USC MUSC 450m - Data Reduction (7 pages)

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Data Reduction Introduction Once you have designed an experiment collected the data and begun thinking about how to communicate the results to other people your next step will almost always be to construct some kind of graphical representation of your data In some cases deciding what to plot on graphs will be fairly straightforward More often than not however it will not be so obvious what the most effective representation might be In perception experiments it is common to investigate the effects of more than one variable at a time and to obtain several measurements per condition Although we won t be running such extensive experiments in this class some researchers manipulate several variables at once and collect hundreds of measurements per condition But even if we restrict ourselves to fairly simple designs and only ask our observers to make five or ten estimates for each type of trial you can see that there are some fundamental decisions we must make when constructing our graphs For one thing should we plot all the data for each subject that is all the measurements per condition or a condensed version of it If we condense it how should we convey how spread out the data are Should we show individual subject data or average across subjects If there is more than one independent variable which one should we plot on the x axis To illustrate and discuss some of these issues we will refer to hypothetical data from a magnitude estimation experiment investigating the M ller Lyer Illusion An Extended Example The M ller Lyer Illusion Imagine that we ran an experiment to investigate the M ller Lyer illusion and that we manipulated two independent variables arrow angle and line length Below is a set of hypothetical data from that experiment In column A you can see that there are three values of line length 80 100 and 120 Likewise there are three values of arrow angle 45 90 and 135 degrees in column B For each combination of line length and arrow angle the subject made four