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FOREIGN TRADE UNIVERSITY

Faculty of Economics and International Business

---------------------------

DISSERTATION

THE EFFECT OF SMARTPHONE ON THE STUDENT RESULT OF THE STUDENT IN FOREIGN TRADE UNIVERSITY BRANCH II

Student ID

Ph m Th H nhạ ị ạ 1201017091

Nguy n Ng c Huyễ ọ 1201017131

Đinh Ti n Phúế 1201017261

Tr n Bích Ph ngầ ượ 1201017283

Nguy n Ng c Thúy Quỳnhễ ọ 1201017300

Nguy n Th Bích Trâmễ ị 1201017394

Ho Chi Minh City – April, 2014

List of members

No Full Name Student ID

1 Ph m Th H nhạ ị ạ 1201017091

2 Nguy n Ng c Huyễ ọ 1201017131

3 Đinh Ti n Phúế 1201017261

4 Tr n Bích Ph ngầ ượ 1201017283

5 Nguy n Ng c Thúy Quỳnhễ ọ 1201017300

6 Nguy n Th Bích Trâmễ ị 1201017394

THE EFFECT OF SMARTPHONE ON THE STUDENT RESULT OF THE STUDENT IN FOREIGN TRADE UNIVERSITY BRANCH II

ABSTRACT

In this digitization world, media is constantly improved day by day. From the laptop to the cell phone, especially smart phone. Using modern technology to serve for the purpose of learning is really a powerful tool. However, beside the good side, it still has some downsides surround the use of phone. Almost student use smart phone more than 2 hours each day. Spending too much time using phone may be the reason makes scores student feel lack of concentration, lacking of sleeping or even though it can makes student live in digital world and live far away from people around them. So how about student of Foreign Trade University II? Using smart phone too much make effect on learning outcomes? Isn’t it? Thus, our group researched, reviewed and analyzed the relationship between the time using smart phone of student at FTU2 in Ho Chi Minh City and their learning outcomes. Purposes of researching is understanding clearly the degree of influence of time using smart phone to the result of learning and review that if spending lots of times on using smart phone, Will they really effect to your learning outcomes? If yes, we will show some ways to surmount this situation. Through the using of factor ANOVA variance methods from the direct survey 245 students, we have a conclusion that using smart phone too much can effect to student’s results. Finally, our group will expose some ways to restrict using smart phone excessively and how to use efficiently to promote the learning outcomes of each student.

KEY WORDS: Learning outcome, time of using, analysis of variance, foreign trade university 2, students, one-way ANOVA, Tukey's HSD test

1. Introduction

Nowadays, along with the evolution of Information Technology, telephones are not only for texting and calling purposes, but they also help us to connect with each other via social networks, email and other online services... These smartphones are becoming more modern and helpful day after day. However, overusing smartphone may cause many negative effects on everyone especially college students. These effects include decline in health, waste of time and decrease in study result... The decrease in study result is the most serious consequence when smartphones are getting commoner among the students.

The phenomenon that smartphones are addictive and affect many respects of life is no new problem. It has appeared so many times on the media. This is an

unsolvable problem for the students as well as a deep concern for the parents. Therefore we decide to carry on the topic “The effect of smartphone on the study result of the student in Foreign Trade University branch II” by analyzing One - factor ANOVA. We find down how the percentage, scale and usage of smartphone of the second-year student of Foreign Trade University branch II in HCMC change their study result as well as propose some solution to overcome this worrying problem.

2. Theory and research methodology

2.1. Theoretical basis and Analysis framework

While the analysis of variance succeeded in the 20th century, antecedents extend centuries into the past according to Stigler. These include hypothesis testing, the partitioning of sums of squares, experimental techniques and the additive model. Laplace was performing hypothesis testing in the 1770s. The development of least-squares methods by Laplace and Gauss circa 1800 provided an improved method of squares. By 1827, Laplace was using least squares methods to address ANOVA problems regarding measurements of atmospheric tides.

The phrase “analysis of variance” was coined by Sir Ronald Aylmer Fisher, a statistician of the twentieth century, who defined it as “the separation of variance ascribable to one group of causes from the variance ascribable to the other groups. Tests hypotheses are made about differences between two or more means. If independent estimates of variance can be obtained from the data, ANOVA compares the means of different groups by analyzing comparisons of variance estimates. There are two models for ANOVA, the fixed effects model, and the random effects model (in the latter, the treatments are not fixed).

The purpose of analysis of variance is to see if there is any difference between groups on some variable. In research, analysis of variance is used as a way to consider the effect of a cause factor to the results factor.

The method contains:

Supposing we have k groups 1, 2, 3 …k (may be different from size). Calling µ1,µ2,µ3….µk

Xij: observation j of group i

Group 1 Group 2 … Group k

x11

x21

… xk1

x12

…

x1n1

x22

…

x2n2

…

…

…

xk2

…

xknk

Hypothesis: Ho µ1=µ2=……µk

H1: µ1differnt µ2……different µk

Step 1 : Find the average xi each group : xi=∑j=1¿

xij

¿

Find the average each group: ): x=∑i=1n

∑j=1

n

xij

n

Step 2 : Find total sum of squares

SSW- Within groups sum of squares:

SSW=SS1+SS2+…SSK= ∑i=1

k

∑j=1

nj

(xij−xi)2

SSG –Between group sum of squares

SSG=∑i=1

k

¿(x i−x )2

Total sum of squares SST=SSW+SSG=∑i=1

k

∑j=1

¿

( xij−x )2

Step 3: Find variances

MSW (mean square within): MSW=SSWn−k

MSB (mean square between): MSB=SSBk−1

Step 4: One-way ANOVA Table

Source of variation

SS

(sum of square)

Df

(degrees of freedom)

MS

(mean of square)

F ratio

Between Samples

SSB k-1 MSB=SSBk−1

F=MSBMBW

Within Samples

SSW n-k MSW=SSWn−k

Total SST n-1

In: k number of populations

N Sum of the sample size from all populations

df Degrees of freedom

HSD (honest significant difference) test

The purpose of the analysis of variance is to test the hypothesis H0 that the overall average is equal. After the analysis and conclusions, there are two cases which can occur: H0 hypothesis is accepted or rejected .If the hypothesis H0 is accepted, analysis will end. If the hypothesis H0 is rejected, the overall average is not equal. So the next further issue is to analyze and identify that any group is different from other groups, the average of groups is greater or smaller.

There are many methods to calculate when hypothesis H0 is rejected. We use Tukey method. The content of this method is to compare pairs of the average groups at a significance level for all possible tested pairsα to detect the different groups .

Example:

Research by U.S. scientists at Kent State University of Ohio found that students-students using smart phone too much can lead to anxiety and learning outcome decline. The researcher surveyed 500 students on daily smartphone usage , lifestyle analysis and academic scores for the purpose of considering whether smartphones can help improve their lives or not.

The result shows that using smartphone too much has scores of disadvantage. Students who use too much have the worst score but they are at the highest level of anxiety.

The team reported in the journal Computers in Human Behavior majors: "When the frequency of mobile phone use is too high, the degree of success in learning and in life fell comfortable. Statistical modeling suggests that such relationships are clear.

Research of Dr. Karla Murdock at Washington Lee (USA) University has the same result. This research shows that students who send a lot of message usually less sleep and more stress than others.

Based on that, we decide to use analysis of variance and Tukey's HSD test to survey whether using smartphone affects to the study result of the second-year student of FTU II or not.

2.2 Methods of data collection and model estimation technique

The data used in this research is collected from the researchers’ questionnaires. Because of time and resources restriction, the researchers only carry survey on 238 people. Therefore, the result cannot generalize for the whole set because each individual surveyed has their own features and cannot represent for the whole set.

We had to choose the one factor ANOVA to analyze. Compare the average of

many populations based on the average of models. Consider the effect of one

factor reason to result factor.

3. Results and discussion

3.1 Descriptive data

Below is the data collected from 238 people, questioned about how many

hours the sophomore of FTU II use smartphone and their study result / average

mark? Base on their using hour, we divided them into 4 groups (as shown in the

table). The unit of measure is hour.

Groups of factor

from 0h to 2h >2h to 4h >4h to 6h >6h8,88 7,20 7,90 7,118,12 8,17 8,00 7,907,90 8,04 7,00 7,808,79 8,30 7,80 7,008,00 8,00 6,00 8,797,80 7,60 5,70 7,207,00 8,07 6,70 8,798,40 7,80 6,80 5,608,90 8,00 7,60 8,007,50 8,40 7,00 6,809,00 7,60 7,80 6,408,50 8,00 7,80 6,908,70 8,70 7,00 6,609,20 8,40 8,20 7,208,90 8,30 7,00 7,308,00 8,00 7,90 6,707,90 7,60 7,80 6,808,20 7,90 8,46 7,008,50 7,80 7,20 5,807,80 8,00 6,20 8,008,00 7,80 8,20 6,808,50 7,40 8,40 7,008,90 6,70 8,50 7,208,00 8,60 8,10 7,607,00 8,00 8,30 6,00

8,70 9,10 7,50 7,106,80 7,90 6,60 8,009,00 7,00 7,70 5,807,80 9,03 8,80 7,007,60 8,00 8,60 7,127,60 7,90 8,00 7,127,30 7,70 7,00 5,008,20 8,80 7,00 7,127,50 7,70 7,00 7,127,90 9,00 7,40 6,198,00 6,90 7,40 6,957,80 7,70 7,90 6,958,80 8,00 8,00 5,547,60 7,85 7,44 6,688,20 7,00 7,67 6,688,50 8,50 7,12 6,347,00 8,60 7,32 5,008,00 7,40 7,32 6,457,80 8,30 7,67 6,458,30 8,29 7,55 5,548,29 8,50 7,12 5,626,80 6,00 7,12 5,627,00 7,83 7,44 5,148,16 7,00 6,95 7,807,60 7,46 7,85 7,008,23 8,20 7,67 4,006,50 7,60 6,00 7,906,50 8,19 7,80 5,007,58 8,15 6,866,80 5,008,16 6,008,87 7,508,458,458,458,898,908,457,407,607,907,309,128,80

9,278,348,506,508,00

3.2 One-way ANOVA model

Hypothesis:

Ho: There is no different in the average monthly food cost between 4 group. (µ1

= µ2 = µ3 = µ4)

H1: The average monthly food cost of them are not equal.

Table 1: Anova: Single Factor by Excel

SUMMARY

Groups Count Sum Average Variancefrom 0h to 2h 74 595,6

8,048648649

0,485674861

>2h to 4h 54427,9

87,92555555

60,35457987

4

>4h to 6h 57421,6

6 7,397543860,56968314

5

>6h 53356,5

26,72679245

30,96192220

6

ANOVASource of Variation SS df MS F P-value F crit

Between 63,1585489 3 21,0528496 36,1782729 3,02055E- 2,6431

Groups 3 4 8 19 8Within Groups

136,1692091 234

0,581919697

Total 199,327758 237

If F > F crit, we reject the null hypothesis. As we can see in the table above: 36.178273 > 2.6432. Therefore, we reject the null hypothesis. The means of the four populations are not all equal. At least one of the means is different. Therefore, we can say that the hour for using smart phone does affect how much to the result / average mark of students.

3.3 HSD (honest significant difference) test

Because the null hypothesis has been rejected, the result / average mark of four groups are not equal. However, in order to find out how they differ from each other, we need to do Tukey’s HSD (honest significant difference) test and compare each couple of group.

t-Test: Two-Sample Assuming Equal Variances

F rom 0h to 2h and >2h to 4h

Hypothesis:

Ho: µ1 - µ2 =0

Table 2: Tukey’s HSD test (from 0h to 2h and >2h to 4h) by Excel

from 0h to 2h >2h to 4h

Mean 8,0486486497,9255555

56

Variance 0,4856748610,3545798

74Observations 74 54Pooled Variance 0,430531732Hypothesized Mean Difference 0df 126t Stat 1,048187211P(T<=t) one-tail 0,14827941t Critical one-tail 1,657036982P(T<=t) two-tail 0,296558819

t Critical two-tail 1,978970602

In this table: -1.978970602 < 1.048187211 < 1.978970602 . Therefore, µ1 - µ2 = 0 , the result / average mark of the two groups are different.

F rom 0h to 2h and >4h to 6h

Hypothesis:

Ho: µ1 - µ3 = 0

Table 3: Tukey’s HSD test (from 0h to 2h and >4h to 6h) by Excel

from 0h to 2h >4h to 6h

Mean 8,0486486497,3975438

6

Variance 0,4856748610,5696831

45Observations 74 57Pooled Variance 0,522143574Hypothesized Mean Difference 0df 129t Stat 5,112973725P(T<=t) one-tail 5,58858E-07t Critical one-tail 1,656751594P(T<=t) two-tail 1,11772E-06t Critical two-tail 1,978524491

If t Stat < -t Critical two-tail or t Stat > t Critical two-tail, we reject the null hypothesis. In this table: 5.112973725 > 1.978524491. Therefore, µ1≠ µ3 , the result / average mark of the two groups are different.

F rom to 2h and >6h

Hypothesis:

Ho: µ1 - µ4 = 0

Table 4: Tukey’s HSD test (from to 2h and >6h) by Excel

from 0h to 2h >6h

Mean 8,0486486496,7267924

53Variance 0,485674861 0,9619222


If t Stat < -t Critical two-tail or t Stat > t Critical two-tail, we reject the null hypothesis. In this table: 8.883284671 > 1.979124109. Therefore, µ1 ≠ µ4 , the result / average mark of the two groups are different.

>2h to 4h and >4h to 6h

Hypothesis:

Ho: µ2 - µ3 = 0

Table 5: Tukey’s HSD test (>2h to 4h and >4h to 6h) by Excel

>2h to 4h >4h to 6h

Mean 7,9255555567,3975438

6

Variance 0,3545798740,5696831


If t Stat < -t Critical two-tail or t Stat > t Critical two-tail, we reject the null hypothesis. In this table: 4.077059893 > 1.98196749. Therefore, µ2 ≠ µ3 , the result / average mark of the two groups are different.

>2h to 4h and >6h

Hypothesis:

Ho: µ2 - µ4 = 0

Table 6: Tukey’s HSD test (>2h to 4h and >6h) by Excel

>2h to 4h >6h

Mean 7,9255555566,7267924

53

Variance 0,3545798740,9619222


If t Stat < -t Critical two-tail or t Stat > t Critical two-tail, we reject the null hypothesis. In this table: 7.65837593 > 1.982815274. Therefore, µ2 ≠ µ4, the result / average mark of the two groups are different.

>4h to 6h and >6h

Hypothesis:

Ho: µ3 - µ4 = 0

Table 7: Tukey’s HSD test (>4h to 6h and >6h) by Excel

>4h to 6h >6hMean 7,39754386 6,726792453Variance 0,569683145 0,961922206Observations 57 53Pooled Variance 0,758538989Hypothesized Mean Difference 0df 108t Stat 4,03600465P(T<=t) one-tail 5,09091E-05

t Critical one-tail 1,659085144P(T<=t) two-tail 0,000101818t Critical two-tail 1,982173483

If t Stat < -t Critical two-tail or t Stat > t Critical two-tail, we reject the null hypothesis. In this table: 4.03600465 >1.982173483. Therefore, µ3 ≠ µ4, the result / average mark of the two groups are different.

Through the analyzing process above, we can say that the result / average mark differs significantly as the hour for using smart phone change, except the case of group 1 and 2, cause the difference in the hour for using smart phone between them is not big enough.

4. Conclusion and Policy Implication

After researching influences of using smartphones on the study results of Foreign Trade University II students, we can know that the time of using smartphones plays an important role in their study results.

Although study results depend on many impacts such as intelligence, hard-working, study method and so on, time for studying is also a very essential one. The more time students spend on using smartphones, the less time they spend on studying. When a student spend more time on learning, his or her study results will be certain better and vice versa. It is very clear that screen time right before bed is bad for sleep. And using your smartphone late at night also makes you feel depleted in the morning, thereby making you less focused and engaged at studying.

To have a good study result, a student need know how to arrange study time and reasonable entertainment. Time for using smartphones should be within a certain limit. It will be better for students’ health as well as study results if they do not use smartphone after 9 pm. Smartphones only brings much benefit and convenience when students know how to use them reasonably.

REFERENCE:

1. Fisher .Ronald Aylmer ,sir (1890-1962) -The analysis of variance with various binomial transformations

2. Landau S, Everitt BS. A Handbook of Statistical Analyses Using SPSS, Chapman & Hall/CRC, 2004.

3. Fisher, Ronald Aylmer, Sir, 1890-1962-Answer to query 114 on the effect of errors of grouping in an analysis of variance

4. Wikipedia, Articles about Tukey’s HSD test.

5. Hoang Tran Van and Van Le Hong (2013), Principles of Statistics, Vietnam National University-Ho Chi Minh City Press, Ho Chi Minh City.

6. David F.G., Patrick W.S., Phillip C.F. and Kent D.S. (2010), Business Statistics 8th edition, Pearson.

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