Data Analysis – Step 2

Section 1:

Evaluation Purpose and Partners

Section 2:

Describing the Intervention

Section 3:

Evaluation Design

Section 4:

Methodology and Data Collection

Section 5:

Data Analysis

Section 6:

Reporting and Dissemination

Step 1

Step 2

Step 3

Step 4

Step 5

Step 6

Step 2: Identify variables and themes

Variables or themes are key “ingredients” in quantitative and qualitative data analysis “recipes”, respectively.

A variable or theme can represent an attribute of a person, place, thing, or idea.
Variables can be quantitative (numeric) or qualitative (categorical, non-numeric); alternatively, themes are all qualitative.
Themes are typically descriptive in nature and, therefore, more complex than variables to analyze.

An original set of variables or themes is derived from measures already available in your secondary data sources or those obtained through your primary data collection tools.

Analysis variables can be created to summarize one or more of the original variables. Through data reduction approaches, themes can also be transformed into variables, if desired.

Examples of analysis variables derived from original variables designed to characterize intervention exposure and impact include:

  • “High-risk population reach” = The proportion of racial, ethnic, and/or lower-income populations exposed to the intervention;
  • “Intervention dose” = Scale or size of the intervention setting (e.g., # of feet of roadway) + stage of implementation (e.g., policy adopted, funds allocated, environment modified, enforcement underway) + quality of implementation (e.g., high, low); and
  • “High-risk population impact” = High-risk population reach + intervention dose.

Many types of variables or themes may be included in quantitative and qualitative data analysis reflecting populations or samples, interventions, outcomes, and contextual conditions. See the table below for examples of variables in each of these categories.

View The Resource Table

Consider how your variables may align with the following categories:

Independent variables - Variables related to populations or subpopulations, interventions, and contextual factors;
Dependent variables - Also known as "outcome variables"; and
Confounding variables - Variables related to populations or subpopulations and contextual factors.
Next: Continue to Step 3
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