LEARNING CHECK
Identify the advantages of using multiple groups in a between-subjects experiment.
Comparing proportions for two or more groups
Often, the dependent variable in a research study is measured on a nominal or ordinal scale. In this case, the researcher does not have a numerical score for each participant, and cannot calculate and compare averages for the different groups. Instead, each individual is simply classified into a category, and the data consist of a simple frequency count of the participants in each category on the scale of measurement. Examples of nominal scale measurements are;
. gender ( male, female)
. academic major for college students
. occupation
Examples of ordinal scale measurements are;
. college class freshman, sophomore, and so on
. birth order first born, second born
. high, medium, or low performance on a task
Because you cannot compute variables, you cannot use an independent-measures t test or an ANOVA F test to compare means between groups. However, it is possible to compare proportions between groups using a chi – square test for independence see Chapter 14, p. 438. As with other between-subjects experiments, the different groups of participants represent different treatment conditions manipulated by the researcher. For example, Loftus and palmer 1974 conducted a classic experiment demonstrating how language can influence eyewitness memory. A sample of 150 students watched a film of an automobile accident and were then questioned about what they saw. One group was asked, ‘About how fast were the cars going when they smashed?’ Another group received the same question except that the verb was changed to ‘hit’ instead of ‘smashed into.’ A third group served as a control and was not asked any question about the speed of the two cars. A week later, the participants returned and were asked a number of questions about the accident, including whether they remembered seeing any broken glass in the accident . There was no broken glass in the film. Notice that the researchers are manipulating the form of the initial question and then measuring a yes/no response to a follow-up question 1 week later. Figure 10.6 shows the structure of this design represented by a matrix with the independent variable different groups determining the rows of the matrix and the two categories for the dependent variable yes/no determining the columns. The number in each call of the matrix is the frequency count showing how many participants are classified in that category. For example, of the 50 students who heard the word the word smashed, there were 16 32% who claimed to remember seeing broken glass even though there was none in the film. By comparison, only 7 out of 50 14% of the students who heard the word bit claimed to have seen broken glass. The chi-square test compares the proportions across one row of the matrix (one group of participants) with the proportions across other rows. A significant outcome means that the pro portions in one row are different from the proportions in another row,and the difference is more than would be expected if there was not a systematic treatment effect. Loftus and Palmer found that participants who had been asked a leading question about the cars smashing into each other were significantly more likely to recall broken glass than participants who were not asked a leading question.