HCM Advanced Research Methods part 3: Combining methods (lectures/working groups)
29 views 3 purchases
Course
Advanced Research Methods (GW4003MV)
Institution
Erasmus Universiteit Rotterdam (EUR)
Notes of the lectures and working groups from the third part of the course: advanced research methods. In this part the quantitative and qualitative methods are combined.
My exam grade: 7.8
Master Healthcare Management.
Combining quantitative and qualitative methods – Advanced Research Methods – Joyce Rommens
Lecture 6: Synthesis: quantitative and qualitative methods
Quantitative part
Objectives segment 1:
- Explain the three different goals for quantitative research (associations)
- Explain how the strategy for descriptive research differs from the strategy for causal inference
1960-2010: methodological development focused on statistical methods
- Development of new techniques
- Improvements in computers and software
- Standardized tests, ‘objectivity’
- Helpful and harmful ® we can do more and better research, but people stopped thinking
themselves and rely on the software
® Associations can be seen in the data, but it is nothing more. What do they mean is up to the
researcher to determine.
Newer developments (not black and white)
- Causal theory
- What should be part of quantitative analysis?
- Interpretation: meaning of results depends on context
The largest part of a quantitative analysis is not about numbers.
Three possible aims to investigate associations:
- Causal inference
- Prediction
- Description
Distinguishing them makes sense, they have crucial differences in design, statistical methods,
interpretation, evaluation, role for theory/subject knowledge.
Causal inference Prediction Description
Goal Find causal effects Predict the future (or the past, or Describe patterns
- Counterfactual the current) - Identify patterns in
prediction: what if - Given what you observe the data
- Not only about what is, - No if - Matter of fact, goal
but also about what - What, given in itself
could be If you know A/B/C, what can you ® Potential starting point
say about D? for policy or further
(causal) research
Example 1. How does Netflix know what Excess mortality due to
films I like? ® watching one film coronavirus in different
makes it more likely that you will countries
like the other one too
2. Diagnosis: recognizing a
disease by the symptoms (reverse
causality a problem? ® no
because this is not about causal
inference)
1
, Combining quantitative and qualitative methods – Advanced Research Methods – Joyce Rommens
Research Why do some groups put more What kind of people will want Which groups are less open
questions value on screening than others: screening in the future? to screening?
causal inference (mediation
analysis) Background: What should the Background: In which
screening capacity in different groups could extra
Background: areas be? promotion be important?
development/testing of theory,
role of culture
Methods 1. Theory Driven 1. May be data driven: try what Bivariate associations:
2. DAG’s (exchangeability) works - Proportions/means
3. To block backdoor paths 2. Regression analysis can be /ratios per group
(randomisation, regression, used ® equation can be used for - Continuous
stratification, predictions on the individual level independent
weighting/matching) (with the first dataset, you variable ®
4. Consider blocking causal develop the regression equation correlation
paths (mediation) to predict outcomes in the coefficient
second dataset) (outdated, not
® sophisticated methods for intuitive); corm
regression may be required categories of
(prevent overfitting, the equation continuous
fits a certain dataset so well that independent
it is less suited for another variable; regression
dataset) with one
independent
No randomisation ® you do not variable
want to interfere.
No stratification ® will probably
not lead to precise predictions.
No weighting/matching ® would
require defining one exposure
Adjustment Yes! Otherwise, the results are No! This would obscure,
s affected by confounding bias remove, or increase the
due to a lack of exchangeability associations.
Results have no
interpretation anymore.
Interpretati Results have intrinsic meaning: Usually, no interest in 1. Direct, intuitive
on - Coeff represents interpretation of coeff for interpretation of
estimates of the individual predictors. Not useful: comparison of
(average) effect test whether there is an means/proportions/ratios
- Coeff in OLS association. 2. OLS coefficient
- Adjusted proportions - No clear intrinsic meaning
(average adjusted - No causal interpretation
predicted probabilities) - Coeff: ‘people with A were more
- RR, RD likely to have B/higher B, given all
- How strong is the other variables’
association?
CI Performance is crucial: evaluation
P-value may play a role of the model.
2
The benefits of buying summaries with Stuvia:
Guaranteed quality through customer reviews
Stuvia customers have reviewed more than 700,000 summaries. This how you know that you are buying the best documents.
Quick and easy check-out
You can quickly pay through credit card or Stuvia-credit for the summaries. There is no membership needed.
Focus on what matters
Your fellow students write the study notes themselves, which is why the documents are always reliable and up-to-date. This ensures you quickly get to the core!
Frequently asked questions
What do I get when I buy this document?
You get a PDF, available immediately after your purchase. The purchased document is accessible anytime, anywhere and indefinitely through your profile.
Satisfaction guarantee: how does it work?
Our satisfaction guarantee ensures that you always find a study document that suits you well. You fill out a form, and our customer service team takes care of the rest.
Who am I buying these notes from?
Stuvia is a marketplace, so you are not buying this document from us, but from seller joycerommens. Stuvia facilitates payment to the seller.
Will I be stuck with a subscription?
No, you only buy these notes for $5.93. You're not tied to anything after your purchase.
