Once you have collected the data, it is time to clean and analyze it! This process helps you identify important trends to answer your overall objective.
Once data have been entered, they must be thoroughly reviewed – or “cleaned” – to check for inconsistencies, implausible entries, missing data, and skip pattern errors. Next, the clean data can be analyzed.
For the Fundamental approach, your data analysis plan will include:
- Calculating frequency scores and percentages of close-ended survey questions
- Coding for themes in open-ended survey responses or interview/focus group responses
Helpful tip 1: Before the cleaning begins, always save a new copy of the database file so that any errors that may occur during cleaning and analysis do not corrupt the original, raw data.
Helpful tip 2: If you have questions on how to analyze your data, consult your agency’s data analyst or statistician. Or, consider reaching out to a statistician or public health researcher at a nearby university for assistance with analysis.
Quantitative analysis
Quantitative data analysis can be conducted using Microsoft Excel. Appendix G provides cleaning instructions, codebooks in Microsoft Excel, Microsoft Excel sheets to input and analyze data, and Microsoft Word analysis sheets to input and analyze data.
The Excel sheets also include codebooks to reference question variable names, and associated response values. If you do not have access to Excel, use the Adobe PDF analysis sheets and the coding/analysis instructions to tabulate counts (i.e., frequency scores) and percentages.
Qualitative analysis
In this toolkit, qualitative data analysis will be conducted using Microsoft Word. You may also have access to qualitative coding software, such as Vivo, ATLAS.ti, Provalis Research Text Analytics Software, Quirkos, MAXQDA, or Dedoose.
If you do not have access to Microsoft Word, you can export or log qualitative comments into Microsoft Excel.
The CDC Field Epidemiology Manual provides the following guidance for analyzing qualitative data.
Analyzing qualitative data is an iterative and ideally interactive process that leads to rigorous and systematic interpretation of textual or visual data. At least four common steps are involved:
- Reading and rereading. The core of qualitative analysis is careful, systematic, and repeated reading of text to identify consistent themes and interconnections emerging from the data. The act of repeated reading yields new themes, connections, and deeper meanings from the first reading. Reading the full text of interviews multiple times before subdividing according to coded themes is key to appreciating the full context and flow of each interview before subdividing and extracting coded sections of text for separate analysis.
- Coding. A common technique in qualitative analysis involves developing/identifying codes for labeling sections of text. Different approaches can be used for textual coding. One approach, structural coding, follows the structure of the interview guide. Another approach, thematic coding, labels common themes that appear across interviews, whether by design of the topic guide or emerging themes assigned based on further analysis. To avoid the problem of shift and drift in codes across time or multiple coders, qualitative investigators should develop a standard codebook with written definitions and rules about when codes should start and stop. Coding is also an iterative process in which new codes that emerge from repeated reading are layered on top of existing codes. Development and refinement of the codebook is part of the analysis.
- Analyzing and writing memos. As codes are being developed and refined, answers to the original objective should begin to emerge. Write memos to record evolving insights and emerging patterns in the data and how they relate to the original research questions. Writing memos is intended to further thinking about the data, thus initiating new connections that can lead to further coding and deeper understanding.
- Verifying conclusions. Analysis rigor depends as much on the thoroughness of the cross-examination and attempt to find alternative conclusions as on the quality of original conclusions. Cross-examining conclusions can occur in different ways. One way is encouraging regular interaction between analysts to challenge conclusions and pose alternative explanations for the same data. If alternative explanations for initial conclusions are more difficult to justify, confidence in those conclusions is strengthened.
For the Fundamental approach, code open-ended responses from surveys or code themes from responses collected by individual interviews or focus groups.
