Leadership scale for sports - Invariant across gender?
Bibliographic record
Abstract
Leadership has been of interest to sport psychology researchers for decades. One of the most popular models of leadership in sport is Chelladurai’s (1978) Multidimensional Model of Leadership. Its accompanying questionnaire, the Leadership Scale for Sports (LSS), has been used in a multitude of studies as a measurement tool to assess a coach’s leadership. Given the popularity of the scale, it is rather surprising that the factorial validity of the LSS has been examined by only few studies (e.g., Fletcher & Roberts, 2013; Riemer & Chelladurai, 1998) with relatively small sample sizes (N<400). Furthermore, a structured examination if the LSS is invariant across gender is missing. Consequently, the purpose of the study is two-fold: (a) identifying a best-fit model for the LSS with a large sample (i.e., N<1000) and (b) testing the invariance across gender for this model. In total, data from 1065 CIS and NCAA athletes (female N=599, Age M=20.29, SD=2.06) were analyzed. Four dimensions of the LSS (training and instruction, positive feedback, social support, democratic behavior) were measured. Autocratic behavior was not considered for this study because previous research indicated psychometric and conceptual problems with this dimension. Confirmatory factor analysis was used to find a model of best fit for the LSS. Once this model was identified, it was tested for invariance across gender. The results indicated that the original factor structure of the LSS had an acceptable fit of the data (CFI=.90, NNFI=.90, RMSEA=.060, 90%CI .058-.062, 4 error variances correlated). The invariance analysis revealed that the model is invariant for measurement weights and intercepts (change CFI <.01) across gender. Taken together, this study supports the view that the LSS is a useable tool to measure leadership in sports for male and female athletes. Limitations and future research directions will be discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".