Today’s Plan:
- Surveys
- Work Logs
- Homework: Finish Proposals
Surveys
Surveys typically collect three kinds of information:
- Attitudes and Preferences [data]: Generally leans towards what we should do.
- Opinions and/or Reactions [data]: Generally measures thought about what we have done.
- Demographic information [What do I need to know about my audience to frame my data]
Generally, you measure attitudes and preferences using multiple choice, ranking (favorite to least favorite) or likert scales. While the term likert scale might be unfamiliar, I can almost guarantee you’ve encountered one before.
- It is very likely you have encountered a Likert scale
- It is likely you have encountered a Likert scale
- It is neither likely or unlikely you have encountered a Likert scale
- It is unlikely you have encountered a Likert scale
- It is very unlikely you have encountered a Likert scale
Note: social scientists and marketers often omit the middle option above. Doing so forces a respondent to make a decision (the middle option provides them an opt out).
Note: If you do a ranking scale, make sure you tell someone whether 1 is their favorite or 1 is their least favorite. That is, if you ask someone to rank their preferences from 1 to 5, then be sure to write something like: please rank the following options from 1 (least favorite) to 5 (favorite).
We can collect more information in surveys via open ended, free write questions. There’s a few issues with these though. One is that people are likely to skip them. If you have more than one of these in a survey, your response rate is likely to plummet. The other difficulty is that these require quite a bit of time to “code”: that is, to go through and synthesize responses. However, that time is usually rewarded; for instance, I’ve published several articles on experimental class projects and I often get the best data from open-ended student responses, but this data takes much more time to analyze than a likert question.
Collecting demographic information is tricky because (some) people are skeptical of surveys. People can become suspicious if they think they know what your survey is attempting to prove. This can, if they disagree with you, create animosity, and lead to survey trolling. This is one reason it is important to create neutral, objective, balanced questions that do not preference a particular response.
Survey skepticism can often surface as a resistance to supplying demographic information. You have to think about what information you need to meaningfully code and analyze your data. A lot of the time, I complete a survey and wonder why they need to know how old I am or my sexuality. However, sometimes demographic information is extremely important–especially if we hypothesize that subject position informs outlook. So–an important preliminary question is to ask whether I *really* need to know demographic data.
If you need to collect demographic data, particularly data relating to race, sex, gender, and/or sexuality, then you need to be careful, diligent, and respectful. I think gender and sexuality are particularly difficult these days, given the rapid reconceptualization of those concepts (which is a good thing). So we should spend some time investigating how to ask demographic questions, particularly about gender and sexuality.
There’s more information on question types and some tips in this article.
What Not to Do in a Survey
Some general tips (emphasis–avoid loaded words). Some more tips (emphasis–use audience’s language).
A classic example of how not to construct a survey.
Okay, now let’s try crafting a survey question.
Work Logs
Wednesday we are going to be working in our old group workspaces.
The plan will be to take every article from your proposal–whether a preliminary research article or a future research article–and add it to a collaborative annotated bibliography. This will involve revising the material you have and, in situations where more than one person has written an annotation, merging them.
Beyond the annotated bibliography, I expect you will spend the next 3 weeks doing two things:
- Reading the future research you listed in your proposal. You will add annotations to the group bibliography (that is, share your work)
- Developing & conducting your primary research (survey, focus group, interview, textual analysis)
- Writing Work Logs in your own personal workspace
A work log is a 3-4 sentence description of the labor you invested in your project that week. It details how many hours you spent, and what you did during that time. These do not need to be extensive, especially if I can see the work in your workspace. For instance, you might say “this week I spent and hour and a half writing an annotation and another 1/2 hour developing the survey questions.” Clear cut.
You might also write something like: “This week I spent an hour revising two of the annotations in the shared workspace (Brunell 2013 and Higgins et al 2020). I also spend a half hour searching for more relevant articles on Google Scholar (found this article here, skimmed it and plan on using a few survey questions). Finally, I spent a half an hour writing up my methodology section.
As you can see from these examples, I expect you to invest two hours a week into our writing projects outside of class. I use work logs here because everyone writes in different ways. I cannot rigidly demand that you do X amount of research or draft Y amount of pages. I can tell you that around November 1st I will ask you to have completed your primary research project and that you’ll be expected to have a full draft of your paper the Friday before Thanksgiving. Rewarding the incremental progress you make via Work Logs should help keep you productively on track, however you chose to approach those goals.
Homework
Complete your proposals!
