There is never enough data
Prof. Dr. Stef van Buuren
Missing data imputation for small datasets
Prof. Dr. Albert Satorra
Barcelona Graduate School of Economics
Small Sample Size SEM
Dr. Dan McNeish
Arizona State University
Mixture Models for Longitudinal Data with Small Samples
Dr. Milica Miočević
Bayesian mediation analysis with N = 1
Single-Case Experimental Designs (SCEDs) are a useful tool for evaluating therapy effectiveness in heterogeneous and low-incidence conditions, and mediation analysis informs researchers about the mechanism through which the intervention leads to changes in the outcome of interest. This talk describes Bayesian methods for mediation analysis in SCEDs.
San Diego State University
Power for Dyadic Models
Many psychological researchers examine the degree to which members of a dyad relate to one another. However, researchers often face challenges while attempting to identify the minimal sample size required to detect a specific dyadic effect (i.e., perform a power analysis). More specifically, researchers often have considerable challenge identifying effect sizes (e.g., exact values for fixed effects and variances for random effects) required to perform power analysis. This talk describes those challenges, provides new methodology for identifying effect sizes (i.e., via pseudo R-square values), demonstrates how to perform power analysis using those new effect sizes (i.e., via Monte Carlo simulation), and presents general results for minimal sample sizes to detect dyadic effect under a range of conditions.
Aggregating continuous streams of sensor data
Sensors included on smartphones or other wearables potentially allow for the precise study of human behavior. Accelerometers typically measure movement behavior in three dimensions (x,y,z) for people at a rate of 60 times per second. Geolocation sensors measure someone physical location in latitude and longitude at a rate that in practice varies between once per second, and once every several minutes. This presentation will focus on explaining the challenges of working with intensive longitudinal data from smartphones and wearables, and present ideas on how to aggregate data from those sensors.