Dear colleagues,
It is my great pleasure to announce that next Friday, September 21st, the
McGill Psychology Department will be hosting Dr. Laura Stapleton, who will
deliver a Hebb Lecture at 3:30pm in MCMED 552. Dr. Stapleton is a Professor
in Measurement, Statistics and Evaluation (EDMS) in the Department of Human
Development and Quantitative Methodology at the University of Maryland.
Additionally, she serves as the Associate Director of the Research Branch
of the Maryland State Longitudinal Data System. This will be followed by a
wine and cheese reception that is open to all. We hope to see you there and
please share with anyone you think may be interested to attend!
Dr. Stapleton's research includes the analysis of administrative data and
survey data obtained under complex sampling designs, multilevel latent
variable models, and tests of mediation within a multilevel framework.
*About*: *https://education.umd.edu/directory/laura-stapleton
<https://education.umd.edu/directory/laura-stapleton>*
*Title*: *Measurement Modeling in Psychology: Construct Validation in
Nested Settings*
*Abstract*: In social science research, latent constructs are often
inferred from sets of items intended to measure those constructs. When data
are collected in multilevel settings (e.g., students within schools or
children within families) the construct of interest might exist at multiple
levels. In this talk, I will consider how researchers might approach
construct meaning and construct validation when working with data that are
nested. I will first present extensions of the single-level confirmatory
factor analysis (CFA) approach to a simple multilevel CFA (MCFA) when data
are nested. I then will wade through the murky conceptual landscape that
exists when considering measurement models at both the individual and
cluster levels and introduce conceptual distinctions between constructs
across levels and among different types of constructs at the cluster level.
Specifically, I will discuss how items might be used to measure “shared”
and “configural” cluster-level constructs. While shared constructs would
reflect a shared element of the cluster (wherein individuals would be
viewed as exchangeable within a cluster), configural constructs represent
aggregation of characteristics of the individuals within the cluster.
Additionally, an often-overlooked characteristic of configural constructs
would be an evaluation of differential dispersion within clusters. Although
empirical data may show cluster dependency, theoretically the construct may
be an individual level one only but the data reflect a spurious intraclass
correlation (ICC) or a spurious contextual effect due to measurement
non-invariance. The appropriate CFA modeling approach will depend on the
hypothesized constructs to be measured; examples based on empirical data
and simulated data will be shown.
Best,
--
Jessica Kay Flake, PhD
Quantitative Psychology and Modelling
McGill University
