You asked for help with SPSS output. It is possible that you need help rather with SPSS input. You reported 2-way ANOVA output as Noise Quiet Noise*Quiet which, as Jos Jansen pointed out, is nonsense. However, it is not clear whether it is nonsense arising from incorrectly choosing which two of your four conditions apply to the two levels of "Noise" and to the two levels of "Quiet", or arising from having mislabelled your factors and their levels but actually produced the ANOVA your data demand. See below ...
On 22 Aug 2003, Pingu wrote in part: > I was interested to know: > > A) Which was faster on successful dials > B) Which had a higher error rate (either failed dial attempts or wrong > names dialled) > C) Which was faster OVERALL, once the errors had been included > D) Which ones performance was more affected detrimentally by > background noise > > 12 subjects took part. Each subject had their own 10 name phonebook. > Each subject had to dial every name in their phonebook (randomly > presented) under four different conditions (also randomised): > > 1) Voice dialling (VD) + quiet > 2) VD + noise > 3) TTS dialling + quiet > 4) TTS dialling + noise In terms of these category labels (1) - (4), your two factors are dialling type (VD or TTS) and background noise level (noisy or quiet), which I'll abbreviate as DT and BNL: 1) Voice dialling (VD) + quiet DT = VD BNL = quiet 2) VD + noise DT = VD BNL = noisy 3) TTS dialling + quiet DT = TTS BNL = quiet 4) TTS dialling + noise DT = TTS BNL = noisy (although I presume you've coded those category values as (1,2) or (0,1): for example, DT = 1 if VD, DT = 2 if TTS, and so on.) One thing you ought to do, fairly early on, is to find the means and variances (or std. deviations) for each of your four cells [(1) to (4) above], for each dependent variable you want to analyze. Of course, the means are what you're mostly interested in, but you need to know if the variances are reasonably similar in all four cells. As Jos remarked, this may not be true in your case. If it be reasonable to assume approximately equal variances, tests based on the ANOVA within-cell variance will be more stable (and possibly more sensitve) than your t-tests are, owing to the increased number of degrees of freedom associated with the error variance -- and your tutors are correct. If the variances are distinctly different -- suppose, e.g., that variances in the two noisy conditions are similar and much larger than variances in the two quiet conditions, which themselves are similar -- then you may need to take that fact into account; possibly by using the t-test based on different variances for comparisons between "noisy" and "quiet", and using the t-test based on equal variances for comparisons between "TST" and "VD". At this point you probably actively need the advice of your tutors, or at least of someone in the same room with you and your SPSS protocol and output. > On the data i collected i did the following (all t tests): And did you adjust the significance level of the t-tests for the multiple tests (e.g., via the Bonferroni correction)? > 1) speed of successful dials, VD vs TTS, with quiet > 2) speed of succesful dials, VD vs TTS, with noise > > etc... for aspects A, B, C listed above. > > For D (effect of background noise) i did: > > 1) Speed of succesful dials, TTS in quiet vs TTS in noise > 2) Speed of succesful dials, VD in quiet vs VD in noise > etc... for error rates and speed of dialling including errors < snip, the rest > ----------------------------------------------------------------------- Donald F. Burrill [EMAIL PROTECTED] 56 Sebbins Pond Drive, Bedford, NH 03110 (603) 626-0816 . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
