dmv-ndb

[dmv-ndb]

Homework #2 for Data Management and Visualization

Data run includes the following variables:

The sex of each data subject

Whether the data subject was raised by adoptive parents

Whether the data subject has full-time work at least 35 hours a week

Interpretation of the data run via the code as follows:

A total of 18,518 (or 42.97%) data subjects showed a "1" which means they are male

A total of 24,575 (or 57.03%) data subjects showed a "2" which means they are female

No missing data for the sex variable

A total of 378 (or 0.88%) data subjects showed a "1" which means they were raised by adoptive parents before they turned 18

A total of 824 (or 1.91%) data subjects showed a "2" which means they were not raised by adoptive parents before they turned 18

A total of 212 (or 0.49%) data subjects showed a "2" which means they do not know whether they were raised by adoptive parents or not before they turned 18

A total of 41,679 (or 96.72%) data subjects have missing data

A total of 22,267 (or 51.67%) data subjects showed a "1" which means they have full-time work

A total of 20,826 (or 48.33%) data subjects showed a "2" which means they do not have full-time work

No missing data for the full-time work variable

[dmv-ndb]

Homework #1 for Data Management and Visualization

DATA SET

For the purpose of this assignment, I will be looking at potential factors which may affect a person’s weight (code: WEIGHT) in relation to a person’s origin of descent and current or most recent occupation (code: S1Q9B).

I chose this topic to provide insight for potential solutions given the rise of obesity incidence among the general population nowadays. Specifically, I will be looking at a variable that may have a potential correlation to weight which is current or most recent occupation. Current or most recent occupation generally impacts a person’s stress and activity levels, which in turn may impact eating and exercise habits.

RESEARCH QUESTION

Does occupation impact a person’s weight?

LITERATURE REVIEW

The correlation of weight and occupation has been previously explored in the following literature.

Occupation and Obesity: Effect of Working Hours on Obesity by Occupation Groups (Barlin and Mercan, 2016) Methodology - Explored the relationship between occupation and obesity using data on 10,127 respondents aged 20-59 from the 2009 National Health Examination Survey - Obesity measured using waist circumference - Modelling carried out using an approach known as Multiple Regression with Post-Stratification (MRP) Results - No clear relationship between the overall sedentary nature of occupations and obesity - Obesity appears to vary occupation by occupation

Obesity and occupation in Thailand: using a Bayesian hierarchical model to obtain prevalence estimates from the National Health Examination Survey (Rittirong et al, 2021) Methodology - Obesity data used from the National Health and Nutrition Examination Survey (NHANES) between 2003 and 2004 - BMI (weight in kg/ height x height in m2) was used to determine obesity Results - Six occupation groups statistically significantly reduce the probability of being obese: engineers, architects and scientists, writers, artists, entertainers, and athletes; construction trades; other mechanics and repairers; fabricators, assemblers, inspectors, and samplers; and freight, stock, and material movers - Exact mechanism leading to reduced risk of obesity for these six occupations are not known

INITIAL HYPOTHESIS

Rittirong et al (2021) concluded that “there is no clear relationship between the overall sedentary nature of occupations and obesity”, given that obesity levels may tend to vary for each occupation. Meanwhile, Barlin and Mercan (2016) saw that certain occupations tend to have a low level of obesity, including: • Engineers, architects, and scientists • Writers, artists, entertainers, and athletes • Construction trades • Mechanics and repairers • Fabricators, assemblers, inspectors, and samplers • Freight, stock, and material movers The exact rationale behind the lower incidence of obesity among the six above-mentioned professions however may vary. Certain professions included such as engineers and scientists tend to be sedentary professions rarely requiring manual labor. Meanwhile, other included professions such as mechanics and construction trades are highly mobile professions, likely causing the decreased obesity levels.

As such, there is a need to further explore the subject with the use of primary information in order to come up with a fitting conclusion.

REFERENCES

Barlin, H. and Mercan, M. (2016). Occupation and Obesity: Effect of Working Hours on Obesity by Occupation Groups. Applied Economics and Finance. https://www.researchgate.net/publication/295878085_Occupation_and_Obesity_Effect_of_Working_Hours_on_Obesity_by_Occupation_Groups

Rittirong, J., Bryant, J., Aekplakorn, W., Prohmmo, A., and Sunpuwan, M. (2021). Obesity and occupation in Thailand: using a Bayesian hierarchical model to obtain prevalence estimates from the National Health Examination Survey. BMC Public Health. https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-021-10944-0