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These are the user uploaded subtitles that are being translated: 1 00:00:00,000 --> 00:00:03,510 post hoc tests you can ask for more more 2 00:00:03,510 --> 00:00:05,819 comparisons here but not in a one way I 3 00:00:05,819 --> 00:00:07,919 know but you have to have between I 4 00:00:07,919 --> 00:00:09,240 believe you have to have a between 5 00:00:09,240 --> 00:00:10,889 subjects effect to actually get these 6 00:00:10,889 --> 00:00:12,929 and when you combine the repeated 7 00:00:12,929 --> 00:00:15,179 measures and between subjects effect in 8 00:00:15,179 --> 00:00:17,279 the same ANOVA you're talking about a 9 00:00:17,279 --> 00:00:19,619 split plot ANOVA or what some people 10 00:00:19,619 --> 00:00:22,320 unfortunately call a mixed design and 11 00:00:22,320 --> 00:00:24,480 OVA which is confusing because sometimes 12 00:00:24,480 --> 00:00:26,130 people confuse it with a mixed effects 13 00:00:26,130 --> 00:00:27,539 ANOVA which is actually a totally 14 00:00:27,539 --> 00:00:30,900 different analysis so those are the the 15 00:00:30,900 --> 00:00:32,308 main things I'm going to choose for this 16 00:00:32,308 --> 00:00:34,290 basic one-way repeated measures ANOVA 17 00:00:34,290 --> 00:00:36,030 and I'm going to click OK to run the 18 00:00:36,030 --> 00:00:41,790 analysis so SPSS is going to give me the 19 00:00:41,790 --> 00:00:44,399 first box here which is this first box 20 00:00:44,399 --> 00:00:45,870 here is a warning and it's just telling 21 00:00:45,870 --> 00:00:47,730 me that it couldn't do one of the 22 00:00:47,730 --> 00:00:50,579 homogeneity of variance tests which is 23 00:00:50,579 --> 00:00:52,530 the Levine's test of homogeneity of 24 00:00:52,530 --> 00:00:54,270 variance in fact you know I'm not even 25 00:00:54,270 --> 00:00:56,370 too sure if I had to click that 26 00:00:56,370 --> 00:00:58,739 homogeneity button because I know it 27 00:00:58,739 --> 00:01:00,750 might actually give the test that I'm 28 00:01:00,750 --> 00:01:02,280 going to take a look at in a second just 29 00:01:02,280 --> 00:01:05,610 automatically so within subjects table 30 00:01:05,610 --> 00:01:07,229 here is just telling me what my my 31 00:01:07,229 --> 00:01:08,850 factor looks like it's got three levels 32 00:01:08,850 --> 00:01:11,610 time one time two times three so people 33 00:01:11,610 --> 00:01:14,700 took the same IQ test three times in a 34 00:01:14,700 --> 00:01:19,290 row let's just say a month apart and we 35 00:01:19,290 --> 00:01:20,670 can see the means and the standard 36 00:01:20,670 --> 00:01:22,710 deviation see and we can see the mean is 37 00:01:22,710 --> 00:01:25,530 increasing linearly so time one to time 38 00:01:25,530 --> 00:01:28,320 two an increase of about one point three 39 00:01:28,320 --> 00:01:32,040 five a one point one point in the means 40 00:01:32,040 --> 00:01:34,200 and then it goes up again by about about 41 00:01:34,200 --> 00:01:36,360 one and we can see the standard 42 00:01:36,360 --> 00:01:38,009 deviations here and we can also see that 43 00:01:38,009 --> 00:01:39,930 the standard deviations are increasing 44 00:01:39,930 --> 00:01:41,909 across and usually in repeated measures 45 00:01:41,909 --> 00:01:44,369 designs in the real world the standard 46 00:01:44,369 --> 00:01:47,220 deviation does increase over time people 47 00:01:47,220 --> 00:01:48,990 at the last time period will usually 48 00:01:48,990 --> 00:01:50,759 have the largest standard deviation and 49 00:01:50,759 --> 00:01:52,680 it's larger usually sometimes 50 00:01:52,680 --> 00:01:54,600 substantially larger than the time one 51 00:01:54,600 --> 00:01:56,700 and one of the assumptions of the 52 00:01:56,700 --> 00:01:58,860 repeated measures ANOVA is that there is 53 00:01:58,860 --> 00:02:01,049 homogeneity of variance and it tests it 54 00:02:01,049 --> 00:02:03,000 with a test called much less Tessa's 55 00:02:03,000 --> 00:02:04,320 veracity which I'm going to get to in a 56 00:02:04,320 --> 00:02:06,060 minute this is the multivariate 57 00:02:06,060 --> 00:02:08,669 estimates and it's outputted by SPSS 58 00:02:08,669 --> 00:02:10,800 automatically it is a legitimate 59 00:02:10,800 --> 00:02:12,959 analysis that something people often do 60 00:02:12,959 --> 00:02:13,270 it 61 00:02:13,270 --> 00:02:15,100 PETA measures design and I'm going to 62 00:02:15,100 --> 00:02:17,650 follow that up in a future video I'm 63 00:02:17,650 --> 00:02:19,120 going to talk about repeated measures 64 00:02:19,120 --> 00:02:21,820 and over in the multivariate manova 65 00:02:21,820 --> 00:02:24,550 context but i'm not going to talk about 66 00:02:24,550 --> 00:02:28,060 it in this analysis except to say when 67 00:02:28,060 --> 00:02:30,640 you violate the assumption of modulus 68 00:02:30,640 --> 00:02:33,910 tests for its diversity some people 69 00:02:33,910 --> 00:02:36,280 often go to the multivariate test 70 00:02:36,280 --> 00:02:38,110 because it doesn't assume that what is 71 00:02:38,110 --> 00:02:40,270 much least tessa sphericity is something 72 00:02:40,270 --> 00:02:43,750 that typically you do not want to reject 73 00:02:43,750 --> 00:02:46,390 the null hypothesis for marshalese test 74 00:02:46,390 --> 00:02:49,240 of sphericity is indirectly testing the 75 00:02:49,240 --> 00:02:52,870 assumption that your variances or 76 00:02:52,870 --> 00:02:54,640 standard deviations are actually going 77 00:02:54,640 --> 00:02:57,040 to be the same as well as the 78 00:02:57,040 --> 00:02:59,380 covariances between time 1 time 2 and 79 00:02:59,380 --> 00:03:02,560 time 3 now typically I'm going to talk a 80 00:03:02,560 --> 00:03:05,470 little bit more about this right now to 81 00:03:05,470 --> 00:03:06,820 get an understanding what's going on 82 00:03:06,820 --> 00:03:09,330 unfortunately spss doesn't give you the 83 00:03:09,330 --> 00:03:11,590 correlation between time 1 time 2 and 84 00:03:11,590 --> 00:03:14,230 times 3 so people who scored high at 85 00:03:14,230 --> 00:03:17,080 time 1 on this IQ test most likely also 86 00:03:17,080 --> 00:03:19,570 scored high at time 2 and also most 87 00:03:19,570 --> 00:03:21,880 likely scored time scored high at time 3 88 00:03:21,880 --> 00:03:23,950 so there's a correlation between the 89 00:03:23,950 --> 00:03:26,800 dependent variable scores from time one 90 00:03:26,800 --> 00:03:31,840 time to two times three the repeated 91 00:03:31,840 --> 00:03:33,790 measures ANOVA assumes that that 92 00:03:33,790 --> 00:03:36,280 correlation between time one and time 93 00:03:36,280 --> 00:03:38,500 two and between time 1 and time three 94 00:03:38,500 --> 00:03:40,300 and between time to and times three is 95 00:03:40,300 --> 00:03:42,880 going to be equal so let's actually take 96 00:03:42,880 --> 00:03:44,709 a look at this let's look at the 97 00:03:44,709 --> 00:03:49,480 correlation so we get the correlations 98 00:03:49,480 --> 00:03:52,180 between time 1 time 2 and time 3 and we 99 00:03:52,180 --> 00:03:53,860 can see that there is some deviations 100 00:03:53,860 --> 00:03:56,260 time 1 the time two are about the same 8 101 00:03:56,260 --> 00:03:58,720 point 8 5 in time 2 and time three point 102 00:03:58,720 --> 00:04:00,970 eight between time - and times 3 and 103 00:04:00,970 --> 00:04:03,700 then time one time 0.85 roughly the same 104 00:04:03,700 --> 00:04:06,459 but then only 0.65 between time 1 and 105 00:04:06,459 --> 00:04:08,890 time 3 so the correlation is actually 106 00:04:08,890 --> 00:04:11,110 going down as we get further and further 107 00:04:11,110 --> 00:04:12,970 away from time 1 which is what usually 108 00:04:12,970 --> 00:04:18,250 you see in practice and much less esses 109 00:04:18,250 --> 00:04:20,858 erisa t indirectly tests that these 110 00:04:20,858 --> 00:04:22,590 correlations are the same 111 00:04:22,590 --> 00:04:24,750 I won't go into too much detail about 112 00:04:24,750 --> 00:04:26,910 exactly what monsters Texas versity is 113 00:04:26,910 --> 00:04:29,040 testing except to say that it's not 114 00:04:29,040 --> 00:04:31,350 actually testing directly that the 115 00:04:31,350 --> 00:04:33,360 variances and covariances or 116 00:04:33,360 --> 00:04:37,430 correlations are the same you can get a 117 00:04:37,430 --> 00:04:40,860 variance covariance matrix in SPSS by 118 00:04:40,860 --> 00:04:42,810 going into the reliability analysis 119 00:04:42,810 --> 00:04:45,570 utility and click on statistics and 120 00:04:45,570 --> 00:04:47,610 getting covariances here interitum will 121 00:04:47,610 --> 00:04:50,070 present pretend that our variables are 122 00:04:50,070 --> 00:04:52,050 items they're not the dist variables but 123 00:04:52,050 --> 00:04:54,030 we can get the variance covariance 124 00:04:54,030 --> 00:04:57,360 matrix and what repeated measures ANOVA 125 00:04:57,360 --> 00:04:59,669 assumes is that these variances here 126 00:04:59,669 --> 00:05:02,460 time one's variance is 7.85 that's 127 00:05:02,460 --> 00:05:04,970 simply this 9091

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