Keenan et al. (2001) used anesthesia to investigate which brain hemisphere is in
ID: 3170913 • Letter: K
Question
Keenan et al. (2001) used anesthesia to investigate which brain hemisphere is involved in self-recognition. Ten subjects were randomly assigned to two groups. The left hemisphere was anesthized in one group, whereas the right hemisphere was anesthized in the other group. Each subject was then shown a picture generated by averaging ("morphing") images of the face of a famous celebrity (e.g., Marilyn Monroe) and their own face, and told to remember the picture. After recovery from anesthesia, patients were presented with two pictures and asked to choose the one they had been shown earlier while under anesthesia. The two pictures were the original two images from which the morphed image had been generated (i.e., "self" and "celebrity," but seperate this time). All five patients whose left hemisphere had been inactivated chose the picture of self. Four of the five patients whose right hemisphere had been anesthetized chose the celebrity picture, instead (the fifth chose self). State what test you would use to determine whether the treatment (left vs. right hemisphere anesthesia) influenced recognition of self versus celebrity. Explain why you would use this test.
At this point in the book, t-tests have not yet been taught.
*IMPORTANT* The range of tests that are available for answers are chi squared contingency test, fisher's exact test, chi squared goodness of fit test, and the binomial test. please limit to one of these with reasoning.... thank you.
Explanation / Answer
Fisher's exact test is used when you have two nominal variables. A data set like this is often called an "R×C table," where R is the number of rows and C is the number of columns. Fisher's exact test is more accurate than the chi-squared test or G-test of independence when the expected numbers are small.
The G*Power program will calculate the sample size needed for Fisher's exact test. Choose "Exact" from the "Test family" menu and "Proportions: Inequality, two independent groups (Fisher's exact test)" from the "Statistical test" menu. Enter the proportions you hope to see, your alpha (usually 0.05) and your power (usually 0.80 or 0.90). If you plan to have more observations in one group than in the other, you can make the "Allocation ratio" different from 1.
As an example, let's say you're looking for a relationship between bladder cancer and genotypes at a polymorphism in the catechol-O-methyltransferase gene in humans. Based on previous research, you're going to pool together the GG and GA genotypes and compare these "GG+GA" and AA genotypes. In the population you're studying, you know that the genotype frequencies in people without bladder cancer are 0.84 GG+GA and 0.16 AA; you want to know how many people with bladder cancer you'll have to genotype to get a significant result if they have 6 percent more AA genotypes. It's easier to find controls than people with bladder cancer, so you're planning to have twice as many people without bladder cancer. On the G*Power page, enter 0.16 for proportion p1, 0.22 for proportion p2, 0.05 for alpha, 0.80 for power, and 0.5 for allocation ratio. The result is a total sample size of 1523, so you'll need 508 people with bladder cancer and 1016 people without bladder cancer.
Note that the sample size will be different if your effect size is a 6 percent lower frequency of AA in bladder cancer patients, instead of 6 percent higher. If you don't have a strong idea about which direction of difference you're going to see, you should do the power analysis both ways and use the larger sample size estimate.
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