Data management and analysis

Data collection

Many IR studies require a team to collect new, purpose-specific data (known as primary data) to address a specific research objective. As described in Module 3, this is typically conducted through the use of survey tools such as questionnaires or interview guides. Before commencing data collection, there are several decisions and steps that must be taken. A key component of data collection is determining the most appropriate way to sample and recruit participants. This will depend on the choice of research methods. Your data collection strategy should describe your approach to identifying and recruiting participants and the process for collecting data. Ensuring the quality and appropriate management of collected data is also a key component; these issues are discussed below.

Sampling and recruitment

Sampling refers to how you will select participants for your research. Sampling methods can be either probability- or non-probability-based. Probability-based or random sampling enables every individual in a population to have equal chances of being selected into a study. Probability sampling techniques are preferable for quantitative research as this type of research typically requires a sample that accurately reflects the characteristics of the broader population from which it was selected. To do this, probability-based sampling requires a sampling frame, that is a complete list of units – individuals, households, clinic facilities etc. – that your sample will be drawn from.

However, in some situations, random sampling is not the preferred option due to factors such as lack of specific resources (e.g. a list of the entire population to provide a sampling frame), time or cost constraints. Additionally, your research may be interested in data that are collected from a specific category of subject, or in a specific context; this is known as non-probability, or non-random sampling and participants are deliberated selected for a specific purpose or based on specific criteria. Given that IR takes place in real-world settings and under real-world conditions, non-probability sampling methods are likely to be more appropriate and feasible for your study.

Sample size

Qualitative research generally requires far fewer participants than quantitative research. A typical sample size for a focus group discussions (FGD), for example, is around 6–10 participants, while key informant interviews (KIIs) are typically conducted with around 10–20 participants in total, given the time they require and the richness of data they provide. FGDs and KIIs are usually stopped when no new information is being generated during data collection – this phenomenon is known as 'data saturation'.

In quantitative research, sample size is determined by statistical power parameters; that is, having enough 'power' or probability within your study to detect an effect under investigation. Sample size can also be guided by the level of expected precision when there is no comparison. Sample size calculation formulae and calculation procedures can be found in standard biostatistics reference materials.¹ Early involvement and discussion with a statistician are strongly advised to calculate the appropriate sample size needed for various types of research methods and to deal with any adjustments that may be needed as the study evolves.

Secondary data collection

Many IR studies also use existing data (known as secondary data) to address a specific research objective. By using existing data that is routinely collected, research teams can reduce potential costs and time associated with conducting a study. TB registers are an example of routinely collected data that could be utilized and analysed as a secondary data source.

Additionally, a feature unique to digital interventions is that many have the ability to automatically collect and store electronic data that can be useful to a study team. This type of 'system-generated' data is often under-utilised, yet when combined with purposefully collected primary data, can provide important information useful for monitoring implementation outcomes.

If your study involves the development and evaluation of a new digital intervention, the study team should consider which data points may be useful from an IR perspective and ensure they are built into the back-end process. These data points should be explicitly discussed with any developers or technologists responsible for building or designing the digital intervention, rather than assuming they will automatically be captured.

For studies using secondary data, either alone or as part of a mixed-methods approach, it is still necessary to describe your data collection process, including how you will identify which data to use (for example, will you take all records from an electronic TB register, or just a sample?) and the methods for extracting and preparing the data for analysis.