Home › Entities › academia › Effect size measures for longitudinal growth analyses: Extending a framework of multilevel model R‐squareds to accommodate heteroscedasticity, autocorrelation, nonlinearity, and alternative centering strategies

Effect size measures for longitudinal growth analyses: Extending a framework of multilevel model R‐squareds to accommodate heteroscedasticity, autocorrelation, nonlinearity, and alternative centering strategies

Research article (New Directions for Child and Adolescent Development, 2021) · cited 29× · AI/ML
academia openalex:W3125052843
Press Enter · cited answer in seconds

Effect size measures for longitudinal growth analyses: Extending a framework of multilevel model R‐squareds to accommodate heteroscedasticity, autocorrelation, nonlinearity, and alternative centering strategies

Summary

Effect size measures for longitudinal growth analyses: Extending a framework of multilevel model R‐squareds to accommodate heteroscedasticity, autocorrelation, nonlinearity, and alternative centering strategies is a scholarly article[1].

Key Facts

  • Effect size measures for longitudinal growth analyses: Extending a framework of multilevel model R‐squareds to accommodate heteroscedasticity, autocorrelation, nonlinearity, and alternative centering strategies's instance of is recorded as scholarly article[2].

References

Programmatic citations — every numbered marker resolves to a verifiable graph row below.

Direct Wikidata claims

  1. [2] ↑ . wikidata.org.

Class ancestry

  1. [1] ↑ . Wikidata. wikidata.org.

📑 Cite this page

Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.

APA 4ort.xyz Knowledge Graph. (2026). Effect size measures for longitudinal growth analyses: Extending a framework of multilevel model R‐squareds to accommodate heteroscedasticity, autocorrelation, nonlinearity, and alternative centering strategies. Retrieved May 24, 2026, from https://4ort.xyz/entity/effect-size-measures-for-longitudinal-growth-analyses-extending-a-framework-of-multilevel-model-rsquareds-to-accommodate
MLA “Effect size measures for longitudinal growth analyses: Extending a framework of multilevel model R‐squareds to accommodate heteroscedasticity, autocorrelation, nonlinearity, and alternative centering strategies.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/effect-size-measures-for-longitudinal-growth-analyses-extending-a-framework-of-multilevel-model-rsquareds-to-accommodate.
BibTeX @misc{4ortxyz_effect-size-measures-for-longitudinal-growth-analyses-extending-a-framework-of-multilevel-model-rsquareds-to-accommodate_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Effect size measures for longitudinal growth analyses: Extending a framework of multilevel model R‐squareds to accommodate heteroscedasticity, autocorrelation, nonlinearity, and alternative centering strategies}}, year = {2026}, url = {https://4ort.xyz/entity/effect-size-measures-for-longitudinal-growth-analyses-extending-a-framework-of-multilevel-model-rsquareds-to-accommodate}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Effect size measures for longitudinal growth analyses: Extending a framework of multilevel model R‐squareds to accommodate heteroscedasticity, autocorrelation, nonlinearity, and alternative centering strategies — https://4ort.xyz/entity/effect-size-measures-for-longitudinal-growth-analyses-extending-a-framework-of-multilevel-model-rsquareds-to-accommodate (retrieved 2026-05-24)

Canonical URL: https://4ort.xyz/entity/effect-size-measures-for-longitudinal-growth-analyses-extending-a-framework-of-multilevel-model-rsquareds-to-accommodate · Last refreshed: