Social Cohesion, Mutual Efficacy, and Informal Social Control: A Confirmatory Factor Analysis of Collective Efficacy

Background: Collective efficacy theory examines the process by which communities act together to solve problems. Collective efficacy is typically comprised of two constructs: social cohesion and informal social control. Social cohesion is the extent of trust, solidarity, and shared values among community members. Informal social control reflects a community’s willingness to enforce social norms.

Recent research suggests that mutual efficacy, which is defined as, “Community members’ beliefs that collective action can be successful,” mediates the relationship between social cohesion and informal social control. However, previous research on mutual efficacy is limited because they utilize a two-item proxy measure of mutual efficacy.

This study will contribute to the literature by using primary data set that was created for developing a mutual efficacy scale. The relationships among social cohesion, mutual efficacy, and informal social control are examined using exploratory factor analysis and confirmatory factor analysis.

Method: Data for this study were collected from Prolific.co, an online survey platform. A sample of 750 individuals was recruited using Prolific.co was recruited.

The majority of respondents were white (n = 574, 76.1%), female (n = 374, 49.1%) and the average age of respondents was 45.7 years old (s.d. = 16.1). Most respondents were married/cohabiting (n = 373, 49.5%), employed (n = 348, 46.2%), and held a Bachelor’s degree (n = 275, 36.5%) at the time of survey completion.

Mutual efficacy was measured using nine items (e.g. residents in your neighborhood can work together successfully to influence positive change). Social cohesion and informal social control are measured using items based on the original measure of collective efficacy.

We first ran three exploratory factor analyses (EFA) on the social cohesion, mutual efficacy, and informal social control items; testing one through four factor solutions. We then conducted two confirmatory factor analyses (CFA) to identify the factor structures among these constructs.

Results: Findings from the EFA suggest the three-factor (CFI = 0.942 TLI = 0.910 SRMR = 0.050) and four-factor (CFI = 0.983 TLI = 0.969 SRMR = 0.029) solutions fit the data comparatively well. In the three-factor solution, social cohesion, mutual efficacy, and informal social control items load on their expected factors. The four-factor solution divided the mutual efficacy items across two, highly correlated factors (r = 0.848).

Findings from the CFA mirror those from the EFA. Specifically, the three-factor (CFI = 0.927 TLI = 0.914 SRMR = 0.053) and four-factor (CFI = 0.928 TLI = 0.913 SRMR = 0.054) fit the data comparatively well. However, the two-factor measure of mutual efficacy shared a correlation of 1.00, indicating that they are the same factor. Thus, we retained the three-factor solution.

Conclusions: Findings suggest that mutual efficacy is distinct from, though positively associated with social cohesion and informal social control. Future research can increase our understanding of how to raise mutual efficacy and facilitate collective actions in communities. Findings also highlight the importance of building social networks in communities that emphasize fostering a shared vision among members, and community-based planning for social change efforts.