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Record W2108275656 · doi:10.1177/1073191110382845

What Does the Narcissistic Personality Inventory Really Measure?

2010· article· en· W2108275656 on OpenAlexaff
Robert A. Ackerman, Edward A. Witt, M. Brent Donnellan, Kali H. Trzesniewski, Richard W. Robins, Deborah A. Kashy

Bibliographic record

VenueAssessment · 2010
Typearticle
Languageen
FieldPsychology
TopicPersonality Traits and Psychology
Canadian institutionsWestern University
Fundersnot available
KeywordsNarcissismPsychologyExhibitionismEntitlement (fair division)Nomological networkGrandiosityFacet (psychology)Social psychologyPersonalityNarcissistic personality disorderPersonality Assessment InventoryDimension (graph theory)Big Five personality traitsPersonality disordersOperationalizationEpistemology

Abstract

fetched live from OpenAlex

The Narcissistic Personality Inventory (NPI) is a widely used measure of narcissism. However, debates persist about its exact factor structure with researchers proposing solutions ranging from two to seven factors. The present research aimed to clarify the factor structure of the NPI and further illuminate its nomological network. Four studies provided support for a three-factor model consisting of the dimensions of Leadership/Authority, Grandiose Exhibitionism, and Entitlement/Exploitativeness. The Leadership/Authority dimension was generally linked to adaptive outcomes whereas the other two dimensions, particularly Entitlement/Exploitativeness, were generally linked to maladaptive outcomes. These results suggest that researchers interested in the psychological and behavioral outcomes associated with the NPI should examine correlates at the facet level. In light of the findings, we propose a hierarchical model for the structure of the NPI and provide researchers with a scoring scheme for this commonly used instrument.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.044
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.044
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.044
GPT teacher head0.386
Teacher spread0.343 · 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 source (direct Gemma or distilled Codex), 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

Citations869
Published2010
Admission routes1
Has abstractyes

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