HDFS617B

This course provides an introduction to working with dyadic data (e.g., nested data with only two upper level units, such as two partners in a relationship or two physiological variables in a person). We will cover cross-sectional dyadic data (e.g., each pair of variables is only measured once, such as survey data from both partners in relationships), but the focus is on repeated measures dyadic data (e.g., each pair of variables is measured multiple times for each higher level unit, such as diary data from both partners in relationships). We will use both traditional Null Hypothesis Significance Testing (NHST) and the more contemporary approach of Bayesian inference. All analyses will be done with the statistical computing platform 'R'. Topics to be covered include a review of NHST and Bayesian perspectives; cross-sectional and repeated-measures dyadic models using multilevel modeling; an introduction to a dynamic systems perspective; an introduction to 'rties', which is an R package that supports dynamic systems modeling of dyadic data; the Inertia-Coordination and Coupled-Oscillator models; State-Space Grids; and Cross Recurrence Quantification Analysis. Other topics may vary year to year.

GIDP - STATD (Statistics and Data Science)