Sample Size Recommendations for Continuous-Time Models: Compensating Shorter Time Series with Larger Numbers of Persons and Vice Versa

Hecht, Martin; Zitzmann, Steffen

doi:10.1080/10705511.2020.1779069

Cited by 48 publications

(37 citation statements)

References 31 publications

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“…For study planning, other criteria might be of interest as well. For instance, Hecht and Zitzmann (2021) illustrated the dependence of model performance (an aggregated measure consisting of convergence, bias, and coverage) on number of persons N and time points T. So even if a sufficient statistical power is achieved with some N and T, model performance might still be suboptimal.…”

Section: Discussionmentioning

confidence: 99%

Exploring the Unfolding of Dynamic Effects with Continuous-Time Models: Recommendations Concerning Statistical Power to Detect Peak Cross-Lagged Effects

Hecht

Zitzmann

2021

Structural Equation Modeling: A Multidisciplinary Journal

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Cross-lagged panel models have been commonly applied to investigate the dynamic interplay of variables. In such discrete-time models, the size of the cross-lagged effects depends on the length of the time interval between the measurement occasions. Continuous-time modeling allows to explore this interval dependence of cross-lagged effects and thus to identify the maximal "peak" cross-lagged effects. To detect these peak effects, sufficient statistical power is needed. Based on results from a simulation study, we employed machine learning algorithms to identify a highly accurate prediction model. Results are incorporated into a Shiny App (available at https://psychtools.shinyapps.io/ ContinuousTimePowerCalculation) for easy power calculations. Although limitations apply, our results might be helpful for study planning.

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Section: Discussionmentioning

confidence: 99%

Exploring the Unfolding of Dynamic Effects with Continuous-Time Models: Recommendations Concerning Statistical Power to Detect Peak Cross-Lagged Effects

Hecht

Zitzmann

2021

Structural Equation Modeling: A Multidisciplinary Journal

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“…Another issue concerns the small T situation in the presence of multiple subjects. On the one hand, multiple subjects can be an additional source of information for model estimation and can compensate for small T to some degree (Hecht and Zitzmann 2020 ). On the other hand, boundary effects may occur (i.e., the first and the last couple of rows in a fully embedded time series may exhibit bias that does not cancel, especially with short time series and large embedding dimensions; Boker et al 2018 ) and add up.…”

Section: Discussionmentioning

confidence: 99%

A Note on the Usefulness of Constrained Fourth-Order Latent Differential Equation Models in the Case of Small T

2020

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Constrained fourth-order latent differential equation (FOLDE) models have been proposed (e.g., Boker et al. 2020) as alternative to second-order latent differential equation (SOLDE) models to estimate second-order linear differential equation systems such as the damped linear oscillator model. When, however, only a relatively small number of measurement occasions T are available (i.e., $$T=50$$ T = 50 ), the recommendation of which model to use is not clear (Boker et al. 2020). Based on a data set, which consists of $$T=56$$ T = 56 observations of daily stress for $$N=44$$ N = 44 individuals, we illustrate that FOLDE can help to choose an embedding dimension, even in the case of a small T. This is of great importance, as parameter estimates depend on the embedding dimension as well as on the latent differential equations model. Consequently, the wavelength as quantity of potential substantive interest may vary considerably. We extend the modeling approaches used in past research by including multiple subjects, by accounting for individual differences in equilibrium, and by including multiple instead of one single observed indicator.

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“…Finally, the sample size also matters for CTMs. A simulation study by Hecht and Zitzmann (2020) indicated that for a design like ours with five measurement occasions, 250 to 500 people are needed to obtain reliable estimates. Thus, our sample size was sufficient to compute CTMs.…”

Section: Assumptions Preconditions and Required Sample Sizes Of Continuous Time Modelsmentioning

confidence: 91%

Stability and Change of Perceived Characteristics of Major Life Events

Haehner¹,

Kritzler²,

Faßbender³

et al. 2021

Preprint

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The occurrence of major life events is associated with changes in mental health, well-being, and personality. To better understand these effects, it is important to consider how individuals perceive major life events. Although theories such as Appraisal Theory and Affective Adaptation Theory suggest that event perceptions change over time and that these changes are relevant for personality and well-being, stability and change of the perceptions of major life events have not been systematically examined. The present paper aims to fill this gap using data from a longitudinal study (N = 619 at T1). In this study, participants rated nine characteristics of the same major life event up to five times within one year with the Event Characteristics Questionnaire. We estimated rank-order and mean-level stabilities as well as intraclass correlations of the nine life event characteristics with continuous time models. Furthermore, we computed continuous time models for the stability of affective well-being and the Big Five personality traits to generate benchmarks for the interpretation of the stability coefficients. Rank-order stabilities of the life event characteristics were lower than for the Big Five, but higher than for affective well-being. Furthermore, we found significant mean-level changes for the life event characteristics extraordinariness, change in world views and external control. Most of the variance in life event characteristics was explained by between-person differences. Future research should examine whether these changes in perceived event characteristics are associated with changes in other constructs and which factors contribute to the stability and change of perceived event characteristics.

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Sample Size Recommendations for Continuous-Time Models: Compensating Shorter Time Series with Larger Numbers of Persons and Vice Versa

Cited by 48 publications

References 31 publications

Exploring the Unfolding of Dynamic Effects with Continuous-Time Models: Recommendations Concerning Statistical Power to Detect Peak Cross-Lagged Effects

Exploring the Unfolding of Dynamic Effects with Continuous-Time Models: Recommendations Concerning Statistical Power to Detect Peak Cross-Lagged Effects

A Note on the Usefulness of Constrained Fourth-Order Latent Differential Equation Models in the Case of Small T

Stability and Change of Perceived Characteristics of Major Life Events

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