Topic: Do Men and Women Differ in the Way They Remember Location Information?
Statistical Concepts Covered: In this applet you will learn more on how to interpret charts, especially misleading charts, and distinguish between significant and chance differences.
The study of cognitive differences between men and women has been a fertile ground for researchers over the past 20 years. In this chapter you read about the types of encoding processes people use to create enduring memories. Are gender-based differences in memory rare? In 1992, Silverman and Eals investigated gender differences in object location memory. Participants were asked to study an image containing several objects. Then, after a short delay, a new image was presented. In this image, some of the objects were in the same locations as in the original image, but others’ locations had been swapped with other objects. The goal of the task was to determine which objects had been moved. (If you would like to take the experiment yourself, you can find an online version of the task at the Online Psychology Laboratory.) Silverman and Eals’ results showed that women outperformed men at this task.
The applet for this unit will allow you to explore results from the online version of the experiment linked above. Experimental data from 100 male and 100 female college students will be presented for analysis. You will be able to analyze the data from multiple perspectives, some of which may suggest clear differences, and others may be harder to interpret. You might be surprised by how much you can influence interpretation of the data just by changing the layout of the graph. You will also investigate the role of practice on performance and try to determine just how large a difference between groups needs to be before you are comfortable saying that a true and reliable difference really exists.
The following legend should help in identifying the different categories of data in the graphs:
• CrMv – Correctly identifies object has moved
• CrSt – Correctly identifies the object did not move but was stationary
• InMv – Incorrectly identifies object has moved
• InSt – Incorrectly identifies the object did not move but was stationary
• DI – differential index, which indicates the proportion of overall responses that were correct
Statistical Lesson. If you play with the range of the y-axis, you can make the difference between the men’s scores and the women’s scores appear to be much larger or smaller, but that does not change the actual numerical difference between the data values. When creating graphs of your data, you should strive to make them as unbiased as possible, which includes selecting a y-axis range that does not artificially inflate the perceived differences. Similarly, whenever you are looking at someone else’s data, you should get into the habit of checking the range of the y-axis so that you do not fall prey to a misleading graph.
Statistical Lesson. In many of these graphs there is only a small numerical difference between the women’s scores and the men’s scores. In situations like this it can be hard to look at the graph and determine if the difference is significant – if it is the result of an actual difference between the two groups – or if the difference was just a random occurrence based on the specific individuals in the sample. One way that researchers try to clarify this is by adding error bars to their graphs. Error bars indicate the range of values that may reasonably represent the actual population value based on the data from the sample. Therefore, if the error bars around one value overlap the error bars for the second value, there’s a reasonable chance that the populations are not truly different. But if the error bars do not overlap the comparison value, then that is an indication that there may be significant differences between the groups.