Different types of data, or variables, used in the health field

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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)
Given scenarios supported by population data, apply sampling techniques and explain potential pitfalls and bias in data collection.

CO 2: Given datasets with qualitative and quantitative data, differentiate between the types of data and how they can be applied in statistical studies for everyday life.

 

Sample Solution

 

 

Data in the Health Field: Types, Measurement, and Sampling

Data Types:

  1. Blood Pressure (Quantitative): Blood pressure is a numerical measurement recorded in millimeters of mercury (mmHg). It provides a specific value and allows for comparison with established norms.
  2. Pain Rating Scale (Qualitative): Pain rating scales typically use descriptive words or numerical values with subjective interpretations (e.g., 0 = No pain, 10 = Worst pain imaginable). While numbers are used, they represent a perception rather than an objective measure.

Levels of Measurement:

  1. Blood Pressure (Interval): Blood pressure measurements have a constant interval between units (mmHg). However, the zero point (0 mmHg) doesn’t represent a complete absence of pressure.
  2. Pain Rating Scale (Ordinal): Pain scales establish order (higher numbers indicate greater pain), but the intervals between values may not be equal. It’s difficult to say that a difference of 2 points represents twice the pain intensity.

Sampling Techniques:

Scenario 1: Analyzing the effectiveness of a new flu vaccine in a large city.

  • Suitable Sampling: Stratified Sampling. We could divide the city’s population by age groups (children, adults, seniors) and randomly select participants from each stratum to ensure representation of different age demographics.
  • Potential Pitfalls: Difficulty in obtaining a representative sample from each stratum if certain groups are harder to reach.

Scenario 2: Studying sleep patterns in a college dormitory.

  • Suitable Sampling: Cluster Sampling. We could select a few dormitories from the college and then survey all residents within those dormitories. This is efficient but might not capture the full diversity of the student population.
  • Potential Bias: Residents within the same dorm might have similar sleep habits due to shared routines, leading to biased results.

Convenience Sampling: While easy to implement by selecting readily available participants, it often leads to biased samples that don’t represent the target population. It’s generally not recommended for reliable research.

CO2: Qualitative vs. Quantitative Data in Everyday Life

Qualitative Data: Describes characteristics or experiences. Examples:

  • Food preferences (sweet, salty, spicy) used by food companies to develop targeted marketing campaigns.
  • Patient satisfaction surveys can help healthcare providers improve patient experience.

Quantitative Data: Provides numerical values for measurement or comparison. Examples:

  • Blood sugar levels used by diabetics to monitor their health and adjust medication.
  • Average daily steps tracked by fitness apps can motivate users to be more active.

By understanding the difference between qualitative and quantitative data, we can choose the most appropriate data collection methods to answer specific questions and make informed decisions in various aspects of daily life.

 

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