Please write out how to do this, all the way to the answer. Use table if you can
ID: 2935846 • Letter: P
Question
Please write out how to do this, all the way to the answer. Use table if you can! SPSS not needed.
The following table presents the results of a two-way design with two levels of IV #1, two levels of IV #2, and n-10 participants in each condition. Each value in the table is the mean score for the participants in that particular condition. Notice that one of the mean values is missing: IV#2 B1 B2 A1 10 50 IV #1 A2 40 What value should be assigned to the missing mean so that the resulting data shows... A. a main effect for IV #1? B. no main effect for IV #1? C. a main effect for IV #2? D. no main effect for IV #2? E. an interaction between IV's? F. no interaction between IV's?Explanation / Answer
Solving this by using an example suppose this is effect of room temperature on test taking To do this we compare test scores of students who take a test in a 90 degree room vs. those who take a test in a 50 degree room. Here we take B1= 90 degree and B2= 50 degree
This is a case of one independent variable (i.e., room temperature) and two levels (i.e., 50 and 90 degrees), and the appropriate analysis would be a between-subjects’ t-test (assuming the two groups are made up of two separate groups of students). Let’s extend this design by adding a third group, 70 degrees. We now have an experiment with one independent variable (i.e., room temperature) and three levels (50, 70, and 90 degrees). We have not added an additional independent variable; rather we have simply added a third level to the already existing independent variable. It is crucial that you understand the difference between a variable and a level in order to select and interpret the analysis for a given experiment. So a 2x2 design that will become is as follows -
(IV) Room Temperature
(IV) Test Difficulty (Level) 50 degrees (Level) 90 degrees
(Level) Hard Test Hard Test in 50 degrees Hard Test in 90 degrees
(Level) Easy Test Easy Test in 50 degrees Easy Test in 90 degrees
The mean of the means for a given independent variable, collapsing across the levels of another independent variable, are referred to as "marginal means" and these means aid us in interpreting a main effect.
(IV) Room Temperature
(IV) Test Difficulty (Level) 50 degrees (Level) 90 degrees Marginal test difficulty
(Level) Hard Test 10 50 30
(Level) Easy Test 40 ? -> 37.5 38.75
Marginal Means 25 25
So if the value will be 37.5 then marginal means of both will be same.
We could find a main effect of Room Temperature, by reversing this hypothetical example
change the means for both test difficulty groups The examples thus far might lead you to the conclusion that a main effect for one independent variable precludes the possibility of finding a main effect for the other.Notice that in this case, we would conclude from the results that students perform best
Note that it is possible to find both a significant main effect and a significant interaction with the same set of means, and in this case, the lines will not be parallel. In interpreting such a case, the main effect is usually ignored, in that it is misleading.
This effect is an interaction because the effect of one independent variable depends on, the effect of the other. If we found these means, and we were asked a question about one independent variable, such as: "Did students do better with the hard test or easy test?", our answer would be something like: "That depends, with room temperature of 50 they did better on the hard test, with room temperatures of 90 they did better on the easy test". Conversely, if someone asked: "Did students do better in ninety degrees or 50 degrees?", your answer would be: "That depends, with the hard test they did better in 50 degrees, with the easy test they did better in 90 degrees.