Quant QUISª:  Information About Quantitative Analysis

This note outlines several approaches that have been taken in the analysis of QUISª data.

Forming Data Sets:  Once the QUISª data has been collected, the first decision is whether to analyze the data as a whole or in groups.  Obviously, if you are comparing two different pieces of software you will want to group the data by, say, Software Package A and Software Package B.  On the other hand, if you are interested in differences between types of users of the same package, you would group on groups of individuals, say, User Group 1 and User Group 2.  When forming data sets, we will be interested in statistics within each group as well as hypotheses tests between groups.  LetÕs first consider the statistics within groups.

Profiles and Diagnostic Tests:  One of the most useful analyses, particularly for iterative testing and design, is the profile.  The profile reveals the strengths and weaknesses of the software program or workstation by showing the deviations of the means above and below a criterion.  A profile is generated by calculating the means and standard deviations for each item in the QUIS.  The means are then graphed on a scale from 1 to 9 as shown in the figure below:

The midpoint of the rating scale (5) can be used as a criterion.  If the item is above 5, it is perceived as being better than an arbitrary, mediocre value.  However, that is generally not good enough.  We may also use the overall mean of the group as a criterion.  Such a mean is shown in the figure.

It is useful to plot a confidence interval around each mean in order to determine its reliability.  The confidence interval also indicates whether the mean of an item is significantly above or below some criterion.  For example, if a 95% confidence interval includes 5 within its boundaries, then it indicates that the mean is not significantly different from 5 at the .05 level of significance.

The profile can be used to identify the areas in the application which are particularly good or particularly bad.  Start with the item having the lowest mean.  Identify flaws in the software that may have led to this low mean.  Then go to the next lowest item and repeat.  Do this until you are satisfied that you have identified the major problems.  Then start with the item having the highest mean.  Ask yourself why this aspect was rated so high and how it can be used to further enhance the software.  Then go the next highest item and repeat.  Again, do this until you are satisfied that you have identified all of the strong points of the software.

A more sensitive and statistically powerful technique for identifying the strengths and weaknesses is to use a within-subject approach.  For each respondent, compute a mean of all of his or her ratings.  Then for each of the respondentÕs ratings get the deviation between that rating and the respondentÕs mean, (dik = Xik - M.k;  where Xik is the rating for item k by respondent i and Mi. is the mean for respondent i across all items).  A simple t-test on these deviations to see if they are significantly different from zero for a particular item will indicate whether the item is perceived as high or low relative to each respondentÕs average rating.  In general, it is nice to have a sample size of at least 20 for statistical purposes.  However, realizing that many usability limit their samples to about 10, it is suggested that one avoid statistical tests and generalizations by presenting only means and focusing on the highest and lowest ratings.

Comparing Groups:  When your data is composed of groups, you make comparisons between groups at the overall level and at the level of individual items.  However, remember that the more statistical tests you run, then greater your probability of a Type I Error (a spurious result).  To guard against that, you should consider only using the .01 or .001 level of significance.

For overall comparisons you may find the mean of the Overall Ratings (3.1 to 3.6) for each respondent in each group.  Then compare the group means using a t-test.  Means may also be computed for sections of the QUISª or for all of the items on the QUISª and compared between groups.  Or, of course, you may make comparisons at the individual item level.  But again, beware of Type I Error.

More Sophisticated Analyses:  The number of analyses that one can play with is nearly endless, if you have enough data points.  One should be cautious of over-analyzing the data.  You will be bound to find something fascinating but unreliable.  Nevertheless, some interesting additional analyses can be used to investigate the correlational structure of the items.  These analyses can reveal the underlying importance or relevance of items to the users and to overall satisfaction.  These include:  factor analysis, item analysis, and hierarchical regression analysis.