In a short essay discuss the different types of extraneous variables and their effects on experiments in marketing research

What will be an ideal response?

The discussion on extraneous variables should focus on the following categories of extraneous variables: history, maturation, testing, instrumentation, statistical regression, selection bias, and mortality.

History
Contrary to what the name implies, history (H) does not refer to the occurrence of events before the experiment. Rather, history refers to specific events that are external to the experiment but occur at the same time as the experiment. For example, general economic conditions may decline during the experiment, thereby contaminating the post-treatment observation. The longer the time interval between observations, the greater the possibility that history will confound an experiment.

Maturation
Maturation (MA) is similar to history except that it refers to changes in the test units themselves. These changes are not caused by the impact of independent variables or treatments but occur with the passage of time. In an experiment involving people, maturation takes place as people become older, more experienced, tired, bored, or uninterested. Tracking and market studies that span several months are vulnerable to maturation, since it is difficult to know how respondents are changing over time.

Testing Effects
Testing effects are caused by the process of experimentation. Typically, these are the effects on the experiment of taking a measure on the dependent variable before and after the presentation of the treatment. For example, the respondents try to maintain consistency between their pre- and post-treatment attitudes. As a result of the testing effect, post-treatment attitudes may be influenced more by pretreatment attitudes than by the treatment itself.

Instrumentation
Instrumentation (I) refers to changes in the measuring instrument, in the observers or in the scores themselves. Sometimes, measuring instruments are modified during the course of an experiment. Consider an experiment in which dollar sales are being measured before and after exposure to an in-store display (treatment). If there is a non-experimental price change between measurements, this results in a change in instrumentation because dollar sales will be measured using different unit prices. Instrumentation effects are likely when interviewers make pre- and post-treatment measurements. The effectiveness of interviewers can be different at different times.

Statistical Regression
Statistical regression effects (SR) occur when test units with extreme scores move closer to the average score during the course of the experiment. In the advertising experiment, suppose that some respondents had either very favorable or very unfavorable attitudes. On post-treatment measurement, their attitudes might have moved toward the average. People's attitudes change continuously. People with extreme attitudes have more room for change, so variation is more likely. This has a confounding effect on the experimental results because the observed effect (change in attitude) may be attributable to statistical regression rather than to the treatment (test commercial).

Selection Bias
Selection bias (SB) refers to the improper assignment of test units to treatment conditions. This bias occurs when selection or assignment of test units results in treatment groups that differ on the dependent variable before the exposure to the treatment condition. If test units self-select their own groups or are assigned to groups on the basis of the researchers' judgment, selection bias is possible. For example, consider a merchandising experiment in which two different merchandising displays (old and new) are assigned to different department stores. The stores in the two groups may not be equivalent to begin with. They may vary with respect to a key characteristic, such as store size. Store size is likely to affect sales regardless of which merchandising display was assigned to a store.

Mortality
Mortality (MO) refers to the loss of test units while the experiment is in progress. This happens for many reasons, such as test units refusing to continue in the experiment. Mortality confounds results because it is difficult to determine if the lost test units would respond in the same manner to the treatments as those that remain. Consider again the merchandising display experiment. Suppose that during the course of the experiment three stores in the new display treatment condition drop out. The researcher could not determine whether the average sales for the new display stores would have been higher or lower if these three stores had continued in the experiment.