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Peer-reviewedCC BY 4.0Frontiers in Psychology

Piecewise modeling for longitudinal psychological research: a tutorial using R

  • Shuncheng He1
  • Wooyeol Lee1
  1. 1Department of Psychology, Chungbuk National University, Cheongju, Chungbuk, Republic of Korea
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Abstract

Longitudinal psychological research often centers on key developmental transitions, where patterns of change may differ across stages. Piecewise modeling is well suited to capturing these stage-specific changes. This tutorial introduces three piecewise methods in longitudinal psychological research: piecewise multilevel modeling (MLM), piecewise latent class growth modeling (LCGM), and latent transition analysis (LTA). Drawing on a six-wave longitudinal study of national identity development, we show how each method can be implemented in R and highlight the types of research questions each is designed to address, from estimating average developmental trajectories to identifying heterogeneous trajectory patterns and examining transitions between latent developmental states.

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Cite

BibTeXRIS
BibTeX
@article{he2026piecewise,
  title = {{Piecewise modeling for longitudinal psychological research: a tutorial using R}},
  author = {Shuncheng He and Wooyeol Lee},
  journal = {Frontiers in Psychology},
  year = {2026},
  month = oct,
  doi = {10.3389/fpsyg.2026.1949552},
  url = {https://doi.org/10.3389/fpsyg.2026.1949552},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Piecewise modeling for longitudinal psychological research: a tutorial using R
AU  - Shuncheng He
AU  - Wooyeol Lee
PY  - 2026
DA  - 2026/10/22
JO  - Frontiers in Psychology
DO  - 10.3389/fpsyg.2026.1949552
UR  - https://doi.org/10.3389/fpsyg.2026.1949552
AB  - Longitudinal psychological research often centers on key developmental transitions, where patterns of change may differ across stages. Piecewise modeling is well suited to capturing these stage-specific changes. This tutorial introduces three piecewise methods in longitudinal psychological research: piecewise multilevel modeling (MLM), piecewise latent class growth modeling (LCGM), and latent transition analysis (LTA). Drawing on a six-wave longitudinal study of national identity development, we show how each method can be implemented in R and highlight the types of research questions each is designed to address, from estimating average developmental trajectories to identifying heterogeneous trajectory patterns and examining transitions between latent developmental states.
SN  - 1664-1078
ER  -