This article is the third in a series of four articles providing practical guidance for conducting high-quality qualitative research in primary care. It addresses frequently asked questions about sampling, data collection, and analysis. The data collection plan should be broadly defined and open, becoming flexible during data collection. Sampling strategies should be chosen to yield rich information and align with the methodological approach. Data saturation determines sample size and varies by study. Common data collection methods include participant observation, face-to-face interviews, and focus group discussions. Analysis in ethnographic, phenomenological, grounded theory, and content analysis studies yields different narrative findings: a detailed description of a culture, the essence of lived experience, a theory, and a descriptive summary, respectively. Sampling involves selecting participants and situations that provide rich data. Qualitative research uses deliberate sampling strategies such as purposive, criterion, theoretical, convenience, and snowball sampling. Sampling strategies vary depending on the qualitative design, with ethnography using purposive sampling, phenomenology using criterion sampling, and grounded theory starting with purposive sampling and later using theoretical sampling. Content analysis often uses purposive, convenience, or snowball sampling. Sampling is influenced by the setting's characteristics, such as access, time, and participant vulnerability. Sampling also affects data analysis, as decisions about whom or what to sample next are based on what is still missing for rich findings. Data saturation is achieved when no new analytical information arises, and the study provides maximum information on the phenomenon. Sample size depends on data richness, participant variety, research question breadth, and data collection method. Data saturation is determined by the availability of in-depth data showing patterns, categories, and variety of the phenomenon. Researchers often conduct additional observations or interviews to confirm data saturation. Data collection methods include participant observation, interviews, and focus group discussions. Participant observation involves participating in and observing a group or individual over time. Interviews involve asking questions to understand the meaning of central themes in participants' lives. Focus group discussions involve a small group discussing a topic, often guided by a moderator. Data collection is unstructured and flexible, with decisions made during fieldwork. Interviews are typically semi-structured, with a guide to ensure coverage of topics. Focus group discussions are often used to explore patients' experiences, evaluate programs, and understand health professionals' roles. Analysis begins with organizing data into manageable units. Qualitative analysis often involves inductive coding, creating categories and abstraction. Ethnography analyzes behaviors and thoughts to understand culture. Phenomenology describes and interprets the meaning of experiences. Grounded theory generates a theory through constant comparison. Descriptive content analysis identifies themes and patterns in data. Analysis software can assist in managing data, but researchers make analytical decisions based on data. The next article in the series will focus on trustworthiness and publishing qualitative research.This article is the third in a series of four articles providing practical guidance for conducting high-quality qualitative research in primary care. It addresses frequently asked questions about sampling, data collection, and analysis. The data collection plan should be broadly defined and open, becoming flexible during data collection. Sampling strategies should be chosen to yield rich information and align with the methodological approach. Data saturation determines sample size and varies by study. Common data collection methods include participant observation, face-to-face interviews, and focus group discussions. Analysis in ethnographic, phenomenological, grounded theory, and content analysis studies yields different narrative findings: a detailed description of a culture, the essence of lived experience, a theory, and a descriptive summary, respectively. Sampling involves selecting participants and situations that provide rich data. Qualitative research uses deliberate sampling strategies such as purposive, criterion, theoretical, convenience, and snowball sampling. Sampling strategies vary depending on the qualitative design, with ethnography using purposive sampling, phenomenology using criterion sampling, and grounded theory starting with purposive sampling and later using theoretical sampling. Content analysis often uses purposive, convenience, or snowball sampling. Sampling is influenced by the setting's characteristics, such as access, time, and participant vulnerability. Sampling also affects data analysis, as decisions about whom or what to sample next are based on what is still missing for rich findings. Data saturation is achieved when no new analytical information arises, and the study provides maximum information on the phenomenon. Sample size depends on data richness, participant variety, research question breadth, and data collection method. Data saturation is determined by the availability of in-depth data showing patterns, categories, and variety of the phenomenon. Researchers often conduct additional observations or interviews to confirm data saturation. Data collection methods include participant observation, interviews, and focus group discussions. Participant observation involves participating in and observing a group or individual over time. Interviews involve asking questions to understand the meaning of central themes in participants' lives. Focus group discussions involve a small group discussing a topic, often guided by a moderator. Data collection is unstructured and flexible, with decisions made during fieldwork. Interviews are typically semi-structured, with a guide to ensure coverage of topics. Focus group discussions are often used to explore patients' experiences, evaluate programs, and understand health professionals' roles. Analysis begins with organizing data into manageable units. Qualitative analysis often involves inductive coding, creating categories and abstraction. Ethnography analyzes behaviors and thoughts to understand culture. Phenomenology describes and interprets the meaning of experiences. Grounded theory generates a theory through constant comparison. Descriptive content analysis identifies themes and patterns in data. Analysis software can assist in managing data, but researchers make analytical decisions based on data. The next article in the series will focus on trustworthiness and publishing qualitative research.