G'day Andy, On Wed, 26 Nov 2008 14:51:50 +0000 Andrew Choens <[EMAIL PROTECTED]> wrote:
> I was asked by my boss to do an analysis on a large data set, and I am > trying to convince him to let me use R rather than SPSS. Very laudable of you. :) > This is the output from R: > > chisq.test(test29) > > Pearson's Chi-squared test > > data: test29 > X-squared = 9.593, df = 4, p-value = 0.04787 > > But, the same data in SPSS generates a p value of .051. It's a small > but important difference. Chuck explained already the reason for this small difference. I just take issue about it being an important difference. In my opinion, this difference is not important at all. It would only be important to people who are still sticking to arbitrary cut-off points that are mainly due to historical coincidences and the lack of computing power at those time in history. If somebody tells you that this difference is important, ask him or her whether he or she will be willing to finance you a room full of calculators (in the sense of Pearson's time) and whether he or she wants you to do all your calculations and analyses with these calculators in future. Alternatively, you could ask the person whether he or she would like the anaesthetist during his or her next operation to use chloroform given his or her nostalgic penchant for out-dated rituals/methods. > I played around and rescaled things, and tried different values for > B, but I never could get R to reach .051. Well, I have no problem when using simulated p-values to get something close to 0.051; look at the last try. The second one might also be noteworthy. Unfortunately, I didn't save the seed beforehand. > test29 <- matrix(c(110,358,71,312,29,139,31,77,13,32), byrow=TRUE, > ncol=2) test29 [,1] [,2] [1,] 110 358 [2,] 71 312 [3,] 29 139 [4,] 31 77 [5,] 13 32 > chisq.test(test29, simul=TRUE) Pearson's Chi-squared test with simulated p-value (based on 2000 replicates) data: test29 X-squared = 9.593, df = NA, p-value = 0.04798 > chisq.test(test29, simul=TRUE) Pearson's Chi-squared test with simulated p-value (based on 2000 replicates) data: test29 X-squared = 9.593, df = NA, p-value = 0.05697 > chisq.test(test29, simul=TRUE, B=20000) Pearson's Chi-squared test with simulated p-value (based on 20000 replicates) data: test29 X-squared = 9.593, df = NA, p-value = 0.0463 > chisq.test(test29, simul=TRUE, B=20000) Pearson's Chi-squared test with simulated p-value (based on 20000 replicates) data: test29 X-squared = 9.593, df = NA, p-value = 0.0499 > chisq.test(test29, simul=TRUE, B=20000) Pearson's Chi-squared test with simulated p-value (based on 20000 replicates) data: test29 X-squared = 9.593, df = NA, p-value = 0.0486 > chisq.test(test29, simul=TRUE, B=20000) Pearson's Chi-squared test with simulated p-value (based on 20000 replicates) data: test29 X-squared = 9.593, df = NA, p-value = 0.05125 Cheers, Berwin =========================== Full address ============================= Berwin A Turlach Tel.: +65 6516 4416 (secr) Dept of Statistics and Applied Probability +65 6516 6650 (self) Faculty of Science FAX : +65 6872 3919 National University of Singapore 6 Science Drive 2, Blk S16, Level 7 e-mail: [EMAIL PROTECTED] Singapore 117546 http://www.stat.nus.edu.sg/~statba ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.