Quantitative methods involve the collection and analysis of objective data, often in numerical form. The research design is determined prior to the start of data collection and is not flexible. The research process, interventions and data collection tools (e.g. questionnaires) are standardized to minimize or control possible bias. Table 8 provides an overview of quantitative data collection strategies.
Qualitative research is generally used to explore values, attitudes, opinions, feelings and behaviours of individuals and understand how these affect the individuals in question. Researchers using qualitative methods are concerned with individuals’ perceptions of specific topics, issues or situations and the meanings they assign to their lives. This kind of research is important for generating theory, developing policy, improving educational practice, justifying change for a particular practice, and illuminating social issues. It may also be used to explain the results of a previous quantitative study or to prepare for the development of a quantitative study.
If your research team decides to use qualitative methods in your study, you will need to describe how qualitative methods will provide the information to help you address your research objectives and research question(s). For example, qualitative research may be appropriate because you intend to explore the values and behaviours of individuals in the study area in relation to a public health intervention, and to understand how these affect the phenomena in question. For example, why do some households have bed nets but do not use them? Or, why do individuals in a study area decline services from a specialized antenatal clinic? Qualitative methods can provide context, a deeper understanding of stakeholders’ needs and participants’ perspectives.
When collecting qualitative data, it is preferable to use more than one data collection method. Obtaining information on the same phenomena in a variety of ways allows the researcher to triangulate the data, adding rigour to the research. By nature, qualitative data collection is emergent and the design is intentionally flexible to enable the researcher investigate themes (findings) in more detail as they emerge.
Qualitative methods use data collection methodologies such as interviewing, observation, discussions and review of documents (e.g. diaries, historical documents). The results of qualitative research are descriptive or explanatory rather than predictive, and are typically time-consuming to collect and analyse. The following table may be helpful to you as you decide which qualitative tools and techniques are most appropriate for your IR project (Table 9).
Unlike quantitative data collection, qualitative data collection can be more flexible allowing the research to incorporate emerging themes in the ongoing data collection. This allows the researcher to test and validate findings as they collect the data. For example, perhaps in one in-depth interview, the researcher learns that people do not attend the lymphatic filariasis mass drug administration because they use traditional medicines and therefore feel that they are already under treatment. The researcher may then add a related question to subsequent in-depth interviews to see how prevalent this phenomenon is in the study population.
Table 10 describes situations when various qualitative data collection techniques can be used.
All study instruments (quantitative and qualitative) should be pre-tested to check the validity and reliability of data collection tools. Pre-testing allows the research team to check whether the research instructions and questions are clear, context specific, and that adequate time has been allowed to administer the questionnaire, etc. Pre-testing should be conducted from a comparable study population and environment. Since data management is critical to the success of the research, the data management team should be available during the discussion that follows the pre-test, in order to incorporate changes into the final design of the tool and facilitate the incorporation of appropriate checks into the data entry system. This stage includes designing the forms for recording measurements, developing programmes for data entry, management and analysis; and planning dummy tabulations to ensure the appropriate variables are collected.