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Related Experiment Video

Updated: Jan 26, 2026

ScanLag: High-throughput Quantification of Colony Growth and Lag Time

ScanLag: High-throughput Quantification of Colony Growth and Lag Time

Published on: July 15, 2014

Time to criterion latent growth models.

Tessa L Johnson¹, Gregory R Hancock¹

¹Department of Human Development and Quantitative Methodology, University of Maryland, College Park.

Psychological Methods

|April 19, 2019

Summary

The new Time to Criterion (T2C) model treats time as a variable, not just an index. This allows for modeling individual differences in achieving outcomes and predicting time-related factors in longitudinal research.

Area of Science:

Quantitative Psychology
Longitudinal Data Analysis
Structural Equation Modeling

Background:

Latent growth models (LGMs) are used to study change over time.
However, time is typically an index, not a focal parameter.
Existing methods lack flexibility for complex longitudinal data.

Purpose of the Study:

Introduce the Time to Criterion (T2C) model.
Reparameterize latent growth models to treat time to achieve an outcome as a random effect.
Enable modeling of predictors and outcomes of time within the SEM framework.

Main Methods:

Derived the T2C model from linear latent growth models.
Utilized structural equation modeling (SEM) principles.
Illustrated with real data and discussed implementation in SEM software.

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Main Results:

The T2C model allows modeling individual variability in time to reach a criterion.
It accommodates complex data issues like missingness and nonnormality.
An extension for nonlinear growth was also presented and illustrated.

Conclusions:

The T2C model offers a novel parameterization for understanding change over time.
It enhances the utility of latent growth models for applied research.
Provides a flexible framework for analyzing time-dependent processes.