Examining the Effects of State Anger on Self-reported Psychopathic Trait Inventories

Research indicates that experiencing anger is linked to social undesirability and temporarily induces a host of significant changes within an individual, such as hostile attribution bias, lower trust, increased impulsivity, poorer decision-making, and preferential recall. Such changes theoretically may inflate psychopathy scores due to a greater ability or willingness to endorse attitudes, behaviors, cognitions, and emotions that are regarded as reflective of psychopathic traits and may potentially introduce random error variance into measurement. Such distortion due to mood may bear important implications for applied settings where one individual’s scores are measured at a single time point, rather than research where variables are aggregated across a sizeable sample and mood effects may be negligible. To that end, this dissertation project examined the effects of a mood induction procedure (MIP) that elicited state anger on self-reported psychopathic traits through an experimental design. A total of 92 participants from Amazon’s Mechanical Turk were randomized into an anger MIP using autobiographical recall or into a control group. A series of mean level comparison analyses tested the effects of state anger on self-reported psychopathic traits across three prominent measures. Results indicated that, despite an effective mood induction of anger, the scores on self-report psychopathic traits on both total and subscale scores did not significantly differ between the anger group and the control group. Further, results were unchanged when covarying for trait anger levels or impact of the contemporary COVID-19 pandemic and held up when considering normative personality traits. In all, these findings support the reliability of three well-established self-reported psychopathy measures during state anger, however, replication and extension to other populations is warranted