Learn from the Data | Origin: OP106
This is a general discussion forum for the following learning topic:
Developing New Programs: Research and Selection --> Learn from the Data
Post what you've learned about this topic and how you intend to apply it. Feel free to post questions and comments too.
The data can tell the team pertinent information about a potential program as well as an existing program.This information may be used to modify and improve the existing program.
Using data is incredibly important. Often times we have a "feeling" about what we want to do, but this is not enough. When using scarce university resources, we should be data-driven in our decisions.
Data is really important! We have to analyze and look at it very carefully!
One thing that jumps out at me from the Analyze Your Data module is the notion that improvement of existing programs and research for new programs are entertwined and inseparable parts of the same data analysis. Practically speaking, I think many of us envision those as separate processes or interpretations even if they use the same data (at least I have) and I like the notion of being holistically minded and not losing the forest in the trees.
Data analysis is almost always tied to the concept of Evidence Based Practice. Because our organization is tied to the healthcare industry we rely very heavily on our research, analysis of data and subsequent interpretation. I have now learned that it is possible to analyze different programs together to see how they fit and or accomplish the goals identified by our stakeholders and constituents.
Data analysis is very important when consideerng a new program. It may indiccate that a new program is or is not needed at this time. Data may indicate that it is better to make changes to an existing program instead.
A new program is not always the answer. It could be a waist of time and money.
Careful analysis of the data may help establish the following:
Competition thru co-ordination
Availability of resources
Identification of critical success factors
When collected properly, the data will not lie. Whatever the data tells us should be followed, even if it is not the outcome we wish for. Even if the data shows that a new program would be contraindicated, it may teach us things about existing programs or processes we can improve.
This is why I'm a mixed methodologist as it relates to research because quantitative data only reveals WHO & WHAT is occurring and HOW often. Qualitative data reveals the WHY which, to me is far more interesting and actionable.
Data will provide justification for 'the ask' when it comes funding. Data can also be manipulated any which way to support whatever it is you are pitching. It's a sales ally.
Program research yeilds interdsting data and results