ARC-PA 5th Edition Standards: Four Key Elements of Data Analysis

scott's thoughts Jul 27, 2022

In our previous blog, we outlined the five challenges facing PA programs as they strive to meet ARC-PA’s 5th Edition Standards. Of the five, the most important when it comes to meeting requirements is that the commission expects all action plans and modifications in the SSR to tie directly to a data source and be documented in program minutes. You must be able to support an evidential connection. 

Data analysis carries the burden of sounding must harder, scarier, and more troublesome than it actually is. No compilation and analysis of numbers are as difficult as the one left unattended until the last minute. The biggest mistake we can make is to procrastinate or ignore the fundamental elements of data analysis until they truly are problems. In that light, we recommend following these four tenets to keep data analysis under control:

  1. COLLECTION. Data collection should be regular and ongoing. Keeping a database of the data as it becomes available is usually a matter of someone sitting down to do it, and of course, this is where the earliest and most avoidable problems arise. Determine who enters the data, who  ensures that the data is available to that person, and the date/time by which entry should be completed. A number of data points may be collected during other processes; for example, if your admissions staff has a database of applicant GPAs, there is no need to collect that data again in another format. Software compatibility across the department is a valuable time-saver and allows access to data for approved individuals. If the collection does not occur, the following steps cannot, either.
  2. ANALYSIS. For ease of use and interpretation, the collected quantitative and qualitative data must be clearly displayed in tables and charts. Programs are available (Excel, for example) that can convert your data to spreadsheets or charts that can make outcomes easier to see. Demonstrate your analysis through narratives highlighting the correlations, relationships, and trends as they relate to the expectations of your program. Displaying data that is easily interpreted is also key. Don’t assume that readers can understand what you are trying to present – seek a reader or two to confirm. There are templates available that help compartmentalize the data for easy display, which we’ll present in upcoming blogs.
  3. APPLICATION. Based on the analysis you performed, you can now apply results and develop conclusions. The application must be a succinctly-stated demonstration of the link between analysis and conclusions. Some programs trip up here because they draw conclusions or suggest modifications that are not based on data. Even when identifying strengths and areas in need of improvement, your narrative must support where your analysis demonstrated these conclusions.
  4. ACTION PLAN. In the ultimate step, you develop an action plan to operationalize the conclusions drawn in the application phase. An action plan should logically result from those conclusions. There must also be longitudinal follow-up for that action plan. 

These four steps will guide your program through the data analysis process. However, the analysis doesn’t happen organically when we lump data together. There remains the question of who does the work! In our next blog, we’ll discuss the problems of the assessment workload, and we’ll present an organizational chart template for the workflow through the four phases of data analysis.


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