1. Applied quantitative analysis a practical introduction SOSM-405 (5 cr) Session 7
Tue 8-10, Period III, Jan-Feb 2018
Faculty of Social Sciences / University of Helsinki
Teemu Kemppainen

2. Contents
Logistic regression: Young Shin, PHD Candidate, University of Helsinki
Regression results revisited
Course objectives
What we did not talk about (but what is still important!)
Final exercises

3. Interpretation of regression results
OLS, logistic etc…the basic logic is the same
Main effect
”effect”…no causality implied!
Cf. interaction
One unit increase in the IV  what happens with the DV?
OLS: DV increases/decreases by ’B’ units
On average, not for all cases  residuals
Dummies, continuous IV’s…the same idea
Logistic: the odds of ”an event” are higher or lower (OR)
Other variables kept constant (controlled, adjusted)

4. Example: OLS and logistic

5. Example continues: education as the main IV (’eisced’ recoded)

6. OLC: crude and adjusted

7. Logistic: crude and adjusted
”OR”
Key question in OLS: is B different from 0?
Key question in logistic: is OR different from 1?

8. Logistic regression example: CAM study
Kemppainen L, Kemppainen T, Skogberg N, Kuusio H & Koponen P (2017): Immigrants’ use of healthcare in their country of origin: The role of social integration, discrimination and the parallel use of healthcare systems. Scandinavian Journal of Caring Sciences, Online 4 SEP 2017, DOI: /scs.12499

9. OLS example: Weckroth et al. 2017
Weckroth M, Kemppainen T & Dorling D (2017). Socio-economic stratification of life satisfaction in Ireland during an economic recession: A repeated cross-sectional study using the European Social Survey. Irish Journal of Sociology. DOI: /

10. So…our course objectives were:
To gain an overview of QUAN research process and key concepts
To get familiar with the basic techniques and logic of cross-sectional data analysis
To gain (even) more confidence to attend more advanced courses and to engage in QUAN or mixed methods research in thesis

11. What we did not talk about (that much)
Statistical details and formulas
Interactions
Weighting
Sampling designs
Imputation
Clustered / hierarchical data, multilevel analysis
Register data
Qualitative analysis
Longitudinal analysis
And many other methods, e.g. MCA, LCA/clustering
You’ll find more information on these later if you need: other courses, books, internet resources etc.

12. Final exercises
More extensive: it may take some days to do them. It’s fair (5 credits) …and good for learning
Nothing really new: just apply what you have learned
Please, read carefully the instructions on Learning Diary and all exercises
I’ll publish the exercises tomorrow
Final DL Sun 11th March
Pass / Corrections / Fail
Max one week late: lots of extra work
More than one week: I cannot promise anything, probably fail
Ps. Don’t mind the page limit in the original instructions (20-30 pages…my mistake).

13. Questions?
Thank you so much!