Data collection methods and statistical analysis

Description

This course will provide a comprehensive overview of main data collection and statistical data analysis procedures over five major modules (see below). Students are strongly advised to bring their personal computers with PsychoPy and R studio installed (also better if they have MATLAB and EEGLAB toolbox for ERP analyses). The timeline may change depending on availability of the labs.

Module 1) General introduction, Accuracy data

At the end of this module, students will be able to (i) program a simple experiment to acquire accuracy data, and (ii) be able to apply parametric (e.g. t-test, ANOVA) and non-parametric statistical tests (e.g. Wilcoxon test) using R studio, and (iii) the module will also introduce mixed-effects linear regression models.

Module 2) Response times data

Response times (RTs) are one of the most widely used measurements in the domain of psychology and linguistics. In this module, students will be provided with (i) step-by-step tutorials to program an experiment to collect RTs data, and then lean about the procedures for simply visualizing and pre-processing RTs data. The module will also introduce linear mixed-effects regressions using R studio.

Module 3) Single case, patient and/or treatment studies

This module will provide the experimental settings and statistical procedures involved in single case and single case series studies in neuropsychological patient research, such as aphasia and in other individuals with brain damage. Single case designs are often used in calculating experimental assessment in one single individual or to compare two single individuals to each other. This module we will focus on the most used statistical techniques used for single case data, including Chi-square tests and Crawford & Howell’s test.

Module 4) Eye movements data

This module will provide an overview of how eye-movements data are collected, and which designs are available in doing so. At the end of the module, the expected learning outcomes are (i) important issues with programming visual-world and eye-tracking during reading experiment, and (ii) analyzing eye-tracking data from a visual world experiment using mixed-effects logistic regression (i.e., participants’ proportions of looks to target pictures).

Module 5) Event-related potentials (ERP) data

ERPs are among the most widely used designs in neuropsychological research using EEG. Oftentimes, ERPs show non-linear fluctuations across time. This module will look into (i) a quick overview of how ERPs data are acquired, and then demonstrate Generalized Additive Models (GAM), a non-linear analysis method, to analyze ERPs data.