Aggression is broadly defined by researchers as any action that inflicts harm on another person who does not wish to be harmed (e.g., Allen et al., 2018). Myriad literature examines the correlates, predictors, and other factors that lead to aggressive actions. Indeed, aggression has been a core topic of interest in psychological science for many decades (e.g., Bandura, 1976). Aggression is generally studied in one of two modes: as a behavior or as a disposition (i.e., trait aggression). Behavioral aggression studies typically take the form of laboratory experiments wherein aggressive behavior is measured using some form of computer task that asks participants to enact aggression against an ostensible other person (e.g., the Taylor Aggression Paradigm, Taylor, 1967; West et al., 2020). Trait aggression is typically studied using self-report inventories that utilize different formulations of this construct. Regardless of the mode of aggression measurement, one variable that has emerged consistently as a (negative) correlate of both aggressive behavior and dispositions is that of self-control (e.g., Denson et al., 2012; Keatley et al., 2017 ). Self-control refers to one’s ability to inhibit impulses or natural drives in the face of ethical, moral, legal, social, or other constraints that make a given impulse inappropriate or even harmful. This feature of human psychology has long been upheld as a critical component that allows us to pursue long-term goals and supports our ability to maintain social relationship networks (e.g., Fujita, 2011). In this sense, self-control is a deliberative process that requires conscious effort. Self-control, like aggression, is typically measured using behavioral decision-making tasks or as a disposition using self-report instruments (i.e., trait self-control). Despite the extant behavioral measures of self-control (or inverse measures of impulsivity), self-reports of self-control are highly common in studies of self-control and aggression (e.g., West et al., 2022). A sizeable portion of the aggression literature points to failures of self-control as a primary cause of aggressive behaviors (e.g., Denson et al., 2012; DeWall et al., 2011). Experimental work indicates that negative affective experiences (e.g., interpersonal provocation) temporarily impair self-control, which coupled by a feeling of urgency to resolve the affective state may lead to aggressive actions (Chester et al., 2016; Heatherton and Wagner, 2011, Schmeichel and Tang, 2015). Indeed, aggression, especially retaliatory aggression, is pursued as a means of mood repair and is a rewarding experience (e.g., Bushman et al., 2001). As such, self-control appears to be a critical element to bolster against engaging in aggressive behaviors despite their potentially rewarding nature as impaired self-control has been implicated as a causal factor in impulsive retaliation (Denson et al., 2011; Finkel, DeWall, Slotter, Oaten, & Foshee, 2009). Aggressive behaviors are also commonly co-morbid with psychological disorders typified by impaired executive functioning necessary for effortful self-control (e.g., attention deficit hyperactive disorder; Saylor & Amaan, 2016 ). Impaired self-control has also been implicated in other domain-specific forms of aggression and more extreme cases of violence such as in cases of domestic violence (e.g., Bushman et al., 2014; Fruzzetti & Levensky, 2000 ; Shamai & Buchbinder, 2010 ). Much of the extant literature thus paints a picture of self-control as a critical element in preventing aggression. However, other research calls into question our current understanding of the link between aggression and self-control. Several perplexing features regarding the aggression-self-control link exist in the literature. For example, impaired self-control is also a reliable correlate of violent victimization (Pratt et al., 2014). This suggests that both perpetrators and victims of aggressive encounters are low in self-control. Experimental research has pursued self-control trainings aimed at bolstering the ability of self-control and in turn reducing aggression. Indeed, such self-control focused treatments for aggression have been reported in the literature for decades (e.g,. Camp et al., 1977; Huntsinger & Nay, 1977 ). More contemporary studies have evinced significant effects, indicating that training-improved self-control reduced aggressive behavior (e.g., Denson et al., 2011 ). However, more recent work indicates that such trainings do impact self-control but does not have an effect on aggression (Beames et al., 2023; Kip et al., 2021). Similarly, self-control training do not reduce feelings of anger, an emotional precursor to aggression (Beames et al., 2020). Further, studies of delayed retaliation indicate that such planful revenge is more likely among those higher in trait physical aggression and is not meaningfully related to self-control (West et al., 2022; Book et al., 2019). Other work has revealed that trait self-control is positively linked with a greater willingness to fight when the individual views fighting as morally appropriate (i.e., fighting in defense of another person; Rai, 2019 ). As such, the literature examining the link between aggression and self-control is marred with inconsistent and contradictory findings leaving the relationship between self-control and aggression unclear. Yet how could such a large body of literature miss the mark? Such inconsistencies may be due to features of the measures commonly used in such studies. Multiple self-report measures exist for both trait self-control and trait aggression. One of the most utilized measures of trait aggression is the Buss-Perry Aggression Questionnaire (BPAQ; Buss & Perry, 1992). This measure of trait aggression has been cited 9,673 times (estimates from Google Scholar, retrieved 5/18/2023). The wide use of this instrument has also led researchers to broadly adopt the four-facet model of trait aggression instantiated in the BPAQ. This formulation of trait aggression comprises four lower-order facets of physical aggression, verbal aggression, anger, and hostility. Whereas the total trait aggression factor itself is a lower-order trait of agreeableness in the five-factor model (FFM) framework of personality (e.g., West & Chester, 2021; Chester & West, 2020). Similarly, a commonly used measure of trait self-control is the Brief Self-Control Scale (BSCS; Tangney et al., 2004 ). The BSCS has been cited 8,231 times (estimates from Google Scholar, retrieved 5/18/2023) and measures trait self-control as a unitary factor formulation, though some work indicates that a two-factor structure may also be appropriate for the BSCS (Maloney et al., 2012 ). Trait self-control as measured by the BSCS is a lower-order facet of Conscientiousness in the FFM personality framework (Jones, 2017 ). Due to the wide use of these measures, much of what we know about aggression and self-control is based around these two instruments. Beyond simple correlations these measures are commonly included in studies utilizing behavioral measures of these constructs and in measure development studies to examine construct validity (e.g., West et al., 2022; Chester & Lasko, 2019 ). Further, trait aggression and trait self-control are commonly included in more complex statistical models (e.g., structural equation models, cross-lagged models, indirect effect models) wherein the ultimate conclusions drawn from these models depends in part on the associations between trait aggression and trait self-control (e.g., Jeong et al., 2020; Sheppard et al., 2015 ; Chen et al., 2019 ; Moroń et al., 2018). Despite the evidence favoring the validity and reliability of these measures, one common feature that may impact the associations between them in the literature is the reliance on aggregated fixed-effect estimates. Psychometric instruments developed under the latent variable approach (e.g., the BPAQ and BSCS) assume that a latent (i.e., unobservable) common cause accounts for participant responses and the variation therein. This is a common practice in psychological science – scales developed under this approach are typically used such that the participant responses to all items in a scale are collected in service of computing a single average value as their score for the construct of interest. These scores are then commonly included in various statistical analyses (e.g., linear regression) as predictors or outcomes. Despite the ubiquity of this approach, it necessarily restricts the total variance observed in participant’s responses by aggregating all variability into a single fixed effect. This approach inherently assumes that every item used in service of that aggregate measurement shares homogenous slopes with the variable(s) of interest. Recent work indicates that solely relying on the aggregate model approach inflates the likelihood of making Type-I errors (i.e., false positives) in relation to hypothesis testing (Donnellan et al., 2023). In short, such models are more likely to produce Type-I errors because they do not model the population-level variability emergent from individual item slopes. Making such models more problematic, the likelihood of making a Type-I error increases with statistical power and standard errors are prone to underestimation when extant random effects are not accounted for (Donnellan et al., 2023; Barr et al ., 2013; Usami & Murayama, 2018). As such, the literature examining the self-control-aggression link may be plagued by such Type I error inflation in studies that rely on latent factor formulations of aggression and self-control. A recent advancement in mixed-effects modeling allows researchers to account for random slopes at the item-level. This analytic approach known as Random Item Slopes Regression (RISR) models the individual item data as the outcome at the participant level allowing the slopes of each item to vary randomly (Donnellann et al., 2023). 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