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 teemu.t.kemppainen@helsinki.fi https://teemunsivu.wordpress.com/applied-quantitative-analysis/

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

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)

Example: OLS and logistic

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

OLC: crude and adjusted

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

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: 10.1111/scs.12499

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: 10.1177/0791603517697326

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

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.

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).

Questions? Thank you so much!