Assignment Prompt & Instructions
Needed by 4am CST 5/5/ 25 (central standard time) you will need to download free Jasp program
– a year ago
**files (2)**
– ## Week7WrittenCaseNumber3.docx
– TexasSTAARFilterANOVAWeek7Data-3case3.csv
**## Week7WrittenCaseNumber3.docx**
BUS 5253-W2 Spring 2025 Case 3
**Description:**
The State of Texas Assessments of Academic Readiness, commonly referred to as its acronym STAAR, is a series of standardized tests used in Texas public primary and secondary schools to assess a student's achievements and knowledge learned in the grade level. Starting in third grade, Texas students are required to take the State of Texas Assessment of Academic Readiness test, or STAAR, every year. This dataset contains test scores and demographic information for randomly selected third grade students from the Texoma region
This data set contains only data from randomly selected third-grade students in from the Texoma region in the 2023-2024 academic year. The data were collected for the purpose of identifying significant predictors in both the home environment and the scholastic setting that may influence educational outcomes. Of particular interest in this study is the evaluation of a new Prep Course designed to prepare children to be able to navigate and succeed on standardized tests. This course was introduced to select schools on a trial basis to determine the efficacy of the program. Also of interest is the family socioeconomic status, indicated by the highest education level of either parent (or guardian) and the indication whether the child qualifies for free or discounted lunch. Test scores are percentile ranks relative to the statewide performance of third grade students in the three facets of the STAAR test: math, reading, and writing. The following measures were included in the data:
**Variables:**
·
**StudentID** – Participant's identification number which looks strangely similar to the original row number of the dataset.
·
**Gender –** The biological sex of the child, i.e. Female/Male.
·
**Testing Location –** Represents the Independent School District in which the Prep Course and Testing were given Anna, TX and McKinney, TX
·
**Parental Level of Education –** the highest education level completed by either parent (or guardian), categorized by: High School, Associates Degree, or Bachelors Degree.
·
**Lunch –** Binary measure indicating whether or not the child is eligible for free or discounted lunch, indicated by free/reduced for qualifying students and standard for students paying the standard fees.
·
**Test Prep Course–** Binary measure to indicate the completion of the newly introduced Prep Course before STAAR testing began in Spring 2024. This measure is indicated as Complete for those whose schools were included in the initiative in time to complete the program or None if the student’s school was not included.
**Research Question:**
In this case we will be designing a study to determine if math test scores are significantly improved depending on whether or not the child completed the Test Prep Course, and if the effect differs based on economic status. Upon loading, examining, and cleaning your data, choose the correct analytical technique to test the following hypotheses:
0
where TespPrep is the population of kids who have completed the Prep Course, Reduced is the population of kids who are eligible for a Free or Reduced Price Lunch, and TestPrep*Reduced is the interaction between the two.
**(You may use or quickly recreate the same Frequency and Descriptive Sections from Case 1 to satisfy Accuracy and Outliers if you easily remember what you did to clean the data.)**
**Accuracy:**
1. Check the data for out of range scores.
a. Include a summary showing you do/do not have out of range scores.
b. If necessary, fix the out of range scores.
i. Indicate what the problems were in the dataset.
ii. Make all out of range values NA.
iii. Include a summary showing that you fixed the accuracy issues.
2. Fix the factored columns to have nice labels (i.e. Proper Case, Fully Spelled out). Only factor the IV, do not factor the DV.
a. Use the data editor in JASP to change the Test Location so that the label for Group A shows Anna, TX and the label for Group B shows McKinney, TX.
b. Use the data editor in JASP to change the Parent Education variable so that the value for High School, Associate, and Bachelor reads 1, 2, and 3. This will ensure they line up correctly in your graphs.
**Missing data:**
3. Exclude all missing data using listwise deletion. Use the filter function to eliminate any cases with missing data.
**Outliers:**
4. Use the boxplot feature to show that you have no outliers.
a. Include a summary of those z-scores.
b. Do you have any outliers?
c. Exclude those outliers.
**Normality:**
5. Perform the proper test to show that the assumption of normality is met.
**Linearity:**
6. Include the multivariate QQ plot.
**Homogeneity:**
7. Include the multivariate residuals plot.
8. Interpret the graph. Does it indicate homogeneity?
**Power:**
9. Calculate the number of participants you would need for this study, assuming a medium effect size.
a. Include a screen shot or summary of the numbers you typed into G*Power, so we can give you partial credit if you get a different sample size than us.
**ANOVA and Levene’s:**
10. Include the ANOVA and Levene’s test output.
11. Do you meet the homogeneity assumption given the results from Levene’s test?
12. Was the overall test significant?
a. Include the APA/AMA style write up for F (just the statistics):
**Post Hocs:**
13. Calculate the means, standard deviations, and group sizes for your levels.
14. Post hocs:
a. What type of post hoc
_test_ did you run?
b. What type of post hoc
_correction_ did you run?
c. Include the t-test output.
15. Effect size:
a. Calculate the effect size for your pairwise comparisons.
b. Include the effect size output or MOTE screen shot.
16. Fill in the table below with the information from the above calculations (like the one from the notes):
| | | | | |
| — | — | — | — | — |
| Mean 1 | Mean 2 | P-value | Explain? | Effect size |
| | | | | |
| | | | | |
| | | | | |
**Graph:**
17. Include a graph of the means and confidence interval for your ANOVA. Be sure to check the following:
a. X-axis label
b. Y-axis label
c. X-axis group labels
d. Error bars
e. Cleaned up graph (no gray backgrounds)
**Write up:**
18. Write up an analysis of what you find in this data, including all the information you answered above. Use the example in the notes for a guide. This write up should include the following for credit:
19. Result section style (APA and AMA):
a. Double space
b. Times New Roman 12 point
c. Two decimals
d. Centered, bolded Results
20. Short description of the study/variables.
21. Data screening summary:
a. Accuracy – did you have problems? What did you do to fix it?
b. Missing data – did you have problems? What did you do to fix it?
c. Outliers – did you have problems? What did you do to fix it?
d. Assumptions:
i. Normality
ii. Linearity
iii. Homogeneity and Levene’s
22. ANOVA
a. Overall F statistic
b. Post hoc tests / corrections and results
c. Effect size for all tests
23. Graph with reference to the figure in the text.
**TexasSTAARFilterANOVAWeek7Data-3case3.csv**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/texasstaarfilteranovaweek7data-3case3-csv)
**TexasSTAARFilterANOVAWeek7Data-3case3.csv**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/texasstaarfilteranovaweek7data-3case3-csv)
**## Week7WrittenCaseNumber3.docx**
BUS 5253-W2 Spring 2025 Case 3
**Description:**
The State of Texas Assessments of Academic Readiness, commonly referred to as its acronym STAAR, is a series of standardized tests used in Texas public primary and secondary schools to assess a student's achievements and knowledge learned in the grade level. Starting in third grade, Texas students are required to take the State of Texas Assessment of Academic Readiness test, or STAAR, every year. This dataset contains test scores and demographic information for randomly selected third grade students from the Texoma region
This data set contains only data from randomly selected third-grade students in from the Texoma region in the 2023-2024 academic year. The data were collected for the purpose of identifying significant predictors in both the home environment and the scholastic setting that may influence educational outcomes. Of particular interest in this study is the evaluation of a new Prep Course designed to prepare children to be able to navigate and succeed on standardized tests. This course was introduced to select schools on a trial basis to determine the efficacy of the program. Also of interest is the family socioeconomic status, indicated by the highest education level of either parent (or guardian) and the indication whether the child qualifies for free or discounted lunch. Test scores are percentile ranks relative to the statewide performance of third grade students in the three facets of the STAAR test: math, reading, and writing. The following measures were included in the data:
**Variables:**
·
**StudentID** – Participant's identification number which looks strangely similar to the original row number of the dataset.
·
**Gender –** The biological sex of the child, i.e. Female/Male.
·
**Testing Location –** Represents the Independent School District in which the Prep Course and Testing were given Anna, TX and McKinney, TX
·
**Parental Level of Education –** the highest education level completed by either parent (or guardian), categorized by: High School, Associates Degree, or Bachelors Degree.
·
**Lunch –** Binary measure indicating whether or not the child is eligible for free or discounted lunch, indicated by free/reduced for qualifying students and standard for students paying the standard fees.
·
**Test Prep Course–** Binary measure to indicate the completion of the newly introduced Prep Course before STAAR testing began in Spring 2024. This measure is indicated as Complete for those whose schools were included in the initiative in time to complete the program or None if the student’s school was not included.
**Research Question:**
In this case we will be designing a study to determine if math test scores are significantly improved depending on whether or not the child completed the Test Prep Course, and if the effect differs based on economic status. Upon loading, examining, and cleaning your data, choose the correct analytical technique to test the following hypotheses:
0
where TespPrep is the population of kids who have completed the Prep Course, Reduced is the population of kids who are eligible for a Free or Reduced Price Lunch, and TestPrep*Reduced is the interaction between the two.
**(You may use or quickly recreate the same Frequency and Descriptive Sections from Case 1 to satisfy Accuracy and Outliers if you easily remember what you did to clean the data.)**
**Accuracy:**
1. Check the data for out of range scores.
a. Include a summary showing you do/do not have out of range scores.
b. If necessary, fix the out of range scores.
i. Indicate what the problems were in the dataset.
ii. Make all out of range values NA.
iii. Include a summary showing that you fixed the accuracy issues.
2. Fix the factored columns to have nice labels (i.e. Proper Case, Fully Spelled out). Only factor the IV, do not factor the DV.
a. Use the data editor in JASP to change the Test Location so that the label for Group A shows Anna, TX and the label for Group B shows McKinney, TX.
b. Use the data editor in JASP to change the Parent Education variable so that the value for High School, Associate, and Bachelor reads 1, 2, and 3. This will ensure they line up correctly in your graphs.
**Missing data:**
3. Exclude all missing data using listwise deletion. Use the filter function to eliminate any cases with missing data.
**Outliers:**
4. Use the boxplot feature to show that you have no outliers.
a. Include a summary of those z-scores.
b. Do you have any outliers?
c. Exclude those outliers.
**Normality:**
5. Perform the proper test to show that the assumption of normality is met.
**Linearity:**
6. Include the multivariate QQ plot.
**Homogeneity:**
7. Include the multivariate residuals plot.
8. Interpret the graph. Does it indicate homogeneity?
**Power:**
9. Calculate the number of participants you would need for this study, assuming a medium effect size.
a. Include a screen shot or summary of the numbers you typed into G*Power, so we can give you partial credit if you get a different sample size than us.
**ANOVA and Levene’s:**
10. Include the ANOVA and Levene’s test output.
11. Do you meet the homogeneity assumption given the results from Levene’s test?
12. Was the overall test significant?
a. Include the APA/AMA style write up for F (just the statistics):
**Post Hocs:**
13. Calculate the means, standard deviations, and group sizes for your levels.
14. Post hocs:
a. What type of post hoc
_test_ did you run?
b. What type of post hoc
_correction_ did you run?
c. Include the t-test output.
15. Effect size:
a. Calculate the effect size for your pairwise comparisons.
b. Include the effect size output or MOTE screen shot.
16. Fill in the table below with the information from the above calculations (like the one from the notes):
| | | | | |
| — | — | — | — | — |
| Mean 1 | Mean 2 | P-value | Explain? | Effect size |
| | | | | |
| | | | | |
| | | | | |
**Graph:**
17. Include a graph of the means and confidence interval for your ANOVA. Be sure to check the following:
a. X-axis label
b. Y-axis label
c. X-axis group labels
d. Error bars
e. Cleaned up graph (no gray backgrounds)
**Write up:**
18. Write up an analysis of what you find in this data, including all the information you answered above. Use the example in the notes for a guide. This write up should include the following for credit:
19. Result section style (APA and AMA):
a. Double space
b. Times New Roman 12 point
c. Two decimals
d. Centered, bolded Results
20. Short description of the study/variables.
21. Data screening summary:
a. Accuracy – did you have problems? What did you do to fix it?
b. Missing data – did you have problems? What did you do to fix it?
c. Outliers – did you have problems? What did you do to fix it?
d. Assumptions:
i. Normality
ii. Linearity
iii. Homogeneity and Levene’s
22. ANOVA
a. Overall F statistic
b. Post hoc tests / corrections and results
c. Effect size for all tests
23. Graph with reference to the figure in the text.
**TexasSTAARFilterANOVAWeek7Data-3case3.csv**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/texasstaarfilteranovaweek7data-3case3-csv)
**## Week7WrittenCaseNumber3.docx**
BUS 5253-W2 Spring 2025 Case 3
**Description:**
The State of Texas Assessments of Academic Readiness, commonly referred to as its acronym STAAR, is a series of standardized tests used in Texas public primary and secondary schools to assess a student's achievements and knowledge learned in the grade level. Starting in third grade, Texas students are required to take the State of Texas Assessment of Academic Readiness test, or STAAR, every year. This dataset contains test scores and demographic information for randomly selected third grade students from the Texoma region
This data set contains only data from randomly selected third-grade students in from the Texoma region in the 2023-2024 academic year. The data were collected for the purpose of identifying significant predictors in both the home environment and the scholastic setting that may influence educational outcomes. Of particular interest in this study is the evaluation of a new Prep Course designed to prepare children to be able to navigate and succeed on standardized tests. This course was introduced to select schools on a trial basis to determine the efficacy of the program. Also of interest is the family socioeconomic status, indicated by the highest education level of either parent (or guardian) and the indication whether the child qualifies for free or discounted lunch. Test scores are percentile ranks relative to the statewide performance of third grade students in the three facets of the STAAR test: math, reading, and writing. The following measures were included in the data:
**Variables:**
·
**StudentID** – Participant's identification number which looks strangely similar to the original row number of the dataset.
·
**Gender –** The biological sex of the child, i.e. Female/Male.
·
**Testing Location –** Represents the Independent School District in which the Prep Course and Testing were given Anna, TX and McKinney, TX
·
**Parental Level of Education –** the highest education level completed by either parent (or guardian), categorized by: High School, Associates Degree, or Bachelors Degree.
·
**Lunch –** Binary measure indicating whether or not the child is eligible for free or discounted lunch, indicated by free/reduced for qualifying students and standard for students paying the standard fees.
·
**Test Prep Course–** Binary measure to indicate the completion of the newly introduced Prep Course before STAAR testing began in Spring 2024. This measure is indicated as Complete for those whose schools were included in the initiative in time to complete the program or None if the student’s school was not included.
**Research Question:**
In this case we will be designing a study to determine if math test scores are significantly improved depending on whether or not the child completed the Test Prep Course, and if the effect differs based on economic status. Upon loading, examining, and cleaning your data, choose the correct analytical technique to test the following hypotheses:
0
where TespPrep is the population of kids who have completed the Prep Course, Reduced is the population of kids who are eligible for a Free or Reduced Price Lunch, and TestPrep*Reduced is the interaction between the two.
**(You may use or quickly recreate the same Frequency and Descriptive Sections from Case 1 to satisfy Accuracy and Outliers if you easily remember what you did to clean the data.)**
**Accuracy:**
1. Check the data for out of range scores.
a. Include a summary showing you do/do not have out of range scores.
b. If necessary, fix the out of range scores.
i. Indicate what the problems were in the dataset.
ii. Make all out of range values NA.
iii. Include a summary showing that you fixed the accuracy issues.
2. Fix the factored columns to have nice labels (i.e. Proper Case, Fully Spelled out). Only factor the IV, do not factor the DV.
a. Use the data editor in JASP to change the Test Location so that the label for Group A shows Anna, TX and the label for Group B shows McKinney, TX.
b. Use the data editor in JASP to change the Parent Education variable so that the value for High School, Associate, and Bachelor reads 1, 2, and 3. This will ensure they line up correctly in your graphs.
**Missing data:**
3. Exclude all missing data using listwise deletion. Use the filter function to eliminate any cases with missing data.
**Outliers:**
4. Use the boxplot feature to show that you have no outliers.
a. Include a summary of those z-scores.
b. Do you have any outliers?
c. Exclude those outliers.
**Normality:**
5. Perform the proper test to show that the assumption of normality is met.
**Linearity:**
6. Include the multivariate QQ plot.
**Homogeneity:**
7. Include the multivariate residuals plot.
8. Interpret the graph. Does it indicate homogeneity?
**Power:**
9. Calculate the number of participants you would need for this study, assuming a medium effect size.
a. Include a screen shot or summary of the numbers you typed into G*Power, so we can give you partial credit if you get a different sample size than us.
**ANOVA and Levene’s:**
10. Include the ANOVA and Levene’s test output.
11. Do you meet the homogeneity assumption given the results from Levene’s test?
12. Was the overall test significant?
a. Include the APA/AMA style write up for F (just the statistics):
**Post Hocs:**
13. Calculate the means, standard deviations, and group sizes for your levels.
14. Post hocs:
a. What type of post hoc
_test_ did you run?
b. What type of post hoc
_correction_ did you run?
c. Include the t-test output.
15. Effect size:
a. Calculate the effect size for your pairwise comparisons.
b. Include the effect size output or MOTE screen shot.
16. Fill in the table below with the information from the above calculations (like the one from the notes):
| | | | | |
| — | — | — | — | — |
| Mean 1 | Mean 2 | P-value | Explain? | Effect size |
| | | | | |
| | | | | |
| | | | | |
**Graph:**
17. Include a graph of the means and confidence interval for your ANOVA. Be sure to check the following:
a. X-axis label
b. Y-axis label
c. X-axis group labels
d. Error bars
e. Cleaned up graph (no gray backgrounds)
**Write up:**
18. Write up an analysis of what you find in this data, including all the information you answered above. Use the example in the notes for a guide. This write up should include the following for credit:
19. Result section style (APA and AMA):
a. Double space
b. Times New Roman 12 point
c. Two decimals
d. Centered, bolded Results
20. Short description of the study/variables.
21. Data screening summary:
a. Accuracy – did you have problems? What did you do to fix it?
b. Missing data – did you have problems? What did you do to fix it?
c. Outliers – did you have problems? What did you do to fix it?
d. Assumptions:
i. Normality
ii. Linearity
iii. Homogeneity and Levene’s
22. ANOVA
a. Overall F statistic
b. Post hoc tests / corrections and results
c. Effect size for all tests
23. Graph with reference to the figure in the text.
**TexasSTAARFilterANOVAWeek7Data-3case3.csv**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/texasstaarfilteranovaweek7data-3case3-csv)