Principles, criteria and preliminary data analysis

The Provost’s Committee on Integrated Planning is at work in the early stages of the analysis portion of the analysis and implementation plan phase of TransformUS and we wanted to provide you all with an update on our early work. PCIP has developed a set of principles for process management and a set of criteria for the future evaluation of projects/initiatives as we begin to identify these in the development of the implementation plan.

Our principles for process management are: 1) transparency and accountability; 2) evidence informed; and 3) collaborative. What this means is that in all we do, we commit to being accountable and communicating about our work; we will ensure all actions we present in our implementation plan are based on evidence; and that we continue to work with leaders and key stakeholders to ensure all actions are decided on and acted on in a collaborative manner.

In terms of the criteria we will use for the evaluation of projects/initiatives to be included in the implementation plan, all will take in to consideration, at a minimum:

  1. Institutional benefit/transformational
  2. Alignment with strategic directions and foundational documents
  3. Financial sustainability
  4. Materiality
  5. Build organizational capacity
  6. Coherence

You can view the principles and criteria in their entirety on the website.

In addition, PCIP has been reviewing data analysis in five key areas to help support evidence informed decision making – Aboriginal programs, interdisciplinary programs, research programsresults by discipline and the relationship between task-force composition and quintile results. This information is available on the TransformUS website.

We will continue to provide updates to the campus community in the coming weeks regarding our progress. Please watch our blog for the latest updates.

If you have questions, we encourage you to ask them of the leaders in your units or continue to visit our blog to be part of the ongoing conversation.

Brett and Greg

 

4 thoughts on “Principles, criteria and preliminary data analysis

  1. How about a statistical analysis, such as an exact test or randomization test, that can be applied when the hypotheses of the chi-square test fail?

    Many thanks.

  2. In the “relationship between task-force composition and quintile results”, membership “is meant to be a way to describe whether a college or administrative unit did or did not have a member from their unit participate on the task force.”

    How about a chi-square analysis with “college” replaced by “department”?

    Thanks in advance.

    • Although not a statistician myself, I asked whether one of the university’s very small number of analysts could add this to their work queue. Here is the reply:

      “Although it would be of interest to conduct further analysis at the department level, this type of granularity cannot be achieved while still using statistical methods. An assumption underlying the chi-square test is that the expected frequency in each cell can be no less than five. Since the task forces represented a considerable number of departments, to further parse the colleges by looking at individual departments would violate this assumption. Thus, the analysis remained at an aggregate level of the colleges.”

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