Hi there. If you don't know by now, designing a survey isn't the easiest thing in the world to do. There are a lot of factors to consider. And there are an infinite number of ways to phrase questions. But you want to make sure you phrase a question just right to get that information you want. It isn't always a simple task. And there are a lot of ways you can make mistakes that affect the data you capture. In this lesson, we're going to discuss some of these pitfalls and talk about ways to avoid them. After this lesson, you will be able to identify tasks you can outsource, identify common pitfalls in designing and implementing quantitative research, and also be able to explain techniques to avoid and mitigate them. All right then, let's get started. In the next module of this course, you will begin designing your quantitative survey. But before you begin this task, let me point out some of the most common pitfalls so that you can avoid them. Since I've covered the ground many times, trust me, I know where the pitfalls lie. Of course, there may be others. But if you anticipate and avoid these ones, you'll definitely save yourself a lot of trouble and produce a much better survey. One common pitfall is surveying the wrong target market. This can happen when your client wants you to bite off more than you can chew. They may identify too broad of a target market. An example would be simply identifying students age 18 to 24. To avoid surveying this incorrect target market that is too broad, I would suggest to the client narrowing it to students age 18 to 24 who have purchased a new smartphone in the last year. Another common pitfall in surveying is not getting enough responses. This can happen when you choose the incorrect research approach for the designated target market. For example, instead of doing a mail survey with the students age 18 to 24, you would probably want to do on online survey instead, right? That particular target market is plugged in and online, so you want to meet them where they are. Most of them aren't going to bother filling out a mailed survey, let alone putting it in the mailbox and returning it. Another common pitfall is not including N/A or other as an option in a response. This is when you design a question on a survey that will be avoided if the respondent can't fit the response in the correct slot. They will probably just skip it and you will risk losing valuable data. The N/A response means not applicable. An example would be asking an 18 to 24 year old, who has an iPhone, how satisfied are you with your Android phone? Not at all, somewhat, satisfied or very satisfied. To avoid this, I would recommend putting in N/A response because the question is not applicable to their situation. They may have an iPhone and don't have an Android phone. Another common pitfall is poorly worded questions. You've probably experienced this when you've been taking a survey and said yourself, what? What are they asking? Or that makes no sense. Here's an example. What do you think about your new cell phone? Is it too big, too fast, too cheap, or not fancy enough? The provided answers from which a respondent is supposed to choose are all over the place and don't quantify well. They also mix language modes, meaning some of these can be seen as a positive attribute and some can be seen as a negative attribute. To avoid poorly worded questions, pay attention to the lesson in the next module where I discuss how to write good questions. Another common pitfall is bias from the survey administrator. This is when the interviewer tells the respondent too much information about the question that will likely lead their response. These are known as leading questions. An example would be, the next question I have is a really hard one. To avoid this type of bias from the survey administrator, I would advise you merely asking the question as it was written exactly without trying to qualify the question in any way. Another common pitfall is unprofessional interaction. This can happen for a variety of reasons. One main reason is that your interviewer was not properly trained about what to do and what not to do when interacting with a respondent. An example could be a phone interviewer who makes up data to get a response, as I've mentioned before. To avoid interacting unprofessionally, I recommend screening the people you hire very carefully. Get their references and call them and be very clear with them. For a phone interviewer in particular, it may even make sense to draft a script in a conversational tone for the interviewer to use. One common pitfall is bias from intentionally surveying some people and not others. This is when you don't follow the randomly chosen procedure designated in your research. This is also referred to as sampling error. And my colleague Olivier will go over this in more detail later in the course. An example of this sampling bias would be picking only people from the phonebook who have a last name starting with a A. Clearly people who have last names starting with the letter A don't represent views adequately of your entire sample. It isn't random enough. To avoid bias from intentionally surveying some and not others, I advise that you give your interviewers an exact list of who they should be interviewing, in the order in which you would like them to be interviewed. There is also a software you can use, including in Excel, that can help you further randomize a list of names to survey in order to get a good random sample. Okay, with that, that's a wrap of the most common pitfalls I'd like you to be aware of. As I suggested earlier, there are definitely others. But these are pretty common ones that are seen often when surveying people. And I'd like you to follow the suggestions I've outlined to avoid them.
