Experimental and Causal-Comparative Designs. Purpose. Examine the possible influences that one factor or condition may have on another factor or condition cause-and-effect relationships ideally, by controlling all factors except those whose possible effects are the focus of investigation.
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X O O X O
Treatment or manipulation of independent variable
Observation or measurement of dependent variableOne-Shot Case Study
An example is an employee education campaign about new technologies without prior measurement of employee knowledge. Results would reveal only how much the employees know after the campaign, but there is no way to judge the effectiveness of the campaign. The lack of pretest and control group make this design inadequate for establishing causality.
O X O
Pretest Manipulation Posttest
Can be used for the educational example, but how well does it control for history? Maturation? Testing effect?
This design provides for two groups, one of which receives the experimental stimulus while the other serves as a control. A forest fire or other natural disaster is the experimental treatment, and the psychological trauma (or property loss) suffered by the residents is the measured outcome. A pretest before the fire would be possible … but. The control group, receiving the posttest, would consist of residents whose property was spared. Weakest link, no way certain that the two groups are equivalent.
R O1 X O2
R O3 O4
The effect of the experimental variable is
E = ( O2 –O1 ) – ( O4 –O3 )
In this design, the seven major internal validity problems are dealt with fairly well, although there are still some difficulties. Local history may occur in one group and not the other, communication between people in test and control groups, and mortality.
R O1 X O2
R O3 O4
R X O5
The addition of the two groups that are not pretested provides a distinct advantage. If the researcher finds that O5 and O do not differ from the top two groups observation, the researcher can generalize findings to situations where no pretest was given. The Solomon Four-Group Design enhances the external validity
R X O1
In this design the pretest measurements are omitted. Pretests are not really necessary when it is possible to randomize.
Experimental effect is ( O1 – O2 )
Since the subjects are measured only once, the threats of testing and instrumentation are reduced.
R O1 X1 O2
R O3 X2 O4
R O5 X3 O6
Experiment: to determine the ideal difference in price between a store’s private brand of vegetables and national brands. There will be three price spreads (treatment levels) of 7, 12 and 17 cents. 18 stores are randomly divided (6 to each treatment group). The price differential is maintained for a period and then a tally is made of the sales volumes and gross profit of the cans for each group of stores.
The critical reason for randomize block design is that the sample size is too small that is risky to depend on random assignment alone. Small samples such as 18 stores are typical in field experiments because of high costs. Another reason for blocking is to learn whether treatments bring different results among various groups of subjects.
Assume there is reason to believe that lower-income families are more sensitive to price differentials than are higher-income families. This factor could seriously distort our results unless we stratify the stores by customer income.
Active Factor – Blocking Factor – Customer Income
Price Difference High Medium Low
7 cents R X1 X1 X1
12 cents R X2 X2 X2
17 cents R X3 X3 X3
The O’s have been omitted. The horizontal rows no longer indicate a time sequence but various levels of the blocking factor. Before and after measurements are associated with each of the treatments.
One can measure both main effects and interaction effects.
Store Size High Medium Low
Large X1 X1 X1
Medium X2 X2 X2
Small X3 X3 X3
Latin square may be used when there are two major extraneous factors. Continuing the store example, we decide to block on size of the store and income (9 stores). One treatment per cell.
Assumes there is no interaction between treatments and blocking factors. With the above design we cannot determine the interrelationships among store size, customer income, and price spreads. (this would require 27 cells)
Unit Price Information 7cents 12 cents 17 cents
Yes X1 Y1 X1 Y2 X1 Y3
No X2 Y1 X2 Y2 X2 Y3Factorial Design
One misconception is that a researcher can manipulate only one variable at a time. With factorial designs you can deal with more that one simultaneously. Our pricing experiment. We are interesting in finding the effect of posting unit prices on the shelf to aid shopper decision making. Above includes both price differentials and the unit pricing. This is known as a 2x3, with two levels and three levels of intensity. Stores are randomized, assigned to one of six treatments. Results can answer the following questions:
What are the sales effects of different price spreads between company and national brands?
What are the sales effects of using unit-price marking on the shelves?
What are the sales-effect interrelations between price spread and the presence of unit-price information?
O1 X O2
This differs form the pretest-posttest group design, because the test and control groups are not randomly assigned. There are two varieties. One intact equivalent design, in which membership is naturally assembled. ( use different classes in a school) The second, self-selected experimental group design, are recruited (weaker). Comparison of pretest (O1O2 ) is one degree of equivalence.
R O1 (X)
R X O2
This design is most applicable when we cannot know when and to whom to introduce the treatment but we can decide when and whom to measure. The bracketed treatment is shown to suggest that the experimenter cannot control the treatment. Assume a company is planning an intense campaign to change its employee’s attitudes toward energy conservation. It might draw 2 random samples of employees, one of which is interviewed about energy use attitudes before the information campaign. After the campaign the other group is interviewed.