The experimental and survey designs described in Chaps. 7, 8, 9, and 12 are grounded on the positivist paradigm and involve the collection and presentation of data in numerical form. While these methods are commonly used in agriculture, they are not suited to every research question, as not all aspects of agriculture can be measured quantitatively. This necessitates the consideration of qualitative and participatory approaches, which focus on elements that cannot be expressed numerically. Qualitative research emphasizes narratives, meanings, and in-depth exploration of phenomena and context. It is particularly useful for understanding the complex interactions between human behavior and the farm environment. Rooted in the disciplines of sociology, psychology, and anthropology, qualitative research is based on the philosophical assumptions that multiple realities exist, meanings are complex, and research is value laden. Though the use of qualitative research in agriculture is not as developed as in education and sociology, it is increasingly being used to address agricultural social and development challenges. We mostly find qualitative approaches used in gender, policy analysis, and market research. We argue that the limited use in agricultural research potentially loses more embedded knowledge, particularly useful in understanding the complex farming systems. Qualitative approaches offer several benefits suited to understanding complex issues, such as the multifaceted social processes of farmers. In gender studies, qualitative approaches are suited for illuminating issues of males and females. In agricultural markets, they are useful for mapping the value chains, as well as visualizing roles and relationships, and understanding the formal and informal systems that govern interactions among the actors. Qualitative methods are particularly useful in capturing the contextual perspectives of those affected by policies, providing a more comprehensive understanding of the issues at hand. Basic characteristics of qualitative research approaches include a lack of a hypothesis and the nonuse of statistics in data analysis. It also operates on a central phenomenon that aims to extract details of specific settings. It is context-bound. Data collection is more of a depth than a breadth of analysis, and aims to present information-rich cases, where “thick description” is emphasized. It uses multiple sources of data collected in a natural setting. Steps to follow in qualitative research closely resemble those of quantitative research, but qualitative research is complex, with backward and forward movements and steps overlapping. It is process-oriented, as opposed to quantitative research, which is outcome-oriented. Overall, key differences exist between the quantitative and qualitative approaches in conception, data collection, analysis, and reporting. The main limitations of qualitative approaches include subjectivity, time-consuming, inadequate rigor, and limited generalization.

Nonexperimental Designs 2: Qualitative and Participatory Approaches

章节
发表日期：

2026年

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Jayne Njeri Mugwe,
Steven Runo

摘要

The experimental and survey designs described in Chaps. 7, 8, 9, and 12 are grounded on the positivist paradigm and involve the collection and presentation of data in numerical form. While these methods are commonly used in agriculture, they are not suited to every research question, as not all aspects of agriculture can be measured quantitatively. This necessitates the consideration of qualitative and participatory approaches, which focus on elements that cannot be expressed numerically. Qualitative research emphasizes narratives, meanings, and in-depth exploration of phenomena and context. It is particularly useful for understanding the complex interactions between human behavior and the farm environment. Rooted in the disciplines of sociology, psychology, and anthropology, qualitative research is based on the philosophical assumptions that multiple realities exist, meanings are complex, and research is value laden. Though the use of qualitative research in agriculture is not as developed as in education and sociology, it is increasingly being used to address agricultural social and development challenges. We mostly find qualitative approaches used in gender, policy analysis, and market research. We argue that the limited use in agricultural research potentially loses more embedded knowledge, particularly useful in understanding the complex farming systems. Qualitative approaches offer several benefits suited to understanding complex issues, such as the multifaceted social processes of farmers. In gender studies, qualitative approaches are suited for illuminating issues of males and females. In agricultural markets, they are useful for mapping the value chains, as well as visualizing roles and relationships, and understanding the formal and informal systems that govern interactions among the actors. Qualitative methods are particularly useful in capturing the contextual perspectives of those affected by policies, providing a more comprehensive understanding of the issues at hand. Basic characteristics of qualitative research approaches include a lack of a hypothesis and the nonuse of statistics in data analysis. It also operates on a central phenomenon that aims to extract details of specific settings. It is context-bound. Data collection is more of a depth than a breadth of analysis, and aims to present information-rich cases, where “thick description” is emphasized. It uses multiple sources of data collected in a natural setting. Steps to follow in qualitative research closely resemble those of quantitative research, but qualitative research is complex, with backward and forward movements and steps overlapping. It is process-oriented, as opposed to quantitative research, which is outcome-oriented. Overall, key differences exist between the quantitative and qualitative approaches in conception, data collection, analysis, and reporting. The main limitations of qualitative approaches include subjectivity, time-consuming, inadequate rigor, and limited generalization.