Designing a study includes developing good research question(s), choosing an appropriate methodology, estimating sample size, selecting data collection tools, and creating an analysis plan.
Your study design is guided by the research question(s). For example, does your question start with “how?” or “why?” If so, your questions might be better addressed using qualitative methods. If you are asking “What?”, “When?, “Where?” or “How much?” you could consider quantitative methodologies. You might even combine both into a mixed methods approach.
Example Study Designs:
- Descriptive (case reports, case series, descriptive surveys)
- Observational/Analytic (cross-sectional, case-control, cohort, hybrid)
- Experimental/Intervention (randomized controlled trials, quasi-experimental designs)
- Mixed Methods (quantitative and qualitative methodologies combined)
Sample size calculations are a key part of a research study. Sample size calculations should be run for each of your main/primary outcomes so you know that your study won’t be underpowered for any of the questions you plan to address.
The sample size calculation depends on your hypothesis test, the significance level (usually set as 5%), and the power and results from your pilot study. There are many formulas available for different research situations. Please submit an online service request for assistance with these calculations.
Another important part of study planning is selecting a sampling technique. How will you select your participants? Will it be a convenience sample? Random sample? Cluster sample? Snowball sample?
Your choice will depend on the research question(s) and study design. Note that different sampling methods have corresponding potential biases. For example, if your sample was a “convenience sample,” the results may not be generalizable or may be biased in other ways (e.g. selection bias).
The analysis plan is your road map for data management and analysis. Writing up an analysis plan is a great way to keep things on track.
Conducting a complete analysis of the data you have collected will enable you to:
- Answer your research question(s)
- Determine the impact of your work
- Have scientific validation of your work
Plans also help with timelines and standardizing analytic approaches (e.g. treatment of missing values, inclusion and exclusion criteria).
Data will have to be entered, coded and checked, new variables created, etc., even when doing secondary data analyses.
Create a data dictionary containing all of your variables, any derived variables and notes about how you coded them. This is useful not only if you will be sharing the dataset with other, but for yourself (e.g. if you need to come back to the data after some time away). Keep in mind that data cleaning is a process that will likely involve you revisiting the data several times over.