The Design of Experiments: Correlational & Observational Design

Correlational Analysis

Not all research uses an experimental design. Correlational analysis examines a number of instances and asserts, based on various initial assumptions (which we will examine in the section on Statistics: Correlation), that there is a co-relationship between variables. Because a correlational study does not manipulate which variable precedes the other, it cannot attribute causal direction to the relationship. Also, correlational studies usually show a much weaker effect than experimental ones, because of the effect of the many uncontrolled variables which are mixed into each individual instance.

Observational Design

Field observation is an uncontrolled source of data about a phenomenon. It assumes a causal ordering (A precedes B, therefore A caused B). The data are usually presented descriptively, providing information on “what” and “who.” But it can provide information that will inform a more controlled design (moving back and forth between observation and experiment is sometimes called “Grounded Design”).

The most common form of observational design is the case study:

· Method: Case studies uses multiple sources of evidence (Campbell calls it “triangulation”) to confirm a single observation. Usually, there is some pre-designed method for recording observations so the database can be mined later. And there should be an explicit chain of evidence required (to make sure that the data are both necessary and sufficient).

· Analysis: There are three common ways to mine the data from a field study:

o Pattern-matching

o Explanation-building

o Time-series analysis

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