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Summary Week 6. Moving beyond the Qualitative/Quantitative dichotomy - LECTURE + WORKGROUP R87,11   Add to cart

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Summary Week 6. Moving beyond the Qualitative/Quantitative dichotomy - LECTURE + WORKGROUP

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This document contains my notes of the lecture & my notes of my workgroup meeting.

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  • November 26, 2023
  • 17
  • 2023/2024
  • Summary
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2023-2024, Block 1 GW4003MV. Advanced Research Methods


WEEK 6
Moving beyond the Qualitative /
Quantitative dichotomy
Inhoud
Lecture 6 (6 okt)................................................................................................................................................2
2. Why do quantitative research?.................................................................................................................2
Descriptive research.................................................................................................................................3
1. Description versus 3. Causal inference..................................................................................................3
Predictive research...................................................................................................................................4
2. Prediction versus 3. Causal inference....................................................................................................5
Causal inference research.........................................................................................................................5
*Two perspectives on observational studies................................................................................................6
3. Why do qualitative research?...................................................................................................................7
How do the WG’s relate to a thesis?.........................................................................................................8
4. Qualitative vs. Quantitative research........................................................................................................9
Workgroup meeting 8.....................................................................................................................................11
Homework assignment...............................................................................................................................11
Present: Quantitative case 2...................................................................................................................11
Discuss: Qualitative case 2......................................................................................................................14
Qualitative case 1....................................................................................................................................15
Quantitative case 1.................................................................................................................................16
Literature........................................................................................................................................................17




1

,2023-2024, Block 1 GW4003MV. Advanced Research Methods



Lecture 6 (6 okt)




2. Why do quantitative research?




Slide 14




Slide 16




 Based on:
Hernan et al. (2019)
A Second Chance to
get causal inference
right: a classification
of data science
tasks.

Slide 18
2

, 2023-2024, Block 1 GW4003MV. Advanced Research Methods


Distinguishing between these 3 reasons makes sense. There are crucial differences in:
- Design
- Statistical methods
- Interpretation of results
- Evaluation
- Role of theory and subject knowledge


Descriptive research
Slide 19 – 26
Goals:
 To identify patterns in data
 To obtain factual information




1. Description: Design and interpretation

There is a slight difference in using population data or using sample data. Both are summaries of
observations and/or of differences/associations.

If you use population data (e.g., election results) there is no uncertainty.

If you use sample data, there is quantify uncertainty (e.g. using confidents intervals). Technically, null-
hypothsis significance testing can be performed, but this is not very informative (and probably not aligned
with the RQ).

1. Description: Evaluation
Are the results:
 Interesting?
 Starting point for policy or further (causal) research?


1. Description versus 3. Causal inference
Description: you ruin your analysis by statistical adjustment. The results no longer make sense.
Causal inference: you may ruin your analysis by lack of statistical adjustment. The results are then biased
(confounders).
 In the PC labs, we used description to explore the data before running (adjusted) regression analysis
and draw causal inference. The study objective was not description.


3

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