Research Article: Hostile attribution bias and angry rumination: A longitudinal study of undergraduate students

Date Published: May 31, 2019

Publisher: Public Library of Science

Author(s): Yueyue Wang, Shen Cao, Yan Dong, Ling-Xiang Xia, Angel Blanch.

http://doi.org/10.1371/journal.pone.0217759

Abstract

Angry rumination and hostile attribution bias are important cognitive factors of aggression. Although prior theoretical models of aggression suggest that aggressive cognitive factors may influence each other, there are no studies examining the longitudinal relationship between angry rumination and hostile attribution bias. The present study used cross-lagged structural equation modeling to explore the longitudinal mutual relationship between hostile attribution bias and angry rumination; 941 undergraduate students (38.5% male) completed questionnaires assessing the variables at two time points. The results indicate that hostile attribution bias showed a small but statistically significant effect on angry rumination 6 months later, and angry rumination showed a quite small but marginally significant effect on hostile attribution bias across time. The present study supports the idea that hostile attribution bias influences angry rumination, and argue that the relationship between angry rumination and hostile attribution bias may be mutual. Additionally, the results suggest that there may be a causal relation of different aggression-related cognitive factors.

Partial Text

Angry rumination is prolonged thinking about personally meaningful angry events and is accompanied by angry feelings or thoughts about revenge [1, 2]. Angry rumination is also a pattern of thinking that specifically intensifies anger and increases aggressive tendencies [3]. Previous behavioral studies have shown that angry rumination is associated with negative outcomes, such as aggressive behavior [4], negative emotion, and depressive symptoms [5]. Furthermore, previous studies further revealed that angry rumination is one of the several important aggression-related cognitive factors [6]. In sum, angry rumination is a negative mental factor that should be subjected to intervention and changed. Thus, understanding the mental mechanisms of the development of angry rumination in daily life is both important and necessary. Recently, researchers have focused on the influencing factors in angry rumination, such as executive control [2], self-control [4], and self-compassion [7]. However, the aggression-related cognitive mechanism of the development of angry rumination in daily life is still unclear. Thus, we first aimed to discover one of the aggressive cognitive mechanisms that influence the frequency of angry rumination in daily life.

Descriptive statistics and correlation analysis were performed using SPSS 22.0 software. Then, confirmatory factor analyses (CFAs) and cross-lagged model analysis were conducted with Mplus 7.0. Latent Variable Structural Equation Modeling was used in the present research because, compared with the manifest variable model, it has several advantages, such as the ability to take measurement error into account, involving whole systems of conceptual relationships, and the potential to improve scale development in the field by providing statistical tests of construct dimensionality [23]. In the present study, all variables in the model at Time 1 and Time 2 were latent variables. The latent variables were created using parcels of items from scales, because there are several strengths of item parceling: increase the stability of the parameter estimates; improve the variable-to-sample-size ratio; help mitigate the problem of non-normality; enhance the communality; increase the common-to-unique ratio for each indicator; and reduce random error [24]. In order to balance the load of items, the items of hostile attribution bias and angry rumination were assigned to four parcels by item-to-construct balance method [25], respectively. For hostile attribution bias, the original 16 items were replaced by 4 parcels of 4 items each; for angry rumination, the 19 items were replaced by 3 parcels of 5 items and 1 parcel of 4 items. Then, we tested the model fit of the measurement model of hostile attribution bias and angry rumination at the two time points, respectively. Finally, we conducted the longitudinal cross-lagged panel analysis between the hostile attribution bias and angry rumination.

 

Source:

http://doi.org/10.1371/journal.pone.0217759