We'll now be learning how to perform chi-square tests in SPSS. Most of the elements will be straightforward (observed and expected frequencies, the overall chi-square, degrees of freedom, and significance). There are a few aspects of the output that may be a little confusing, however, so I've made another handy-dandy guide to aid interpretation (below).
Perhaps the most confusing aspect of a chi-square table is how to report on percentages of respondents. The important thing to remember is that, in a statement of the form "the percentage of people in group A have characteristic B," the order of A and B is not interchangeable.
Here's an example. I would estimate that about 80% of the players in the National Basketball Association (NBA) are from the U.S. (players such as the Dallas Mavericks' Dirk Nowitzki and the Houston Rockets' Yao Ming are part of the growing international presence).
However, we would never claim the reverse -- that 80% of the people in the U.S. are NBA basketball players! You might call this the "Reversal Error."
We thus have to be careful about phrasing the results of a chi-square analysis. A good practice is to request only ROW percentages from SPSS for the cells in the table. If you follow this practice, then you can always phrase your results in the following form. For any given cell, you can say something like:
"Among [category represented by the row], ___% [shown in cell] were [characteristic represented by the column]."
This format corresponds to the SPSS output in the following manner:
Update, November 13, 2007: Television host Keith Olbermann of MSNBC's "Countdown" awarded himself third place in his nightly "Worst Person in the World" competition. Olbermann's offense? He committed a statistical reversal error of the type described above. Quoting from the transcript of the show:
The bronze to me. We inverted a statistic last night. The study based on stats from the Veterans Affairs and Census Bureau indicating the heart breaking percentage of homeless veterans. I said one in every four veterans is homeless. In fact, one of every four homeless is a veteran. That makes the number smaller. It is, quote, only, unquote, 194,000; 1,500 of them, according to the V.A., veterans of Afghanistan and Iraq, already on the streets. I apologize for the statistical mistake.
Here are some other tips:
1. The statistical significance of a chi-square analysis pertains to the table as a whole. You're saying that the overall chi-square (which is the sum of the cell-specific chi-squares) exceeds the critical value for a given degrees of freedom and significance level.
2. To see if one or more cells are making particularly large contributions to the overall significance of the chi-square, you can have SPSS provide the unstandardized (regular) and standardized residuals for each cell. The regular residual is just the difference between the observed and expected frequency counts for a given cell. The standardized residual then puts the residual in z-score form, so that any standardized residual that's 1.96 or greater (in absolute value) could be said to be a major contributor to the overall chi-square. Standardized residuals may not be that informative, however, as with large sample sizes, many cells may have standardized residuals greater than 1.96. That makes it hard to pinpoint any one or two cells where the "action" is. For further information, see the links section to the right, under Chi-Square.
3. If your overall chi-square for an analysis is significant, you should describe your findings in a way that emphasizes contrast. For example: "Among women, 39% followed the election campaigns closely, whereas only 28% of men did."