Hierarchical Linear Modeling of Individual Athlete Performance-Affect Relationships

Tate Richard L, Tenenbaum Gershon and Delpish Ayesha
Additional contact information
Tate Richard L: Florida State University
Tenenbaum Gershon: Florida State University
Delpish Ayesha: Florida State University

Journal of Quantitative Analysis in Sports, 2006, vol. 2, issue 2, 29

Abstract: A current approach to the empirical study of the relationship between affect and the performance of athletes before and during a competition is idiographic in nature. Affect-performance zones are estimated for each athlete based on a sufficient number of paired affect and performance observations. Though extremely important for practitioners, the idiographic approaches introduced in the literature until now do not readily support generalizations across different populations (e.g., for different genders, levels of experience, and levels of expertise). This article illustrates how hierarchical linear modeling (HLM) can be effectively used to retain this idiographic focus, while also adding a nomothetic perspective describing the variation of individual affect-performance relationships across athletes. The article illustrates the computational and graphical options that, when appropriately used, can expand our understanding of the affect-performance linkage for both individual cases and populations of interest.

Keywords: modeling; optimal performance (search for similar items in EconPapers)
Date: 2006
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.2202/1559-0410.1024 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:jqsprt:v:2:y:2006:i:2:n:2

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/jqas/html

DOI: 10.2202/1559-0410.1024

Access Statistics for this article

Journal of Quantitative Analysis in Sports is currently edited by Mark Glickman

More articles in Journal of Quantitative Analysis in Sports from De Gruyter
Bibliographic data for series maintained by Peter Golla ().