Description
this would be the statitiscs homework I have to do, please follow instructions in the documents attached. these are important homework that is large portion of my grades, so please do your best job
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U3.2 DA
Answer the following questions on your own paper and upload your work into Canvas. Be sure to
write your name on your answer sheet, number your answers, show your work, organize your
work in a logical and readable order, and upload a single submission (if you have multiple pages
of work, take pictures of your work and copy/paste/drop into a single document).
Researchers investigated students’ attitudes about the use of “Content Warnings” on sensitive
lecture content*. Researchers put students into one of three groups: no CW, low CW, and high
CW. In the low CW group, students received only one CW while those in the high CW received
four CWs. The lecture material was identical for all three groups.
Students rated their attitudes about the use of CWs on a scale of 1 – 7, where higher values
indicate better attitudes.
*THIS IS AN ENTIRELY FICTIONAL STUDY. ALL DATA ARE FICTITIOUS.
Use the tutorial posted in Canvas to see how to run the analysis using VassarStats. Then, answer
the questions below.
Data:
No CW
2
5
4
4
2
3
1
2
5
4
3
3
Low CW
6
5
4
5
6
4
3
4
3
5
2
6
High CW
1
4
3
2
4
3
3
3
3
1
2
1
1. Review the output. List the means for each condition. (Hint: be sure to label the means so
that your reader knows which mean goes with which condition.)
2. Review the ANOVA Summary table. Is there a significant effect for the CWs on students’
attitudes? (Hint: look at P in the table. If it is less than .05 then the researcher would
reject the null hypothesis. If it is greater than .05 then the researcher would retain the
null hypothesis.)
3. The result of the F-test tells you whether or not there’s an effect. Post hoc tests are
necessary if the researcher rejects the null hypothesis with more than two groups. Post
hoc comparisons identify the pairs of means that are statistically different from one
another. Which pairs of conditions were statistically different? (Hint: look at the Tukey
HSD Test table.)
4. Based on the F-test and post hoc tests, which condition produced the best attitude
toward CWs? Which condition produced the worst attitude toward CWs? (Hint: You may
have to look back at the means to determine this.)
This is a friendly reminder that this study and the data listed above are entirely fictitious and do
not originate from any real study (that I know of).
U3.2 HW
Due 11/14/2021 by 11:59 p.m. in Canvas
Answer the following questions on your own paper and upload your work into Canvas. Be sure to
write your name on your answer sheet, number your answers, show your work, organize your
work in a logical and readable order, and upload a single submission (if you have multiple pages
of work, take pictures of your work and copy/paste/drop into a single document).
The following data summarize the result from an independent-measures study comparing three
treatment conditions.
TX1
n=8
M=1
T=6
SS = 32
TX2
n=8
M=5
T = 32
SS = 35
TX3
n=8
M=6
T = 36
SS = 39
N = 24
G = 74
Use ⍺ = .01 to determine whether there are differences between conditions.
1. Step 1
a. State the hypotheses in symbols NOT words.
2. Step 2
a. Go to vassarstats.net. Select Distributions | F-Distributions | enter dfbetween in the
first prompt and dwithin in the second prompt.
3. Step 3
a. Calculate MSbetween
b. Calculate MSwithin
c. Calculate the test statistic.
i. If you need to find the formulas for these tests, view the formula sheet in
the Course Resources button on the landing page of Canvas.
4. Step 4
5. Step 5
a. Calculate effect size.
b. Interpret effect size: is there no effect size, a small effect size, a moderate effect
size, or a large effect size?
c. Determine if you made an inferential error in step 4.
6. Step 6
a. If it’s warranted, calculate Tukey’s HSD.
i. Go to vassarstats.net. Select Utilities | Statistical Table Calculator | Critical
Values of Q | enter k and dfwithin into prompts. Choose q for the original
alpha.
b. Calculate all pairwise comparisons.
7. Are the results sufficient to conclude that there is a difference between groups and if so,
which ones?
U3.3 DA
Answer the following questions on your own paper and upload your work into Canvas. Be sure to
write your name on your answer sheet, number your answers, show your work, organize your
work in a logical and readable order, and upload a single submission (if you have multiple pages
of work, take pictures of your work and copy/paste/drop into a single document).
Description:
This data set provides heart rates of male and female runners and generally sedentary
participants following 6 minutes of exercise.
Quasi-Independent Variables:
• Gender – Participant’s gender (Female, Male).
• Group – Group of ‘Runners’ (averaging more than 15 miles per week) and ‘Control’ group
(generally sedentary participant).
Dependent Variable:
• Heart Rate – Heart rate after six minutes of exercise.
The output below demonstrates the use of a 2 x 2 between subjects ANOVA. Specifically, it tests
whether heart rates differ between gender and groups.
ANOVA:
The output below reflects the results of a two-way ANOVA of differences in heart rates between
Males and Females, Runners and Controls and their interaction. All terms are statistically
significant
Cases
df
Mean Square
F
p
η²
Gender
Group
1
1
45030.005
168432.080
185.980
695.647
< .001
< .001
0.110
0.413
Gender x Group
1
1794.005
7.409
0.007
0.004
Descriptives
Heart Rate
Gender
Female
Male
Group
Control
Runners
Control
Runners
Mean
148.000
115.985
130.000
103.975
SD
16.271
15.972
17.100
12.499
N
200
200
200
200
Descriptive plots
Marginal Means
Marginal Means - Gender
95% CI for Mean Difference
Gender
Marginal Mean
Lower
Upper
SE
Female
131.992
130.465
133.520
0.778
Male
116.987
115.460
118.515
0.778
Marginal Means - Group
95% CI for Mean Difference
Group
Marginal Mean
Lower
Upper
SE
Control
139.000
137.473
140.527
0.778
Runners
109.980
108.453
111.507
0.778
The above plot shows the results. There is a clear difference between the Control group and the
group of Runners, both for Females and Males. Women appear to have a higher heart rate than
men. The fact that the two lines are not exactly parallel signifies the interaction effect, which
suggests that the differences between the Males and Females is larger in the Control group than
in the group of Runners.
In the narrated PPT and in class, we’ve defined main effects as a difference in marginal means in
a 2 x 2 table. You have all the information in this output to complete a similar 2 x 2 table.
1. Complete this 2 x 2 table using the relevant information from the output (you do NOT
need to do any calculations). The white cells of the table should contain the means for
the variable pairs (e.g., the mean for Control, Female; the mean for Control, Male; etc.).
The gray cells should contain the marginal means for the QIVs.
Gender
Females
Males
Marginal
Means for
Group
Control
Group
Runners
Marginal Means for Gender
In the narrated PPT and in class, we’ve defined interactions as non-parallel lines in a graph. You
have all the information in this output to complete a similar graph.
2. Complete this graph using the relevant information from the output (or the table you
completed in #1) using the x-axis and legend included on the graph. The y-axis is
incomplete. You’ll need to add the heart rate values to the y-axis. (Note: Be careful! The
graph below is NOT just a duplication of the graph in the output!)
References:
Moore, D. S., McCabe, G. P., and Craig, B. A. (2012). Introduction to the Practice of Statistics (7th
ed.). New York: Freeman.
Wood, P.D, Haskell, W. L., Stern, M. P., Lewis, S. and Perry, C. (1977). Plasma lipoprotein
distributions in male and female runners. Annals of the New York Academy of Sciences, 301: 748763.
U3.3 HW
Answer the following questions on your own paper and upload your work into Canvas. Be sure to
write your name on your answer sheet, number your answers, show your work, organize your
work in a logical and readable order, and upload a single submission (if you have multiple pages
of work, take pictures of your work and copy/paste/drop into a single document).
Repeated-Measures ANOVA
1. The repeated-measures ANOVA can be viewed as a two-stage (two-parsing) process.
a. How is the first parsing in the repeated-measures ANOVA alike or different from
the first parsing in a one-way ANOVA?
b. How is the within-groups variance further parsed in the repeated-measures
ANOVA?
c. Name the source of variance in which the variance due to individual differences is
eliminated by virtue of the repeated-measures design.
d. Name the source of variance in which the variance due to individual differences is
removed mathematically.
Factorial ANOVA
2. The following matrix presents the results from an independent-samples, factorial ANOVA
with a sample of n = 10 in each treatment condition. Note that one treatment mean is
missing.
a.
b.
c.
d.
How many total participants were there in this study?
What value for the missing mean would result in no main effect for factor A?
What value for the missing mean would result in no main effect for factor B?
What value for the missing mean would result in no interaction?
U3.4 DA
Answer the following questions on your own paper and upload your work into Canvas. Be sure to
write your name on your answer sheet, number your answers, show your work, organize your
work in a logical and readable order, and upload a single submission (if you have multiple pages
of work, take pictures of your work and copy/paste/drop into a single document).
A researcher is investigating the relationship between shyness and salary*. The researcher
measures shyness (in which higher values on the measure indicate greater shyness) and salary
for a group of employees.*This is a fictional study with fictitious data.
Use the tutorial posted in Canvas to see how to run the analysis using VassarStats. Then, answer
the questions below.
Data:
Shyness
53
27
34
22
57
33
43
27
33
45
38
41
Salary
35500
40000
39000
41500
40500
42500
41000
45500
42500
41500
38500
39500
1. Review the output. List the means for each variable. (Hint: be sure to label the means so
that your reader knows which mean goes with which variable.)
2. Review the r table.
a. Find r among the Data Summary output. What is the value for r?
b. What is p if this had been a one-tailed test? What is p if this had been a two-tailed
test?
c. Remember that the threshold for rejecting/retaining is p ≤ .05. Should the
researcher reject or retain the null hypothesis if it had been a one-tailed test?
Should the research reject or retain the null hypothesis if it had been a two-tailed
test?
3. With the results of the study, is there a significant relationship between shyness and
salary if the test is a one-tailed test? if the test is a two-tailed test?
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