Ordinal Outcome State-Space Models for Intensive Longitudinal Data

Teague R. Henry (), Lindley R. Slipetz, Ami Falk, Jiaxing Qiu and Meng Chen
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Teague R. Henry: University of Virginia
Lindley R. Slipetz: University of Virginia
Ami Falk: University of Virginia
Jiaxing Qiu: University of Virginia
Meng Chen: University of Oklahoma

Psychometrika, 2024, vol. 89, issue 4, No 7, 1203-1229

Abstract: Abstract Intensive longitudinal (IL) data are increasingly prevalent in psychological science, coinciding with technological advancements that make it simple to deploy study designs such as daily diary and ecological momentary assessments. IL data are characterized by a rapid rate of data collection (1+ collections per day), over a period of time, allowing for the capture of the dynamics that underlie psychological and behavioral processes. One powerful framework for analyzing IL data is state-space modeling, where observed variables are considered measurements for underlying states (i.e., latent variables) that change together over time. However, state-space modeling has typically relied on continuous measurements, whereas psychological data often come in the form of ordinal measurements such as Likert scale items. In this manuscript, we develop a general estimation approach for state-space models with ordinal measurements, specifically focusing on a graded response model for Likert scale items. We evaluate the performance of our model and estimator against that of the commonly used “linear approximation” model, which treats ordinal measurements as though they are continuous. We find that our model resulted in unbiased estimates of the state dynamics, while the linear approximation resulted in strongly biased estimates of the state dynamics. Finally, we develop an approximate standard error, termed slice standard errors and show that these approximate standard errors are more liberal than true standard errors (i.e., smaller) at a consistent bias.

Keywords: state-space modeling; intensive longitudinal data; ecological momentary assessment; ordinal measurements; item response theory; particle filtering (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11336-024-09984-3

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