Page 125 - Foundations of Cognitive Psychology : Core Readings

128 Daniel J. Levitin

significant. Ten out of 10 subjects indicates repeatability. The technique just de-
scribedis calledthe sign test,because we are looking only at the arithmetic sign
of the differences between groups (positive or negative).
Often,a good alternative to significance tests is estimates of confidence inter-
vals. These determine with a given probability (e.g.,95%) the range of values
within which the true population parameters lie. Another alternative is an
analysis of conditional probabilities. That is,if you observe a difference between
two groups on some measure,determine whether a subject’s membership in
one group or the other will improve your ability to predict his/her score on the
dependent variable,compared with not knowing what group he/she was in
(an example of this analysis is in Levitin 1994a). A good overview of these al-
ternative statistical methods is contained in the paper by Schmidt (1996).
Aside from statistical analyses,in most studies you will want to compute the
mean and standard deviation of your dependent variable. If you had distinct
treatment groups,you will want to know the individual means and standard
deviations for each group. If you had two continuous variables,you will prob-
ably want to compute the correlation, which is an index of how much one vari-
able is related to the other. Always provide a table of means and standard
deviations as part of your report.

6.8.2 Qualitative Analysis,or ‘‘How to Succeed in Statistics without Significance
Testing’’
If you have not had a course in statistics,you are probably at some advantage
over anyone who has. Many people who have taken statistics courses rush to
plug the numbers into a computer package to test for statistical significance.
Unfortunately,students are not always perfectly clear on exactly what it is they
are testing or why they are testing it.
The first thing one should do with experimental data is to graph them in a
way that clarifies the relation between the data and the hypothesis. Forget
about statistical significance testing—what does the pattern of data suggest?
Graph everything you can think of—individual subject data,subject averages,
averages across conditions—and see what patterns emerge. Roger Shepard has
pointed out that the human brain is not very adept at scanning a table of
numbers and picking out patterns,but is much better at picking out patterns in
a visual display.
Depending on what you are studying,you might want to use a bar graph,
a line graph,or a bivariate scatter plot. As a general rule,even though many
of the popular graphing and spreadsheet packages will allow you to make
pseudo-three-dimensional graphs,don’t ever use three dimensions unless the
third dimension actually represents a variable. Nothing is more confusing
than a graph with extraneous information.
If you are making several graphs of the same data (such as individual subject
graphs),make sure that each graph is the same size and that the axes are scaled
identically from one graph to another,in order to facilitate comparison. Be sure
all your axes are clearly labeled,and don’t divide the axis numbers into units
that aren’t meaningful (for example,in a histogram with ‘‘number of subjects’’
on the ordinate,the scale shouldn’t include half numbers because subjects come
only in whole numbers).