A pub-550: application and interpretation of public health data course introduce learners to the application and interpretation of data to assess, design, and justify public health programs. Learners learn the basics of data management and statistical analysis using real-life public health data sets. Additionally, learners consider the implications of crafting a clear research question, identifying available and quality data, applying appropriate data analysis methods, and effectively communicating the results. Research standards and ethics are emphasized in contributing to evidence-based public health practice.
Table of Contents
What is data interpretation?
Data interpretation refers to the implementation of processes through which data is reviewed for the purpose of arriving at an informed conclusion. The interpretation of data gives meaning to the information analyzed and determines its significance and implications.
Types of scales of data measurement.
Before performing any serious data interpretation, it should be understood that visual presentations of data findings are irrelevant unless a sound decision is made regarding scales of measurement. Additionally, before any serious data analysis can commence, the scale of measurement must be decided for the data as this will have a long-term impact on data interpretation ROI. The following are the varying scales of data measurement:
Nominal Scale.
Non-numeric categories that cannot be ranked or compared quantitatively. Variables are exclusive and exhaustive.
Ordinal Scale.
Exclusive categories that are exclusive and exhaustive but with a logical order. Quality ratings and agreement ratings are examples of ordinal scales (i.e., good, very good, fair, etc., OR agree, strongly agree, disagree, etc.).
Interval.
A measurement scale where data is grouped into categories with orderly and equal distances between the categories. There is always an arbitrary zero point.
Ratio.
Ratio contains features of all three.
How data interpretation is done?
When an analyst is performing data interpretation, he or she must try to discern the differences between correlation, causation, and coincidences, as well as much other bias. However, he or she also has to consider all the factors involved that may have led to a result. There are several data interpretation methods one can use.
The main purpose of why the interpretation of data is designed is to help people make sense of numerical data that has been collected, analyzed, and presented. Moreover, having a baseline method (or methods) for interpreting data will provide the analyst teams a structure and consistent foundation.
The main methods of data interpretation.
There are two main methods of data interpretation namely; quantitative method and qualitative data interpretation method.
1. Qualitative Data Interpretation
Qualitative data analysis can be described using one word as categorical. With qualitative analysis, data is not described through numerical values or patterns, but through the use of descriptive context (i.e., text). Characteristically, narrative data is gathered by employing a wide variety of person-to-person techniques. The following are some of the techniques used to gather data for qualitative data interpretation:
· Observations.
This involves detailing behavioral patterns that occur within an observation group. These patterns could be the amount of time spent in an activity, the type of activity, and the method of communication employed.
· Documents.
Just like how patterns of behavior can be observed, different types of documentation resources can be coded and divided based on the type of material they contain.
· Interviews.
This is one of the best collection methods for narrative data. Inquiry responses can be grouped by theme, topic, or category. Most importantly, the interview approach allows for highly focused data segmentation.
2. Quantitative Data Interpretation
If you would like to explain quantitative data interpretation in one word which you can’t, you would say it’s “numerical.” Thus, you can be sure if you are performing quantitative research, you will come across numbers within your research process.
Quantitative analysis refers to a set of processes by which numerical data is analyzed. The following are some of the statistical terms that you are likely to use:
- Mean.
- Standard deviation.
- Frequency distribution.
Importance of data interpretation.
- Informed decision-making.
- Anticipation needs with trends identification.
- Cost efficiency.
- Clear foresight.
