The aim of this research was to explore the connections between obesity and many facets of the food home environment and in the vicinity. For the variables at each level, descriptive statistics is determined. Standard deviations and means were then identified for constant variables and estimation percentages equal to one for binary variables. The core study of multilevel data structure accounting was based on various logistic intercept models of the area, the household and individual random elements.
Research Results
The findings of the research found that about one third of the whole samples were overweight and another third were obese. This got associated with the figures from the polls of other national authorities. The adult group had a mean of 28.50 BMI equivalent to an estimate by 28.7 for adults in the United States, based on estimated weights and heights between 2009 and 2010. Over 85% of the households were not Hispanic whites and other over half had a college-learned head of the family. Two members were in the household and the average income was reported at 69,000 dollars. However, almost all individual demographic and lifestyle was largely assoc8iated with obesity or overweight ( Morland & Evenson, 2009 ). Results indicated that age was positively associated with overweight status or obesity, also being a woman was negatively influenced with overweight status. Also, a higher diet level was related to being overweight. Broadly, regular exercise was negatively affected by obesity probability of overweight or obesity. Household-level measures showed several socioeconomic disparities in overweight status.
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Results Differences between Groups
The primary calculation of food at home was carried out in a layout compatible with dietary efficiency indices of other scientists. Increased conformity with U.S. citizens' dietary practices was associated with lower obesity risk. Mixed findings were observed for the association between food variables in the community and obesity. In the metro subsample, a food desert measure was positive for obesity ( Chen et al., 2016 ). There were no healthy patterns in relationships between densities of different store types, and obesity levels had no healthy patterns. However, some mixed results were equal to those of other researchers.
Limitations of the Study
Broadly, there were several notable problems encountered in the survey. Next, instead of applying conventional reflective approaches like food frequency questionnaires or 24-hour repetitions, the investigation made use of data from the scanner to offer hypothetically more reliable home experience data. This data showed acquisitions rather than intakes. The scanner details and the USDAScore measurement just compensated for food shopping at home and not food from home. The researcher sought to minimize the effect of statistically missed food-removal variables by testing fast-food intake through the results in the MedProfiler data collection. Second, the analysis relied on IRI's MedProfiler's self-reported results, which may be subject to many errors. The review on past studies comparing self-reported with measured heights and weights finalized that weights are underreported and heights over-reported, which leads to underestimating BMIs.
Advantages and Disadvantages of Statistical Method Used
Data used in this study is secondary data. It means it is borrowed from other sources. The most noticeable advantage of applying secondary data is that it is cost-effective. This is because someone already collected that data, and the researching parties do not have to spend a lot of time and money into the data collecting stages of their study. For instance, in the research, data used was from the US Census Bureau County Business Patterns, information from IRi Consumer Panel Scanner (2008-2012), and the supplementary MedProfiler figures set (2012).
The most significant disadvantage of using secondary data in the analysis is that the data does not answer the researcher's questions to the extent they would want. It means that the secondary data might not have precise information for the researcher. For instance, in the study, the scanner records indicated acquisitions instead of consumption. Plus, the scanner information and the USDAScore portion incorporated buying of “food-at-home” only and not “food away from home.”
Limitations of the Study Method Used
Descriptive research design is used in this study, and its main purpose is to describe, explain, and validate findings. A significant limitation of descriptive research is that it cannot verify the research problem statistically. It is nearly impossible to identify the cause behind the described phenomenon. This data cannot be proven statistically because the information used is from the already collected information.
References
Chen, D., Jaenicke, E., & Volpe, R. (2016). Food Environments and Obesity: Household Diet Expenditure versus Food Deserts. American Journal of Public Health , 106 (5), 881-888. doi:10.2105/ajph.2016.303048
Morland, K., & Evenson, K. (2009). Obesity prevalence and the local food environment. Health & Place , 15 (2), 491-495. doi:10.1016/j.healthplace.2008.09.004
