https://github.com/mihaiconstantin/sample-size-workshop

Workshop on Sample Size Planning for Intensive Longitudinal Studies
https://github.com/mihaiconstantin/sample-size-workshop

data-science power-analysis sample-size statistics time-series workshop

Last synced: 7 days ago
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Workshop on Sample Size Planning for Intensive Longitudinal Studies

• Host: GitHub
• URL: https://github.com/mihaiconstantin/sample-size-workshop
• Owner: mihaiconstantin
• Created: 2023-05-03T11:48:38.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2023-06-08T09:44:39.000Z (almost 2 years ago)
• Last Synced: 2025-02-17T02:16:43.363Z (3 months ago)
• Topics: data-science, power-analysis, sample-size, statistics, time-series, workshop
• Language: TeX
• Homepage: https://samplesize.help
• Size: 39.9 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        



    Workshop on Sample Size Planning for

    


    Intensive Longitudinal Studies





    Ginette Lafit,

    Jordan Revol,

    Mihai A. Constantin, &

    Eva Ceulemans





    



## 📝 Description

In recent years the popularity of procedures to collect intensive longitudinal

data such as the Experience Sampling Method has increased immensely. The data

collected using such designs allow researchers to study the dynamics of

psychological processes, and how these dynamics differ across individuals. A

fundamental question when designing a study is how to determine the sample size,

which is closely related to the replicability and generalizability of empirical

findings. Even though multiple statistical guidelines are available for sample

size planning, it still remains a demanding enterprise in complex designs. The

goal of this workshop is to address this crucial question by presenting

methodological advances for sample size planning for intensive longitudinal

designs. First, we provide an overview of methods for sample size planning with

special emphasis on a priori power analysis. Second, we focus on how to conduct

power analysis in the $N = 1$ case when the goal is to model within-person

processes using $\text{VAR}(1)$ models. Subsequently, we consider the extension

to multilevel data in which multiple individuals are measured over time. We

introduce an approach for conducting power analysis for multilevel models that

explicitly accounts for the temporal dependencies that characterize the data

collected in IL studies. In addition, we showcase how to perform power analysis

for these models using a user-friendly and open-source application. Finally, we

consider an alternative criterion for conducting sample size planning that

targets the predictive accuracy of a model for unseen data. Focusing on

$\text{VAR}(1)$ models in an $N = 1$ context, we introduce a novel approach,

called predictive accuracy analysis, to assess how many measurement occasions

are required in order to optimize predictive accuracy.

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    Check out the materials and more at 


    samplesize.help



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## ✍️ Citation

- Lafit, G., Revol, J., Constantin M. A., & Ceulemans, E. (2023). *Workshop on

  Sample Size Planning for Intensive Longitudinal Studies*.

  [https://doi.org/10.5281/zenodo.8015940](https://doi.org/10.5281/zenodo.8015940)

## ⚖️ License

- 
The scripts, slides, and other materials by Ginette Lafit, Jordan Revol, Mihai A. Constantin, and Eva Ceulemans are licensed under CC BY 4.0  .