辅导案例-FTTZ1
FTTZ1
by Zi Wang
Submission date: 13-Jan-2020 02:09AM (UTC+0000)
Submission ID: 118087904
File name: FTTZ1_1568757_1235762862.docx (134.43K)
Word count: 3384
Character count: 19221

Q1.b causal
1
2
3CI
4

R2
Screenreg
Q2-e
Randomisation
5Changing significance
67
8
Good!
Statistical significance
Good!
Confounding example
9
Statistical significance
Causal
Confounders
11
12
13
7b treatment effects
14
Missing Intro Univariate stats
Nice Plots
Plot - labels
Explanatory variables
15
16
Missing statistical significance
Missing R2
17
18


45%
SIMILARITY INDEX
3%
INTERNET SOURCES
0%
PUBLICATIONS
45%
STUDENT PAPERS
1 44%
2 <1%
3 <1%
4 <1%
5 <1%
Exclude quotes On
Exclude bibliography On
Exclude matches Off
FTTZ1
ORIGINALITY REPORT
PRIMARY SOURCES
Submitted to University College London
Student Paper
Submitted to University of Southampton
Student Paper
www.economicdynamics.org
Internet Source
bitsavers.trailing-edge.com
Internet Source
Submitted to University of Minnesota System
Student Paper
QM
QM
FINAL GRADE
44/100
FTTZ1
GRADEMARK REPORT
GENERAL COMMENTS
Instructor
PAGE 1
PAGE 2
Q1.b causal
To assess whether this is a causal effect, you should consider if there are confounding factors or
differences between treatment and control classes other than their treatment group. Because this
experiment involved random assignment, we expect treatment and control classes to be the same,
on average, regarding all characteristic, and hence we could take the difference in means as an
unbiased estimator of the average treatment effect.
Comment 1
this is not correct. check the material for week 7 for the correct formula
Comment 2
because of your answer to (c), this t-statistic is also incorrect.
however, you show you understand how to interpret it.
PAGE 3
Comment 3
same as (d)
CI
If we took 100 samples of the same size and then calculate their CIs, 95 of them would include the
QM
QM
QM
QM
QM
true average treatment effect.
Comment 4
it should be approximately normal, why?
PAGE 4
PAGE 5
R2
This question mainly asks about R2, which refers to the percentage of DV's variation been explained
by your model
PAGE 6
Screenreg
You can use screenreg to generate a table for all the models
Q2-e
For this question, think about the experimental design in this case, and how does a randomised
experiment can speak to the potential confounding concerns. Try a t-test of the pre.score for
control/treatment group and see if it's significantly different.
Randomisation
In this case, there is actually no serious confounding concern (although grade and pre.score
correlate with post.score) because the randomisation means that these variables are uncorrelated
with the treatment by construction thereby ruling out any omitted variable bias in expectation.
Therefore, even though the three models shows small differences in the treatment effect, there is not
evidence of significant confounding with respect to pre.score as evidenced by the large p-value on
the t.test.
PAGE 7
Comment 5
missing output table
Changing significance
how about the change of statistical significance levels for these coefficients?
PAGE 8
QM
QM
QM
QM
QM
QM
Comment 6
by how much and is it statistically significant?
Comment 7
by how much?
Comment 8
it would be better if you make your argument with statistical evidence from the table
PAGE 9
Good!
Good!
Statistical significance
Is this significant at 95% confidence level?
PAGE 10
Good!
Good!
Confounding example
Democrats are more likely to win state legislative offices in states where they also hold governorship,
and Democrat control of these offices may also influence downstream policy outcomes.
Comment 9
...controlling for whether the Democrats control the House and the Senate
Statistical significance
Is this significant at 95% confidence level?
PAGE 11
Strikethrough.
Causal
We would have to assume that we have controlled for all possible confounding variables to interpret
QM
QM
QM
QM
We would have to assume that we have controlled for all possible confounding variables to interpret
the coefficients causally.
Confounders
Confounders are likely to be correlated both with the presence of a Democratic govern and with the
four policy outcomes.
PAGE 12
Comment 11
you need to discuss more the assumptions behind the RDD, the key thing is that the treatment is as
good as random. The main weakness is low external validity
Comment 12
the numbers are correct but they are the other way around: all the outcomes are lower for democrats
than republican
Comment 13
you need to plot the two ranges of fitted values separately for dem and rep, the ones you used to find
the treatment effects above
PAGE 13
7b treatment effects
The treatment effects are:
-Unemployment: -0.06
-Murder: -0.3
-1% income: -0.12
-House prices: -0.14
Comment 14
They are actually very small
PAGE 14
Missing Intro
You should have provided a brief introduction explaining the aims and hypotheses of the study.
Univariate stats
QM
QM
QM
QM
QM
It would be useful to provide some univariate descriptive stats of all variables included in the
analysis.
E.g. mean & standard deviation of numerical variables, % of observations in each level of categorical
variables.
Nice Plots
Nice plots
Plot - labels
use appropriate labels for the X and Y axes
Explanatory variables
It would be useful to explain why you selected these explanatory variables and how they might
influence the relationship between the Catholic % of the population and NSDAP vote share.
PAGE 15
Comment 15
You should describe the actual coefficient of Catholic population and
also the intercept of the model.
Comment 16
You should have compared the coefficient of Catholic population in Model 1 with that found in Model
2.

Do the additional explanatory variables confound the relationship between Catholic population and
Nazi vote?
Missing statistical significance
You did not discuss the statistical significance of the regression results.
Missing R2
You did not discuss the R2 of the models.
PAGE 16
Comment 17
These estimates cannot be causally identified owing to omitted variable bias resulting from the
observational design of the study.
You should discuss the issue of omitted variable bias in more detail and highlight the limitations of
study.
Comment 18
You should consider alternative analytical approaches that would help to strengthen causal inference
such as RDD and fixed-effects regression.
PAGE 17
PAGE 18
RUBRIC: POLITICAL SCIENCE - DEFAULT
CRITERION 1
MORE WORK
NEEDED (TICK)
CRITERION 2
MORE WORK
NEEDED (TICK)
CRITERION 3
MORE WORK
NEEDED (TICK)
CRITERION 4
MORE WORK
NEEDED (TICK)
CRITERION 5
MORE WORK
NEEDED (TICK)
CRITERION 6
MORE WORK
NEEDED (TICK)
CRITERION 7
MORE WORK
NEEDED (TICK)
CRITERION 8

Structure and organisation
Development and consistency of argument
Extent of research work completed
Use of evidence
Evidence of critical analysis
Overall insight and originality
Adequacy and presentation of bibliographic information
MORE WORK
NEEDED (TICK)
CRITERION 9
MORE WORK
NEEDED (TICK)
Accuracy and appropriateness of referencing
Overall presentation
51作业君 51作业君

Email:51zuoyejun

@gmail.com

添加客服微信: IT_51zuoyejun