FRAME ANALYSIS: A MODERN APPROACH TO FACTOR ANALYSIS
Author(s)
Sanchez, Ryan C.
Advisor(s)
Hunter, Michael D.
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Abstract
We introduce and develop a new statistical method for exploring latent structures: Frame Analysis. Frame Analysis drops the one-to-one correspondence between factor dimensionality and vector space representation found in Factor Analysis. This minor change obviates factor rotations, simple structure, and provides equal status for cross-loaded items. We show that in Frame Analysis, manifest items are defined by only one frame loading and are uniquely characterized as a linear combination of latent variables: a frame vector. Through a series of simulations, we characterize Frame Analysis performance in three scenarios: Exploratory, Constrained, and Partially-Constrained. Finally, we apply Frame Analysis to archival five-factor personality data and provide evidence that hierarchical personality models are disguised frame vectors.
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Date
2021-11-11
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