How to Conduct a Repeated Measures MANCOVA in SPSS

In today's blog entry, I will walk through the basics of conducting a repeated-measures MANCOVA in SPSS. I will focus on the most basic steps of conducting this analysis (I will not address some complex side issues, such as assumptions, power…etc). If you find yourself with lingering questions after walking through this blog, feel free to leave questions in the "comments" section, or visit the MANCOVA section of my discussion forum to find answers and/or ask questions of your own. Full disclosure: the example data used is from the SPSS sample/help files, and it can be downloaded below.

RIGHT-CLICK HERE AND "SAVE AS FILE" FOR SAMPLE DATA

Let's get started:

Repeated-Measures MANCOVA is used to examine how a dependent variable (DV) varies over time, using multiple measurements of that variable, with each measurement separated by a given period of time. In addition to determining whether the DV itself varies, a MANCOVA can also determine wether other variables are predictive of variability in the DV over time. If that wasn't crystal clear, don't worry, just keep reading.

Repeated-Measures MANCOVA Example:

In our example, your local stats store Stats "R" Us launched a marketing campaign, with three different strategies (variable name: promo; value labels: Strategy A, Strategy B, Strategy C). Stats "R" Us launched campaigns in markets of three different sizes (variable name: mktsize; value labels: Small, Medium, and Large), and measured the sales in each store every three months over the course of one year (4 time points; variable names: sales.1, sales.2, sales.3, and sales.4; see data below).

NOTE: Sales are scaled in "thousands" (e.g. 70.63 is actually $70,630). Also, your data should be in person-level (a.k.a. "wide") format (as opposed to person-period, a.k.a. "long", format), meaning each row of data is a single case (store, in our example). If it were in person-period (long) format, each case (store) would have the number of rows equal to the number of repeated measures (four, in our example), because the repeated measures (sales.1, sales.2, sales.3, and sales.4) would be stacked to form a single variable (Sales).

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Put aside your fears and be wrong already!

First of all, if your research progress is slowed by fear of statistics, your are certainly not alone. Being afraid to "mess-up" your stats, and thus your project, is a common lament. But I'm here to tell you that your project is not that fragile! Once your data is collected, entered, cleaned, and ready for analysis, it is time for excitement, not concern! The golden rule here is: BACK UP. I'll say it again: BACK UP. In case I haven't been clear so far BACK UP! By this I mean back up your data. Make double, triple, and quadruple copies of your dataset and KEEP THEM IN DIFFERENT PLACES (e.g. on a server, on an external hard drive, on a flash drive...etc). It doesn't do much good to keep back up copies in the same place as your original, because if something goes wrong where your data is located, the back up copies are likely toast too!

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