Partnership Rubric: A tool for measuring industry relationships

Many of the grant funded projects we provide evaluation services to have an objective to expand industry/college partnerships. Because of the lack of available instruments to measure changes in these relationships, we developed the Partnership Rubric.  The Partnership Rubric was designed as a tool to quantify the involvement of outside partners in a given project or center by measuring the changes in the number of and level of involvement of those partnerships in targeted areas. While the instrument was useful in quantifying changes, a key limitation of the rubric was that it lacked validation.

Beginning in 2018, The Rucks Group and the National Science Foundation (NSF) Advanced Technological Education (ATE) Working Partners research project (DUE #1501176) teams began collaborating to address this key limitation and ultimately to widen the dissemination of the tool. As context, the Working Partners research project, employs a mixed methods approach to document and examine community college/industry partnership models, gain a better understanding of how these models are used in real world situations, and gather data about impacts of the partnerships. 

We have begun piloting the rubric to gather feedback about the utility and areas for improvement. At the 2018 NSF ATE Principal Investigators’ Conference, we facilitated a roundtable discussion to introduce the rubric and gather this feedback. Based on that feedback, a number of revisions were made and we introduced the revised version at the 2019 High-Impact Technology Exchange Conference (Hi-TEC) in July.  We are highlight encouraged by the response and believe that this tool will meet a critical need for many projects. If you are interested in learning more, download the Partnership Rubric and click on this link to provide us with information on your experience with instrument.

Student Success Part II – How is it defined and what promotes it?

While our evaluation firm focuses on measuring the effectiveness of initiatives, ultimately our goal is about identifying effective student success practices.  While serving as the evaluator of John Carroll University’s Aligned Learning Communities and Student Thriving: A First in the World Project, Terry L. Mills, Ph.D., the project director, shared with our team a blog that he had written about how student success is defined.

In this two-part blog on student success, we shared his blog in which Dr. Mills provides his perspective (click here to read).  Be sure to read all the way to the conclusion where he lists questions to consider when one is defining student success. Dr. Mills is assistant provost for Diversity and Inclusion and sociology professor at John Carroll University. He applied for the First in the World grant, and John Carroll University was one 17 institutions to receive this grant from the U.S. Department of Education grant in 2015. 

In this second blog on the topic, I am providing the perspective of a researcher and program evaluator on this key issue.

The current post-secondary educational landscape is vastly different than a few decades ago. The students seeking a post-secondary education are far more diverse now than with previous generations. The diversity is not just based on demographic factors but also on educational motives and academic preparedness. Take for instance, many family-sustaining jobs which historically only required a high school equivalence degree, now require some form of post-secondary credential. Four-year institutions which traditionally saw few students working upwards of 20 hours or more per week, now are witnessing an increased number of students that need to work for more than just discretionary funds. From my own teaching experience, it was not uncommon for a particular community college course to have half of the students in the midst of a career transition and already holding a bachelor’s degree.

This diversity introduces complexity that is not fully reflected in the prevailing definition of student success. It is encouraging that there is an awareness of the limitation of the current “accepted” definition of student success that needs to reflect a more student-centered that involves examining engagement and thriving not just academic performance. Hopefully, these conversations will lead to the extensive system-wide changes needed to fully embrace a more flexible definition. Because as is, the definition impacts on how schools are measured, and in many states funded and how students are able to obtain financial aid. In the absence of a system-wide change, a “work around” to address the issue may be to consider the difference between how student success is defined and what promotes student success. Factors such as engagement, thriving, and student-centeredness can be conceptualized as leading indicators of student success within the current framework. Admittedly, this definition is not in alignment with the goal of every student which is why the conversation on the definition of student success should continue. However, if institutions are able to incorporate these components within the support services provided to students then it could be very impactful on requisite outcomes measure related to degree completing students and all students.

Completing a National Science Foundation Freedom of Information Act Request

You probably have heard of a FOIA (Freedom of Information Act) request, but it was probably in the context of journalism. Often, journalists will submit a FOIA request to obtain information that is not otherwise publicly available, but is key to an investigative reporting project.  

There may be times when your work could be enhanced with information that requires submitting a FOIA request. For instance, while working as EvaluATE’s external evaluator, The Rucks Group needed to complete a FOIA request to learn how evaluation plans in ATE proposals have changed over time. And we were interested in documenting how EvaluATE may have influenced those changes. Toward that goal, a random sample of ATE proposals funded between 2004 and 2017 was sought to be reviewed. However, in spite of much effort over an 18-month period, we still were in need of actually obtaining nearly three dozen proposals. We needed to get these proposals via a FOIA request primarily because the projects were older and we were unable to reach either the principal investigators or the appropriate person at the institution. So we submitted a FOIA request to the National Science Foundation (NSF) for the outstanding proposals.

For me, this was a new and, at first, a mentally daunting task. Now, after having gone through the process, I realize that I need not be nervous because completing a FOIA request is actually quite simple. These are the elements that one needs to provide:

  1. Nature of request: We provided a detailed description of the proposals we needed and what we needed from each proposal. We also provided the rationale for the request, but I do not believe a rationale is required.
  2.  
  3. Delivery method: Identify the method through which you prefer to receive the materials. We chose to receive digital copies via a secure digital system.
  4.  
  5. Budget: Completing the task could require special fees, so you will need to indicate how much you are willing to pay for the request. Receiving paper copies through the US Postal Service can be more costly than receiving digital copies.
 

It may take a while for the FOIA request to be filled. We submitted the request in fall 2018 and received the materials in spring 2019. The delay may have been due in part to the 35-day government shutdown and a possibly lengthy process for Principal Investigator approval.

The NSF FOIA office was great to work with, and we appreciated staffers’ communications with us to keep us updated.

Because access is granted only for a particular time, pay attention to when you are notified via email that the materials have been released to you. In other words, do not let this notice sit in your inbox.

One caveat: When you submit the FOIA request, there may be encouragement to acquire the materials through other means. Submitting a FOIA request to colleges or state agencies may be an option for you.

While FOIA requests should be made judiciously, they are useful tools that, under the right circumstances, could enhance your evaluation efforts. They take time, but thanks to the law backing the public’s right to know, your FOIA requests will be honored.

To learn more, visit https://www.nsf.gov/policies/foia.jsp

A version of this blog was published on EvaluATE’s website (http://www.evalu-ate.org/blog/rucks-july19/) on July 15, 2019.

Correlation vs. Causation: Understand the Difference for Better Interventions

If you have taken a research methods course at some point, you may remember the mantra “correlation does not imply causation.” People say they understand the difference between a correlation and causation, but when I hear them talk, I can tell that they don’t.

As a quick refresher, correlation simply refers to what occurs when two variables co-vary together. Essentially as one variable increases so does another variable (positive correlation, see graph on the left).  Or as one variable increases another variable decreases (negative correlation, see graph on the right). On the other hand, causation can be thought of as a specialized correlation in which two variables are co-varying because of one of the variables.

Figure 1. Simplified representation of a “positive” and “negative” correlation.

The distinction between correlation and causation is clearer when we look at variables that are correlated simply by chance. For example, a correlation exists between letters in the winning word in the Scripps National Spelling Bee and deaths due to venomous spiders (Vigens, 2015). Basically, as the number of letters in the Scripps winning word increased, so too did the number of deaths that year by venomous spiders increase.

If your reaction to that correlation is that the two cannot be correlated because there is no reason for the correlation to occur, what you are actually trying to establish is a causal relationship. In which case you are correct, there is no causal relationship between these two variables.

The lack of a causal relationship is clearer when two variables are in no way conceptually related. However, a causal relationship still has not been established even when there is a correlation established between two variables that appear to be related.

Take, for example, the obvious correlation between class attendance and course performance. The two variables are correlated such that course performance tends to increase with class attendance. If we do not address the possibility of other variables, we cannot say with certainty that class attendance increases performance because class attendance could be a proxy variable for course engagement, for instance, or some other circumstance.

Why is it important to disentangle these two concepts?

Disentangling these two concepts is more than just an interesting intellectual exercise; the distinction is important to achieve optimal outcomes. For example, when making big decisions about what to do to improve student success, we have to be careful that we are pressing on the right levers that will lead to the return on investment. When we think about interventions, the more we understand about the causal variable itself, the better the intervention we will have.

Consider the prevailing understanding that first-generation students are at risk for not completing a degree. It is critical for us to understand what the causal factor is in order to figure out a better approach for helping students who are the first in their families to attend college to persist and complete degrees. Any of the following could be causing the challenge that is “correlated” with a first-generation student not completing a degree: not understanding how to navigate college expectations; not having a strong resource network to troubleshoot issues; or feeling like an “imposter” whose lack of familiarity with campus life can lead to thinking that one does not belong in college.

If we understand what is occurring at the causal level and not simplify or misuse the concept of correlation, then we will be in a better position to design more effective interventions.  

Vigen, T. (2015). Spurious Correlations. New York, NY: Hachette Books.

Obtaining Credible Evidence of “Long” Long-Term Outcomes

Another challenge that The Rucks Group team sees across projects is what we call “aspirational goals.” This phrase is how we refer to goals and objectives that will likely not occur until after a project’s grant funding ends. Many projects have them. The question is: How do you measure them?

We struggled with measuring aspirational goals until, through a conversation with another evaluator, the idea of using the transitive mathematical property to address this challenge created an “aha” moment. 

As you may (or may not) recall from math class, the transitive property is this:

If a = b, and b = c, then a = c.

We can apply this mathematical property to the evaluation of grant-funded projects as well.

If, for instance, a college receives a three-year grant to increase the number of underrepresented individuals in a non-traditional field, progress toward the goal (which is unlikely to occur within the three-year time frame when the first year will be dedicated to implementing the grant) can be gauged using a sequence of propositions that follow the logic of the transitive property:

  • Proposition A = Start with a known phenomenon that is linked to the desired outcome.

Green and Green (2003) [1] argue that to increase the number of workers in the field, the pipeline needs to be increased.

Proposition B = Establish that the project’s outcomes are linked to Proposition A.


The current project has increased the pipeline by increasing the number of underrepresented individuals declaring this field as a major.


  • Proposition C = Argue that while the project (because of time) has not demonstrated the desired outcome, based on established knowledge it likely will.

If the number of individual majors increased, assuming a similar rate of retention, then there will be more individuals graduating and prepared to work in the field.


By using the transitive property it is possible to create a persuasive evidence-based projection that by increasing the number of individuals majoring in the field and in the pipeline to become workers, the project has instigated the changes to achieve its aspirational goals.


[1] This is a fictitious citation of illustration purposes only.

When Fuzzy Wuzzy Isn’t a Bear, But What You Need to Measure

I had a professor who believed that you could measure anything, even the impact of prayer. For many, that may seem like an arrogant pronouncement, but what he was illustrating was that in measuring fuzzy constructs you have to think outside of the box (and besides, there are actual studies that have measured the impact of prayer).

In much of the work at The Rucks Group, we encounter things that are difficult to measure. We often deal with clients’ understandable angst about identifying key nebulous variables such as measuring changes in a coordinated network, the impact of adding a new role like a coach or navigator, or the impact of outreach activities to increase interest in a particular field. 

One approach to measuring difficult-to-measure constructs is through the counterfactual survey (click here to read our blog about counterfactual surveys). 

Whether or not you use a counterfactual survey when measuring difficult-to-measure variables, it is essential to build a case that the intervention is making a difference through the “preponderance of evidence.” There is rarely a single magic bullet. The evidence, instead, usually comes from multiple observable outcomes. In legal terms, it is akin to building a circumstantial case. 

With preponderance of evidence in mind, our team often talks about “telling the story” of a project. Here are two approaches for effectively “telling the story” in an evaluation context.

Incorporate mixed-methods for data gathering

Using a mixed-methods approach in an evaluation can paint a compelling picture. For instance, many of the projects we work with strive to build relationships with industry partners for their important work in curriculum development. Measuring the changes in industry partners’ involvement as well as the impact of these relationships is very challenging. However, we have found three useful ways to measure industry partnerships. They are 

    1. conversations with the project team to obtain information regarding the impact of the industry partnerships (e.g., any stories of donations, assistance in identifying instructors, etc.);
    2. data from industry partners themselves (gathered either through surveys or interviews); and 
    3. rubrics for tallying quantitative changes that result from industry partnerships. 

Incorporating multiple approaches to data gathering is one way to measure otherwise nebulous variables.

Leverage what is easily measurable

Another common challenge is measuring the broader impact of outreach activities. For one client with this goal, our team struggled to find credible evidence because outreach involved two different audiences: individuals within a grant-funded community and the larger general audience of individuals who may be interested in the work of the grant-funded community. 

For some time we really struggled with how to find an approach to demonstrate successful outreach to the general audience. As we reviewed the available data it dawned on us that we could leverage the data related to the visits to the project’s website because the grant-funded audience had a known size. We made an assumption around how many hits the website would have if the known community members were to visit it. By subtracting that number from the total website visitors, we arrived at the number we identified as the general audience of individuals from outside the grant-funded community who accessed the project’s website. 

We then employed a mixture of methods to combine our audience calculation with other data to tell a cogent story. We have used this approach for other clients, sometimes using Google searches and literature searches to find a number as a reference point. 

Hopefully these tips (along with a prayer or two to help with insight) will help the next time you’re confronted with difficult-to-measure variables. 

Questions Frame the Lens for Answers*

Far better an approximate answer to the right question … than the exact answer to the wrong question.

— John Tukey, Statistician

 If I had an hour to solve a problem and my life depended on the solutions, I would spend the first 55 minutes determining the proper question to ask, for once I know the proper question, I could solve the problem in less than five minutes.

 — Albert Einstein, Physicist

Much of what we do both at the individual and organizational levels are driven by questions. Questions are the lens by which we see what is and is not possible.  It has been my experience that teams and organizations, regardless of whether they are working at improving student success or addressing workforce demands, sometimes go astray when they seek answers to the wrong questions.

This error generally happens not because of lack of intelligence, work ethic, or even passion, but because those working on the problem respond too quickly to the high pressure for a solution. The perception that they have to hurry often results in teams moving too quickly from the problem-space into the solution-space because we are often metaphorically “building the plane while we are also flying it.”

This pressure is keenly felt when attempting to evaluate an initiative. Consequently, the focus generally is on “What can we measure?” which on the surface would be the exact question that should be asked. But I have found that frustrations mount when the question of what to measure becomes the focus of the evaluation before the project team and evaluator together address other important questions such as: “What do we want to know?”

One time I had a client project team grappling with what should be measured for the evaluation to demonstrate project outcomes. Rather than dwelling on their dilemma about what to measure, I asked a series of questions such as “What do you want to learn about your project?” “How does this project change behavior?” “What do your stakeholders want to know?” As team members answered those questions, I pointed out how their responses led to what they really should be measuring regardless of how difficult it could be to obtain relevant data.

This experience reminded me that once you focus on what people want to learn from an intervention, it is easier to figure out how to measure outcomes.

My advice is do not skip over the questions of what you want to learn. Sure, those questions can be challenging because of a fear that those items cannot be measured. But doing this deeper thinking up-front avoids angst at the end about inadequate data or measures that lack meaning and often reveals novels ways of measuring outcomes that may have at first seemed impossible to measure.  Yes, the preliminary work will take time, but the benefits are so worth it.

*Portions were originally published in October 2012 issue of the Dayton B2B Magazine.

Using Counterfactual Surveys to Improve the Evidence-Gathering Process

Contextual information plays an important role in interpreting findings. Many of us have experienced this when a child has come home and said they have gotten 43 points on a test. Was it 43 out of 45, 100, or some other point system? Depending on the response, there is either praise or a very serious conversation.

In evaluation and research the same need for context to interpret findings exists. But how you get to that context can vary widely.

One common approach to create context is to utilize a pre-/post-testdesign (pre-/post-test). The purpose of a pre-/post-test is to compare what was occurring before an intervention to what is occurring after that intervention by focusing on particular outcome measures.

One challenge to the pre/post-test is responders’ standards for the basis of a judgment may shift because of the intervention. With additional information your perception of what “good” is and how good you are can change. This occurrence can result in similar pre-intervention and post-intervention responses.

One solution to this problem that our team has successfully incorporated into much of our work is the use of a counterfactual survey, also called retrospective survey. In these types of surveys, respondents are asked to consider their current attitudes or perceptions and their attitudes or perceptions prior to participating in the intervention at the same time. In this way respondents are able to make their own adjustments about how they perceive the intervention.

To understand completely how a counterfactual survey looks in practice, let’s examine one of our first projects in which we incorporated this approach.

In this project, STEM academic administrators were participating in a year-long professional development opportunity to enhance their leadership skills. Prior to participating in any activities, we disseminated a survey for participants to rate on a scale of 1 (least like me) – 7 (most like me) their self-perceptions as a leader. Consistent with a traditional pre-/post-test, participants were then asked to complete the survey at the end of the professional development opportunity as shown in the figure below.

03.04.19 - Image 1

To incorporate the counterfactual survey, after participants answered items about how they perceived themselves as leaders, participants were presented with items asking them to rate how they would have rated the items before participating in the professional development opportunity. Therefore, the counterfactual design looks like this:

03.04.19 - Image 2

It should be noted that a counterfactual design does not require including a pre-test questionnaire; in this situation we just happened to do so.

What is interesting is that the pre-/post-test responses on several items were very similar, which is not that uncommon of an occurrence (selected items presented below).

03.04.19 - Image 3

However, when you add in the counterfactual design responses, an interesting pattern emerges—respondents rated themselves lower using a counterfactual than they had in reality.03.04.19 - Image 4

In follow-up interviews with participants, it was apparent that the standard that participants used had indeed shifted. In other words, they didn’t know what they didn’t know and so rated themselves higher on items before the intervention than after learning more about leadership.

A counterfactual survey holds a lot of promise, particularly in conjunction with gathering other data points. A counterfactual survey is only appropriate for attitudinal or perception data and not for objective measures of skill or knowledge. But utilizing a counterfactual survey may serve to illuminate changes that would otherwise go undetected.

 

 

 

Looking for an Individual to Join Our Team!

Last Team Meeting - Team Photo July 10 2018We have recently experienced transitions in our team: Jeremy who had been with us since 2015 left to work on his doctorate degree at Penn State and Maggie Jaeger who started with us as a research assistant is now working on her doctorate degree at the University of Minnesota. We are sad to have them leave us, but are excited about the opportunities that are ahead for them!

As a consequence, we are seeking to bring another individual on our team. If you want to work at a firm that discusses the nuisances of survey design, optimal non-parametric tests, best practices in data visualization, and yes, gets excited about Pi Day, then we invite you to review the job description and submit an application!

Job Description – Research and Evaluation Associate – Final – 09.14.18rev

Happy Pi Day!

I love celebrating Pi Day largely because it’s such a gloriously geeky thing to do! What makes it even “geekier” Pi - Day Picturewas the cool Pi pen holder (using about the first 300 digits of Pi) The Rucks Group team made through additive manufacturing (3D Printing) to celebrate a team member’s birthday. Why?

We have the pleasure of working with Iowa State University on a project funded by the National Science Foundation Division of Engineering Education and Centers to “promote a platform to bring together a network of under-represented minority (URM) women in engineering” towards increasing participation in advanced manufacturing and towards career advancement for URM women faculty in engineering.  As part of that work, I participated in a 2 ½ day workshop in October that covered a variety of topics including several presentations on additive manufacturing.

I shared with the team many of the advances in additive manufacturing and got the team excited about the topic as well. So of course, we needed to experience it firsthand, so we went over to our local 3D printing bar (yes, there is one just around the corner from our office and yes, they serve beer) and made our Pi pen holder.

In much of our evaluation work, we strive to gain a deep understanding of the subject to optimally implement the evaluation. For this project, it brought together all our geeky tendencies!

To learn more about the Advanced Manufacturing Workshop: Preparing the Next Generation of Researchers project, visit https://www.imse.iastate.edu/advanced-manufacturing-workshop/