A Network Approach to Understanding Narcissistic Grandiosity via the Narcissistic Admiration and Rivalry Questionnaire and the Narcissistic Personality Inventory

ABSTRACT

The narcissistic admiration and rivalry concept (NARC) model of grandiose narcissism posits that striving for uniqueness, grandiose fantasies, and charmingness define narcissistic admiration, whereas striving for supremacy, devaluation, and aggressiveness define narcissistic rivalry. Given these complex interrelationships, we explored the structure of grandiose narcissism using the Narcissistic Admiration and Rivalry Questionnaire (NARQ) and Narcissistic Personality Inventory (NPI) via network analysis in four separate samples which allowed us to assess the extent to which these networks replicated across these samples (total N = 3,868). Overall, grandiose cognitions from the NARQ emerged as a highly central node in each network, providing compound evidence for its replicability and generalizability as an important feature of grandiose narcissism within the NARC model. Charmingness from the NARQ emerged as a central node throughout Samples 1, 2, and 3, with strong connections to features of narcissistic admiration and narcissistic rivalry (e.g., grandiose fantasies and aggressiveness), but was less central in Sample 4. To our knowledge, this is the first research to examine the replicability of the network structure of grandiose narcissism across various samples. These findings add to an increasingly important dialogue regarding replicability in psychological network science.

KEYWORDS:

• Grandiose narcissism
• network analysis
• narcissistic admiration
• narcissistic rivalry

Acknowledgments

We thank Dr. Kaitlyn Burnell for providing the data used in Sample 2, Dr. Michael Grosz for providing the data used in Sample 3, and Dr. Mitja Back for providing the data used in Sample 4. We also thank these researchers for their commitment to open science practices.

Disclosure of potential conflicts of interest

No potential conflict of interest was reported by the author(s).

Open Practices

Materials and data from Sample 1 are available at https://osf.io/3pzuw/.

Notes

1. Given the use of network analysis as an exploratory procedure, the data analytic plan was not pre-registered in an independent, institutional registry.

2. The purpose of the Fruchterman-Reingold algorithm is to provide a graph based on minimizing the number of crossing edges. Importantly, the position of the nodes are not considered to have meaningful positions or correspond to certain factors; rather, the nodes are placed in a manner that allows for a more easily interpretable graph (Jones et al., 2018).

3. These supplemental figures include the bootstrapped confidence intervals for estimated edge weights plots, case-dropping bootstrap plots, bootstrapped difference tests of strength centrality plots, and bootstrapped edge weight difference test plots.

4. More specifically, these communities are based on information independent of the network structure itself and are not based on any network estimation procedure (such as community detection analyses).

5. The netSimulator function in bootnet was also used to examine the degree of sensitivity and specificity for this network using the network from Sample 1 as input data. Similarly, a sample size of 656 was deemed appropriate for detecting the presence of edges in this network.

6. We thank Joshua Miller for this suggestion.