Community Psychology

Malpert, Adele V.; Suiter, Sarah V.; Kivell, Natalie; Perkins, Douglas D.; Bess, Kimberly D.; Evans, Scotney D.; Hanlin, Carrie E.; Conway, Patricia L.; McCown, Diana; Prilleltensky, Isaac

doi:10.4135/9781526405555.n19

Cited by 3 publications

(4 citation statements)

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“…It is a relationally and context-sensitive focus group method (see Barbour, 2014; Chilisa, 2020), focusing on creating an optimal interpersonal space for data collection that enables research participation irrespective of participants’ or researchers’ background (socio-economic or socio-cultural) and by its composition and application provides a transformative context. Since its development in 2002, the method has been applied in more than 30 different research topics (social science, environmental science, community psychology, youth studies, socio-gerontology), thereby demonstrating its applicability across subject disciplines and contexts (Dlamini & Tesfamichael, 2021; Liebenberg & Theron, 2015; Malpert et al, 2017; Romm, 2018; Theron, 2016).…”

Section: Example Of Mmogo-methods Participant-created Visual Datamentioning

confidence: 99%

An Analytical Tool for Visual Data Analysis: Application to the Mmogo-Method^®

Baart

Roos

2022

International Journal of Qualitative Methods

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An analytical tool that enables experienced researchers and non-experts to analyse participant-created visual data has so far remained underexplored. Existing frameworks to analyse participant-generated visual data tend to use selected theoretical frameworks, guided by participants’ interpretations, to generalise results, test hypotheses, or to identify representational meanings. Analyses of visual data as separate units employ mainly content analysis or (social) semiotic analysis. This article presents a tool for conducting a systematic and transparent analysis of image-based research data by adopting an empiricist and pragmatist approach. We designed a step-by-step procedure to guide researchers individually or in a group to conduct an analysis of such data. Our prototype—which we named Created-Image Data Analysis (CIDA)—was developed by applying design-based research. The CIDA tool consists of five phases, each with an analytical focus and operational questions. Phase 1 covers the basic information; Phase 2 examines the elements and organisation of the visual representation; Phase 3 analyses its logic or cohesion; Phase 4 interprets meaning; and Phase 5 concludes with an evaluation. We applied CIDA to an example of visual data obtained by applying the Mmogo-method in which participants use materials such as clay, dried grass stalks, and beads of different sizes and colours to respond to a researcher-introduced prompt. The CIDA tool is applicable to all static participant-created visual data obtained during research, but it has not yet been tested for analysing moving image-based data. The tool qualifies as heuristic; it offers a systematic procedure to guide an analysis with data-grounded interpretations. The analysis is replicable and open to scrutiny. When the findings from the textual data are considered in combination with the participant-created visual data, the trustworthiness of the interpretations of these datasets is enhanced. This analytical tool enables a rigorous procedure applicable to visual data across subject disciplines and for different research purposes.

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Section: Example Of Mmogo-methods Participant-created Visual Datamentioning

confidence: 99%

An Analytical Tool for Visual Data Analysis: Application to the Mmogo-Method^®

Baart

Roos

2022

International Journal of Qualitative Methods

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“…Values familiar to community psychology such as empowerment, social justice, and health and well‐being (Malpert et al., forthcoming) informed my decision‐making related to understanding my role and proceeding with my partnership in an ethical manner. There were, of course, times when these values were in conflict with one another just as the differing views of poverty were.…”

Section: Values Toolboxmentioning

confidence: 99%

Navigating Community Development Conflicts: Contested Visions of Poverty & Poverty Alleviation

Suiter

2017

American J of Comm Psychol

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This article explores an ethical dilemma that arose from the author's involvement in a project intended to identify and address the needs of seven batey communities in the Dominican Republic. In the summer of 2014, the author conducted a large-scale needs assessment to inform strategic planning for a foundation that was invested in community development work in those communities. Through a collaborative process, the author worked with the foundation, representatives of the migrant communities, and other researchers to develop survey and focus group tools that were administered to 1,360 and 54 people, respectively. Data collection, analysis, and dissemination proceeded as planned. The conflict and resulting ethical dilemma emerged at the point of data interpretation and strategic planning, processes that revealed that the different parties held conflicting theories of poverty. The ideological conflicts related to why poverty exists, who is responsible for it, and what should be done about it made proceeding with the project ethically tenuous for the author. The author describes the values and theories of ethics on which she relied to navigate this challenge, as well as reflections on her continued participation in the community development process.

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“…As this idea comprises, instead of studying individual experiences and choices, community psychologists understand well‐being as a positive state that entails both individual and community levels (Evans & Prilleltensky, 2007), shifting the view of well‐being as an individual phenomenon toward one that is relational and a matter of social justice (Prilleltensky, 2012). Consequently, individual and collective well‐being experiences are connected and, in turn, inseparable from the social conditions that determine the structure of opportunities in which people operate and develop (Malpert et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, individual and collective well-being experiences are connected and, in turn, inseparable from the social conditions that determine the structure of opportunities in which people operate and develop (Malpert et al, 2017).…”

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confidence: 99%

Child and adolescent well‐being: Potentials of intersectionality for community psychology

Bilbao-Nieva

Alfaro

2023

Community & Applied Soc Psy

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This theoretical article aims to recognize the potential of intersectionality theory for understanding well‐being during childhood and adolescence, particularly from a community psychology perspective. Following the tenets of intersectionality, the article analyses and proposes three relevant dimensions for community psychologists, researchers and professionals interested in studying and intervening in children's and adolescents' well‐being. Those dimensions consider (1) adultism as an intersecting power axis fundamental for understanding well‐being, (2) the acknowledgment of diversity in well‐being conceptions and experiences as a critical issue and (3) the need for advancing studies that involve praxis and engagement in transformative changes for child and adolescent well‐being.

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Community Psychology

Cited by 3 publications

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An Analytical Tool for Visual Data Analysis: Application to the Mmogo-Method^®

An Analytical Tool for Visual Data Analysis: Application to the Mmogo-Method^®

Navigating Community Development Conflicts: Contested Visions of Poverty & Poverty Alleviation

Child and adolescent well‐being: Potentials of intersectionality for community psychology

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Community Psychology

Cited by 3 publications

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An Analytical Tool for Visual Data Analysis: Application to the Mmogo-Method®

An Analytical Tool for Visual Data Analysis: Application to the Mmogo-Method®

Navigating Community Development Conflicts: Contested Visions of Poverty & Poverty Alleviation

Child and adolescent well‐being: Potentials of intersectionality for community psychology

Contact Info

Product

Resources

About

An Analytical Tool for Visual Data Analysis: Application to the Mmogo-Method^®

An Analytical Tool for Visual Data Analysis: Application to the Mmogo-Method^®