Survey research and design in psychology/Lectures/Correlation

Outline
This lecture overviews non-parametric and parametric approaches to (bivariate) measures of association (dependence), i.e., correlational statistics and graphing. The lecture is accompanied by a computer-based tutorial.

This lecture explains:
 * 1) The purpose of correlation (what types of question(s) are we trying to answer?)
 * 2) Nature of covariation (what does it mean if two variables covary or “vary together”?)
 * 3) Correlational analyses
 * 4) Types of answers – What can we conclude?
 * 5) Types of correlation – Selecting appropriate correlations and graphs based on the variables' level of measurement
 * 6) Interpretation – of correlational relations and graphs
 * 7) Assumptions and Limitations
 * 8) Dealing with several correlations

Slides

 * Lecture slides (Google Slides]
 * 2018 handouts:
 * [[Media:SRDP Lecture04Handout Correlation 6slidesperpage.pdf|Download 6 slides to a page]]: SRDP Lecture04Handout Correlation 6slidesperpage.pdf
 * [[Media:SRDP Lecture04Handout Correlation 3slidesperpage.pdf|Download 3 slides to a page]]: SRDP Lecture04Handout Correlation 3slidesperpage.pdf

Readings

 * 1) Howitt and Cramer (2014a):
 * 2) Chapter 07: Relationships between two or more variables: Diagrams and tables (pp. 86-97
 * 3) Chapter 08: Correlation coefficients: Pearson correlation and Spearman’s rho (pp. 98-119)
 * 4) Chapter 11: Statistical significance for the correlation coefficient: A practical introduction to statistical inference (pp. 143-156)
 * 5) Chapter 15: Chi-square: Differences between samples of frequency data (pp. 196-217)