Is There a Correlation between Practicing Music and a Student’s Performance on School Work?

Introduction:

My high school set manager has ever told me that pupils who are involved in music do much better in school than pupils who are non involved with music. I have ever wondered if this statement is truly true. I have noticed that many IB pupils here at Walnut High School are someway involved in the music section, whether it is band, orchestra, choir, or any other type of musical activity. That got me inquiring if there was a connexion between what my set manager said, and why there are so many musically involved pupils in the IB plan.

A major ground as to why I am taking this subject is because music has been a major portion of my life. Music is something that provides me a safe oasis, a topographic point where all my problems seem to travel off. Besides, this subject is of import to me because I am to a great extent involved in our school’s music section, particularly the set. We have an improbably big music plan here at Walnut High School, and we hope to enlarge it even more. However, because of budget cuts and the province of our economic system, the simple school music plan is under the menace of being removed. Personally, I believe that it is improbably of import to present music to simple school childs so that they may happen an involvement in life at an early age. If the simple school music plan is to be removed, this will be improbably traumatising for the high school plan because it lowers the figure of pupils who will take set as an elective in in-between school and finally high school. If there is a correlativity between musical engagement and public presentation in school, hopefully this information can be a manner to carry the territory to maintain our simple school music plan. Besides, if this experiment supports a correlativity, so this information can be used as a manner for more immature pupils to hold an increased involvement in musical engagement. Music is an of import portion of our lives and it should non be eliminated.

Before I can get down my probe, I must research some background about old research that has been done on music and its consequence on a person’s rational public presentation in different state of affairss. A research done by Gordon Shaw, Katherine Ky, and Frances Rauscher published inNature, an international diary of scientific discipline, showed that when listening to music, specifically Mozart’s Sonata for two pianos or more famously known as K4481, trial subjects’ public presentation on IQ trials improved somewhat. The IQ tonss of the trial topics increased approximately 8 to 9 points, on norm, each clip they took the different trials [ 1 ] . The findings of these three research workers, combined known as Rauscheret Al., theorized that music has the ability to increase focal point and accordingly better our public presentation on many different undertakings. Another research, done by a group of research workers at the Richard’s Institute of Education and Research conducted an experiment on kindergartners to prove if music can better their public presentation in their job work outing abilities. A group of kindergartners out of three groups were given softly keyboard lessons for six months. At the terminal of the six months, the research workers noticed higher tonss on trial tonss from the group that received keyboard developing than the other two groups. They concluded that long-run engagement can better a person’s rational public presentation [ 2 ] .

Statement of Undertaking:

In my geographic expedition, I hope to be able to happen a decision on whether engagement in music has any consequence on a person’s public presentation on trials in their day-to-day categories. I will make a random sampling of pupils who are involved in music at Walnut High School and to see if there is any correlativity between the average figure of hours practiced per hebdomad and their overall un-weighted G.P.A. For my probe, I asked a sum of 51 pupils from both the Band and the Orchestra here at Walnut High School to reply a speedy questionnaire. In order to hold informations that is widely dispersed, I made certain to inquire pupils from the 9^Thursday-12^Thursdayclass to take a speedy questionnaire. All of the pupils surveyed play an instrument, and I asked pupils to bespeak the average figure of hours that they pattern their instrument outside of category and besides their entire un-weighted GPA. Their GPA will be able to state me how good they are presently making in school. Now, what is my hypothesis? I believe that if there is a correlativity between the average figure of hours a pupil patterns a hebdomad on an instrument and the entire un-weighted GPA, so the more hours that a pupil patterns on a musical instrument will increase his or her overall GPA.

Plan of Probe

I will garner up all of the surveyed informations and do a spread secret plan. Then, I will cipher the standard divergence (Second_tenandSecond_Y) of both the ten and Y values in order to cipher the equation of the additive arrested development subsequently. Then, I will cipher the correlativity coefficient, or theRvalue, to find if my collected information has any correlativity at all. If it does hold correlativity, I will necessitate to happen out if it is positive, negative, weak, or strong. After ciphering theRvalue and the standard divergence, I can utilize those Numberss to cipher the equation of the additive arrested development and find a line of best tantrum. A bulk of my computations will be done by an copycat version, “WabbitEMU” , of a TI-84 Plus C Silver Edition charting reckoner. Any graphs will be made by the spread secret plan map in Microsoft Excel 2013.

Probe

Collected Datas

The average figure of hours spent practising on an instrument per hebdomad and the entire un-weighted GPA of 51 pupils at Walnut High School. Here I will set all the collected information values onto a tabular array and subsequently I will chart the information values.

Table 1:

Average # of Hours Practicing Music per Week	Entire Un-Weighted GPA
14	3.6
0	2.84
7	4
0	4
1	3.83
7	3.75
4	3.66
6	3.5
1	3
4	3
4.5	3.71
15	3.2
0	3.88
1	3.7
2	4
0	3.77
2	3.96
0	3.75
10	3.9
7	3.5
8	4
12	3.98
10	4
6	3.2
0	3.12
3	2.99
1	2.98
11	3.79
1	3.4
2	3.9
0	3.75
2	3.98
2	3.95
4	4
3.5	3.5
0	4
2	4
0	3.83
12	4
12	3.7
5	3.27
6	3.7
0	3.81
0	3.98
0.5	3.6
4	4
7	4
7	3.85
0	3.73
2	2.97
3	3.45

Table 1shows the average figure of hours that a pupil spends per hebdomad practising an instrument and the corresponding GPA for that pupil. As you can see from the tabular array, there are some persons who have practiced for long hours that have high GPAs as a consequence. However, this is non consistent all the manner through. Some pupils who have perfect 4.0 GPAs pattern really small or no hours outside of category. Why is this? Possibly it is because these pupils merely do non hold adequate clip outside of category to pattern their instrument, as an IB pupil I can understand. Those pupils likely spent all their clip analyzing or seeking to catch up on their prep, and as a consequence, they will high classs because of their difficult work, non because of practising their instrument.

To better understand the collected information, I graphed the informations values on a spread secret plan utilizing Microsoft Excel 2013.

Figure 1:

As we can see fromfigure 1, the spread secret plan, the information values are non really strongly correlated. The information values are practically on the same line merely by visualising it. This is likely a consequence of merely appraising 51 pupils out of the about four hundred pupils in the set and orchestra plan. Possibly appraising all of them would give a drastically different consequence. What are some possible grounds for holding the information points dispersed in the form of a level line? One possible account is that about all of the pupils who filled out the questionnaire had similar GPAs. As we can see from the spread secret plan, a big bulk of the pupils who filed out the questionnaire had a GPA between 3 and 4. A solution to this would be to study the full school, or at least pupils with lower GPAs every bit good. I predict from this spread secret plan that the correlativity coefficient is traveling to be a value that represents a weak correlativity.

Datas Processing

Standard Deviation

In order to cipher the Pearson’s product-moment correlativity coefficient (R) and the equation of the additive arrested development, I must foremost cipher the standard divergence (Second_tenandSecond_Y) , or the step of scattering. I will cipher the step of dispersion of the average Numberss of hours spent practising an instrument per hebdomad ( ten ) and the entire non-weighted GPA ( Y ) of the 51 surveyed pupils. The standard divergence is a manner to mensurate the sum of spread that a set of information has.

Formula:

Second_ten

Where ten_Iis each single value in the informations,is the mean of all the information values, andNis the entire figure of values in the information set [ 3 ] .

Since there are so many pieces of informations to input into the expression, I used an copycat version, called “WabbiEMU, ” of a TI-84 Plus C Silver Edition charting reckoner to cipher theSecond_tenandSecond_Y. First, I inputted the x-values into L₁and y-values into L₂by pressing “STAT” and so taking “Edit” from the “EDIT” bill of fare_.Then, I pressed “STAT” and selected the “CALC” bill of fare. Finally, I selected “1-Var Stats” from the “CALC” bill of fare and calculated each list individually. First L₁and so L₂. Below are screenshots taken from the copycat.

Second_ten? 4.21

Figure 2:The standard divergence of the average # of hours a pupil spends practising an instrument per hebdomad ( L₁) is about 4.21, intending that our x information values are good spread out from that of the mean. Since my informations is good spread out, that means there will be a good distribution of x-values.

Second_Y? 0.349

Figure 3:The standard divergence of entire un-weighted GPA ( L₂) is about 0.349, intending that the Y informations values are really near together and non good distribute out from that of the mean.

Now allow us see if there is an existent correlativity between the average # of hours spent practising an instrument per hebdomad and the entire un-weighted GPA. As we have already seen from the graph, there is either a really weak correlativity or none at all. To cognize if it is positive or negative, we must happen the correlativity coefficient, orR.

Pearson’s Product-Moment Correlation Coefficient (R)

In order to find if our information has a correlativity or non, I must cipherR, the correlativity coefficient. My hypothesis is that there is most likely a really weak positive correlativity as revealed by the graph infigure 1.

To cipherR, I will utilize the “LinReg ( ax+b ) ” map on my TI-84 Plus C SE charting reckoner. First, I pressed “STAT” and so selected the “CALC” bill of fare. Then, I chose the “LinReg ( ax+b ) ” map from the “CALC” bill of fare.

R? 0.101 really weak positive correlativity

Figure 4: 0 & A ; lt ;R & A ; lt ;0.3 is considered to be a weak, positive correlativity. So, our our information, harmonizing to the correlativity coefficient, has a really weak, positive correlativity, as expected from my anticipation made by the spread secret plan infigure 1.

Why is there such a low correlativity? Well, once more, it is most likely the cause of holding a little sample size. If the sample size was much larger, the correlativity might be much stronger. Besides, if I did non restrict this probe to merely band and orchestra pupils, the informations would be able to change more.

Linear Regression and Line of Best Fit

The equation of the additive arrested development ( ax + B ) will give us the line of best tantrum for our informations. With this additive equation, we can utilize it to do anticipations with different values.

First away, we need to happen the incline ( m ) for the additive equation.

mwhereRis the correlativity coefficient oftenandY, andSecond_tenandSecond_Yare the standard divergences of X and Y.

0.101

m= 0.00837264841

m

In order to happen the Regression line, we must now happen the y-intercept of the additive map. Then, we must compose the equation in additive signifier ;Y = maxwell + B

BwhereBis the y-intercept andandare the agencies of thetenandYvalues [ 4 ] .

B =3.66627451

B =3.631563628

B? 3.63

Now we can compose the equation of our arrested development line in additive signifier (Y = maxwell + B ).

Y = 0.00837x + 3.63

Now, I must mention back tofigure 4and see if I found the arrested development line right. When cipheringR, the reckoner besides calculates the incline ( a ) and y-intercept ( B ) of the arrested development line. Now that I looked back atfigure 4, my equation was derived right. The values given by the reckoner were a = 0.0083662541 and b = 3.631579038. These values are really near to the values that I got, a = 0.00837264841 and b = 3.631563628. The values merely differ at the hundred thousandths topographic point ( 0.00001) . Although these computations are really near to being exact, the Numberss are still merely somewhat off. Why were my Numberss merely somewhat off? Well, this is because the Numberss that I substituted into the expression were rounded Numberss, so of course, there is traveling to be some sort round-off mistakes.

Now, I will chart the line of best tantrum utilizing the equation of the arrested development line onto the spread secret plan fromfigure 1.

Figure 5

As you can see fromfigure 5, the line of best tantrum is about wholly horizontally level. The correlativity is so weak between the two variables that the incline is about 0. This arrested development line would non be a valid manner to foretell the consequence of practising music and one’s GPA.

Decision

Due to the little sample size, the cogency of the probe is questionable. However, the probe did finally reason a really weak correlativity between musical pattern and a student’s public presentation on school work, as shown by GPA. There were many restrictions to this probe. First off, the sample size was merely 51 people, and there are about two thousand people go toing Walnut High School. The 51 pupils surveyed could non hold perchance represented the full population really good. Second, the sample merely included pupils of the set and orchestra plan. I should hold besides surveyed pupils from other musical plans, such as choir and play. Another defect was that out of the 51 pupils surveyed, a bulk of them had similar GPAs. This was a major ground as to why the line of best tantrum was near to being level. The information points for GPA are non dispersed plenty. However, this merely applied for the y-values, the x-values, or the average # of hours, were good distributed as shown by the standard divergence computations. Another restriction was that most of the pupils who replied with a low figure of hours likely spend their free clip analyzing and catching up on their school work. This should hold been a controlled variable. As a consequence of these pupils, the consequences were skewed.

How could I have perchance done better on this probe? Well, I think the best manner to better this probe is to first garner a big plenty sample where the informations can non be skewed. Second, I should hold besides included a control group of pupils who are non musically involved at all. The contrast in consequences between these two groups might hold revealed more about music’s consequence on rational public presentation than merely the original sample. Third, I should hold set up more controlled variables which would hold improved the cogency of my experiment.

Although there is non important grounds that shows that musical engagement can profit public presentation in school, my consequences showed that there is a little correlativity. This little correlativity might explicate why long term musical engagement can take to some pupils stand outing over others. Future experiments can be done on pupils who are involved in music for a long period of clip, and see if there is a correlativity between their long-run engagement and their GPA. Overall, I still believe music to be something improbably important in everyone’s lives. Whether intellectually or non, music has benefited out lives one manner or the other.

Bibliography

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“ How to Calculate a Regression Line. ”– For Dummies. Web. 18 Feb. 2015. & A ; lt ; hypertext transfer protocol: //www.dummies.com/how-to/content/how-to-calculate-a-regression- line.html & A ; gt ; .

“ How to Calculate Standard Deviation in a Statistical Data Set. ”– For Dummies. Web. 18 Feb. 2015. & A ; lt ; hypertext transfer protocol: //www.dummies.com/how-to/content/how-to-calculate-standard-deviation- in-a-statistic.html & A ; gt ; .

Pela, Robert. “ Listening to Music While Analyzing. ”– University of Phoenix. 29 Aug. 2013. Web. 16 Nov. 2014. & A ; lt ; hypertext transfer protocol: //www.phoenix.edu/forward/student-life/2013/08/should- you-listen-to-music-while-studying.html & A ; gt ; .

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