Consider confounding variables, discrete or continuous data, the effects of outliers, etc.

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In your reference for this assignment, be sure to include both your text/class materials AND your outside reading(s).
Initial Post Instructions
Present two different types of data, or variables, used in the health field. Examples could be blood pressure, temperature, pH, pain rating scales, pulse oximetry, % hematocrit, minute respiration, gender, age, ethnicity, etc.
Classify each of your variables as qualitative or quantitative and explain why they fall into the category that you chose.
Also, classify each of the variables as to their level of measurement–nominal, ordinal, interval or ratio–and justify your classifications.
Which type of sampling could you use to gather your data? (stratified, cluster, systematic, and convenience sampling)
Follow-Up Post Instructions
Respond to at least one peer. Further the dialogue by providing more information and clarification.
Your responses to other students can explain additional analyses that could be done with the variables they selected. Consider confounding variables, discrete or continuous data, the effects of outliers, etc.
Writing Requirements
Minimum of 2 posts (1 initial & 1 follow-up)
APA format for in-text citations and list of references
1st student :
Hello All,
Two variable I chose are BMI and gender.
According to National Heart, Lung, and Blood Institute, Body Mass Index or BMI is defined as a measure of body fat based on height and weight that applies to adult man and woman. It is a screening tool, not a diagnostic tool use by physicians to determine possible health risk of an individual. For accurate diagnosis healthcare providers need to do further assessment or collect more supporting data. Second variable is gender, because there are diseases are more likely affecting one gender than the other. The probability of developing a disease could be higher in male than in female or vice versa.
BMI is quantitative data because it follows numerical range to determine ones category. BMI categories are: Underweight = <18.5, Normal weight = 18.5-24.9, Overweight = 25-29.9, and Obesity = 30 or >. Gender is qualitative data because to determine the difference of possible health risk between male and female it needs to further assess their lifestyles and body characteristics. Example of ratio in gender is female is twice more likely to be obese than male.
BMI is an interval because it follows numerical range that does not include zero while gender is ratio. Ratio is a relation of two variables where in variable may show the same amount or more than the other.
Both are systematic sampling because BMI and gender are variables that are normally part of the general population. Researchers for sure before they came up with reliable numerical range value and ratio to determine the health risk factor, they randomly picked samples from general population and conduct their studies and their result became a part of our health assessment today.

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