![]() * You should also change the way that the plot looks. You should edit the labels on the x-axis and fill legend so that these are displayed as informative text labels to make the graph easier to read. * At the moment, the dataset displays the different conditions as 1s and 0s. Using ` ggplot() ` and ` study4_dat `, write the code in chunk T06 to make a bar plot that shows the number of participants who redeemed the flyer in each experimental condition. ![]() You should use ` read_csv() ` to load the files and NOT ` read.csv() ` as it will change the names of your variables and you won't get the correct answers for the rest of the tasks! You need to replace the ` NULL ` with the code used to load the data. Now we have the tidyverse loaded, edit the below code to load in the data file ` RTA_Study4.csv ` and then run the code (make sure you have loaded the tidyverse). In the code chunk below, write and run the code to load the packages ` tidyverse ` and ` lsr `. Subtract each participant's score from the grand mean, square these differences, and then sum up all of the squared differences. Which of the following is a significant p-value according the standard cut-off criteria for psychology? What was the sample size (N) for this study?Īccording to APA style, which of the below options is the correct way to write up the results of a one-way ANOVA?ġ. Out of the following options, how would you describe the design of the study?Ī one-way ANOVA has degrees of freedom between = 2 and degrees of freedom within = 63. Half of the participants drink caffeine before taking part in the study and half drink decaf coffee to test the effect of caffeine. All participants see both happy and sad faces as stimuli to test the effect of emotion on response time. 11 would be considered:Ī study looks at how fast participants are to respond to different stimuli. There are two main effects and an interaction between A and Bįor a one-way ANOVA, the degrees of freedom between are calculated as:Īccording to Cohen's rules of thumb an effect size of ηp2 =. There is a main effect of factor A but not factor BĤ. There is an interaction between the two factorsĢ. If the effect of factor A is dependent upon the level of factor B we can conclude that:ġ. What is the between-subjects equivalent of the assumption of sphericity? Which of the following is not a multiple comparison correction to prevent family-wise error? Please only use a single number, do not use words, and do **not ** put the number in quotation marks. Please replace the ` NULL ` below with the number of the option you think is correct. The probability of a given result occurring under the alternative hypothesis That the interaction is not significantģ. What is the null hypothesis for an omnibus one-way ANOVA test?ġ. The following 11 questions relate to the lecture content: You can use code or you can enter the number manually, but it must be a single number and it must be rounded correctly. Store this single value in ` answer_t11 `. * In the ` t11 ` code chunk below, replace the ` NULL ` with this p-value rounded to 3 decimal places. Store this single value in ` answer_t8 `įind the p-value for the difference between the false belief and ignorance condition for female participants. * In the ` t08 ` code chunk below, replace the ` NULL ` with the number of the statement below that is correct. Which of the following is true about *t * and *F *? Look at the F-ratio from the one-way ANOVA and the t-statistic from the t-test. The result of the call to ` aov_ez() ` should be stored in the variable ` mod1 `. Use the ` aov_ez() ` function to run a one-way ANOVA with ` looktime ` as the DV and ` condition ` as a between-subjects IV and display the results. Scale_y_continuous(name = "Mean look time difference (ms)") Scale_x_discrete(name= "Condition",labels = c("False belief", "Ignorance"))+ Ggplot(dat, aes(x = condition, y = looktime))+ Recreate the violin-boxplot from the instructions: The result should be a table ` means_cond ` with 2 rows and 3 columns named ` condition `, ` m `, and ` sd `. Now do the same for condition, calculate the means and SD for each condition, ignoring sex. The result should be a table ` means_sex ` with 2 rows and 3 columns named ` sex `, ` m `, and ` sd `. Now calculate the means (and standard deviations) for sex, ignoring condition.
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