Removing the individual differences can substantially reduce variance and lower the standard error. However, this benefit only occurs if the individual differences are consistent across treatment conditions. Similarly , the last two participants consistently had the lowest scores in both treatments. To construct the following data, we started with data and climinated the consistency of the individual differences. For example, the first participant now has the lowest score in treatment 1 but the highest score in treatment 2. a. Assume that the data are from an independent measures study using two separate samples, each with n = 6 participants. Compute the pooled variance and the estimated standard error and the estimated standard error for the mean difference. b. Now assume that the data are from a repeated-measures study using the same sample of n=6 participants in both treatments conditions. Compute the variance for the sample of difference scores and the estimated standard error for the mean difference. (This time you should find that removing the individual differences does not reduce the variance or the standard error.) Treatment 1 Treatment 2 Difference 5 13 8 7 12 8 10 2 6 10 4 12 6 -6 10 9. -1 M = 8 SS = 34 M = 10 SS = 30 Mp = 2 SS= 120
Removing the individual differences can substantially reduce variance and lower the standard error. However, this benefit only occurs if the individual differences are consistent across treatment conditions. Similarly , the last two participants consistently had the lowest scores in both treatments. To construct the following data, we started with data and climinated the consistency of the individual differences. For example, the first participant now has the lowest score in treatment 1 but the highest score in treatment 2. a. Assume that the data are from an independent measures study using two separate samples, each with n = 6 participants. Compute the pooled variance and the estimated standard error and the estimated standard error for the mean difference. b. Now assume that the data are from a repeated-measures study using the same sample of n=6 participants in both treatments conditions. Compute the variance for the sample of difference scores and the estimated standard error for the mean difference. (This time you should find that removing the individual differences does not reduce the variance or the standard error.) Treatment 1 Treatment 2 Difference 5 13 8 7 12 8 10 2 6 10 4 12 6 -6 10 9. -1 M = 8 SS = 34 M = 10 SS = 30 Mp = 2 SS= 120
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
Related questions
Topic Video
Question
100%
Transcribed Image Text:Removing the individual differences can substantially reduce variance and lower
the standard error. However, this benefit only occurs if the individual differences
are consistent across treatment conditions. Similarly , the last two participants
consistently had the lowest scores in both treatments. To construct the following
data, we started with data and eliminated the consistency of the individual
differences. For example, the first participant now has the lowest score in
treatment 1 but the highest score in treatment 2.
a. Assume that the data are from an independent measures study using two
separate samples, each with n = 6 participants. Compute the pooled
variance and the estimated standard error and the estimated standard error
for the mean difference.
b. Now assume that the data are from a repeated-measures study using the
same sample ofn = 6 participants in both treatments conditions. Compute
the variance for the sample of difference scores and the estimated standard
error for the mean difference. (This time you should find that removing the
individual differences does not reduce the variance or the standard error.)
Treatment 1 Treatment 2 Difference
13
7
12
8
10
10
4
12
6
-6
10
9.
-1
МM- 10
SS = 30
M = 8
Mp = 2
SS = 34
SS= 120
Expert Solution
This question has been solved!
Explore an expertly crafted, step-by-step solution for a thorough understanding of key concepts.
This is a popular solution!
Trending now
This is a popular solution!
Step by step
Solved in 2 steps with 4 images
Knowledge Booster
Learn more about
Need a deep-dive on the concept behind this application? Look no further. Learn more about this topic, statistics and related others by exploring similar questions and additional content below.Recommended textbooks for you
MATLAB: An Introduction with Applications
Statistics
ISBN:
9781119256830
Author:
Amos Gilat
Publisher:
John Wiley & Sons Inc
Probability and Statistics for Engineering and th…
Statistics
ISBN:
9781305251809
Author:
Jay L. Devore
Publisher:
Cengage Learning
Statistics for The Behavioral Sciences (MindTap C…
Statistics
ISBN:
9781305504912
Author:
Frederick J Gravetter, Larry B. Wallnau
Publisher:
Cengage Learning
MATLAB: An Introduction with Applications
Statistics
ISBN:
9781119256830
Author:
Amos Gilat
Publisher:
John Wiley & Sons Inc
Probability and Statistics for Engineering and th…
Statistics
ISBN:
9781305251809
Author:
Jay L. Devore
Publisher:
Cengage Learning
Statistics for The Behavioral Sciences (MindTap C…
Statistics
ISBN:
9781305504912
Author:
Frederick J Gravetter, Larry B. Wallnau
Publisher:
Cengage Learning
Elementary Statistics: Picturing the World (7th E…
Statistics
ISBN:
9780134683416
Author:
Ron Larson, Betsy Farber
Publisher:
PEARSON
The Basic Practice of Statistics
Statistics
ISBN:
9781319042578
Author:
David S. Moore, William I. Notz, Michael A. Fligner
Publisher:
W. H. Freeman
Introduction to the Practice of Statistics
Statistics
ISBN:
9781319013387
Author:
David S. Moore, George P. McCabe, Bruce A. Craig
Publisher:
W. H. Freeman