|
Estimates of Fixed Effectsa
|
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|
Parameter |
Estimate |
Std. Error |
df |
t |
Sig. |
95% Confidence Interval |
|
|
Lower Bound |
Upper Bound |
||||||
|
[group=.00] |
8.076798 |
4.563993 |
196 |
1.770 |
.078 |
-.924042 |
17.077637 |
|
[group=1.00] |
-24.972667 |
5.430149 |
196 |
-4.599 |
.000 |
-35.681687 |
-14.263646 |
|
[group=.00] * age |
.681919 |
.437809 |
196 |
1.558 |
.121 |
-.181501 |
1.545340 |
|
[group=1.00] * age |
3.629046 |
.286749 |
196.000 |
12.656 |
.000 |
3.063537 |
4.194554 |
|
a. Dependent Variable: talking on the phone. |
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Carol,Something's wrong with your analysis. Your "Estimates of Fixed Effects" should include three main effect terms (representing intercepts) and three interaction terms (representing slopes). Instead, it looks like you have a reference category for age. Did you run the analysis a couple different ways?RyanOn Wed, May 2, 2012 at 2:34 PM, Parise, Carol A. <[hidden email]> wrote:
Here is the result of using age in its original form and the \TEST statements.What just dawned on me as i was looking at this is that since there is a significant interaction for age3*age, the main effect of age3 is no longer applicable - just like in any other anova.The efffect of age on time, depends on which part of the age continum one is on. So, there is no effect of being under age 38 on finish time but being between 38-51 means you get slower by .20 hrs for every year. If you are over 50, then you still slow down but not as much.
Estimates of Fixed Effectsa
Parameter Estimate Std. Error df t Sig. 95% Confidence Interval
Lower Bound Upper Bound [age3=1.00] .171610 1.293038 13,805 .133 .894 -2.362920 2.706140 [age3=2.00] -6.124152 1.282973 13,805 -4.773 .000 -8.638953 -3.609351 [age3=3.00] 0b 0 . . . . . [age3=1.00] * NewAge .036716 .022293 13,805.000 1.647 .100 -.006981 .080413 [age3=2.00] * NewAge .203924 .016284 13,805 12.523 .000 .172004 .235843 [age3=3.00] * NewAge .082119 .019039 13,805 4.313 .000 .044800 .119438 /TEST = "diff in slopes between <38 and 38-50" age3*newAge 1 -1 0The estimate is simply 0.037-0.203 and it's significant which means that the slopes change from one age group to another. I believe that this provides the rationale for selecting this cutpoint in the data.
Contrast Estimatesa,b
Contrast Estimate Std. Error df Test Value t Sig. 95% Confidence Interval
Lower Bound Upper Bound L1 -.167208 .027493 13,805 0 -6.082 .000 -.221098 -.113318
a. diff in slopes between <38 and 38-50 /TEST = "diff in slopes between <38 and 51+" age3*newAge 1 0 -1The estimate is 0.203-0.082 and it's not significant which means that the slopes aren't really that different.
Contrast Estimate Std. Error df Test Value t Sig. 95% Confidence Interval
Lower Bound Upper Bound L1 -.045403 .029255 13,805 0 -1.552 .121 -.102747 .011940
a. diff in slopes between <38 and 51+ /TEST = "diff in slopes between 38-50 and 51+" age3*newAge 0 1 -1.The difference in slopes between 38-50 and 51+ is significant which means that the cut point at age 50 is justfiable.
Contrast Estimatesa,b
Contrast Estimate Std. Error df Test Value t Sig. 95% Confidence Interval
Lower Bound Upper Bound L1 .121804 .024961 13,805 0 4.880 .000 .072877 .170731
a. diff in slopes between 38-50 and 51+ In the end, people under 38 and over 51 slow down by just around the same amount of time...but you slow down more in those middle ages.Thanks again for taking the time to post. This was actually really fun to work though and see how it affected my results.Carol
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