Cultural Comptence and Attending the AES Conference in Brisbane, Australia

As I begin packing for a trip to Australia for the Australasian Evaluation Society conference I realize I have no idea what the local cuisine is in Australia. What is Australian food? About the only thing that I know is Australian is vegemite, and only because of an 80s pop song. So I feel a bit undeprepared for my trip. It got me thinking about conducting evaluations in locations/areas that you are unfamiliar with. We all learn in graduate school how to utilize the methods of evaluation and we all are aware of the various evaluation theories.....but how many of us are prepared (and competent enough) to conduct evaluations in settings that we are unfamiliar with? Are we culturally competent in all situations?

You can't just ask someone if they are culturally competent....I mean who is going to say "
no I'm not"! So how do we gauge who is culturally competent and who is lacking in this area? When we are hiring an evaluator how can we ensure that they will be able to conduct a competent evaluation in our specific setting with our program clients?

For you evaluators how do you decide if you are able to conduct an evaluation in a setting you are unfamiliar with? If you take the job, how do you prepare yourself? Can we truly conduct evaluations in any setting....just by doing some research to understand the setting that it's being conducted in? I don't know...

So I need to do some research on what is Australian food and customs. While I'm not conducting an evaluation while I'm there I will be conducting a workshop at AES so I need to make sure I understand enough about my setting in order to successfully interact with my workshop attendees.

For those attending AES I can't wait to meet you all. Happy evaluating!

Importance of Dealing with Outliers

I just finished my second day of conducting a webinar (estudy) for AEA on data cleaning. I just covered outliers....one of my favorite topics! As I was preparing my activity for the attendees I was manipulating variables in a bunch of datasets and comparing what happens when you ignore outliers (leave them as raw variables), delete outliers (delete any outliers from your variables), winsorize outliers (change the outlying value to the value that is 3sd from the mean), or you modify the outliers (change the outlying value to one unit lower or higher than 3sds from the mean....as discussed in Tabachnick & Fidell). I will say when I'm looking at all this output and seeing how even a few number of outliers can greatly change the values of my variable...it is a powerful argument to show how important it is to not ignore this step of the data cleaning process.

I've gotten into many debates with colleagues about what to do with outliers. Some are adamant about leaving them alone...keeping them as is. While others are quick to toss them out of the dataset no matter what it does to their sample size and mean value of the variable. I tend to modify my outliers so I can keep those individuals values in the dataset but with less of an impact on my mean value.

Students always ask me "what is the best/correct way to deal with outliers?" I give them my famous "it depends". There really is no agreed upon way to deal with outliers. You can find a citation to support any of the above mentioned ways to deal with outliers. THIS CAN BE FRUSTRATING! My goal is to always present to my client the most accurate, reliable data I can.

So....what does everyone else do with their outlying values? What is common in the Evaluation field? What do we teach our novice evaluators/students to do? I'd love to hear others opinions on this