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

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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

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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

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true average treatment effect.

Comment 4

it should be approximately normal, why?

PAGE 4

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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

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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

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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

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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.

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RUBRIC: POLITICAL SCIENCE - DEFAULT

CRITERION 1

MORE WORK

NEEDED (TICK)

CRITERION 2

MORE WORK

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CRITERION 3

MORE WORK

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CRITERION 4

MORE WORK

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CRITERION 5

MORE WORK

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CRITERION 6

MORE WORK

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CRITERION 7

MORE WORK

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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