The Yuen-Welch and Generalized Linear Model Approaches for Analyzing Skewed and Heteroscedastic Data in Psychology and A brief survey of current statistical methods for meta-analyzing data produced by single-case experimental designs

When:
March 31, 2014 @ 10:15 am – 11:15 am
Where:
Norm Endler Room (BSB 164)
York University
4700 Keele St, Toronto, ON M3J 1P3
Canada

Speaker: Victoria Ng and Joo Ann Lee, York University
Department of Psychology

Speaker: Victoria Ng

Title: The Yuen-Welch and Generalized Linear Model Approaches for Analyzing Skewed and Heteroscedastic Data in Psychology

Abstract: Many psychological studies are designed for testing whether there are group mean differences for some continuous outcome variable. However, the assumptions of normality and heteroscedasticity underlying traditional methods (i.e., ANOVA/OLS regression) are often violated. Two alternative methods are discussed: the Yuen-Welch with trimmed means, and the Generalized Linear Model (GLM). Given the many specifications that are possible in the GLM, selected studies on competing estimators from health outcomes literature are touched upon. With the premise that one would ideally choose the method that yields both adequate power and estimates that represent all relevant data (i.e., including distribution tails), I address the motivation for comparing the Yuen-Welch and the GLM by simulation and discuss potential implementations of such a study.

Speaker: Joo Ann Lee

Title: A brief survey of current statistical methods for
meta-analyzing data produced by single-case experimental designs

Abstract: Single-case experimental designs (SCEDs; also known as n-of-1 trials,
small-n designs, single-subject designs, and interrupted time-series
experimental designs, among others) are a set of experimental designs
that employ repeated data collection over time on a single unit of
interest such as an individual, a family, or an institution. SCEDs are
especially beneficial when the research areas studied have high
variability, or a low prevalence rate, because the unit serves as its
own control. More specifically, SCEDs explicitly focus on
within-individual variability. Unfortunately, a single SCED provides
very little, if any, information about between-individual variability.
This disadvantage however, can be remediated by meta-analyzing results
from separate SCEDs. Nonetheless, the meta-analytic methods of SCEDs
are just beginning to be developed. The presentation will begin with a
review of the type of data common to SCEDs, followed by illustrations
of current popular methods to analyze and meta-analyze SCED data, and
conclude with future research in the area.

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