Analysing and interpreting the data you’ve collected brings you, in a sense, back to the beginning. You can use the information you’ve gained to adjust and improve your program or intervention, evaluate it again, and use that information to adjust and improve it further, for as long as it runs. You have to keep up the process to ensure that you’re doing the best work you can and encouraging changes in individuals, systems, and policies that make for a better and healthier community.
The process of drawing conclusions begins early in the coding process. Even as you first begin reviewing and coding your data, you are beginning to form ideas about the important phenomena they indicate, and beginning to generate propositions about these phenomena, and the relationships among them. Once the data are coded the researcher will begin to pursue those early propositions and to examine the data for other conclusions.
The first step here is often to examine the individual themes which have been identified. The data processor gathers together all of the text passages coded for a theme. Reading all of these passages together (while also referring back to their original contexts for accurate interpretation) can enable the data processor to better understand the theme. Often it becomes clear that there is more than one theme captured by the code, and it must be partitioned. At other times, after reading several themes it becomes clear that several should be combined, or one subsumed within another. Eventually, the data processor is able to write a faithful representation of all the data on that theme. Such a summary captures the common features of the reports of the different respondents but avoids glossing over differences among them. It is important to illustrate each of these theme summaries with quotes. This allows the reader to assess the interpretation the analyst has made. Further, it is important to have a scheme for tracking respondents in these quotes—say with ID numbers, pseudonyms, or fictitious initials—so that the reader can see that you are not overly relying on a subset of respondents. This kind of use of quotes thus constitutes a fundamental tactic for ensuring reliability in qualitative research.
Identifying Patterns and Relationships
There is a broad range of ways to go about identifying patterns and relationships among variables and cases in qualitative research. There is no strict recipe for doing so. Rather, there are many methods and tactics to draw from. Miles and Huberman (1994), as mentioned earlier, suggest a variety of tactics, including but not limited to:
- Noting patterns;
- Counting;
- Making metaphors;
- Clustering;
- Partitioning variables; and
- Building chains of evidence.
Ensure consistency with the information and purpose of the inquiry
Data consistency summarises the validity, accuracy, usability, and integrity of related data between applications and across an IT enterprise. This ensures that each user observes a consistent view of the data, including visible changes made by the user’s own transactions and transactions of other users or processes…
Data are of high quality “if they are fit for their intended uses in operations, decision making and planning” (J. M. Juran). Alternatively, the data are deemed of high quality if they correctly represent the real-world construct to which they refer. Furthermore, apart from these definitions, as data volume increases, the question of internal consistency within data becomes paramount, regardless of fitness for use for any external purpose, e.g. a person’s age and birth date may conflict within different parts of a database. The first views can often be in disagreement, even about the same set of data used for the same purpose
The market is going some way to providing data quality assurance. A number of vendors make tools for analysing and repairing poor quality data in situ, service providers can clean the data on a contract basis and consultants can advise on fixing processes or systems to avoid data quality problems in the first place. Most data quality tools offer a series of tools for improving data, which may include some or all of the following:
- Data profiling – initially assessing the data to understand its quality challenges
- Data standardisation – a business rules engine that ensures that data conforms to quality rules
- Geocoding – for name and address data. Corrects data to Worldwide postal standards
- Matching or Linking – a way to compare data so that similar, but slightly different records can be aligned. Matching may use “fuzzy logic” to find duplicates in the data. It often recognises that ‘Bob’ and ‘Robert’ may be the same individual. It might be able to manage ‘house holding’, or finding links between husband and wife at the same address, for example. Finally, it often can build a ‘best of breed’ record, taking the best components from multiple data sources and building a single super-record.
- Monitoring – keeping track of data quality over time and reporting variations in the quality of data. The software can also auto-correct the variations based on pre-defined business rules.
- Batch and Real-time – Once the data is initially cleansed (batch), companies often want to build the processes into enterprise applications to keep it clean.