Business & Finance Data analytical assignment week 5 assignment 2

please review the assignment instructions attached, will need to download JASP to complete the assignment due 5pm 4/21

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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 x

 Download Week 5 Analytical Assignment – Texas STAAR Exam Data x

Texas STAAR Filter ANOVA Week 5 Data.csv

Download Texas STAAR Filter ANOVA Week 5 Data.csv

 

Top of Form

 

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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Group B
Some College
Standard
Completed
91
96
91

Female
Group B
Associates Degree
Free/Reduced
None
54
65
65

Male
Group A
Associates Degree
Standard
None
67
57
53

Female
Group B
High School
Standard
None
58
68
61

Male
Group B
High School
Standard
None
82
82
80

Female
Group A
Some College
Standard
None
56
58
64

Female
Group B
Bachelors Degree
Standard
None
75
84
80

Female
Group A
High School
Free/Reduced
Completed
53
50
60

Female
Group A
Some College
Standard
None
54
63
67

Male
Group B
Some College
Free/Reduced
Completed
60
62
60

Female
Group B
High School
Free/Reduced
None
8
24
23

Female
Group A
Some College
Standard
Completed
78
87
91

Male
Group A
High School
Standard
None
57
51
54

Male
Group A
High School
Standard
None
63
63
62

Student Gender Testing Group Parental Level of Education Lunch Test Preparation Course Math Score Reading Score Writing Score
1 Female Group B Bachelors Degree Standard None 7 72 7

4
Male Group A Associates Degree Free/Reduced 47 57 44
6 71 8 78
Some College Completed 88 95 92
40 43 39
10 High School 38 60 50
13 65 81 73
14 70
22 75
27 69 54 55
32 63 61
56
53 58
59 66
46
62
41 51 48
76
80
49
82 45
83
89 67
79 86
101
104
109 52
113
117 85
122 91
126 87
1

30
144
15 77 68
153
158
159
161 74
169
171
177
19
200
204
209
210
219
222
225
23
239
24
249 64
259
275
28
286
289
2

96
301
304
306
309
311
312
319
328
3

34 90
3

36
343
347
351
356
357
361
366
380
381
396
3

97
402
403
407
408
42
444
449
450
460
462
465
468
469
475
481
4

84
485
490
491
495
502 94
507
508
520
535
540
552
553
559
565
566
569
571
572
576
577
578
579
587
597
610
615 93
622
624 100
627
632
633
636
6

37
645
646
649
652
654
656
668
676
682
683
685
689
698
703
706
716
725
731
738
742
745
750
760
764
766
768
770
771
775
777
779
783
788
791
795
804
806
810
812
817
819
824
831
833
834
835
838
839
843
844
852
856
857
859
868
871
873
883
897 29
903
907
912
915
920
924
937
945
947
961
970
973
975
977
981
984
986
995

Jeffreys’s Amazing Software Program ($0.00)

·
JASP v19.03 available for free download

·

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 x

 Download Week 5 Analytical Assignment – Texas STAAR Exam Data x

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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