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Catching the catfish: Exploring gender and the Dark Tetrad of personality as predictors of catfishing perpetration

Lauder, Cassandra ; March, Evita (2022) — Computers in Human Behavior

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DOI: 10.1016/j.chb.2022.107599 (opens in a new tab)

Type:   Journal Article
Country:   Global
Tags:   Catfishing, Dark Tetrad, Narcissism, Online Deception, Online Fraud, Personality Predictors, Psychopathy, Sadism
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Synopsis

            
This study examines whether gender and the Dark Tetrad of personality (psychopathy, sadism, Machiavellianism, and narcissism) predict catfishing perpetration, a form of online deception. Framed by an evolutionary cheater-strategy perspective, the authors test whether men engage in catfishing more than women and whether higher levels of each dark trait relate to greater catfishing behavior. A sample of 664 English-speaking adults (55.8% men; mean age 28.84, SD 9.60) completed measures of social desirability, the four Dark Tetrad traits, and a newly developed Catfishing Questionnaire. The researchers used a hierarchical regression approach to assess the unique contributions of gender and the Dark Tetrad traits to catfishing perpetration, controlling for social desirability. In the results, the Catfishing Questionnaire demonstrated high internal consistency and was refined by removing one item to improve model fit. In the hierarchical model, social desirability did not explain variance in catfishing. Entering gender at Step 2 yielded a small but significant increase in explained variance, with men reporting higher catfishing perpetration than women. At Step 3, the four Dark Tetrad traits collectively explained a substantial additional portion of variance (about 59.9%), with psychopathy, sadism, and narcissism emerging as significant positive predictors of catfishing perpetration, while Machiavellianism did not reach significance. The final model accounted for about 62.6% of the variance in catfishing perpetration, indicating a strong association between these traits, gender, and the behavior within this cross-sectional sample. The authors note the study’s correlational design and reliance on self-report, which limit causal inferences.

          

        
                  

            
Identified Gaps

            
Catfishing perpetration, including gender differences and individual predictors, is understudied. The authors identify limited prior research on gender differences in perpetration and present this as the first quantitative investigation of catfishing behavior. The newly developed Catfishing Questionnaire also requires further psychometric validation.

          

        
                  

            
Methods

            
A cross-sectional correlational online survey recruited 664 English-speaking adults through social-media advertisements. Participants completed demographic questions, self-report measures of social desirability and Dark Tetrad traits, and a 15-item author-developed Catfishing Questionnaire. The researchers used descriptive and correlational analyses, three-step hierarchical regression controlling for social desirability, and exploratory PROCESS moderation analyses.

          

        
                  

            
Limitations

            
The new Catfishing Questionnaire had promising initial fit and internal consistency, but its validity and test-retest reliability were unestablished. Social desirability and Machiavellianism scales had lower-than-expected internal consistency (alpha = .66 and .68), limiting reliability and interpretation. The authors also note that the relatively new Machiavellianism measure had not received extensive validation.

          

        
                  

            
Future Work

            
Future research should retest the Catfishing Questionnaire's model fit and establish construct, convergent, divergent, and test-retest validity. Researchers should use additional or alternative measures of social desirability and Machiavellianism, examine multidimensional Dark Tetrad facets, and investigate empathy as a predictor of catfishing.

          

              

    
    

        
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