Experimental Designs

Section 7.5

Variables

Explanatory variable Response Variable

(Independent Variable) (Dependent Variable)

"Explains observed outcomes" "Measures the outcome of a study“

Example: A subject takes a new weight-loss pill (Explanatory) and the weight lost (Response) is measured after one month.

Observational Study In an observational study, a researcher observes

individuals and measures the variables of interest, but does not attempt to influence the responses (surveys, case studies).

Nothing is imposed on the individual by the researcher in an observational study.

Experiment

An experiment is where the researchers deliberately impose some treatment on individuals in order to observe their responses to the treatment.

An experiment is a research situation in which at least one variable, called the experimental variable, is deliberately manipulated or varied by the researcher.

Basic Terminology Experimental Units - the individuals upon which the

experiment is done. When these individuals are human beings, they are called subjects.

Treatments - the specific experimental conditions imposed on the units. This might be aspirin, temperature changes, nutrients, etc.

Factor - a set of related conditions or categories. A Factor is also one of your explanatory variables.

Levels - The conditions or categories making up a factor.

Example 1: One Factor with Three LevelsThis is one explanatory variable!

Example 2: Two Factors - Physical State and DrugThis is an interaction of two explanatory variables!

Rationale for Experiments Experiments are the preferred method for examining

the effect of one variable on another.

By imposing the specific treatment of interest and controlling other influences, we can pin down cause and effect.

A sample survey may show that two variables are related, but it cannot demonstrate that one causes the other.

Example 9 – An Uncontrolled Experiment

Confounding An uncontrolled experiment, like the GMAT example,

has this form:

Online course → Observe GMAT scores or, in general form Treatment → Observe response.

Such an experimental design can suffer from confounding.

Definition: Variables, whether intentionally part of a study or not, are said to be confounded when their effects on the outcome cannot be distinguished from each other.

Confounding

Comparative Experiments Comparative Experiments are a remedy for confounding.

It consists of a treatment group, and a control group (to serve as a baseline).

It is important to ensure that groups are similar to help with control of bias. Thus, good experimental design must also have random selection of individuals into groups.

For example, if we allow students to elect online or classroom instruction, older employed students are likely to sign up for the online course. We don’t want a group with all such students in it, so we randomize the older students between the two groups.

Example 10 A Randomized Comparative Experiment

Advantages of Experiments over Observational Studies:

Experiments can give good evidence for causation.

We can study specific factors we are interested in while controlling the effects of lurking variables (holding constant that which is of no interest to us).

Experiments can allow us to study the combined effects of several factors.

Homework

Worksheet 7.5