Types of Data
Primary data: data that you collect yourself from participants or health records using questionnaires, patient chart reviews, interviews, focus groups, or similar approaches.
Secondary data: data collected by other researchers that you are using to answer your own research questions.
Qualitative vs. Quantitative Data
The type of data you collect depends on the question you want to answer and your resources. Both quantitative and qualitative data have strengths and limitations and may be appropriate for different settings, evaluation designs, and evaluation questions.
Qualitative data consist of words and narratives. The analysis of qualitative data can come in many forms including highlighting key words, extracting themes, and elaborating on concepts.
Quantitative data are numerical information, the analysis of which involves statistical techniques. The type of data you collect guides the analysis process.
Types of Variables
Dependent Variables (Outcome Variables). Dependent variables are the outcome of interest and will answer your research question(s).
Independent Variables (Predictors). Independent variables are those factors that may influence your dependent variable/outcome variable.
Example: Say you’re conducting a study on diet and exercise. Your weight would be your dependent variable and your diet and exercise (which both influence weight) would be your independent variables.
Categorical vs. Continuous Variables
Categorical variables are based on groupings or classification. There are two types: Nominal (no inherent order) and Ordinal (natural order).
Nominal Example – Smoker vs. Non-Smoker
Ordinal Example – Educational Level (Less than High School, High School, Some College, College, Bachelor’s Degree, Graduate Degree)
Continuous variables can take on any score or value within a measurement scale. There are two types: Interval and Ratio Scale. An interval variable can be ordered, and the distance or level between each category is equal and static. A ratio scale variable is similar to an interval variable with one difference: the ratio scale has true zero point (i.e., 0.0 = none/absence of the measurement).
Interval Example – The temperature in Calgary on any given day.
Ratio Scale Example – Newborn weight.