Experiencing Instigations and Trait Aggression Contribute to Harsh Parenting Behaviors

2016 ◽  
Author(s):  
Randy J. McCarthy

Three studies (total N = 1,777 parents) examined whether harsh parenting behaviors would increase when parents experienced an instigation and whether this increase would be especially pronounced for parents who were high in trait aggression. These predictions were tested both when parents’ experience of an instigation was manipulated (Studies 1 and 2) and when parents’ perceptions of their child’s instigating behavior was reported (Study 3). Further, these predictions were tested across a variety of measures of parents’ harsh behaviors: (1) Asking parents to report their likelihood of behaving harshly (Study 1); (2) using proxy tasks for parents’ inclinations to behave harshly (Study 2); and (3) having parents report their past child-directed behaviors, some of which were harsh (Study 3). Both child instigations and parents’ trait aggression were consistently associated with parents’ child-directed harsh behaviors. However, parents’ trait aggression only moderated the extent to which the instigation was associated with their harsh parenting for self-reported physical harsh behaviors (Study 1). The results of the current studies demonstrate that both situational factors, such as experiencing an instigation, and individual difference variables, such as trait aggression, affect parents’ likelihood to exhibit harsh behaviors, but found little evidence these factors interact.

2017 ◽  
Vol 120 (6) ◽  
pp. 1078-1095
Author(s):  
Randy J. McCarthy

Three studies (total N = 1777 parents) examined whether harsh parenting behaviors would increase when parents experienced an instigation and whether this increase would be especially pronounced for parents who were high in trait aggression. These predictions were tested both when parents’ experience of an instigation was manipulated (Studies 1 and 2) and when parents’ perceptions of their child’s instigating behavior was reported (Study 3). Further, these predictions were tested across a variety of measures of parents’ harsh behaviors: (1) asking parents to report their likelihood of behaving harshly (Study 1), (2) using proxy tasks for parents’ inclinations to behave harshly (Study 2), and (3) having parents report their past child-directed behaviors, some of which were harsh (Study 3). Both child instigations and parents’ trait aggression were consistently associated with parents’ child-directed harsh behaviors. However, parents’ trait aggression only moderated the extent to which the instigation was associated with their harsh parenting for self-reported physical harsh behaviors (Study 1). The results of the current studies demonstrate that both situational factors, such as experiencing an instigation, and individual difference variables, such as trait aggression, affect parents’ likelihood to exhibit harsh behaviors, but found little evidence these factors interact.


Author(s):  
Elizabeth M. Oberlander ◽  
Frederick L. Oswald ◽  
David Z. Hambrick ◽  
L. Andrew Jones

1999 ◽  
Vol 9 (2) ◽  
pp. 183-205 ◽  
Author(s):  
Kenneth Bass ◽  
Tim Barnett ◽  
Gene Brown

Abstract:This study examined the relationship between the individual difference variables of personal moral philosophy, locus of control, Machiavellianism, and just world beliefs and ethical judgments and behavioral intentions. A sample of 602 marketing practitioners participated in the study. Structural equation modeling was used to test hypothesized relationships. The results either fully or partially supported hypothesized direct effects for idealism, relativism, and Machiavellianism. Findings also suggested that Machiavellianism mediated the relationship between individual difference variables and ethical judgments/behavioral intentions.


2020 ◽  
Vol 117 (32) ◽  
pp. 19061-19071 ◽  
Author(s):  
Samantha Joel ◽  
Paul W. Eastwick ◽  
Colleen J. Allison ◽  
Ximena B. Arriaga ◽  
Zachary G. Baker ◽  
...  

Given the powerful implications of relationship quality for health and well-being, a central mission of relationship science is explaining why some romantic relationships thrive more than others. This large-scale project used machine learning (i.e., Random Forests) to 1) quantify the extent to which relationship quality is predictable and 2) identify which constructs reliably predict relationship quality. Across 43 dyadic longitudinal datasets from 29 laboratories, the top relationship-specific predictors of relationship quality were perceived-partner commitment, appreciation, sexual satisfaction, perceived-partner satisfaction, and conflict. The top individual-difference predictors were life satisfaction, negative affect, depression, attachment avoidance, and attachment anxiety. Overall, relationship-specific variables predicted up to 45% of variance at baseline, and up to 18% of variance at the end of each study. Individual differences also performed well (21% and 12%, respectively). Actor-reported variables (i.e., own relationship-specific and individual-difference variables) predicted two to four times more variance than partner-reported variables (i.e., the partner’s ratings on those variables). Importantly, individual differences and partner reports had no predictive effects beyond actor-reported relationship-specific variables alone. These findings imply that the sum of all individual differences and partner experiences exert their influence on relationship quality via a person’s own relationship-specific experiences, and effects due to moderation by individual differences and moderation by partner-reports may be quite small. Finally, relationship-quality change (i.e., increases or decreases in relationship quality over the course of a study) was largely unpredictable from any combination of self-report variables. This collective effort should guide future models of relationships.


2021 ◽  
Author(s):  
Patrick McNamara ◽  
Wesley J Wildman ◽  
George Hodulik ◽  
David Rohr

Abstract Study Objectives To test and extend Levin & Nielsen’s (2007) Affective Network Dysfunction (AND) model with nightmare disorder (ND) image characteristics, and then to implement the extension as a computational simulation, the Disturbed Dreaming Model (DDM). Methods We used AnyLogic V7.2 to computationally implement an extended AND model incorporating quantitative effects of image characteristics including valence, dominance, and arousal. We explored the DDM parameter space by varying parameters, running approximately one million runs, each for one month of model time, varying pathway bifurcation thresholds, image characteristics, and individual-difference variables to quantitively evaluate their combinatory effects on nightmare symptomology. Results The DDM shows that the AND model extended with pathway bifurcations and image properties is computationally coherent. Varying levels of image properties we found that when nightmare images exhibit lower dominance and arousal levels, the ND agent will choose to sleep but then has a traumatic nightmare, whereas, when images exhibit greater than average dominance and arousal levels, the nightmares trigger sleep-avoidant behavior, but lower overall nightmare distress at the price of exacerbating nightmare effects during waking hours. Conclusions Computational simulation of nightmare symptomology within the AND framework suggests that nightmare image properties significantly influence nightmare symptomology. Computational models for sleep and dream studies are powerful tools for testing quantitative effects of variables affecting nightmare symptomology and confirms the value of extending the Levin & Nielsen AND model of disturbed dreaming/ND.


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