Changing Rural Institutions and Social Relations

Social Network Analysis (SNA) has received growing attention among diverse academic fields for studying ‘social relations’ among individuals and institutions. Unfortunately, its application has remained limited in the study of livelihood systems of rural poor. Complexity in rural livelihoods has increased sharply in the face of increased pressure on natural resources and rapid shift in farm-based to non-farm based employments. This poses great challenge to successful livelihood intervention in rural areas. On one hand, rural development/extension needs to cater to diverse information and service need of the rural people; on other hand, rural institutions need to deliver livelihood-sustaining services more efficiently, which often need institutional restructuring at multiple levels. To achieve these challenges, a strong innovative analytical tool is required for understanding the complexity of rural livelihoods and the associated role of rural institutions. SNA provides excellent scope to analyse such complex systems and interactions among their components. This article proposes an outline of using SNA in livelihood system analysis. The analysis can provide answer to many questions of practical importance – Who are the influential actors in a livelihood system? Which are the key institutions contributing towards sustainable livelihoods? How do these actors interact among themselves? This will help rural development administrators to deliver livelihood-supporting services more efficiently through informed targeting and capacity building.

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The SRM Approach to Family Assessment

European Journal of Psychological Assessment ◽

10.1027/1015-5759.21.4.216 ◽

2005 ◽

Vol 21 (4) ◽

pp. 216-225 ◽

Cited By ~ 21

Author(s):

William L. Cook

Keyword(s):

High Risk ◽

Risk Behaviors ◽

Social Relations ◽

Family Relationships ◽

Family Dynamics ◽

Family Assessment ◽

High Risk Behaviors ◽

Analysis Group ◽

The Social ◽

Direction Of Effects

Abstract. In family systems, it is possible for one to put oneself at risk by eliciting aversive, high-risk behaviors from others ( Cook, Kenny, & Goldstein, 1991 ). Consequently, it is desirable that family assessments should clarify the direction of effects when evaluating family dynamics. In this paper a new method of family assessment will be presented that identifies bidirectional influence processes in family relationships. Based on the Social Relations Model (SRM: Kenny & La Voie, 1984 ), the SRM Family Assessment provides information about the give and take of family dynamics at three levels of analysis: group, individual, and dyad. The method will be briefly illustrated by the assessment of a family from the PIER Program, a randomized clinical trial of an intervention to prevent the onset of psychosis in high-risk young people.

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Meta-Accuracy and Perceived Reciprocity From the Perception-Meta-Perception Social Relations Model

Social Psychology ◽

10.1027/1864-9335/a000356 ◽

2019 ◽

Vol 50 (1) ◽

pp. 24-37

Author(s):

Ben Porter ◽

Camilla S. Øverup ◽

Julie A. Brunson ◽

Paras D. Mehta

Keyword(s):

Structural Equation Modeling ◽

Social Relations ◽

Latent Variables ◽

Structural Equation ◽

Multilevel Structural Equation Modeling ◽

Equation Modeling ◽

Complete Understanding ◽

Social Relations Model ◽

Flexible Framework ◽

Multilevel Structural Equation

Abstract. Meta-accuracy and perceptions of reciprocity can be measured by covariances between latent variables in two social relations models examining perception and meta-perception. We propose a single unified model called the Perception-Meta-Perception Social Relations Model (PM-SRM). This model simultaneously estimates all possible parameters to provide a more complete understanding of the relationships between perception and meta-perception. We describe the components of the PM-SRM and present two pedagogical examples with code, openly available on https://osf.io/4ag5m . Using a new package in R (xxM), we estimated the model using multilevel structural equation modeling which provides an approachable and flexible framework for evaluating the PM-SRM. Further, we discuss possible expansions to the PM-SRM which can explore novel and exciting hypotheses.

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