Notes Introduction to Quantitative Methods (PUBL055)
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Course
Introduction to Quantitative Methods (PUBL055)
Institution
University College London (UCL)
Summary of lectures and readings for the Introduction to Quantitative Methods (PUBL055) course at UCL.
Includes revision notes on Rstudio, regression, causal inference, counterfactuals, potential outcomes, difference in difference designs, standard errors, p-values, confidence intervals, etc.
Courier font for screenreg!!
Add fully completed essay front cover, including word count.
Use same formatting as saved mock style.
Submit as PDF file
Use candidate number as ‘submission title’
1500-word limit (excluding tables and code) - Figures, tables (including regression tables),
and table / figure titles will not be counted in the word limit.
Double check Turnitin similarity report
Write in full sentences, not bullet points
present your analyses and the output of all statistical tests in a clear and readable
format.
do not include screenshots from R.
Please use screenreg or make a table in Word where relevant
make sure your code is well annotated.
If you are asked to answer a policy-relevant question, you should not simply report a
difference in means without commenting on the substance.
You can use R to answer any question where you think it might be useful.
The annotated code in the appendix will also not be included in the word count. - figure
out/ ask how to do this.
Note that figure titles can be included in Word above the graph or as a title on the plot
itself (using the main option) - either option will be fine.
In mock answers – says ‘here, I made a box plot, and labelled x axis...’ - do we need to do
this. - also, ‘I have pasted in the output from screen reg...’
Should we include appendix of our code?
- Description, prediction and
causality
Week 1
- research design
- R basics
Observational and experimental
studies
The logic of counterfactuals
Week 2 Causality
randomization
Confounding
difference-in-differences
, Describing
- Descriptive statistic
Week 3 quantitative data
- visualizing data
“Measurement”
- Prediction using quantitative
Regression I data
Week 4
(Prediction) - simple linear regression
- multiple linear regression
- Modelling non-linear
Regression II
relationships
Week 5 (Model
- Interaction terms
specification)
- Statistics for model fit
Week 1
Objects
Vectors – a set of information e.g., 0 1 13 14 56
Subsetting a vector e.g., my_first_vector[c,(1,3,4)]
Functions
Arguments – inputs to functions
mean(), median(), max(), min(), sum(), +, -, /, *, sqrt()
13^2 – square function
sum(1:100) - adds up all numbers from 1 to 100
data.frame - object that holds data in a tabular format
data(name_of_dataset) - to look at dataset already in R
View(name_of_dataset) - to view dataset as separate spreadsheet
$ - subsetting - access individual column of a data.frame - name_of_dataset$name_of_column
[,] - to subset a row and column – if [10, ] - subsets entire 10 th row
longley[c(4, 7), ] # elements in 4th and 7th row, all columns
Logical values – TRUE, FALSE
Logical operators - >, <, ==, !=
head()
Week 2
Setting working directory - setwd("~/Desktop/PUBL0055")
Loading data - transphobia <- read.csv("data/transphobia.csv")
Variables
head(transphobia) – shows the first six (by default) rows of the data
str(transphobia) – shows the “structure” of the data
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