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Research Skills Notes to get a 10 on the Final!!

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This is a summary with notes from all lectures, the readings and other sources (supplementary materials and own research) combined together to give you all the knowledge you need to get the best possible grade for the Research Skills final exam. The document is well organised and has a table of contents so you can quickly find the content you need. Save hours of work by downloading this summary!

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November 5, 2020
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Written in
2019/2020
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RESEARCH SKILLS 2
GEO1-2415

,Table of Contents
Lecture 8: Quantitative data analysis, comparing two groups ........................................... 3
Data types .................................................................................................................................. 3
Normality ................................................................................................................................... 4
Testing for differences between two groups ............................................................................... 4
Student’s T-test: ......................................................................................................................... 5
Mann-Whitney U test ................................................................................................................. 7
Chi-squared test ......................................................................................................................... 7
Lecture 9: Quantitative data analysis, correlation and regression ...................................... 8
What is correlation? ................................................................................................................... 8
Measuring the relationship between two variables .................................................................... 9
Pearson’s R ................................................................................................................................. 9
Spearman’s rank correlation (ρ) ............................................................................................... 10
Regression: ............................................................................................................................... 11
Lecture 10: Qualitative Data Analysis .............................................................................. 12
Induction/Deduction ................................................................................................................ 12
Grounded Theory (GT) ............................................................................................................. 14
Analytical Induction (AI) ........................................................................................................... 17
Thematic Analysis (TA) ............................................................................................................. 18
Narrative Analysis (NA) ............................................................................................................ 19
Abductive Analysis (AA)............................................................................................................ 20
Narrative Analysis (NA) ............................................................................................................ 20
Lecture 11: Interdisciplinarity and mixed-methods .......................................................... 21
Philosophy of science ............................................................................................................... 21
Ontology: ................................................................................................................................. 22
Epistomology............................................................................................................................ 23
Methodology:........................................................................................................................... 23
Lecture 12: Reporting and Quality Criteria ....................................................................... 24
Classic structure ....................................................................................................................... 24
Quantitative researchers .......................................................................................................... 25
Qualitative researchers ............................................................................................................ 27
Underlying principles in all research ......................................................................................... 28
Causation and Making good arguments .................................................................................... 28
Identifying causal mechanisms ................................................................................................. 30
Writing and making arguments ................................................................................................ 31


1

,Lecture 13: Philosophy of Science .................................................................................... 32
Epistemology:........................................................................................................................... 33
Ontology: ................................................................................................................................. 33
Paradigms ................................................................................................................................ 34
Implications.............................................................................................................................. 35
Normative research .................................................................................................................. 36




2

, Lecture 8: Quantitative data analysis, comparing two groups




Data types

Nominal data (just names, no relationship across names):
• Did you study for the exam? Answer: Yes/No
• What is your hair color? Hair color: red, blond, brown, black
Ordinal data (puts results on order, but no specific distance):
• How much did you study for the exam?
o Very little
o Little
o A lot
o Really a lot
Scale data (results on a numeric scale of measurement):
• How tall are you?
o Can be any number measured in [cm], or [m]
o Units of scale variables: it is crucial that the units are consistent within and
across measurements




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