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Summary SPS5034 research-design-quantitative-approaches
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lOMoARcPSD|6106013Notes Research Design Quantitative Approaches 30.10.17
Research Design (University of Glasgow)
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Quantitative Approaches: 30.10.17
Note: the class will be here (in Hunter Hall West) until the last week: November 27 th.
Study Groups: encouraged to find students to discuss your research ideas with – it can help
you become more certain about your ideas.
Course representatives: feedback from sociology department. Send any remaining feedback
to personal email which will go up on Moodle tonight.
Documenting Data:
Data can come in many forms: but often it is either CSV (comma separated values) or binary
data. CSV can be read by lots of different software. Whereas binary cannot be opened in all
software formats. But binary compresses data, so saves memory. CSV can be used to
exchange data between different programmes.
Once you start coding up your data, you also want to document them. Code book
(document detailing what different variables are about etc) a good way of doing this (the
codebook explains what all the code is about).
Best to upload data to a repository (like the Harvard Dataverse). They will keep data for at
least 30 years.
Replication script should contain a commentary, explaining what you are doing so that other
people can replicate it.
Report summary statistics for your variables. What’s the standard deviation etc. So, they
have a better understanding of what your data consists of. Wouldn’t usually be contained in
main manuscript but in an online archive.
Replication script should also contain random seed. If you use a random number generator
then different outcomes might be the result. This is important to make sure that the test
can be replicated exactly.
Include package version numbers in replication script. You should specify version numbers
for packages, you need to do everything it takes to replicate the results exactly.
Typical Steps in Quantitative Data Analyses:
You would start by cleaning the data and doing data management.
Descriptive analysis: summary statistics and relation to research question (visualisation of
variables and first hints).
Inferential Analysis. A hypothesis test of or regression model: this is basically an inferential
model where you explain the dependent variable using a few independent variables. Most
important tool in social science research.
Software:
There are commercial packages, and open-source packages.
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