Table 1.
Taxonomy of mixed method designs for economic evaluation
| Element | Categorya | Definitionb |
|---|---|---|
| Structure | ||
| Sequential | Qual ➔ QUAN | Sequential collection and analysis of quantitative and qualitative data, beginning with qualitative data, for primary purpose of testing the economic impact of an implementation activity, e.g., collect qualitative data about implementation costs to inform the design, execution, or analysis of a subsequent economic evaluation. |
| QUAN ➔ Qual | Sequential collection and analysis of quantitative and qualitative data, beginning with quantitative data, for primary purpose of testing the economic impact of an implementation activity, e.g., after an economic evaluation of an implementation initiative, collect qualitative data about the value generated by implementing the target evidence-based practice. | |
| Simultaneous | Qual + QUAN | Simultaneous collection and analysis of quantitative and qualitative data for primary purpose of testing the economic impact of an implementation activity, e.g., use both qualitative and quantitative data to perform cost-benefit analysis calculations for an implementation initiative. |
| QUAN + QUAL | Simultaneous collection and analysis of quantitative and qualitative data, giving equal weight to both testing the economic impact of an implementation activity and exploration/hypothesis generation about its economic costs and impacts, e.g., while measuring costs as an implementation outcome, collect exploratory qualitative data about factors that increase or decrease implementation costs in different settings. | |
| Function | ||
| Convergence | Using both types of methods to answer the same question, through: (a) Comparison of results to see if they reach the same conclusion [MERGE two datasets together], e.g., triangulation to validate quantitative measures of implementation costs using qualitative data. (b) Converting a dataset from one type into another [CONNECT—transform] by quantifying qualitative data (e.g., counting the number of times different categories of impact are mentioned) or qualifying quantitative data (e.g., extracting types of costs from a budget spreadsheet). |
|
| Complementarity | Using each type of method to answer a related question or series of questions for purposes of: Evaluation [EMBED one study within another so that one type of data provides a supportive role to the other dataset], e.g., using quantitative data to evaluate costs and impacts and qualitative data to evaluate the processes through which those costs and impacts arose (was the implementation process efficient?). Elaboration [CONNECT—elaborate, have one dataset build upon another dataset], e.g., when comparing the cost-effectiveness of implementation strategies, use qualitative data to provide depth of understanding and quantitative data to provide breadth of understanding. |
|
| Expansion | Using one type of method to answer questions raised by the other type of method [CONNECT—expand], e.g., collecting follow-up qualitative data to explore why an implementation strategy was not cost-beneficial in a certain setting. | |
| Development | Using one type of method to answer questions that will enable use of the other type of method to answer other questions [CONNECT—initiate], e.g., develop plan for measuring costs or conducting sensitivity analyses based on qualitative data. | |
| Sampling | Using one type of method to define or identify the participant sample for collection and analysis of data using the other type of method [CONNECT—sample], e.g., selecting interview informants to provide perspectives on costs and impacts based on responses to a survey questionnaire. | |
Adapted from Palinkas et al. [10]
aQUAL or Qual qualitative, QUANT or Quant quantitative, upper- or lowercase indicates whether the method was primary/dominant versus secondary/subservient, respectively
bRather than presenting the process elements separately in the table, we note each one that is relevant in brackets (with the type of process in uppercase letters [AS SUCH]) within the definition for a given function element (i.e., indicating the processes by which that function is achieved)