MarketingExperiments Blog: Research-driven optimization, testing, and marketing ideas

I’ve got a research question. Now what do I do with it?

A few weeks ago, Daniel Burstein wrote a blog about writing research questions. In that blog post, we emphasized the importance of asking “which” rather than “what” questions because a “which” question is clearly testable.

You might ask, “Which page format results in the most lead submissions?” or “Which price point generates the most revenue?” Both questions are clearly stated and include two key pieces of information:

• An independent variable you are going to test
• The dependent variable you will use to measure your results

To know if something is better, first you must know if it is different

With the research question on paper, we can easily create a hypothesis. For the former question: “All page formats will result in the same number of lead submissions.” This type of hypothesis is so famous in research circles that it has a name: “The Null Hypothesis.”

In general terms, the null hypothesis states that varying the independent variable will result in no change to the dependent variable.

In other words, you’re testing to see if changing the page (the independent variable) will change the number of leads (the dependent variable). After all, if there is no change, one cannot be any better than the other.

Why not “The new layout will result in the most lead submissions,” you ask. Because there is no concrete reason to know that there will be a change. Besides, if you already knew the effect of A on B, why would you need to test it?

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What do you get when you divide Jacksonville Beach, Fla. by Arden Hills, MN? I’m sure there’s a punch line in there somewhere. However, if you were tracking your customers’ ZIP codes in a database you would have 32250/55112, or 0.585.

Never mind that it doesn’t make any sense to you and me to divide one ZIP code by another, but a statistical software package is happy to do exactly that for us. Most software just isn’t smart enough to realize that each ZIP code holds a discrete meaning from the next. It sees them as numbers: values which can be sorted in order and used in any type of calculation.

That is why researchers and statistical software packages classify variables into four main types: Nominal, Ordinal, Interval and Ratio.

In this post, I’m going to describe each type of variable to help you understand how they should be used, let you know how this can help improve your data collection … and, while we’re at it, help you sound sharp the next time you’re chatting with your data analyst at the water cooler.

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