Last week, I talked briefly about what test to use to analyze your data. Most of the evaluation work conducted by Extension professionals results in one group, often an intact group or population.  Today I want to talk about what you can do with those data.

One of the first things you can do is to run frequencies and percentages on these data.  In fact, I recommend you compute them as the first analyzes you run.  Most softwear (SPSS, SAS, Excel, etc.) programs will do this for you.  When you run frequencies in SPSS, the computer returns an output that looks something like the first image:

When compute frequencies in SAS, the resulting output looks like the second image:

Both images report frequencies, percentages of those frequencies, and cumulative percentage (that is, it adds the percents of frequency A to the percent of frequency B, etc. until 100% is reached).

To compute frequencies in Excel, read here.  Excel has a number of COUNT functions depending on what you want to know.

Once you have computed frequencies and percentages, most people want to know if change occurred.  Although there are other analyses  which can be performed (reliability, validity, correlation, prediction), all of these require that you know what type of data do you have

• nominal–whether people’s answers named something (e.g., gender; marital status);
• ordinal–whether people ordered their responses on how strongly they agreed (e.g., agree or disagree);
• interval–the scores on a standardized scale (e.g., temperature or nutrition test).

If you have nominal data and you want to compute change, you need to know how many times participants are answering the questionnaire and how many categories you have in your questions (e.g.pre/post; yes/no).  If you are giving the questionnaire twice and there are two categories for some of your questions, you can compute the McNemar change test.  The McNemar change test is a non-parametric (meaning that the parameters are not known) that is applied to a 2×2 contingency table.   It tests for changes in responses using the chi-square distribution and is useful for detecting changes in responses due to “before-and-after” designs.  A 2×2 contingency table has two columns and two rows.  The frequencies from the nominal data are in the cells where the rows and columns cross; the totals for rows and columns are in the margins (or the last row and the far right column).  SPSS computes the following statistics when a cross tabs test is run–Pearson’s Chi Square, Continuity Correction, Likelihood Ratio, Fisher’s Exact Test, and Linear by Linear Association. A McNemar test can be specified.