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,
Dear colleagues,
Please share this invitation widely! 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 522. 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
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 -- Jessica Kay Flake, PhD Quantitative Psychology and Modelling McGill University
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Jessica Flake, Dr. -
JK Flake