One of the primary advantages of a repeated-measures design, compared to an inde
ID: 3372183 • Letter: O
Question
One of the primary advantages of a repeated-measures design, compared to an independent-measures design, is that it reduces the overall variability by removing variance caused by individual dfferences. The following data are from a research study comparing three treatment conditions Person Totals P- 27 P-24 P-21 P-12 P-9 P-15 N-18 G-108 x-800 12 M-4 T-24 SS 42 M-6 T-36 SS-28 M-8 T- 48 SS- 34 Assume that the data are from an independent-measures study using three separate samples, each with n- 6 participants. Ignore the column of P totals and use an independent-measures ANOVA with a-.05 to test the significance of the mean differences. (Use three decimal places where needed.) Source Between treatments Within treatments MS Numerator Degroes of Fredom6 eo o What is the value of For?(Use three decimal places) Conclusion: O Reject the null hypothesis. There are no significant ifferences among the three groups O Fail to reject the null hypothesis. There are no significant differences among the three groups O Reject the null hypothesis. There are significant differences among the three groups O Fail to reject the null hypothesis. There are significant differences among the three groupsExplanation / Answer
Result:
Analysis of Variance
Source
DF
SS
MS
F-Value
P-Value
Treatment
2
48.00
24.000
3.462
0.058
Error
15
104.00
6.933
Total
17
152.00
Critical value F( 2,15) = 3.682
Calculated F=3.462 < critical F =3.682. Ho is not rejected.
Fail to reject the null hypothesis. There are no significant difference among the three groups.
Analysis of Variance
Source
DF
SS
MS
F-Value
P-Value
person
5
84.00
16.800
8.40
0.002
Treatment
2
48.00
24.000
12.00
0.002
Error
10
20.00
2.000
Total
17
152.00
F calculated = 12.000
F critical F( 2,10) =4.103
Calculated F=12.000 > critical F =4.103. Ho is rejected.
Reject the null hypothesis. There are significant difference among the three groups.
The repeated measure ANOVA reduces the error variances by removing individual differences. This increases the likelihood that make significant differences.
Source
DF
SS
MS
F-Value
P-Value
Treatment
2
48.00
24.000
3.462
0.058
Error
15
104.00
6.933
Total
17
152.00
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