Generating Hypothesis
1
My main IV is impaired vision. The first level is myopia. The second level is hyperopia.
My moderating IV is Navon Task. The first level is local. The second level is global. # 2
My DV is perfect vision.
3
What is the Effect of Impaired Vision on Identifying Global and Local Features Using the Navon Task?
4
First we make a List variable to hold the column with the DV. The name of the variable becomes the header for the column. DV <- c(“Perfect Vision”)
Next, List variables to hold the levels of the IV. Again, the name of the variable becomes the name of the column.
Myopia <- c(“Highest Score”) Hyperopia <- c(“Lowest Score”)
Next, we’ll make the data.frame() to hold our three lists studyDesignTable <- data.frame(DV, perfect vision, 0% impaired vision)
Finally, print out the table knitr::kable(studyDesignTable, caption = “Study Design Table: 1 IV with 2 levels”)
| IV | Myopia | Hyperopia |
|---|---|---|
| Scores | Highest Scores | Lowest Scores |
| Moderator IV: Navon Task | Main IV: impaired vision |
|---|---|
| Global | Myopia |
| Local | Hyperopia |
5
The variable is named “IV”. The values of the list are Low Power and High Power IV <- c(“Highest Scores”, “Lowest Scores”)
The variable is named DV. The first value is 9. 9 is our prediction for the score participants will have in the Highest Scores condition. The second value is 5. 5 is our prediction for the score participants will have in the Lowest Scores condition.
DV <- c(9, 5) myData <- data.frame(IV,DV) if (!require(ggplot2)){ install.packages(“ggplot2”, dependencies = TRUE) require(ggplot2) } ggplot(myData, aes(x = IV, y=DV)) + stat_summary(fun.y=“mean”, geom=“bar”, width=.4, color=“black”, fill=“white”) + theme_minimal() + theme(panel.grid = element_blank(), axis.line.y = element_line(color=“black”, size = .5)) + geom_hline(yintercept=0, color=“black”, size=.5) + labs (x = “Visual Impairments”, y = “Test Scores”)