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Record W2287968437 · doi:10.14288/1.0055775

A meta-analysis of Fiedler’s Contingency model of leadership effectiveness

2010· article· en· W2287968437 on OpenAlexaff
Ellen Patricia Crehan

Bibliographic record

VenuecIRcle (University of British Columbia) · 2010
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOrganizational Leadership and Management Strategies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsContingency theoryComputer scienceKnowledge management

Abstract

fetched live from OpenAlex

Fiedler's Contingency Model of Leadership Effectiveness is widely cited, yet highly controversial. The present study subjected Fiedler-based studies to a meta-analysis to determine whether a body of consistent findings would emerge. If such findings did emerge, the aim would be to develop a theoretical framework which would adequately explain them. If they did not, the study would try to explain their absence. The original pool of 402 documents contained 112 primary studies. Of these studies, 38 met the criteria of quality, comparability, and extent of adherence to Fiedler's Model needed for retention in the meta-analysis. These 38 studies contained 249 LPC-performance correlations. These correlations were first analyzed for frequency of support for Fiedler's Model using two indicators: (1) the primary study authors' stated conclusions and (2) directional congruency with Fiedler's predictions. Full or partial support was concluded by 15% and 63%, respectively, of the authors. Directional congruency yielded 54% support and 46% non-support. The second phase of the analysis, in which the within-octant correlations were partitioned by eight different variables, examined both the magnitude and direction using median correlations as the common metric. The extensive disparity between the obtained medians and those predicted by Fiedler led to a third examination using Exploratory Data Analysis techniques. This analysis showed that, regardless of the variable used, there were within-octant differences which prevented not only combining the partitioned data sets but also continuing the meta-analysis. An analysis of the methodological variability in the studies yielded findings important to the continued use of Fiedler's Model. These findings led to several recommendations intended to standardize testing procedures. These recommendations included suggestions regarding greater standardization of score division methods for LPC and GAS, assessment of the three situational variables, and criteria by which to assess support for the Model. Until such standardization is achieved, the validity of Fiedler's Model can be neither confirmed nor disconfirmed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.480
Threshold uncertainty score0.968

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.070
GPT teacher head0.203
Teacher spread0.133 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2010
Admission routes1
Has abstractyes

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