I have a dataset structured as follows:
Variable1 Variable2 Variable3 Variable4 Variable5 Variable6 Variable7 Variable8
60 70 75 73 57 69 85 90
55 66 47 79 63 76 82 97
etc.
Each variable represents the detection performance for a certain position and condition. Variables 1 to 4 are meant to represent condition 1, Variables 5 to 8 are meant to represent condition 2. Detection performance for the same locations (but different conditions) are represented by variables 1 & 5, variables 2 & 6, variables 3 & 7, variables 4 & 8.
I now have plotted the detection performance for both conditions as a line chart.
What I want to do, is to compare the slope of both curves using SPSS. What would be the best way to accomplish this? I would like to test if one slope is steeper than the other.
Both curves are representing the detection performance of a target at different locations. I have the directional hypothesis, that the slope of the one curve would be flatter than the other.
I am aware that there are different slopes within the curve, so what I am looking for is the "overall slope" for each curve. I suppose this could be accomplished by calculating a regression line. However I am not aware about how to do this with my dataset. Usually predictor and predicted variable are positioned in two fields of your SPSS file. But now they are in the same field: the location of the target should predict the detection performance. And variable 1 to x represents the detection performance on location x.
Do I have to split those two pieces of information detection performance and location, or can this problem be solved with the existing structure of my dataset?
This is how the plot looks like:
And I want to test if the curve of one condition is steeper / flatter than the curve of the other condition.
Detection performance for condition 1 (4 possible target locations) is represented by variables 1 to 4. Detection performance for condition 2 (4 possible target locations) is represented by variables 5 to 8.
The explanatory variables would be the position of the target, the response variable would be the detection performance. Both pieces of information are represented by the same field: Variable1 stands for detection performance on position a, Variable2 stands for detection performance on position b, and so on.
My study design is a within subjects design, each participant was tested on both conditions. In the sample data I provided, each row stands for one participant. Each number stands for the average detection performance of the respective participant for the respective condition (condition 1: variable 1 – 4; condition 2: variable 5 – 8) and the respective location (the same location is represented in Variable 1 & 5, Variable 2 & 6, Variable 3 & 7, Variable 4 & 8).
Best Answer
It isn't clear to me whether my last post was converted into a comment as such I am re-posting as another answer and will follow up when I am back on line. Answer follows:
Am I correct in responding by saying you are interested in determining whether position and condition affects detection performance? If so, I think that considering the slopes is not the correct approach, although I do think it is possible that a linear model may be suitable. While I am still struggling to understand exactly what you are trying to do, I think a MANOVA (multivariate ANOVA) approach may be applicable.
To give you a bit more context, a MANOVA approach would be useful if you were interested in determining if party affiliation (Democrat, Republic etc) and gender have any effect on voters views on issues such as taxes, gun control etc.