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Summary ARM: lectures, workgroups and literature
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Summary Advances Research Methods: lectures, workgroups and literature
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Week 1 ........................................................................................................................................ 2Dinsdag 1 september: Lecture 1....................................................................................................... 3
Chapter 6: Wheelan ....................................................................................................................................... 17
Chapter 7: Wheelan ....................................................................................................................................... 18
Chapter 13: Wheelan ..................................................................................................................................... 19
Donderdag 3 september: Workgroup 1 ........................................................................................ 23
Vrijdag 4 september: Lecture 2 ..................................................................................................... 28
Hernan MA, Robbins JM (2020): Causal Inference Chapter 1 .................................................................... 48
Hernan MA (2002) Causal Knowledge as a Prerequisite for Confounding Evaluation ............................... 50
Week 2 ...................................................................................................................................... 52
Dinsdag 8 september: Workgroup 2.............................................................................................. 53
Donderdag 10 september: Skills group 1 ...................................................................................... 58
Vrijdag 11 september: Lecture 3 ................................................................................................... 64
Wheelan Chapter 8 ........................................................................................................................................ 85
Viera (2008): Difference and why does it matter? ........................................................................................ 86
Westreich (2013): Table 2 fallacy ................................................................................................................. 87
Week 3 ...................................................................................................................................... 88
Maandag 14 september: Workgroup 3 ......................................................................................... 89
Donderdag 17 september: Skills group 2 ...................................................................................... 95
Vrijdag 18 september: Lecture 4 ................................................................................................. 100
Green J., Thorogood N. (2004), Qualitative methods for health research .................................................. 112
Alvesson M., Karreman D. (2000), Varieties of discourse: On the study of organizations through discourse
analysis ........................................................................................................................................................ 115
Hodges B.D., Kuper, A., Reeves S. (2008), Qualitative research. Discourse analysis, British Medical
Journal ......................................................................................................................................................... 117
Sandberg J., M. Alvesson (2011), Ways of constructing research questions: gap-spotting or
problematization .......................................................................................................................................... 119
Teghtsoonian (2009). Depression and mental health in neoliberal times. .................................................. 124
Week 4 .................................................................................................................................... 125
Dinsdag 22 september: Workgroup 4.......................................................................................... 126
Donderdag 24 september: Workgroup 5 .................................................................................... 128
Vrijdag 25 september: Lecture 5 ................................................................................................. 130
Reeves S., Albert M., Kuper A., Hodges B.D. (2008), Why use theories in qualitative research? ............ 138
Reeves S., Kuper A., Hodges B.D. (2008), Qualitative research methodologies: ethnography. ................ 139
Wilson, W.J. and A. Chaddha (2009). The role of theory in ethnographic research. ................................. 141
Hallett and Barber (2013), Ethnographic research in a cyber era. .............................................................. 143
Week 5 .................................................................................................................................... 146
Dinsdag 29 september: Workgroup 6.......................................................................................... 147
Donderdag 1 oktober: Workgroup 7 ........................................................................................... 151
Vrijdag 2 oktober: Lecture 6........................................................................................................ 153
,Week 1
,Dinsdag 1 september: Lecture 1
Video 1.1
Assessing causal inference studies
- What was the question?
o What was the underlying question?
- What was actually estimated?
o Is the estimated biased or unbiased?
o Is this an estimate of a full or partial effect?
- Is the estimate really an answer to the question?
- How was the analysis designed?
- Were statistical methods applied correctly?
- What is the estimate? Is that big, small, good, bad, etc?
o How uncertain is the estimate?
- What do the researchers conclude? Is that conclusion justified?
- Is this strong or weak evidence for something?
- How does it compare with what we (thought we) knew?
What should you know about statistics?
- Descriptive statistics: mean, median, standard deviation, percentiles, proportions
- Measures of association: correlation, difference in means or proportions, relative risk,
coefficients
- Inferential statistics: standard error, p-value, confidence interval
- Statistical significance, null hypothesis significance testing
- Bivariate tests: t-test, chi-squared test
- Ordinary least squared regression (OLS, linear regression)
- Logistic regression
- What is statistical adjustment in general
- Methods for statistical adjustment
What if you don’t know these terms?
- Material from your own earlier courses
- Online module Research Methods (Canvas)
o Quantitative and qualitative research methods
- Wheelan, Naked statistics
- OLS and logistic are revisited in lectures 2 and 3
- Pratice in PC labs
- Asks questions in PC labs
Lecture 1, workgroup 1
- What is statistical adjustment?
- Methods for statistical adjustment
o Stratification, regression analysis in general
Lecture 2, workgroup 2, PC lab 1
- Ordinary least squares regression
- Null hypothesis significance testing
Lecture 3, workgroup 3, PC lab 2
- Logistic regression
- Null hypothesis significance testing
, Video 1.2: Causal theory
Quantitative research
A reductionistic perspective to the object is used; specific relationships between variables are
studied. The researcher is detached. Research questions are more often closed questions (prevalence
of a phenomenon, associations etc.). The aim is to test a hypothesis, to prove an assumption or
causality. The research process is more or less fixed; it is done in a controlled experiment with a fixed
design. Data collection is about numbers; structured observations, surveys, measurements etc. Data
analysis is presented in tables and calculated.
Causal inference
Drawing conclusions about causation or estimating causal relationships. Statistical inference uses
data to address important questions; it tells what is likely and what is unlikely. Statistical inference is
the process by which the data speak to us, enabling us to draw meaningful conclusions.
Terms used are from epidemiology. Epidemiology does not represent a body of knowledge. It is a
philosophy and methodology that can be applied to a very broad range of health problems. The art
of epidemiology is knowing when and how to apply the various strategies creatively to answer
specific health questions (how can we study what makes you ill) – may be applied to problems
outside the healthcare sector as well.
Loreal beweert dat een bepaalde foundation binnen 4 weken zorgt voor 70% minder imperfecties in
het gezicht. Dit is onderzocht binnen een onderzoek met 41 vrouwen. Conclusie: de foundation
verbetert de kwaliteit van je huid binnen 1 maand.
Het woord ‘verbetert’ impliceert dat het een causaal effect is. A leidt tot B.
Common problems with causal effects (A leads to B)
- Small sample size – whether this is a problem depends on the effect you are proving; when
the effect is very strong a small sample is enough (all study participants die after treatment),
when the effect is small a larger sample is desirable to have some certainty about what is going
on. The data sample needs to be representative of a larger group or population to have
accurate estimates. Easiest to do this to randomly select a subset of the population (every
individual should have the same chance to be in the sample).
- Study performed or financed by a commercial company – not a problem when agreements
exist on what and how the results are published
- No control group – for causal estimates you need to know what happened with the treatment,
and what would have happened without the treatment to predict what will happen
(regression to the mean problem; when having severe pain on day 1, it will probably be better
on day 10). You need the outcome of the treatment group and control group that are broadly
similar (only differ in treatment) and compare the two > try to isolate the impact of one specific
intervention.
Not interested in the outcome per se: ‘how many imperfections.’ Interested in the role of treatment
in achieving that outcome: ‘less imperfections than without true match minerals.’
Conclusion: No meaningful causal conclusion can be drawn from this study
When two outcomes differ and the only difference between two groups is the treatment, the
treatment has a causal effect (causative or preventive) on the outcome.
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