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Research Design: Week 7- 30/10/17
Quantitative Approaches
Slide 2: Venue
October 30: Hunter Hall West, Gilbert Scott Main Building.
November 6: Hunter Hall West, Gilbert Scott Main Building.
November 13: Hunter Hall West, Gilbert Scott Building.
November 20: Hunter Hall West, Gilbert Scott Building.
November 27: Hilton.
Slide 3: Documenting Data
Data Formats: CSV vs. Binary.
Use a codebook for documenting variables and choices.
Write replication script; use comments to annotate it.
Upload replication archive to a data repository, e.g. Harvard Dataverse.
Report summary stats.
Use a random seed in replication script.
Include package version numbers in replication script.
Slide 4: Typical steps in quantitative data analysis
1. Data cleaning and management.
2. Descriptive analysis:
Summary statistics.
Visualisation of relevant variables or associations.
3. Inferential analysis:
Hypothesis test.
Regression model.
Slide 5: Software for quantitative data analysis
Commercial statistical software:
1. Excel.
2. SPSS.
3. Stata.
4. SAS.
5. EViews.
Open source statistical software:
1. R (slow but very versatile and lots of packages).
2. Julia (fast but limited user base and few packages).
3. Python (slow but general-purpose language).
Slide 6: Properties of the R statistical programming language
Free to use. Just download from www.r-project.org
Good editor for getting started: RStudio.
Many tools for increasing reproducibility.
Good documentation and help forums.
Tons of packages with extensions.
Can be connected with faster languages for time-critical analyses, such as CC+ and
Java.
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