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Please Write Up And Interpret The Results For The Following Repeated Measures ANOVA, Using The Activity

Please Write Up And Interpret The Results For The Following Repeated Measures ANOVA, Using The Activity

Please write up and interpret the results for the following repeated measures ANOVA, using the Activity 6.sav data set. Score_0 through Score _12 is the repeated measure (7 levels) and gender is a fixed factor. Discuss especially both main effects and the presence/absence of an interaction between the two.

 

All of the relevant data is given below.

 

 

Within-Subjects Factors

Measure:   MEASURE_1
Score Dependent Variable
1 Score_0
2 Score_2
3 Score_4
4 Score_6
5 Score_8
6 Score_10
7 Score_12

 

Between-Subjects Factors
  Value Label N
Gender F Female 8
M Male 4

 

 

Descriptive Statistics
  Gender Mean Std. Deviation N
Pre-test score Female 28.25 8.172 8
Male 32.25 19.432 4
Total 29.58 12.221 12
Week 2 score Female 29.75 6.319 8
Male 39.75 13.889 4
Total 33.08 10.113 12
Week 4 score Female 33.63 5.181 8
Male 39.00 16.432 4
Total 35.42 9.885 12
Week 6 score Female 35.88 6.556 8
Male 35.25 17.802 4
Total 35.67 10.671 12
Week 8 score Female 39.38 5.370 8
Male 41.00 16.633 4
Total 39.92 9.718 12
Week 10 score Female 44.88 5.743 8
Male 47.25 13.961 4
Total 45.67 8.690 12
Week 12 score Female 48.38 8.518 8
Male 53.25 13.793 4
Total 50.00 10.189 12

 

Multivariate Testsa
Effect Value F Hypothesis df Error df Sig.
Score Pillai’s Trace .961 20.439b 6.000 5.000 .002
Wilks’ Lambda .039 20.439b 6.000 5.000 .002
Hotelling’s Trace 24.526 20.439b 6.000 5.000 .002
Roy’s Largest Root 24.526 20.439b 6.000 5.000 .002
Score * Gender Pillai’s Trace .491 .804b 6.000 5.000 .607
Wilks’ Lambda .509 .804b 6.000 5.000 .607
Hotelling’s Trace .965 .804b 6.000 5.000 .607
Roy’s Largest Root .965 .804b 6.000 5.000 .607
a. Design: Intercept + Gender

Within Subjects Design: Score

b. Exact statistic
 

Mauchly’s Test of Sphericitya

 
Measure:   MEASURE_1  
Within Subjects Effect Mauchly’s W Approx. Chi-Square df Sig. Epsilonb  
Greenhouse-Geisser Huynh-Feldt Lower-bound  
Score .001 56.876 20 .000 .441 .674 .167  
Tests the null hypothesis that the error covariance matrix of the orthonormalized transformed dependent variables is proportional to an identity matrix.  
a. Design: Intercept + Gender

Within Subjects Design: Score

 
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.  

 

Tests of Within-Subjects Effects
Measure:   MEASURE_1
Source Type III Sum of Squares df Mean Square F Sig.
Score Sphericity Assumed 3246.536 6 541.089 20.609 .000
Greenhouse-Geisser 3246.536 2.646 1227.164 20.609 .000
Huynh-Feldt 3246.536 4.045 802.659 20.609 .000
Lower-bound 3246.536 1.000 3246.536 20.609 .001
Score * Gender Sphericity Assumed 182.155 6 30.359 1.156 .342
Greenhouse-Geisser 182.155 2.646 68.853 1.156 .341
Huynh-Feldt 182.155 4.045 45.035 1.156 .344
Lower-bound 182.155 1.000 182.155 1.156 .307
Error(Score) Sphericity Assumed 1575.321 60 26.255    
Greenhouse-Geisser 1575.321 26.456 59.546    
Huynh-Feldt 1575.321 40.447 38.948    
Lower-bound 1575.321 10.000 157.532    

 

 

Tests of Within-Subjects Contrasts

Measure:   MEASURE_1
Source Score Type III Sum of Squares df Mean Square F Sig.
Score Linear 2962.680 1 2962.680 46.905 .000
Quadratic 143.040 1 143.040 4.305 .065
Cubic 51.361 1 51.361 2.242 .165
Order 4 73.724 1 73.724 2.765 .127
Order 5 3.584 1 3.584 .405 .539
Order 6 12.147 1 12.147 4.444 .061
Score * Gender Linear 25.537 1 25.537 .404 .539
Quadratic 21.254 1 21.254 .640 .442
Cubic 66.694 1 66.694 2.911 .119
Order 4 55.767 1 55.767 2.092 .179
Order 5 5.060 1 5.060 .572 .467
Order 6 7.841 1 7.841 2.869 .121
Error(Score) Linear 631.638 10 63.164    
Quadratic 332.272 10 33.227    
Cubic 229.083 10 22.908    
Order 4 266.594 10 26.659    
Order 5 88.403 10 8.840    
Order 6 27.330 10 2.733    

 

 

Tests of Between-Subjects Effects
Measure:   MEASURE_1
Transformed Variable:   Average
Source Type III Sum of Squares df Mean Square F Sig.
Intercept 114349.339 1 114349.339 188.733 .000
Gender 290.720 1 290.720 .480 .504
Error 6058.804 10 605.880    

 

 

 

 

a. Is the assumption of sphericity violated? How can you tell? What does this mean in the context of interpreting the results?

 

Mauchly’s Test of Sphericitya

Measure:   MEASURE_1
Within Subjects Effect Mauchly’s W Approx. Chi-Square df Sig. Epsilonb
Greenhouse-Geisser Huynh-Feldt Lower-bound
Score .001 56.876 20 .000 .441 .674 .167
Tests the null hypothesis that the error covariance matrix of the orthonormalized transformed dependent variables is proportional to an identity matrix.
a. Design: Intercept + Gender

Within Subjects Design: Score

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.

The above table depicts the results of Mauchly’s Test of Spheriicty which tests for one of the assumptions of the ANOVA with repeated measures, namely, sphericity (homogeneity of covariance). This particular table is important for viewing as this assumption is commonly violated. In this case, since p-value is less than .05, I conclude that there are significant differences between the variance of difference. Therefore, the condition of sphericity has not been met.

b. Is there a main effect of gender? Is so, explain the effect. Use post hoc tests when necessary (or explain why they are not required in this specific case).

 

In this case, there is no main effect of gender since gender has a p-value of .504 which means that this is not significant at the 5% level. Also, in this case, the effect is not significant so there is no need for a post hoc test. Moreover, if the effect was significant, then we would not be able to perform the post hoc test since we only have two categories. Post hoc can be run if there are more than two classifications.

c. Is there a main effect tie (i.e. an increase in scores from Week 0 to Week 12)? If so, explain the effect. Use post hoc tests when necessary (or explain why they are not required in this specific case). Examine the output carefully and give as much detail as possible in your findings.

 

Tests of Within-Subjects Effects
Measure:   MEASURE_1
Source Type III Sum of Squares df Mean Square F Sig.
Score Sphericity Assumed 3246.536 6 541.089 20.609 .000
Greenhouse-Geisser 3246.536 2.646 1227.164 20.609 .000
Huynh-Feldt 3246.536 4.045 802.659 20.609 .000
Lower-bound 3246.536 1.000 3246.536 20.609 .001
Score * Gender Sphericity Assumed 182.155 6 30.359 1.156 .342
Greenhouse-Geisser 182.155 2.646 68.853 1.156 .341
Huynh-Feldt 182.155 4.045 45.035 1.156 .344
Lower-bound 182.155 1.000 182.155 1.156 .307
Error(Score) Sphericity Assumed 1575.321 60 26.255    
Greenhouse-Geisser 1575.321 26.456 59.546    
Huynh-Feldt 1575.321 40.447 38.948    
Lower-bound 1575.321 10.000 157.532    

 

Pairwise Comparisons
Measure:MEASURE_1
 

(I) SCORE

 

(J) SCORE

Mean Difference (I-J) Std. Error Sig.a 95% Confidence Interval for Differencea
Lower Bound Upper Bound
  1   2 -4.500 3.383 .213 -12.039 3.039
3 -6.062* 2.412 .031 -11.437 -.688
4 -5.312* 2.117 .031 -10.029 -.596
5 -9.937* 2.693 .004 -15.938 -3.937
6 -15.813* 2.683 .000 -21.791 -9.834
7 -20.563* 3.524 .000 -28.415 -12.710
2   1 4.500 3.383 .213 -3.039 12.039
3 -1.562 1.542 .335 -4.998 1.873
4 -.812 2.324 .734 -5.990 4.365
5 -5.437* 1.914 .018 -9.701 -1.174
6 -11.313* 2.063 .000 -15.909 -6.716
7 -16.063* 3.154 .000 -23.089 -9.036
3   1 6.062* 2.412 .031 .688 11.437
2 1.562 1.542 .335 -1.873 4.998
4 .750 1.097 .510 -1.693 3.193
5 -3.875* 1.058 .004 -6.233 -1.517
6 -9.750* 1.336 .000 -12.726 -6.774
7 -14.500* 2.739 .000 -20.603 -8.397
4   1 5.312* 2.117 .031 .596 10.029
2 .812 2.324 .734 -4.365 5.990
3 -.750 1.097 .510 -3.193 1.693
5 -4.625* 1.019 .001 -6.895 -2.355
6 -10.500* 1.202 .000 -13.177 -7.823
7 -15.250* 2.711 .000 -21.291 -9.209
5   1 9.937* 2.693 .004 3.937 15.938
2 5.437* 1.914 .018 1.174 9.701
3 3.875* 1.058 .004 1.517 6.233
4 4.625* 1.019 .001 2.355 6.895
6 -5.875* .716 .000 -7.471 -4.279
7 -10.625* 2.065 .000 -15.226 -6.024
6   1 15.813* 2.683 .000 9.834 21.791
2 11.313* 2.063 .000 6.716 15.909
3 9.750* 1.336 .000 6.774 12.726
4 10.500* 1.202 .000 7.823 13.177
5 5.875* .716 .000 4.279 7.471
7 -4.750* 1.705 .019 -8.548 -.952
7   1 20.563* 3.524 .000 12.710 28.415
2 16.063* 3.154 .000 9.036 23.089
3 14.500* 2.739 .000 8.397 20.603
4 15.250* 2.711 .000 9.209 21.291
5 10.625* 2.065 .000 6.024 15.226
6 4.750* 1.705 .019 .952 8.548
Based on estimated marginal means
a. Adjustment for multiple comparisons: Least Significant Difference (equivalent to no adjustments).
*. The mean difference is significant at the .05 level.

The mean effect of score is significant at 5% level of significance. From the table, I am able to ascertain the F-value for the score factor, its associated significance level, and the effect size (Partial Eta Squared). Because my data violated the assumption of sphericity, I examine the values in the Greenhouse-Geisser row (if sphericity had not been violated, I would have looked under the Sphericity Assumed row). Thus, I can report that when using an ANOVA with repeated measures with a Greenhouse-Geiseer correction, the mean scores for weeks were statistically significantly different (F(2.646,60) = 20.609, p < 0.0005.

In addition, in looking at the above Paired Comparisons Table, I recognize the labels associated with score in the experiment from the Within-Subject Factors Table. This is a table which gives the significance level of differences between the individual time points. It can be seen that there was a significant difference in scores in training from pre to week 12. The p-values indicate the significant differences between the groups.

 

 

 

 

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