ANCOVA

 

 

 

Suppose a researcher randomly assigns 30 police officers to receive one of two different “dosages” of cognitive interview intervention to enhance eyewitness recollections or to be assigned into a control “business as usual” group. She determines the number of details that each police officer can obtain from 5 eyewitnesses of armed robbery. Further, these details are weighted such that important details are given 10 points, minor but correct details are given 5 points, and incorrect or irrelevant details are given 0 points. Also, she obtained the percentage of cases that were classified as solved and closed by each of the police officers during the last 12 months before the study began. This covariate was included to determine if any group differences in details of a crime by eyewitnesses persisted when police officer’s estimated eyewitness interview skills were statistically controlled for. Her results are as follows:

One Cognitive Interview Session
# of Details % Solved Two Cognitive Interview Sessions
# of details % Solved “Business as Usual”

# of Details % Solved
10.00 100.00 56.00 118.00 24.00 93.00
28.00 105.00 39.00 110.00 38.00 79.00
37.00 111.00 61.00 105.00 23.00 82.00
31.00 94.00 28.00 120.00 29.00 92.00
15.00 95.00 28.00 111.00 32.00 78.00
15.00 76.00 23.00 96.00 19.00 89.00
12.00 88.00 46.00 126.00 24.00 89.00
25.00 77.00 46.00 96.00 33.00 109.00
34.00 99.00 50.00 133.00 36.00 100.00
33.00 99.00 47.00 130.00 27.00 101.00

Please enter the data above into SPSS to answer some of these questions below:
a. What are the categorical independent variable? (1 oint)
b. What is the dependent variable? (1 point)
c. What is the covariate? (1 point)
d. What are the null and alternative hypotheses concerning the potential effects of the treatment conditions without the covariate? (1 point)
e. What are the null and alternative hypotheses concerning the potential effects of the treatment conditions with the covariate included in the analysis? (1 point)
f. Which two groups would you compare to determine if dosage was an important consideration for use of the Cognitive Interview in the field? (1 point)
g. Run an analysis of variance with the covariate. Include the eta squared (η2) in the output, label each of the between participants conditions for credit (paste the ANOVA table results from SPSS in your hw for credit). (1 point)
h. What is your decision regarding the null hypothesis concerning the effects of the interventions (i.e., did you reject or fail to reject the null)? Explain your decision. (1 point)
i. Was this study sufficiently powered? In other words, were there enough participants in this study to warrant strong conclusions or might the sample size be a potential limitation? (show the G*Power output as part of your answer to receive credit). 1 point

 

Sample Solution

throughput. For DPS method, this pairing method only takes into account for the fast fading and not for slow fading. Thus, this method cannot achieve higher throughput. An adjustable factor has been introduced in the proposed scheme, where this adjustable factor is used to adjust the influence of SNR for each user equipment pairing. The ADPS algorithm begins with measuring the SNR value for each user equipment (UE), where this measurement is done by the base station. The channel matrix between the base station and user equipment is measured as well by the base station. Next, the first user will be selected by base station and the second user is selected from the remaining user equipment. The second user is selected by calculating the paring factor with the first user. The criterion for the second user to be selected is depending on the pairing factor. Hence, on the remaining users, the users that achieve the maximum pairing factor is chosen to be paired with the first user. However, the authors only focus on presenting the pairing algorithm with two users. Because the second user will be selected from the remaining user equipment, high computational complexity occurs when more than two users are paired. This is because a single calculation is made for each user equipment in determining the pairing factor before they can be paired.

Another study on the user pairing algorithms has been done by Chen et al. (2008). The uplinks virtual-MIMO (V-MIMO) is considered in this research work, where the double proportional fairness (D-PF) algorithm is proposed for implementing the user paring. For user pairing, the first user is selected using the proposed D-PF algorithm with proportional fairness criterion. Then, the second user is chosen, where second user is selected by implementing a modified proportional fair criterion. The performance comparison has been made between the proposed D-PF algorithm with the single PF (S-PF). The S-PF will choose the first user using the proportional fair criterion. Then, the next user is selected to maximize the overall throughput of the user pair. Tough the S-PF algorithm is able to achieve a high overall throughput, but a good fairness between users may not being offered. The scheduler on the base station will choose the group to share the time-frequency resource blocks. In this study, the author focuses only on implementing the user paring with two users for every group, hence, the number of paired users is fixed to two. However, the proposed D-PF algorithm is able to be implemented with more users per group, where the additional number of receiver antennas is needed. For the uplinks V-MIMO system, the identical resource blocks are shared by at least two users or more, in order to improve the system spectral efficiency. By comparing the performance of the proposed D-PF algorithm with the single PF (S-PF

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