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Question 1 5 pts
There is a statistically significant difference between theory test scores of instrumental and vocal majors.
There is not a statistically significant difference between theory test scores of instrumental and vocal majors
Instrumental majors tended to score 25% higher than vocal majors on their theory test. The Confidence Level for this research 25%.
A college Music Theory professor observed that instrumental students tended to earn higher grades in his theory class in comparison to vocal students. He asked a statistician to run data analysis procedures comparing the average theory test grades of instrumental and vocal majors. Results of this data analysis yielded a significance level of .25.
Based upon these research findings, the researcher can conclude that:
Question 2 5 pts
A Music Education researcher is interested in recruiting students for beginning band and wants to study instrument choice by gender and feeder school. The level of measurement represented by feeder school is:
Nominal Ordinal Interval Ratio
Question 3 5 pts
Which of the following data sources would be most appropriate for data analysis using the Frequencies command in SPSS?
Age
Gender
Numerical Music Aptitude Score ALL of the above
None of the above
Question 4 5 pts
Which of the following data sources would be most appropriate for data analysis using the Descriptives command in SPSS?
Ensemble Enrollment (whether students are enrolled in Band or Choir) Number of Years of School Music Ensemble Experience
Principal Instrument
ALL of the above
NONE of the above
Question 5 5 pts
When sample size decreases:
Confidence level increases Confidence interval decreases Probability of error decreases ALL of the above
NONE of the above
Question 6 5 pts
A college class piano teacher carried out a quasiexperimental study to compare the final Fall semester jury grades of nonmajor piano students studying under two different adult beginning piano methods (Method A and Method B). Data analysis revealed no statistically significant difference (p < .05) between jury grades of students studying under Method A and students studying under Method B, so she concludes to accept (“fail to reject”) the null hypothesis of NO difference between the two methods and decides to use Method B next semester (because Method B costs a few dollars less than Method A). Unfortunately, these results are erroneous! Students actually improved much more under Method A in comparison to Method B, but the data analysis did not show this because jury grades were biased. Different faculty served on the jury on different days. The students in the sections of class piano using Method A were assigned to play their jury on a day when the faculty judges graded much more harshly in comparison to the faculty judges who were much more forgiving in grading students in the sections of class piano using Method B.
This is an example of:
Type I Error
Type II Error
Type III Error
ALL of the above NONE of the above
Question 7 5 pts
A music education researcher wanted to learn if there is a significant difference in high school band directors’ instructional technology use by age level. (His Research Hypothesis predicted that younger teachers would report more technology use.) He ran Crosstabs to compare responses to a survey item about technology use across different age levels. He also ran a
Chi Square to learn if there was a statistically significant difference between observed and expected distributions across these two variables.
Crosstab
Do you have any activities that incorporate technology during your classes?
Total
Yes
No
Age Level
2434
20
4
24
3545
15
1
16
4656
11
1
12
5772
8
3
11
Total
54
9
63
ChiSquare Tests
Value
df
Asymptotic Significance (2 sided)
Pearson ChiSquare
2.817a
3
.421
Likelihood Ratio
2.791
3
.425
LinearbyLinear Association
.253
1
.615
N of Valid Cases
63
What did we learn from this data analysis? (See above Output Tables)
There was a significant difference between band directors’ technology use across different age levels.
There was no significant difference between band directors’ technology use across different age levels
Younger band directors incorporate significantly more technology in comparison to older directors.
ALL of the above NONE of the above
Question 8 5 pts
A Music Education researcher studied the relationship between 164 Alabama high school band directors’ age and their students’ use of electronic devices during band class. Data Analysis revealed a positive correlation coefficient of +.052, and a significance level of .69. Based upon the results of this data analysis, the researcher can conclude that:
A. There is a statistically significant relationship between students’ use of electronic devices during band class and their band directors’ age.
B. Students’ use of electronic devices during band class tends to decease as the age of their band director increases.
C. Younger band directors are more likely to allow students to use electronic devices during band class.
ALL of the above
NONE of the above
Question 9 5 pts
A college Music Department Head is interested in improving the efficiency of their recruiting process. She is interested in targeting high school students who are most likely to be successful music performance majors. She collects data from current performance majors and alumni and runs a Regression analysis to see which variables are the best predictors of students’ jury grade. The Dependent variable in her Regression analysis is the Jury Grade for the final semester of music performance majors’ Senior year in College. The Independent (predictor) variables are music aptitude, High School GPA, years of ensemble experience, and years of private music lessons.
Interpret the R Square statistic from the Regression Model Summary table below.
Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
1
.964a
.929
.926
4.05833
a. Predictors: (Constant), Music Aptitude Score, Years in School Music Ensembles, HS Grade Point Average, Years of Private Music Lessons
The researcher can be about 93% confident (confidence level) that these results did NOT occur by chance.
The significance level is .05833.
The independent variables account for about 93% of the variance in the dependent variable
ALL of the above NONE of the above
Question 10 5 pts
NOTE: This Question uses a different Output Table from the same Regression example used in Question 9.
A college Music Department Head is interested in improving the efficiency of their recruiting process. She is interested in targeting high school students who are most likely to be successful music performance majors. She collects data from current performance majors and alumni and runs a Regression analysis to see which variables are the best predictors of students’ jury grade. The Dependent variable in her Regression analysis is the Jury Grade for the final semester of music performance majors’ Senior year in College. The Independent (predictor) variables are music aptitude, High School GPA, years of ensemble experience, and years of private music lessons.
Interpret the statistics from the tables below. ANOVA Table
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
23276.967
4
5819.242
353.322
.000b
Residual
1778.767
108
16.470
Total
25055.735
112
a. Dependent Variable: Senior Jury Grade
b. Predictors: (Constant), Music Aptitude Score, Years in School Music Ensembles, HS Grade Point Average, Years of Private Music Lessons
Regression Model Summary table
Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
1
.964a
.929
.926
4.05833
a. Predictors: (Constant), Music Aptitude Score, Years in School Music Ensembles, HS Grade Point Average, Years of Private Music Lessons
The Regression Model is statistically significant (p<.05)
Music Aptitude Score, High School GPA, Years of Ensemble Experience, and Years of Private
Music Lessons are all related to College Music Majors’ Senior Jury Grade.
This model (combination of independent variables) is a very good predictor of the Jury Grade that students will achieve during their Senior Year in college.
ALL of the above NONE of the above
Question 11 5 pts
A College Strings Teacher at a large university is interested in comparing music reading skills of students who were initially trained under the Suzuki method with students who were initially trained under a non-Suzuki approach (independent/grouping variable). He administers a sight-reading test that yields two sets of numerical scores (number of correct pitches and number of correct rhythms) as a measure of music reading skills to all students
Question 12 5 pts
A music researcher in Georgia sends out a survey to everyone on the GMEA (Georgia Music Educators’ Association) membership list. His response rate represents about 15% of the total membership. Results of his data analysis will represent:
Parameters Statistics
ALL of the above NONE of the above
auditioning for college admission as music majors (dependent variables). Preliminary data analysis revealed that scores on the sight-reading test were distributed normally.
The most appropriate data analysis procedure to test for a statistically significant difference between the two sight-reading scores of Suzuki and non-Suzuki students would be:
a. Descriptive Statistics – Cross Tabs – Chi Square b. General Linear Model Multivariate (MANOVA) c. General Linear Model Univariate (ANOVA)
ALL of the above
NONE of the above
Question 13 5 pts
A music researcher wanted to learn about requirements and procedures for choral all-state auditions across the entire United States. She obtained quantitative
Question 14 5 pts
Assumptions for statistical data analysis procedures based upon the mean (such as tTest and ANOVA) include:
Normal Distribution Scalelevel Data ALL of the above NONE of the above
information about requirements for all-state auditions for all 50 state music education organizations in the United Stated.
Results of her data analysis will represent:
Parameters Statistics
ALL of the above NONE of the above
Question 15 5 pts
After teaching a unit on basic music reading, an elementary school music teacher wrote and administered a note recognition test. Unfortunately, due to school priorities and scheduling, the children only have one music class per week. The teacher overestimated how much information the children would be able to retain with such limited music instruction and, consequently most children performed very poorly on the test. This is an example of:
BiModel Distribution
Question 16 5 pts
A college music GTA developed and administered a test to his Music Appreciation class. This teacher wrote a very easy test, so most students achieved very high scores. This is an example of a:
BiModal Distribution NegativelySkewed Distribution Normal Distribution PositivelySkewed Distribution
NegativelySkewed Distribution Normal Distribution PositivelySkewed Distribution
Question 17 5 pts
An elementary music teacher developed and taught a new unit on the history of Jazz to her 5th and 6th grade students. She is interested in seeing if this unit was successful and decides to carry out an informal study to compare all children’s pre and posttest scores (numerical data) to see if there was any statistically significant improvement in their knowledge of Jazz history at the end of the unit. Her school serves a large number of English Language Learners (ELLs). Preliminary data analysis revealed a definite BiModel distribution, with ELL students scoring very low on the test in comparison to native English Language speakers who all scored very high. The most appropriate data analysis procedure for this situation is:
ANOVA
Question 18 5 pts
A high school general music teacher experimented (quasi-experimental design) with two different approaches to teaching a unit on the Classical era. He used a project- based approach for one section of the course and used a standard Music Appreciation textbook with worksheets for the other section. He administered a comprehensive written pre-test to his students at the beginning of the semester and a post-test to the students at the end of the semester (the test yields numerical scores). He wants to compare gain scores of students in the two sections to see which group had the highest scores. His preliminary data analysis indicated that the test gain scores have very strong positive skew.
The most appropriate data analysis procedure for this situation is:
ANOVA MannWhitney U tTest
Wilcoxon Test
MannWhitney U tTest
Wilcoxon Test
Question 19 5 pts
The diagram above best represents:
High Reliability but Low Validity High Reliability and High Validity Low Reliability but High Validity Low Reliability and Low Validity
Question 20 5 pts
A music education researcher wants to carry out data analysis to compare student responses to a survey based upon their principal instrument (or voice). His survey
asked students to check their principal instrument/voice, but he now realizes that he needs to collapse the many different individual instrument/voice options into just 6 “families” (brass, keyboard, percussion, strings, vocal, woodwind) to have sufficient numbers in each category for valid data analysis procedures. He wants to keep the individual instrument/voice information in his data base, but also needs to create a new variable representing instrument family. The most appropriate SPSS data transformation procedure for creating a new variable representing instrument/voice “family” is:
(music OR school OR education) AND (vocal OR choral) AND teaching Compute Variable
Recode into Different Variable
Recode into Same Variable
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1. There is not a statistically significant difference between theory test scores of instrumental and vocal majors

2. Nominal

3. Gender...