Balancing Data Quantity And Quality

Balancing Data Quantity And Quality
Have you ever heard the saying “too much of a good thing can be a bad thing”? Often, evaluators make the mistake of accepting information from anyone willing to participate in a survey. Not only is too much data difficult to manage, but when you are willing to accept any data, the quality of the data comes into question. It is important to find a balance between the quantity and quality of data. You need enough data to complete the evaluation, but you want to be selective enough so that the data you collect is representative of your sample population and is reliable.

Consider the following scenario:
You have been asked to help evaluate a nutrition program for former collegiate athletes following their final active season. The athletes are presented with the nutrition program and counselled for the semester following their final active season. You are asked to evaluate the effectiveness of the program over the following year and determine if enough quality data is collected. In addition, the college administrators have also asked you to review a survey that is very short and easy to complete. (See the “Nutrition Program Pre/Post Questionnaire” listed under the Week 3 Learning Resources.)

You must also help determine how a sample of students will be selected to represent the over 100 athletes across 8 different sports that are completing their final season of play. You need to consider how to balance the quality and quantity of the data in the data sampling.

To prepare for this Discussion:

Review the video Program Evaluation and Evaluation Instruments in the Learning Resources. Think about the data collection instruments that were used in the programs featured in this video. Consider whether any survey instruments provided useful data for program evaluation.
Review the scenario presented above.
Review the provided “Data Collection Instrument: Nutrition Program Pre/Post Questionnaire” in the Learning Resources and assess the strengths and limitations of the instrument as it relates to the scenario above.
Think about whether you could gather a sufficient quantity of quality data using the instrument.

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