups. Since food trucks form the new entrepreneurial behavior according to Downing et al. (2018), part-time students should also be involved for insights on industry trends. In summary, four different groups will be targeted; full-time on-campus students, part-time students, part-time university staff, and full-time workers within the institution.
Sampling Techniques
A successful survey depends on the data collection and analysis approaches founded on having the correct sample. In this case, the sampling techniques applied are determined by the target population and the sample size required. In this case, the stratified random sampling is the best choice. Although all the respondents are chosen from within the university, several distinct features of the population are needed. Full-time and part-time staff and students are expected to react differently to the food truck business, warranting the use of stratified random sampling for equal representation in the test sample.
Justification of the Sampling Method
Following the requirement that four different groups are be studied, the sampling method required should entail subdividing the group into distinct segments based on their unique characteristics. Etikan and Bala (2017) define stratified random sampling as a technique that divides a population into distinct groupings known as strata. The strata are constructed based on shared qualities or characteristics among the participants. When matched to the population, a representative sample out of each stratum is selected in a size proportional to the stratum’s number.
The goal of a stratified randomly selected sample is to minimize the chances of human bias in sampling design. As a result, provided that there is minimal missing data, the survey technique presents a researcher with a group that is truly representative of the entire population being researched (Sharma, 2017). In addition, it allows researchers to make statistical inferences (generalizations) from the population sample to the entire group since the units picked for participation in the study are determined through probabilistic methods. This is a significant benefit because such conclusions are likely to be recognized as valid in the outside world.
However, this method has one significant limitation that should be considered I its application. When the populace cannot be completely partitioned into separate subgroups, stratified random sampling is ineffective. Making subgroup sample sizes proportionate to the quantity of data accessible from the segments, rather than adjusting sample lengths to subgroups, would be a misappropriation of the technique. If anticipated variability among the subgroups requires stratified sampling, the date reflecting each grouping is assumed to be of equal relevance (Etikan & Bala, 2017). There is no option to ensure that the subgroup datasets are proportionate (simultaneously) to the segment sizes within the whole population if the variances differ so much among the categories. In this case, the method remains a viable option because the variances are not expected to differ so much as to limit its application.
Application of the Sampling Technique
A sample size of (N=2000) will be used, whereby the size of each strata will be determined using the proportionate stratified random sampling formula: nh = (Nh / N) * n
nh= Sample size for hth stratum
Nh= Population size for hth stratum
The results of this technique are shown in table 1 below.
The sample size identified from each strata will be aggregated into the final research group and given the questionnaire. The results will be analyzed separately for each strata to facilitate the derivation of conclusive data.
Table 1: Stratified random sampling
References
Downing, C. G., James, T. P., & Evans, D. (2018, June). Food for thought: Predicting entrepreneurial behavior. In 2018 ASEE Annual Conference & Exposition. Web.
Middha, B., & Lewis, T. (2021). Pop-up food provisioning as a sustainable third space: Reshaping eating practices at an inner urban university.Australian Geographer, 1-18.
Roy, R., Soo, D., Conroy, D., Wall, C. R., & Swinburn, B. (2019). Exploring university food environment and on-campus food purchasing behaviors, preferences, and opinions.Journal of Nutrition Education and Behavior, 51(7), 865-875.
Sharma, G. (2017). Pros and cons of different sampling techniques.International journal of applied research, 3(7), 749-752.
Etikan, I., & Bala, K. (2017). Sampling and sampling methods. Biometrics & Biostatistics International Journal, 5(6), 00149.
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