The following data a delayed discounting study. The participants are asked how m
ID: 3234103 • Letter: T
Question
The following data a delayed discounting study. The participants are asked how much they would take today instead of waiting for a specific delay period to receive $1000. Each participant responds to all 5 of the delay periods. Use a repeated-measures ANOVA with alpha = .01 to determine whether there are significant differences among the 5 delay periods for the following data: The endorphins released by the brain act as natural painkillers. For example. Gintzler (1980) monitored endorphin activity and pain thresholds in pregnant rats during the days before they gave birth. The data showed an increase in pain threshold as the pregnancy progressed. The change was gradual until 1 or 2 days before birth, at which point there was an abrupt increase in pain threshold. Apparently a natural painkilling mechanism was preparing the animals for the stress of giving birth. The following data represent pain-threshold scores similar to the results obtained by Gintzler. Do these data indicate a significant change in pain threshold? Use a repeated- measures ANOVA with alpha =.01.Explanation / Answer
Result:
Ho: There is no change in pain threshold.
H1: There is a change in pain threshold
Repeated measure ANOVA F(3,12)=101.25, P=0.000 which is < 0.01 level of significance.
Ho is rejected.
We conclude that there is significant change in pain threshold.
Minitab OUTPUT:
General Linear Model: pain versus Subject, Days
Method
Factor coding (-1, 0, +1)
Factor Information
Factor Type Levels Values
Subject Random 5 A, B, C, D, E
Days Fixed 4 1, 3, 5, 7
Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Subject 4 208.00 52.000 19.50 0.000
Days 3 810.00 270.000 101.25 0.000
Error 12 32.00 2.667
Total 19 1050.00
Model Summary
S R-sq R-sq(adj) R-sq(pred)
1.63299 96.95% 95.17% 91.53%
Coefficients
Term Coef SE Coef T-Value P-Value VIF
Constant 45.000 0.365 123.24 0.000
Subject
A 0.000 0.730 0.00 1.000 *
B -1.000 0.730 -1.37 0.196 *
C 5.000 0.730 6.85 0.000 *
D 1.000 0.730 1.37 0.196 *
Days
1 10.000 0.632 15.81 0.000 1.50
3 1.000 0.632 1.58 0.140 1.50
5 -5.000 0.632 -7.91 0.000 1.50
Regression Equation
pain = 45.000 + 0.0 Subject_A - 1.000 Subject_B + 5.000 Subject_C + 1.000 Subject_D
- 5.000 Subject_E + 10.000 Days_1 + 1.000 Days_3 - 5.000 Days_5 - 6.000 Days_7
Equation treats random terms as though they are fixed.
Fits and Diagnostics for Unusual Observations
Obs pain Fit Resid Std Resid
3 49.00 46.00 3.00 2.37 R
4 52.00 55.00 -3.00 -2.37 R
R Large residual
Expected Mean Squares, using Adjusted SS
Expected Mean Square
Source for Each Term
1 Subject (3) + 4.0000 (1)
2 Days (3) + Q[2]
3 Error (3)
Error Terms for Tests, using Adjusted SS
Synthesis
Source Error DF Error MS of Error MS
1 Subject 12.00 2.6667 (3)
2 Days 12.00 2.6667 (3)
Variance Components, using Adjusted SS
Source Variance % of Total StDev % of Total
Subject 12.3333 82.22% 3.51188 90.68%
Error 2.66667 17.78% 1.63299 42.16%
Total 15 3.87298
Related Questions
drjack9650@gmail.com
Navigate
Integrity-first tutoring: explanations and feedback only — we do not complete graded work. Learn more.