Suppose you want to know whether males and females differ in their interest in travel. In your study, you ask males and females whether they like or dislike travel. To describe your results, you will need to calculate the percentage of females who like to travel and compare this with the percentage of males who like to travel. Suppose you tested 50 females and 50 males and found that 40 of the females and 30 of the males indicated that they like to travel. In describing your findings, you would report that 80% of the females like to travel in comparison with 60% of the males. Thus, a relationship between the gender and travel variables appears to exist. Note that we are focusing on percentages because the travel variable is nominal: Liking and disliking are simply two different categories.
After describing your data, the next step would be to perform a statistical analysis to determine whether there is a statistically significant difference between the males and females. Statistical significance is discussed in Chapter 13; statistical analysis procedures are described in Appendix C.
A second type of analysis is needed when you do not have distinct groups of subjects. Instead, individuals are measured on two variables, and each variable has a range of numerical values. For example, we will consider an analysis of data on the relationship between location in a classroom and grades in the class: Do people who sit near the front receive higher grades?
Much research is designed to compare the mean responses of participants in two or more groups. For example, in an experiment designed to study the effect of exposure to an aggressive adult, children in one group might observe an adult “model” behaving aggressively while children in a control group do not. Each child then plays alone for 10 minutes in a room containing a number of toys, while observers record the number of times the child behaves aggressively during play. Aggression is a ratio scale variable because there are equal intervals and a true zero on the scale.
In this case, you would be interested in comparing the mean number of aggressive acts by children in the two conditions to determine whether the children who observed the model were more aggressive than the children in the control condition. Hypothetical data from such an experiment in which there were 10 children in each condition are shown in Table 12.1; the scores in the table represent the number of aggressive acts by each child. In this case, the mean aggression score in the model group is 5.20 and the mean score in the no-model condition is 3.10.
TABLE 12.1 Scores on aggression measure in a hypothetical experiment on modeling and aggression
Page 246For all types of data, it is important to understand your results by carefully describing the data collected. We begin by constructing frequency distributions.