Exploring the lived experiences of young adults (ages 18-25) in Kisumu County, Kenya, regarding their perceptions and engagement with local climate change adaptation initiatives.”

This topic would involve in-depth interviews, possibly focus groups, and potentially ethnographic observations to understand their perspectives, challenges, motivations, and interactions with programs designed to help communities adapt to climate change (e.g., related to agriculture, water management, disaster preparedness like the flooding discussed earlier).

(b) Three Ways My Research May Be Biased by Beliefs, Values, and Expectations:

If I were a human researcher conducting this study, my pre-existing beliefs, values, and expectations could introduce bias in several ways, even with rigorous qualitative methods.

1. Selection Bias and “Optimism Bias” towards Youth Engagement:
   • Belief/Expectation: I might hold a strong personal belief that young people are inherently more environmentally conscious, adaptable, and eager to participate in community-level change, especially concerning climate change, given its long-term impact on their future. I might also expect to find widespread enthusiasm for “modern” adaptation initiatives.
   • How Bias Might Manifest: This could lead me to subconsciously:
     • Recruit participants selectively: I might gravitate towards interviewing young adults who are already vocal or visibly engaged in environmental groups, inadvertently excluding those who are disengaged, apathetic, or facing more pressing immediate survival concerns that overshadow climate change.
     • Interpret data through a positive lens: I might overemphasize positive narratives of engagement and resilience, potentially downplaying or rationalizing expressions of cynicism, skepticism, or practical barriers (like lack of resources or political will) that hinder engagement. I might also be less likely to probe deeply into reasons for non-participation, assuming it’s due to lack of awareness rather than systemic issues.
2. Confirmation Bias towards Specific Adaptation Strategies (e.g., technology-driven solutions):
   • Belief/Value: I might personally value and believe in the efficacy of technological solutions or formally structured programs (e.g., government-led initiatives, NGO projects involving specific technologies like solar irrigation or drought-resistant seeds) as the primary means of climate change adaptation. I might also value formal education as a key driver of understanding and engagement.
   • How Bias Might Manifest: This could lead me to:
     • Frame interview questions: My questions might implicitly steer participants towards discussing their experiences with these specific, formal initiatives, rather than exploring informal, traditional, or community-led adaptation strategies that might be equally or more prevalent but less “modern” or “visible.” For instance, I might ask more about “using improved seeds” than about “community pooling of labor for traditional water harvesting.”
     • Code and analyze data selectively: I might pay more attention to, and prioritize the analysis of, narratives that confirm the importance or impact of technology-driven solutions, potentially overlooking or under-emphasizing the significance of local ecological knowledge, social networks, or cultural practices in adaptation.
     • Discount alternative perspectives: If participants express disillusionment with formal initiatives, I might attribute it to “lack of understanding” rather than critically examining potential flaws in the design or implementation of those initiatives from their lived perspective.
3. Cultural and Socio-Economic Blind Spots/Assumptions:
   • Belief/Expectation: While researching in Kisumu, I might carry assumptions about the homogeneity of “youth experiences” or implicitly apply a Western-centric lens to concepts like “engagement” or “adaptation,” without fully appreciating local nuances of poverty, unemployment, educational access, and cultural priorities. I might also assume a universal understanding of “climate change” as a scientific concept, rather than exploring local interpretations or lived experiences of environmental shifts.
   • How Bias Might Manifest: This could lead to:
     • Misinterpreting non-verbal cues or silence: In interviews, a participant’s hesitation or brief answers might be misinterpreted as disinterest, rather than culturally appropriate deference or a lack of trust.
     • Overlooking context-specific barriers: I might fail to adequately probe how daily struggles for food security, access to clean water, or obtaining basic education might significantly overshadow long-term climate change concerns, leading to an incomplete picture of their “lived experience.”
     • Imposing external definitions: My analysis might inadvertently impose definitions of “successful adaptation” or “meaningful engagement” that don’t align with the community’s own values or priorities, missing locally relevant and effective strategies because they don’t fit my preconceived notions of what adaptation “looks like.”

Recognizing these potential biases is crucial. To mitigate them, I would employ strategies such as prolonged engagement in the field, seeking diverse participant perspectives, engaging in reflexive journaling, conducting member-checking with participants, and actively seeking feedback from local experts or cultural guides.