Source: Gender Symbols created by Joseph Gearin, Sheep in Road, Public Domain http://commons.wikimedia.org/wiki/File:Birkak2.jpg No Smoking, Public Domain: http://en.wikipedia.org/w/index.php?title=File:No_smoking_symbol.svg&page=1
In this tutorial you're going to learn about binomial questions. Now let's break down that word binomial real quick. Bi- means two, -nomial means names. So it's a question with two names.
There are two types of data that we've talked about in this course. Qualitative data are sorted into categories. They're also called categorical data. Quantitative data are numbers. And you can do arithmetic with them. They're also called numerical. So if we go back to that name binomial do you think that this is a qualitative type of question or a quantitative type of question?
A binomial question is a qualitative. They have two possible responses. It's a question with two categories. So the simplest version is yes or no. You probably have seen this back from elementary or middle school. Male and female, you probably filled that out on a questionnaire at some point. Whether you're on time to work or late to work, apparently because there were sheep in the road. Or if you're a smoker or you're not a smoker.
Now this one's a little bit tricky. Because some people feel like they fall sort of between them. Like they're a smoker but they're trying to quit, kind of thing. So sometimes questions have some shades of gray. Something like are you a smoker, or have you never smoked? Well what about people who don't currently smoke but used to. Or do you agree with the recently passed law, or you do think it should be repealed? Well maybe you don't really agree with the law. But you don't think it should be repealed either. That's not an answer choice.
Sometimes things don't neatly fit into two boxes. Nor do they work when the questions have more than two answers or are open ended questions. Like how do you feel about this topic? It doesn't really work to place something into categories that way.
And so to recap binomial questions produce categorical data. And there are only two categories. And it's important to consider whether or not there really are just two categories before you ask something as a binomial question. The terms we used were binomial questions. Good luck. And we'll see you next time.
In this tutorial, you're going to learn about the difference between open ended versus closed questions. So many surveys have a combo platter of open and closed questions. Closed questions have short, definite, usually multiple choice type answers.
So your overall experience with the instructor, the course as a whole. And you'll notice there are multiple choice-- poor, fair, satisfactory, good, and excellent-- and those are your only choices. So yes/no or multiple choice are the selectable answer choices whereas the open questions, or also called open ended questions, are subjective.
And these are areas where on this particular survey, someone can click into this field and start to type things. So they're open to the interpretation of the person being surveyed. And they're also open to the interpretation of the person who's conducting the survey when they do the analysis of that survey. So usually, they need to be analyzed by a person in order to really get the full effect from it.
So oftentimes in the desire for simplicity, someone will have a question in closed form that really should be an open question. So suppose that you were in a court of law and the lawyer asks, were you there at the crime scene? And she says, yes. And she's trying to give an explanation in an open style and he wants it to be a closed question. He says, just yes or no, were you there?
So to recap, open questions allow for more explanation. And they're sometimes difficult to interpret, because they're not very cut and dried like closed questions. Close questions are easier to interpret. But they're not always appropriate for the situation. So open ended, some people call those essay questions and closed, multiple choice type questions. Good luck, and we'll see you next time.