[Ailist] How to makes sense of already-collected data?

[Anne Radford]

Hi Gary,

If you don't have it already, my suggestion would be to get a copy of  
Professor Jan Reeds' book, Appreciative Inquiry: Research for Change  
published by Sage in 2007. Sheila McNamee, professor of communication  
at the University of New Hampshire wrote the Foreword. Jan is  
professor of Healthcare for Older People at the University of  
Northumbria in Newcastle in the UK.

Jan has lots of experience in bringing together the rigours of  
research, needs of different populations including funders and using  
AI to surface meaning information.

Chapter 6: Information Gathering and Generating: Inclusitvity,  
Partnership, and Collaboration has many examples of using AI approach  
in less than ideal data gathering situations.

I hope this helps.

Anne

Anne Radford
AI Practitioner
Thinking partner to leaders and consultants



On 29 May 2009, at 18:03, Gary Robbins wrote:

> Colleagues:
>
> I'm currently doing some research, and was hoping that someone  
> could point me to some literature or ideas that I may be missing.
>
> The evaluation literature seems to be geared largely toward  
> implementing evaluation models and methods in 'ideal' situations,  
> where the evaluators are able to influence the evaluation  
> procedures from the beginning of program implementation, so that  
> the formative and summative evaluation components are built in to  
> the program at the onset.
>
> I'm wondering if there's a model of working with a program that has  
> been running for some time and that has been collecting data in a  
> haphazard, we-think-this-might-be-interesting-to-know-someday sort  
> of way.  In particular, I'm thinking of a smaller psychoeducational  
> program that did not have any particular evaluation guidance at the  
> beginning, so put together a trial pre/post instrument, and has  
> been collecting information for a few years now, but the data is  
> just accumulating in a storage room at the facility.  The program  
> wants to try to wrangle the information that they've collected to  
> see if any of it is informative, or can be used to provide a sense  
> of how the program has done in the past, but there's so much data,  
> and any or all of it may or may not be usable, that they are unsure  
> how to begin wading through it all.
>
> So, does anyone know of any kind of step-by-step process, or list  
> of strategies or rules or best practices for wading through  
> evaluation data in a less-than-ideal evaluation situation like  
> this, where the evaluator is asked to come in and see what they can  
> do with the information that the program has already been collecting?
>
> Thanks so much for any insight into this issue.
> Gary Robbins
> Loma Linda University
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