1. Applied quantitative analysis a practical introduction SOSM-405 (5 cr) Session 5
Molto importante!
Tue 8-10, Period III, Jan-Feb 2018
Faculty of Social Sciences / University of Helsinki
Teemu Kemppainen

2. Contents Description  statistical inference Associations  causality
Or, what a cross-sectional regression analysis can and cannot do

3. Populations and samples in surveys
Random sample
Non-response
Data
Different sampling techniques
Statistical inference, study objectives

4. Statistical inference in a nutshell 1
Let’s consider a bivariate result
lrscale (0-5 / 6-10) & ownrdcc (ESS8)

5. Statistical inference in a nutshell 2
Is the result we see only due to sampling?
 there is nothing there in the population: random sampling just produce sometimes ”false positives”
Or is it really there in the population we study?
Technical term: null hypothesis (H0)  there is nothing there: no differences, no results
p-value (sig.)
How often does random sampling produce such a result when H0 is true?
Or, how much do we have evidence to reject H0?
Old conventions: 0.05, 0.01, … just conventions but often used!
Says nothing about the practical or substantial meaning or significance of the result  with big data you get small p-values for even very very small real differences!

6. Statistical inference in a nutshell 3
SPSS: bivariate linear regression  p <  we can easily reject H0  ”The result is out there!”
- Causality?
- Is this interesting from a practical or substantial point of view?
- Non-response?
- Correct test / regression model type?  ”OLS”, a good starting point, but good to be careful

7. On causality 1 Philosophy of science E.g. J. L. Mackie’ & INUS
Picture from: de Araújo, L. F. S., Dalgalarrondo, P., & Banzato, C. E. (2014). On the notion of causality in medicine: addressing Austin Bradford Hill and John L. Mackie. Archives of Clinical Psychiatry (São Paulo), 41(2),

8. On causality 2 Applied QUAN studies
Often observational (e.g. surveys) cf. RCT
Practical perspective: manipulation of the IV
Language: association etc. contra cause, effect, impact…
Some typically used practical criteria
Association
Temporal order (reverse causality)
No confounders
Interpretation
E.g. Bradford-Hill considerations
see Höfler, M. (2005). The Bradford Hill considerations on causality: a counterfactual perspective. Emerging themes in epidemiology, 2(1), 11.
Easy
not always clear, reciprocal effects…cross-sectional design does not help
regression helps to some extent…but cross-sectional less
regression may help + theory, reasoning

9. Causality and regression
Mediators (intermediate vars, mechanism)
Modifiers
(interaction)
X, IV, predictor
Y, DV, outcome
Confounders
(control vars, adjustments)

10. Causality and regression
Mediators (intermediate vars, mechanism)
Modifiers
(interaction)
THEORY
X, IV, predictor
Y, DV, outcome
E.g. Is Z a confounder or a mediator?
Confounders
(control vars, adjustments)