please review the assignment instructions attached, will need to download JASP to complete the assignment
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JASP v19.03 available for free download
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Download JASP – JASP – Free and User-Friendly Statistical SoftwareLinks to an external site.
This Assignment will use the same Texas STAAR Test Data from the One-Way Assignment. Similar to the first one, you will follow the instructions to create a 2×2 Full Factorial model to test whether the Test Prep Course significantly influences test scores, and whether this effect is greater in Females or Males. To do this, you will use Test Prep and Gender as treatments and/or blocking factors, then test for a significant interaction. You will follow steps to do this for all three test subjects in Questions 1-3, then upload the results in Question 4 just like last time.
(If you are lost, don’t worry. I will post a video in which I work through Reading for both assignments by Wednesday. Then you can use that for direction in how to tackle the other two, so it’s probably worth waiting and not trying to rush through just to be done with it.)
Week 5 Analytical Assignment – Texas STAAR Exam Data.docx
Download Week 5 Analytical Assignment – Texas STAAR Exam Data.docx
Texas STAAR Filter ANOVA Week 5 Data.csv
Download Texas STAAR Filter ANOVA Week 5 Data.csv
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Flag question: Question 1
Question 130 pts
For large complex analyses such as these, you may wish to reboot your computer before you begin. JASP uses a lot of virtual memory. Load the Texas STAAR file into JASP, then make sure the that your data loaded correctly. Since we have already examined the data, we will skip the descriptives this time. Proceed to ANOVA and begin.
Question 1: Analyze Math Scores
Next, you will use statistical analysis to determine whether the parent’s education level significantly influences a child’s test scores in math. Open the correct module and name it ANOVA PrepxGen-Math. Choose the appropriate analytical technique to test the following hypotheses for α=.05.:
H0:μCompleted=μNone
H1:μCompleted≠μNone
H0:μFemale=μMale
H2:μFemale≠μMale
H0:μPrepij∗Female=μPrepij∗Male
H3:μPrepij∗Female≠μPrepij∗Male
Perform all necessary omnibus tests to ensure that the assumptions for the analysis have been met. Include the Q-Q plot as a test for normality, but you may assume that normality has been met for all three dependent variables. We will not be performing nonparametric analyses in this assignment. Test for homogeneity of variances, however if Levene’s test is significant (p < .05), then we will still perform the analyses using Type III Model since there is no variance correction for factorial ANOVA and we cannot test interactions using KW nonparametric techniques.
Use the default Type III sum of squares model if homogeneity of variances can be assumed. Include the descriptive statistics table for this analysis and use partial eta square to estimate the effect size of the treatment, block, and interaction.
Perform post-hoc analysis using Tukey’s test for pairwise comparisons of the treatment, block, and interaction if appropriate. Flag significant values, show the 95% CI, calculate Cohen’s d for effect size, select conditional comparisons for interactions, and also present the simple main effects.
For visual presentation, also include descriptive plots with 95% error bars for Test Prep, with Gender displayed separately. Then include raincloud plots displayed horizontally with Gender plotted separately.
Use the results from ANOVA Parent-Math to answer the following questions:
Q1p1– What is the test statistic (F-value) for the Test Prep Course?
[ Select ] [“14.551”, “”]
Q1p2- What is the test statistic (F-value) for Gender?
[ Select ] [“5.155”, “”]
Q1p3- What is the test statistic (F-value) for the Interaction Term?
[ Select ] [“3.188”, “”]
Q1p4- What is the effect size (partial eta squared) for the Test Prep Course?
[ Select ] [“.067”, “0.067”]
Q1p5- What is the effect size (partial eta squared) for Gender?
[ Select ] [“.025”, “0.025”]
Q1p6- What is the effect size (partial eta squared) for the Interaction Term?
[ Select ] [“.015”, “0.015”]
Q1p7- What is the p-value for Levene’s test?
[ Select ] [“.784”, “0.784”]
Q1p8- What is the p-value for the interaction term?
[ Select ] [“.076”, “0.076”]
Q1p9– Does the Test Prep Course significantly increase math exam scores? (yes/no)
[ Select ] [“yes”, “”]
Q1p10– Does the Test Prep Course benefit Females significantly more than Males? (yes/no)
[ Select ] [“no”, “”]
Move on to Question 2, where you will repeat this process using the reading exam scores. Do not close or delete the previous modules as you will only upload ONE results file for this assignment.
Flag question: Question 2
Question 230 pts
Question 2: Analyze Reading Scores
Next, you will use statistical analysis to determine whether the parent’s education level significantly influences a child’s test scores in reading. Open the correct module and name it ANOVA PrepxGen-Reading. Choose the appropriate analytical technique to test the following hypotheses for α=.05.:
H0:μCompleted=μNone
H1:μCompleted≠μNone
H0:μFemale=μMale
H2:μFemale≠μMale
H0:μPrepij∗Female=μPrepij∗Male
H3:μPrepij∗Female≠μPrepij∗Male
Perform all necessary omnibus tests to ensure that the assumptions for the analysis have been met. Include the Q-Q plot as a test for normality, but you may assume that normality has been met for all three dependent variables. We will not be performing nonparametric analyses in this assignment. Test for homogeneity of variances, however if Levene’s test is significant (p < .05), then we will still perform the analyses using Type III Model since there is no variance correction for factorial ANOVA and we cannot test interactions using KW nonparametric techniques.
Use the default Type III sum of squares model if homogeneity of variances can be assumed. Include the descriptive statistics table for this analysis and use partial eta square to estimate the effect size of the treatment, block, and interaction.
Perform post-hoc analysis using Tukey’s test for pairwise comparisons of the treatment, block, and interaction if appropriate. Flag significant values, show the 95% CI, calculate Cohen’s d for effect size, select conditional comparisons for interactions, and also present the simple main effects.
For visual presentation, also include descriptive plots with 95% error bars for Test Prep, with Gender displayed separately. Then include raincloud plots displayed horizontally with Gender plotted separately.
Use the results from ANOVA PrepxGen -Reading to answer the following questions:
Q2p1– What is the test statistic (F-value) for the Test Prep Course?
Q2p2- What is the test statistic (F-value) for Gender?
Q2p3- What is the test statistic (F-value) for the Interaction Term?
Q2p4- What is the effect size (partial eta squared) for the Test Prep Course?
Q2p5- What is the effect size (partial eta squared) for Gender?
Q2p6- What is the effect size (partial eta squared) for the Interaction Term?
Q2p7- What is the p-value for Levene’s test?
Q2p8- What is the p-value for the interaction term?
Q2p9– Does the Test Prep Course significantly increase reading exam scores? (yes/no)
Q2p10– Does the Test Prep Course benefit Females significantly more than Males? (yes/no)
Move on to Question 3, where you will repeat this process using the writing exam scores. Do not close or delete the previous modules as you will only upload ONE results file for this assignment.
Flag question: Question 3
Question 330 pts
Question 3: Analyze Writing Scores
Next, you will use statistical analysis to determine whether the parent’s education level significantly influences a child’s test scores in writing. Open the correct module and name it ANOVA PrepxGen-Writing. Choose the appropriate analytical technique to test the following hypotheses for α=.05.:
H0:μCompleted=μNone
H1:μCompleted≠μNone
H0:μFemale=μMale
H2:μFemale≠μMale
H0:μPrepij∗Female=μPrepij∗Male
H3:μPrepij∗Female≠μPrepij∗Male
Perform all necessary omnibus tests to ensure that the assumptions for the analysis have been met. Include the Q-Q plot as a test for normality, but you may assume that normality has been met for all three dependent variables. We will not be performing nonparametric analyses in this assignment. Test for homogeneity of variances, however if Levene’s test is significant (p < .05), then we will still perform the analyses using Type III Model since there is no variance correction for factorial ANOVA and we cannot test interactions using KW nonparametric techniques.
Use the default Type III sum of squares model if homogeneity of variances can be assumed. Include the descriptive statistics table for this analysis and use partial eta square to estimate the effect size of the treatment, block, and interaction.
Perform post-hoc analysis using Tukey’s test for pairwise comparisons of the treatment, block, and interaction if appropriate. Flag significant values, show the 95% CI, calculate Cohen’s d for effect size, select conditional comparisons for interactions, and also present the simple main effects.
For visual presentation, also include descriptive plots with 95% error bars for Test Prep, with Gender displayed separately. Then include raincloud plots displayed horizontally with Gender plotted separately.
Use the results from ANOVA PrepxGen -Writing to answer the following questions:
Q3p1– What is the test statistic (F-value) for the Test Prep Course?
Q3p2- What is the test statistic (F-value) for Gender?
Q3p3- What is the test statistic (F-value) for the Interaction Term?
Q3p4- What is the effect size (partial eta squared) for the Test Prep Course?
Q3p5- What is the effect size (partial eta squared) for Gender?
Q3p6- What is the effect size (partial eta squared) for the Interaction Term?
Q3p7- What is the p-value for Levene’s test?
Q3p8- What is the p-value for the interaction term?
Q3p9– Does the Test Prep Course significantly increase writing exam scores? (yes/no)
Q3p10– Does the Test Prep Course benefit Males writing significantly more than Females? (yes/no)
Export the Results of the entire assignment to pdf, then move on to Question 4, where you upload it for credit.
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