Hiệu quả sử dụng smartphone về kết quả của học sinh tại trường đại học ngoại thương chi nhánh II( ANOVA )
FOREIGN TRADE UNIVERSITY Faculty of Economics and International Business - DISSERTATION THE EFFECT OF USING SMARTPHONE ON THE RESULT OF THE STUDENT IN FOREIGN TRADE UNIVERSITY BRANCH II (ANOVA) List of members No Ho Chi Minh City – April, 2014 Full Name Student ID Phạm Thị Hạnh 1201017091 Nguyễn Ngọc Huy 1201017131 Đinh Tiến Phú 1201017261 Trần Bích Phượng 1201017283 Nguyễn Ngọc Thúy Quỳnh 1201017300 Nguyễn Thị Bích Trâm 1201017394 TABLE OF CONTENTS ABSTRACT Introduction Theory and research methodology 2.1 Theoretical basis and Analysis framework 2.2 Methods of data collection and model estimation technique Results and discussion 3.1 Descriptive data 3.2 One-way ANOVA model 3.3 HSD (honest significant difference) test 11 Conclusion and Policy Implication 16 REFERENCE 18 THE EFFECT OF USING SMARTPHONE ON THE RESULT OF THE STUDENT IN FOREIGN TRADE UNIVERSITY BRANCH II (ANOVA) 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 the students use smart phone more than hours each day Spending too much time using phone may be the reason makes students’score feel lack of concentration, lacking of sleeping or even though it can make 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 238 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 Smartphone Effective Page 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 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 leastsquares 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 Smartphone Effective Page 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, …k (may be different from size) Calling µ1,µ2,µ3….µk Xij: observation j of group i Group Group … Group k x 11 x 12 … x … x … x x 1n1 x 21 x 22 … 2n2 k1 … k2 … … x knk Hypothesis: Ho µ1=µ2=……µk H1: µ1differnt µ2……different µk Step :Find the average each group : = Find the average each group:): = Step :Find total sum of squares Smartphone Effective Page SSW- Within groups sum of squares: SSW=SS1+SS2+…SSK= SSG –Between group sum of squares SSG= Total sum of squares SST=SSW+SSG= Step 3: Find variances MSW (mean square within): MSW= MSB (mean square between): MSB= Step 4:One-way ANOVA Table Source of SS Df MS F ratio variation (sum of (degrees of (mean of square) freedom) square) Between SSB k-1 MSB= F= Samples Within SSW n-k MSW= Samples 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 Smartphone Effective Page 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 α for all possible tested pairs to detect the different groups Example: Research byU.S.scientistsatKent State UniversityofOhiofound thatstudents-students usingsmart phonetoomuchcan lead toanxietyandlearning outcomedecline 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 teamreportedin the journalComputersinHumanBehaviormajors: "When the frequency ofmobile phoneuseis toohigh, the degree of successinlearning andinlifefellcomfortable Statistical modelingsuggests thatsuchrelationshipsareclear 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 Smartphone Effective Page 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 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 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 >6h 8,88 7,20 7,90 7,11 Smartphone Effective Page 8,12 8,17 8,00 7,90 7,90 8,04 7,00 7,80 8,79 8,30 7,80 7,00 8,00 8,00 6,00 8,79 7,80 7,60 5,70 7,20 7,00 8,07 6,70 8,79 8,40 7,80 6,80 5,60 8,90 8,00 7,60 8,00 7,50 8,40 7,00 6,80 9,00 7,60 7,80 6,40 8,50 8,00 7,80 6,90 8,70 8,70 7,00 6,60 9,20 8,40 8,20 7,20 8,90 8,30 7,00 7,30 8,00 8,00 7,90 6,70 7,90 7,60 7,80 6,80 8,20 7,90 8,46 7,00 8,50 7,80 7,20 5,80 7,80 8,00 6,20 8,00 8,00 7,80 8,20 6,80 8,50 7,40 8,40 7,00 8,90 6,70 8,50 7,20 Smartphone Effective Page 10 8,00 8,60 8,10 7,60 7,00 8,00 8,30 6,00 8,70 9,10 7,50 7,10 6,80 7,90 6,60 8,00 9,00 7,00 7,70 5,80 7,80 9,03 8,80 7,00 7,60 8,00 8,60 7,12 7,60 7,90 8,00 7,12 7,30 7,70 7,00 5,00 8,20 8,80 7,00 7,12 7,50 7,70 7,00 7,12 7,90 9,00 7,40 6,19 8,00 6,90 7,40 6,95 7,80 7,70 7,90 6,95 8,80 8,00 8,00 5,54 7,60 7,85 7,44 6,68 8,20 7,00 7,67 6,68 8,50 8,50 7,12 6,34 7,00 8,60 7,32 5,00 8,00 7,40 7,32 6,45 7,80 8,30 7,67 6,45 8,30 8,29 7,55 5,54 Smartphone Effective Page 11 8,29 8,50 7,12 5,62 6,80 6,00 7,12 5,62 7,00 7,83 7,44 5,14 8,16 7,00 6,95 7,80 7,60 7,46 7,85 7,00 8,23 8,20 7,67 4,00 6,50 7,60 6,00 7,90 6,50 8,19 7,80 5,00 7,58 8,15 6,86 6,80 5,00 8,16 6,00 8,87 7,50 8,45 8,45 8,45 8,89 8,90 8,45 7,40 7,60 7,90 7,30 Smartphone Effective Page 12 9,12 8,80 9,27 8,34 8,50 6,50 8,00 3.2 One-way ANOVA model Hypothesis: Ho: There is no different in the average monthly food cost between 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 from 0h to 2h Average Variance 8,04864864 74 595,6 0,485674861 7,92555555 >2h to 4h 54 427,98 0,354579874 >4h to 6h 57 421,66 7,39754386 0,569683145 >6h 53 356,52 6,72679245 0,961922206 Smartphone Effective Page 13 ANOVA Source Variation of SS df Between Groups MS F P-value F crit 36,17827298 3,02055E-19 2,64318 21,0528496 63,15854893 Within 0,58191969 Groups 136,1692091 234 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 Tukey’s HSD (honest significant difference) test and compare each couple of group t-Test: Two-Sample Assuming Equal Variances Smartphone Effective Page 14 From 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 7,92555555 Mean 8,048648649 0,35457987 Variance 0,485674861 Observations 74 54 Pooled Variance 0,430531732 Hypothesized Mean Difference Df 126 t Stat 1,048187211 P(T4h to 6h) by Excel Mean from 0h to 2h >4h to 6h 8,048648649 7,39754386 0,56968314 Variance 0,485674861 Observations 74 57 Pooled Variance 0,522143574 Hypothesized Mean Difference Df 129 t Stat 5,112973725 P(T 1.978524491 Therefore, µ1≠ µ3 , the result / average mark of the two groups are different Smartphone Effective Page 16 From 0h to 2h and >6h Hypothesis: Ho: µ1 - µ4 = Table 4: Tukey’s HSD test (from 0h to 2h and >6h) by Excel from 0h to 2h >6h 6,72679245 Mean 8,048648649 0,96192220 Variance 0,485674861 Observations 74 53 Pooled Variance 0,683793757 Hypothesized Mean Difference Df 125 t Stat 8,883284671 P(T1.979124109 Therefore, µ1 ≠ µ4 , the result / average mark of the two groups are different >2h to 4h and >4h to 6h Hypothesis: Ho: µ2 - µ3 = Table 5: Tukey’s HSD test (>2h to 4h and >4h to 6h) by Excel Mean >2h to 4h >4h to 6h 7,925555556 7,39754386 0,56968314 Variance 0,354579874 Observations 54 57 Pooled Variance 0,465091647 Hypothesized Mean Difference Df 109 t Stat 4,077059893 P(T1.98196749 Therefore, µ2 ≠ µ3 , the result / average mark of the two groups are different >2h to 4h and >6h Hypothesis: Ho: µ2 - µ4 = Table 6: Tukey’s HSD test (>2h to 4h and >6h) by Excel >2h to 4h >6h 6,72679245 Mean 7,925555556 0,96192220 Variance 0,354579874 Observations 54 53 Pooled Variance 0,655358934 Hypothesized Mean Difference Df 105 t Stat 7,65837593 P(T1.982815274 Therefore, µ2 ≠ µ4, the result / average mark of the two groups are different >4h to 6h and >6h Hypothesis: Ho: µ3 - µ4 = Table 7: Tukey’s HSD test (>4h to 6h and >6h) by Excel >4h to 6h >6h Mean 7,39754386 6,726792453 Variance 0,569683145 0,961922206 Observations 57 53 Pooled Variance 0,758538989 Hypothesized Mean Difference Df 108 t Stat 4,03600465 P(T1.982173483 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 From 0h to 2h and >2h to 4h, cause the difference in the hour for using smart phone between them is not big enough 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, hardworking, 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 not use smartphone after pm Smartphones only brings much benefit and convenience when students know how to use them reasonably Smartphone Effective Page 21 REFERENCE: David F.G., Patrick W.S., Phillip C.F and Kent D.S (2010), Business Statistics 8th edition, Pearson Fisher Ronald Aylmer ,sir (1890-1962) -The analysis of variance with various binomial transformations Fisher, Ronald Aylmer, Sir, 1890-1962-Answer to query 114 on the effect of errors of grouping in an analysis of variance Hoang Tran Van and Van Le Hong (2013), Principles of Statistics, Vietnam National University-Ho Chi Minh City Press, Ho Chi Minh City Landau S, Everitt BS A Handbook of Statistical Analyses Using SPSS, Chapman & Hall/CRC, 2004 Smartphone Effective Page 22 Wikipedia, Articles about Tukey’s HSD test Smartphone Effective Page 23 ... (mean square within): MSW= MSB (mean square between): MSB= Step 4:One-way ANOVA Table Source of SS Df MS F ratio variation (sum of (degrees of (mean of square) freedom) square) Between SSB k-1... “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. .. 3.1 Descriptive data 3.2 One-way ANOVA model 3.3 HSD (honest significant difference) test 11 Conclusion and Policy Implication 16 REFERENCE 18 THE EFFECT OF USING SMARTPHONE ON THE RESULT OF THE