I am pleased that you are joining me in this new segment of my series, in which I’ll be guiding you through some advanced assessment methods for your PA program’s data.

The situation at hand is this: PA programs must implement a comprehensive assessment process to ensure compliance with 5th Edition Standards and ensure annual completion of all components of Appendix 14. In our “5th Edition Standards” blog series and webinar, we carefully unpack the various sections of Appendix 14 for you to better understand what the commission wants and how you can manage the intimidating amount of data you are required to collect to respond to their requirements.

Data collection is usually considered a chore. Teaching responsibilities, scholarship, and service already stretch faculty thin. I have been in PA education for 32 years and I know what it is like to have teaching responsibilities among others, and then you have assessment on top of it all. Finding time for assessment activities is often daunting. 

Thinking of accreditation as a burden is just as common; it is another complicated entry on a long to-do list. But, as an advocate of using data to drive programs towards higher levels of excellence, I will tell you that if you embrace all these elements, and you have a highly functional assessment process, accreditation requirements will only be the beginning. You will aspire to a higher level of excellence and find ways to improve your program. 

A PA program itself is ever-changing and organic in nature. All these data elements are informing you that there are components you should observe, divided into the different “alphabet soup” of the appendices of the SSR.

This will be our topic for the next several weeks. We will begin first with the foundations, which form the key components for looking at your data assessment through a different lens. After that, we will look at applied statistics in PA education and discuss the key tools used for these purposes. Finally, I will walk you through risk modeling for academic success, where we will take the application of the statistics and look at how we can use that for purposes of identifying your students at risk.

Over the next few blogs, I hope to guide you through these learning objectives:

1. Primarily, I want to demystify this entire process. From a distance, this can look unnecessarily complex. If you already feel overwhelmed by SSR requirements, how could diving further into the data help? The answer is simple. By venturing into advanced assessment, you can understand your own program better and make meaningful, measurable suggestions and improvements. You will learn how to explore new frontiers, such as predictive modeling, or enhancing student outcomes by identifying risk factors.

2. I will conceptualize how to fully implement advanced assessment system into your individual program.

3. I will describe how to applied parametric analysis can enhance your ability to improve your curriculum and student outcomes. 

4. I will describe how the documentation of critical analysis is essential to maintain compliance with the ARC-PA standards. This is oftentimes a key element that is missing in self-study reports, so I will emphasize not only its importance, but how to put that together. 

5. I will Identify how to generate applied scholarship from program outcome data. There is an opportunity for scholarship here, and I will share examples of published works that I have generated from my work involving outcomes-based research.

6. I will explore how more advanced methods can enhance your assessment process and uncover deeper layers of program phenomenon that you may not really think about when just using descriptives.

In the next blog, I will delve further into the theory behind the constructs of the assessment model to create a SSR with demonstrable connections between its various responses to the sections of Appendix 14. I look forward to sharing this fascinating approach with you.