Biometrics - ANOVA repeated measures
Before application conditions require analysis of variance between groups are independent, i.e., the result data of the same measured at certain time factors, but . 4 month to . 5 month data are related, it is necessary to consider the results of the different measurement periods under certain factors data analysis, i.e., repeated measures of variance, i.e., the processing * repeated measures on the time factor * repeated measurements at the same time.
The advantage is that to overcome the effect of time, in case where a small number of sample data is not too small an amount, but repeated measurements so that the object has three effects. Assuming that the measurement time has no effect on objects are paired samples t premise test, or repeated measures analysis of variance.
Conditions are independent between individual samples, i.e. A patient with B patients does not matter. Homogeneity of variance is the variance of each treatment was the same, i.e., data for all patients receiving different treatments, the patient A of all data associated with the patient B all variance data are the same; covariance spherical symmetry, i.e. symmetrical by ball test , or it is biased, which need to adjust the degree of freedom.
Total variation = individual room (differences in different patients treated) + (time difference of different patient) in an individual
1. if the assumptions 2. Test symmetry (different test methods)
Common is the same, if not choose the first one
Multiple alignment must be symmetrical to inspect the ball: the p-value must be non-obvious:
| Mauchly's Test of Sphericitya |
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| Measure: MEASURE_1 |
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| Within Subjects Effect |
Mauchly's W |
Approx. Chi-Square |
df |
Sig. |
Epsilonb |
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| Greenhouse-Geisser |
Huynh-Feldt |
Lower-bound |
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| time |
0.208 |
12.131 |
5 |
0.034 |
0.595 |
0.733 |
0.333 |
| Tests the null hypothesis that the error covariance matrix of the orthonormalized transformed dependent variables is proportional to an identity matrix. |
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| a. Design: Intercept Within Subjects Design: time |
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| b. May be used to adjust the degrees of freedom for the averaged tests of significance. Corrected tests are displayed in the Tests of Within-Subjects Effects table. |
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If the difference is significant, the dissatisfaction football test, you need to optimize this table of degrees of freedom: freedom appear in the group is within the influence after optimization:
