Qualitative Data
Please see the newly published CFIR User Guide which contains 6 tools and templates to guide using CFIR in implementation research:
CFIR Implementation Research Worksheet: A worksheet (see Additional File 6) to guide users through using CFIR in study design, data collection, data analysis, data interpretation, and knowledge dissemination. Last updated June 2025.
CFIR Construct Example Questions: Examples of open-ended questions for each CFIR construct along with broader implementation questions (see Additional File 1); these questions must be customized to meet the needs of the project and can then be used in data collection instruments. Last updated June 2025.
CFIR Construct Coding Guidelines: Detailed coding guidelines for each construct (see Additional File 2); these guidelines should be operationalized for each project and further developed throughout the coding process by adding new inductively identified constructs and subconstructs as needed. Last updated June 2025.
Inner Setting Memo Template: A template to aggregate, summarize, and rate data at the Inner Setting (unit of analysis) level (see Additional File 3). Last updated June 2025.
CFIR Construct Rating Guidelines: Detailed rating guidelines for each construct (See Additional File 4); these guidelines should be operationalized for each project to ensure consistency across ratings for each construct and Inner Setting. Last updated June 2025.
CFIR Construct x Inner Setting Matrix Template: A template designed to compare construct ratings (with short summaries of the data and supporting rationale) within and across each Inner Setting in a project (See Additional File 5). Last updated June 2025.
Resources for Coding Data – based on the Updated CFIR:
- Dedoose qualitative coding software template in Excel, prepopulated with updated CFIR constructs, thanks to Bridget Fowler King with the Shirley Ryan AbilityLab.
- A generic template in Excel, prepopulated with updated CFIR constructs, that can be imported using other coding software that supports this format.
If you use other qualitative analysis software (e.g., MAXQDA, ATLAS.ti) and are willing to share, please send us a template and we will post it to the site.
We have retained this guidance based on the outdated 2009 CFIR. However, we strongly advise data interpretation based on the updated CFIR.
A common evaluation objective is to identify constructs that appear to distinguish between organizations with high and low implementation success, which provides insights into the key constructs that influence implementation. This information can be used to design implementation strategies for individual or larger-scale implementation efforts.
After applying ratings at the appropriate levels (see Data Analysis: Rating Data above), analysts can be unblinded to implementation outcomes in order to determine distinguishing constructs.
Analysts can identify patterns by sorting sites by implementation outcome. This approach is particularly useful with small samples. For example, in our evaluation of the MOVE! weight management program, the pattern of ratings (-2, +1, +1, +2, +2) for Relative Advantage (within the Innovation Characteristics domain) appeared to be different between the lower and higher implementation facilities. See table below. It is clear that implementation strategies for MOVE! should include effective communication about the Relative Advantage of MOVE! compared to other available options.
If sample size is sufficient, analysts can conduct simple correlation analyses between construct ratings and outcomes (e.g., referral rates) by organization. For example, in our evaluation of a telephone-based lifestyle coaching program, we identified distinguishing constructs based on correlational analyses with a priori determined cut-offs. See table below.
Analysts can use QCA to provide in-depth information about clusters of constructs that contribute to success or failure of implementation. QCA considers “equi-finality,” meaning that more than one combination of positively (or negatively) rated constructs may lead to success (e.g., Cragun et al 2016) and can be a powerful approach for building knowledge.
If sample size is sufficient, analysts can use regression or other more sophisticated models, especially if quantitative measures are used.