Research Analysis – What Attracts You To

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Research Analysis What attracts you to facebook?

Client Mrs Lopamudra Ghosh

Researchers

Neeraj Singh 09BS0001377 Neha Gupta 09BS0001399 Nilankan Dey 09BS0001439 Nishant Vora 09BS0001462Prakash Krishnamoorthy 09BS0001615Preha Sharma 09BS0001667Rachit Sharma 09BS0001742

Ramkumar Venkiteswaran 09BS0001822

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Acknowledgement

I like to take this opportunity to thank all those who havecontributed to make this finding successful.

Special thanks to my peers and colleagues who have givenvaluable input.

I am also indebted to Mrs Lopamudra Ghosh to havegiven this project of great magnitude to this respectable group.

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Letter of Authorization:

IBS MumbaiDate: 5th January, 2010

TO WHOM SO EVER CONCERNED

This is to authorize Section ACE group to conduct a research onFACEBOOK and interact with the faculty and the students ofthe college.

Please assist in their endeavor.

IBS MUMBAI

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TABLE OF CONTENTS

Sr No TOPIC Page No

1 List of Tables 6

2 List of Graphs 10

3 Executive Summary 11

4 Problem Definition 12

5 Approach to the Problem 13

6 Research Design 14

7 Data Analysis 16

8 Results 21

9 Limitations 23

10 Conclusions and Recommendations 24

11 Exhibits 26

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LIST OF TABLES

Correlation Matrix

KMO and Bartletts Test

Communalities

Total Variance Explained

Scree Plot

Component Matrix

Rotated Component Matrix

Component Transformation Matrix

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Correlation Matrix(a)

Playnon-

interactive

gamesCha

t

Chec

k outhowyourfriendsare

doing

Update

yourprofil

e

Topasstime

Simplicity

Horoscope

CommunityPersonalizati

onFlirtin

gFlexibili

ty

Correlation

Play non-interactivegames

1.000 .465 .375 .443 .365 .321 .324 .318 .264 .222

Chat .465 1.000 .791 .524 .540 .540 .321 .313 .392

Checkout howyourfriends

aredoing

.375 .791 1.000 .527 .599 .413 .126 .244 .229

Updateyour

profile.443 .524 .527

1.000

.558 .459 .391 .447 .406

To passtime

.365 .540 .599 .558 1.000 .446 .174 .314 .090

Simplicity

.321 .540 .413 .459 .446 1.000 .436 .449 .253

Horosco

pe.324 .321 .126 .391 .174 .436 1.000 .647 .399

CommunityPersonalization

.318 .313 .244 .447 .314 .449 .647 1.000 .252

Flirting

.264 .392 .229 .406 .090 .253 .399 .252 1.000

Flexibility

.222 .487 .440 .329 .306 .502 .315 .443 .232

a Determinant = .010

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of SamplingAdequacy. .820

Bartlett's Test ofSphericity

Approx. Chi-Square 366.124

Df 45

Sig. .000

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Communalities

Initial Extraction

Play non-interactivegames 1.000 .364

Chat 1.000 .764

Check out how yourfriends are doing 1.000 .811

Update your profile 1.000 .588

To pass time 1.000 .638Simplicity 1.000 .533

Horoscope 1.000 .791

CommunityPersonalization 1.000 .673

Flirting 1.000 .352

Flexibility 1.000 .408

Extraction Method: Principal Component Analysis.

Total Variance Explained

Component Initial EigenvaluesExtraction Sums of Squared

Loadings Rotation Sums of Squared Loadings

Total

% of

Variance

Cumulative

% Total

% of

Variance

Cumulative

% Total

% of

Variance

Cumulative

%1 4.55

445.543 45.543

4.554

45.543 45.5433.32

933.292 33.292

2 1.368

13.677 59.2211.36

813.677 59.221

2.593

25.929 59.221

3 .932 9.322 68.543

4 .813 8.131 76.673

5 .616 6.156 82.829

6 .502 5.023 87.853

7 .438 4.381 92.233

8 .331 3.313 95.546

9 .288 2.875 98.421

10 .158 1.579 100.000

Extraction Method: Principal Component Analysis.

Component Matrix(a)

Component

1 2

Play non-interactivegames .603 -.032

Chat .819 -.304

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Check out how yourfriends are doing .733 -.523

Update your profile .766 -.040

To pass time .674 -.428

Simplicity .726 .076

Horoscope .591 .665

CommunityPersonalization .646 .506

Flirting .492 .331

Flexibility .637 .052

Extraction Method: Principal Component Analysis.a 2 components extracted.

Rotated Component Matrix(a)

Component

1 2

Play non-interactive

games.493 .349

Chat .831 .269

Check out how yourfriends are doing .899 .044

Update your profile .625 .444

To pass time .794 .083

Simplicity .523 .510

Horoscope .051 .888

CommunityPersonalization .193 .797

Flirting .181 .565

Flexibility .468 .436

Extraction Method: Principal Component Analysis.Rotation Method: Varimax with Kaiser Normalization.

a Rotation converged in 3 iterations.

Component Transformation Matrix

Component 1 2

1 .785 .620

2 -.620 .785

Extraction Method: Principal Component Analysis.Rotation Method: Varimax with Kaiser Normalization.

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EXECUTIVE SUMMARY

Facebook is one of the leading networking sites in theworld. We plan to analyze why it is so!!

Various features and application attracts a user. We haveundertook a research and found some key factors that act asinfluencers for users to stay loyal to the site and on the contraryrope in more users. In our research we included 12 factors thatare there to attract to people to use face book. We used aquestionnaire where in we asked the respondents to rate those

factors which they thought were important factors to use the site.

After getting all the respondents answers we then did thedata entry using SPSS. Here we used factor analysis to interpretthe data by using KMO, Bartler Test, Communalities and ScreePlot

Our analysis showed that there were basically 2 main

factors in which we could analyze all the other 12 factors withthe help of data reduction.

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PROBLEM DEFINI TION

a)Problem Statement: Here the basic problem that the researchwill probe is whether the factors which are attractive to use facebook are correlated to each other or are independent. Byunderstanding these factors we will be able to see whether wecan combine 2 or more factors into one which would be easy forus to interpret. So the null hypothesis in this problem would bethat the factors are not correlated to each other.

b)Background of the Problem: Facebook as we all know is oneof the worlds largest social networking site. Social networkingin the earlier years only was about interaction between people.But due to tough competitions from many networking sites it isnecessary that there need to be some extra facilities on the basisof technology as well as on the basis of needs of the people.Facebook has been successful in doing that. Facebook has somany new and different features that it is very difficult to

understand what kind of things does people like in Facebook. Tounderstand this problem we are doing a research where in weanalyze whether some factors really are correlated toattractiveness and whether those factors are related to each otheror not.

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Approach to Problem

Once we understood what our problem was, we started to planon how should we proceed with the research. We decided tostart our survey from college itself. Here we provided ourquestionnaire to people at specific areas where we may get alarge amount of respondents for e.g: Library, Reading Hall,Canteen etc. We also tried to do this research with different setof age group people also that may help in analyzing the problem

better. While creating questionnaire our first criteria was to findthe factors that are relevant to the problem. After a lot ofresearch we decided to analyze on the basis of following 12factors1)New Friends2)Interactive Games3)Non Interactive Games4)Chat5)Wall6)Profile Update7)Passing Time8)Simplicity9)Horoscope10)Community11)Flirting

12)Flexibility

We prepared a questionnaire relating to the rating of thesefactors.

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We decided to use Factor Analysis along with Semantic Scale torate the factors and then analyze Research Design

a)Type of Research Design: Factor AnalysisHere we used factor analysis because our main problem talksabout correlation among various factors. To understandattractiveness towards facebook, we need to understand theinterdependence among variables. All the factors could begrouped together with respect to specific characteristics and thenanalyze it well.

b)Information needs:There were various types of informationthat was needed while taking the survey. First information waswhether they use facebook or not. If the answer was yes thenonly we used to proceed with the other informations. Amongthem was there name, age, contact number and how often do

you use facebook. This was some of the basic information alongwith the rating of factors that was needed.

c)Data Collection from Secondary Sources:Here secondary sources include data entry of primary sourcesinto SPSS and then using factor analysis interpreting therelationship between the primary data.

d)Data Collection from primary sources: Primary sourcesincluded most of the students of IBS who were the respondentsof this survey along with others who generally use face book.

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e)Scaling Techniques: Semantic Scale

Here we needed ratings. Hence we used semantic scale wherewe asked the respondents to rate the factors from 1-7. Here 1

being least important factor and 7 being most important factor.

Rating will help us to understand the importance of these

factors.

f)Questionnaire development and pretesting : The most

important factor that is necessary for a research is to prepare a

good questionnaire. Before starting our questionnaire, we made

the necessary research to find the factors involved .Here the

most important which the respondents may relate to has to be

used. The most important question being asked in the

questionnaire was whether they use facebook. If no, then there is

no point in proceeding with further questions. There were some

other relevant question as to how often do you use facebook.

Also some basic questions like respondents age, contact number

etc.

g)Sampling Techniques : Simple Random sampling.All items in the population had an equal chance of being chosenin the sample. This was done to avoid bias.

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h)Field Work:To conduct a research we need to do some fieldwork. Hence even this research involved certain field work. Ourinitial respondents were IBS students. Hence we went to main

areas of IBS campuses like library, canteen, reading hall,classroom etc to take the survey. Others were been also givenpersonally.

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Data Analysis

a)Methodology:

i)Correlation Matrix: First set of analysis that can be done isthrough correlation matrix. From our table, we can see that thereis correlation between individual variables (e.g. chat has 1correlation with chat itself but it has .487 with flexibility). Fromthis method we are able to reject the hypothesis that correlation

matrix is an identity matrix since the correlation with othervariables is not 0. Hence accept the alternative hypothesis thatthere is a correlation between variables.

ii)KMO and Bartletts Test: According to this case we test thesignificance and adequacy level of samples.KMO checks the adequacy level of samples. The condition tocheck the adequacy level is that if the level is greater than 0.5 itis fit. More it is closer to1 better would be the research. In ourcase the level is .820. Hence we can say the sampling is goodenough since it is more than .5. It is conducive enough toconduct factor analysis. Hence we can proceed further.

The next set of test is Bartletts test of significance: Here thecondition is if significance level is less than 0.05, we can say

that there exits significant correlation between variables. In ourcase it was 0.00.Hence the case is significant enough to findcorrelation.

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v)Scree Plot: This is another way to find the number of factors

used. Scree plot is a graph where you are able to find the numberof factors with the help of flatness of the graph. In our case thegraph becomes flat after 2 factors. Hence according to thecondition in our case due to its flatness we can say that thenumber of factors here is 2.

vi)Component Matrix: Component Matrix is used forreloading. This explains how much one attitude has explainedeach of the 2 factors.Here some of the attitudes are best explained by factor 1 whilesome factors are best explained by factor 2.Highest loading formost of the attitudes is attitude 1 while for horoscope highestloading is factor 2.

Factor 1 Factor 2Chat- 81% Horoscope- 66% New Friends- 73%Update Profiling -76%Pass Time -65%Simplicity -74%Community

Personalization- 65%Flirting -49%Flexibility -65%

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vii)Rotation Component Matrix: If almost all the factors are

same then we tend to rotate to get an accurate picture of thesample. Maximum rotation can be done 25 times .Here we haveused Varimax method for rotation. Here we have some changesin the loading factor. Even the percentage of loading has alsoincreased after rotation. Eg: Communality Personalization andFlirting in the previous condition was best explained by factor1but then after rotation now it is best explained by factor 2.

viii)Component Transformation Matrix: Componenttransformation matrix tells us the way the overall attitudes havebeen explained by 2 components. Here it 77% and 63%

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Plan of Data Analysis :

1. After completing our questionnaire, we did the statisticalanalysis of our research. We used SPSS, factor analysis to carryforward our research.2. First process was data entry. While adding our data, we firstdescribed our variables in Variable View, which included Name,Type, Width, Decimal etc.3. Then data was entered into the Data View.

4. To obtain the output, we selected Data Reduction, where weselected factor.5. We selected all the variable and choose Initial and KMO fromdescriptives.6. Then Principal component method was selected from theextraction option. Correlation matrix was used to analyze andunrotated factor solution and scree plot was used to display. Thevalue for the Eigen value was given over 1.7. Varimax rotation was used.8. Listwise cases were excluded.

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Results:

We rejected the hypothesis that correlation matrix is an identitymatrix since the correlation with other variables is not 0. Henceaccept the alternative hypothesis that there is a correlationbetween variables.KMO checks the adequacy level of samples. In our case thelevel is .820. Hence the sample was good enough, since it ismore than .5. It is conducive enough to conduct factor analysis.

Hence we proceeded further.The next set of test was Bartletts test of significance. In ourcase it was 0.00. Hence the case was significant enough to findcorrelation.Communalities help us to explain how much the research hasexplained the factors. New friends has the maximum 82%explanation in this research while flirting has just 35.2%explained in the case. Non interactive games has 36.4%. Chatwas 76.4%. Checking out how friends are doing was 81%.Profile update was 58.8%. Passing time constituted 63.8%.Simplicity accounted to 53.3%. Horoscope was about 79%.Community personalization and Flexibility accounted to 67.3%and 40.8% respectively.Then we analyzed the variances of various factors and determineeigen values of that factors. In our case till the second factor the

eigen value was more than 1. After that it reduced to less than 1.Hence we determined the number of factors as 2 since after thatthe eigen values was reduced to less than 1. Cumulative herewas 62% in case of 2 factors.Some of the attitudes were best explained by factor 1 whileother factors by factor 2. Highest loading for most of the

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attitudes is attitude 1 while for horoscope highest loading isfactor 2.

Factor 1 Factor 2Chat- 81% Horoscope- 66% New Friends- 73%Update Profiling -76%Pass Time -65%Simplicity -74%CommunityPersonalization- 65%Flirting -49%Flexibility -65%Component transformation matrix tells us the way the overallattitudes have been explained by 2 components. Here it was77% and 63%.

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Limitations

1. We have taken 12 question in our survey to analyzefacebook. And because of using 12 questions only we havelimited our research. We have considered various factorslike chat, friends, games etc through which we have cometo a conclusion.

2. Our survey was limited to Mumbai only. So the results

would be biased to a metro city where such networkingsites are very common so this might not give us the rightsurvey results as small cities are totally ignored.

3. As our survey was mostly among the students only so thissurvey lags the interest of other age group people liketeenagers and people above the age of 30.

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Conclusions

Here our main aim was to find the significance of each andevery factor that was related to attractiveness to facebook. Withfactor analysis using SPSS we were able to find the followingconclusions:

1)Using KMO we measured the sample adequacy and weproved that the samples were fit to do the research.2) Using Bartlett we concluded that there is a significantcorrelation between the variables.3)Scree plot and Variances helped us to reduce the factors to 2and then help us to analyze the data.4)Using Component matrix we saw that factors like noninteractive games, chat, new friends, pastime, simplicity etcwere all explained most by factor1 due to high loading whileHoroscope was best explained by factor 2.

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Recommendations:

The privacy security should be increased:The current features can be upgraded with certain latesttechnologies.For eg Currently the photo which areuploaded can be easily edited by any person and use it inany manner.This may lead to some undesirableconsequences.

Facebook can also introduce calling facility:Upgarding the facebook with calling facility wouldincrease the usage of it to a larger extent.The callingfacility which is availbe in Skype pr GTalk can beincorporated which would lead to overall improvement inthe facebook features.

Videos and songs download can be added:Songs are backbone of each and very individual.Whenever people are online they do always work while listeningto music.So inclusion of songs and videos would entyertainthe people using facebook.

Repair and maintenance regularity

Exhibits:

QUESTIONNAIRE ON ATTRACTION TO FACEBOOK

Do you use Facebook?

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Yes No

How frequently do you log into Facebook?Several times a day Daily

Weekly Monthly

Less than monthly

What do you use Facebook for? Please rate the following 7activities according to the frequency of usage with 1 being theleast frequent and 7 being the most.

1 2 3 4 5 6 7

1 New friends

2Playinteractive

games

3Play non-interactivegames

4

Chat(includingcomments and

wall)

5

Check out howyour friendsaredoing(photos,walls etc)

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6Update yourprofile

7 To pass time8 Simplicity

9 Horoscope

10Communitypersonalization

11 Flirting

12 Flexibility

Name:

Mobile:

Gender:Age:

Research Analysis – What Attracts You To

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