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“A study of Awareness, Opportuni t ies & Problems for Retai l

Investors wi th Reference to Mutual Funds In Gujara t Sta te” G a n p a t U n i v e r s i t y

208 D a t a A n a l y s i s a n d I n t e r p r e t a t i o n

CHAPTER – 6

DATA ANALYSIS AND INTERPRETATION

Sr. No. Content Page No.

6.1 Introduction 212

6.2 Reliability and Normality of Data 212

6.3 Descriptive Analysis 213

6.4 Cross Tabulation 218

6.5 Chi– Square Test 223

6.5.1 Association between Gender of Respondents and

Method of Investment in Mutual Funds

223

6.5.2 Association between Age of Respondents and Method

of Investment in Mutual Funds

224

6.5.3 Association between Education Level of Respondents

and Method of Investment in Mutual Funds

225

6.5.4 Association between Employment Status of

Respondents and Method of Investment in Mutual

Funds

226


Preference for Mutual Funds Type

227

6.5.6 Association between Age of Respondents and


228


and Preference for Mutual Funds Type

229


Respondents and Preference for Mutual Funds Type

229




6.5.9 Association between Monthly Income of Respondents

and Preference for Mutual Funds Type

230

6.5.10 Association between Monthly Investment of

Respondents and Preference for Mutual Funds Type

231

6.5.11 Association between Gender of Respondents and Level

of Risk Preference

232

6.5.12 Association between Age of Respondents and Level of

Risk Preference

232


and Level of Risk Preference

233


Respondents and Level of Risk Preference

234


and Level of Risk Preference

235


Respondents and Level of Risk Preference

236


Duration of Investment

236

6.5.18 Association between Age of Respondents and Duration

of Investment

237


Respondents and Duration of Investment

238


and Duration of Investment

239


Respondents and Duration of Investment

240

6.6 Friedman ANOVA Test

241




6.6.1 Preference of Investors for Source of Information

about Mutual Funds

241

6.6.2 Preference of Investors for Different Mutual Funds

Schemes

243

6.6.3 Preference of Investors for Different Sponsors Mutual

Funds.

245

6.6.4 Preference of Investors for Different Investment

Avenues

246

6.7 Factor Analysis 249

6.8 Factor Analysis for Awareness About Mutual Funds 250

6.8.1 Bartlett Test of Spherity 250

6.8.2 Measures of Sampling Adequacy (MSA) 250

6.8.3 Anti-Image Correlation Matrix 251

6.8.4 Method of Factor Analysis 252

6.8.5 Method of Factor Rotation 252

6.8.6 Communalities 254

6.8.7 Eigenvalue and Total Variance Explained 264

6.8.8 Factor Loading 266

6.8.9 Revised Factor Extraction 268

6.8.10 Naming of Factors 270

6.9 ANOVA Analysis for Awareness 272

6.10 ANOVA Analysis for Awareness and Demographic

Variables

273

6.10.1 ANOVA Analysis for Awareness * Gender 273

6.10.2 ANOVA Analysis for Awareness * Age 274

6.10.3 ANOVA Analysis for Awareness * Education Level 276

6.10.4 ANOVA Analysis for Awareness * Employment Status

279




6.11 Factor Analysis for Opportunities to Investment in

Mutual Funds

282



6.12 ANOVA Analysis for Opportunities and Demographic

Variables

287

6.12.1 ANOVA Analysis for Opportunities * Gender 287

6.12.2 ANOVA Analysis for Opportunities * Age 288

6.12.3 ANOVA Analysis for Opportunities * Education Level 289

6.12.4 ANOVA Analysis for Opportunities * Employment

Status

290

6.13 Factor Analysis for Problems of Investing in Mutual

Funds

295



6.14 ANOVA Analysis for Problems and Demographic

Variables

300

6.14.1 ANOVA Analysis for Problems * Gender 300

6.14.2 ANOVA Analysis for Problems * Age 301

6.14.3 ANOVA Analysis for Problems * Education Level 302

6.14.4 ANOVA Analysis for Problems * Employment Status 303

6.15 Summary 305




6.1 Introduction

Once the raw data is collected, the next step is to analyze it to draw logical inferences

from them. The data collected in a survey could be voluminous in nature, depending

upon the size of the sample. It is necessary to arrange raw data collected during

primary survey in useful fashion. The purpose of this chapter is to analyze and test the

hypothesis created to achieve objective. To test the hypothesis analysis could be

univariate, bivariate and multivariate in nature. In the univariate analysis one variable

is analyzed at a time. In the bivariate analysis two variables are analysed together and

examined for any possible association between them. In the multivariate analysis, the

concern is to analyze more than two variables at a time. In present research various

data analysis techniques have been used depending on the type of data and hypothesis

framed.

6.2 Reliability and Normality of Data

A multi item scale should be evaluated for accuracy and applicability (Malhotra,

2009). It is used to measure the strength of the scale. Questionnaire consist twenty

two different statements on a 5-point scale. The content validity of the survey was

assessed in a pre-test with 36 respondents not included in the sampling frame. Pre-test

participants were asked to evaluate all aspects of the questionnaire, including the

wording of individual items, the general flow and structure of the instrument and its

comprehensiveness. Participants’ suggestions were then incorporated into the survey

prior to its final use. The most widely used reliability measure is Cronbach’s Alpha.

So, in reliability analysis, the alpha (α) coefficient is calculated to find out the internal

consistency of the items on the scale from 36 respondents.

Table – 6.1 Reliability Statistics

Cronbach’s alpha

Number of

questions Construct

Cronbach’s alpha

value

63 Overall 0.932

22 Awareness 0.964

20 Opportunities 0.938

21 Problems 0.799




Alpha value of 0.6 or less generally indicates unsatisfactory level (Malhotra, 2009).

But in this survey the overall and individual test value was found more than the

required value that indicates good consistency among items and tools developed for

study are reliable and hence researcher can proceed further.

In case of normality, sample size has the effect of increasing statistical power by

reducing sampling error Hair et. al. (2009), Larger sample size reduces the

detrimental effects of non-normality. In small samples of 50 or less, significant

departures from normality can have a substantial impact on the results but for sample

size of 200 or more, however, the same effects may be negligible. Moreover, when

group comparisons are made, such as ANOVA, the differing sample sizes between

groups, if large enough, can even cancel out the detrimental effects. Thus, in most

instances, as the sample sizes become large, researcher can be less concerned about

non-normal variables. Normality can have serious effects in small samples (less than

50 cases), but the impact effectively diminishes when sample sizes reach 200 cases or

more. Here in this research as sample size is 463, researcher has assumed that

normality does exist.

6.3 Descriptive Analysis

Descriptive analysis refers to transformation of raw data into a form that will facilitate

easy understanding and interpretation. Descriptive analysis deals with summary

measures relating to the sample data. The common ways to summarizing data are by

calculating average, range, standard deviation, frequency and percentage distribution.

The first thing to do when data analysis is taken up is to describe the sample. In

following section characteristics of sample data gathered during primary survey are

described.




Table – 6.2 – Descriptive Analysis for Demographic Variables

Variable Range Frequency %

Gender Male

Female

348

115

75.2

24.8

Age 21-30

31-40

41-50

51-60

More than 60

195

132

88

41

7

42.1

28.5

19.0

8.9

1.5

Marital Status Single

Married

Others

164

297

03

35.5

64.1

0.4

Education School

Graduate

Post graduate

Professional

Doctorate

Others

40

156

195

61

8

3

8.6

33.7

42.1

13.2

1.7

0.6

Family Size 1 person

2-3 persons

4-5 persons

More than 5 persons

8

137

245

73

1.7

29.6

52.9

15.8

Employment Status Salaried

Businessman/Self Employed

Housewife

Student

Retired

Unemployed

262

127

12

36

17

9

56.6

27.4

2.6

7.8

3.7

1.9

Type of House Stay Own

Rented

412

51

89.0

11.0

Investment in

Mutual Funds

Yes

No

463

00

100

00

Monthly Income Less than 10000

10001 to 15000

15001 to 20000

20001 to 25000

More than 25000

81

69

99

85

129

17.5

14.9

21.4

18.4

27.9

Monthly Investment

in Mutual Funds

Less than 5000

5001 to 10000

10001 to 15000

15001 to 20000

More than 20000

257

118

62

11

15

55.5

25.5

13.4

2.4

3.2




Preference for Method

of Investment

Online

Offline

Both

101

197

165

21.8

42.6

35.6

Preference for

Mutual Funds type

Open Ended

Close Ended

Both

146

96

221

31.6

20.7

47.7

Preference for Risk High Risk

Medium Risk

Low Risk

65

249

149

14.0

53.8

32.2

Preference for


Less Than 1 Year

1 – 3 Years

3 – 5 Years

5 – 7 Years

More Than 7 Years

53

174

139

57

40

11.5

37.6

30.0

12.3

8.6

Table 6.2 provides a summary of the demographic profile of the respondents. The

result shows that a majority of the respondents were male (n = 348, 75.2 percent) with

only 24.8 percent of the female respondent (n = 115). Age is represented by middle

age respondents who represent almost 70 percent of the sample and which includes

age group 21-30 and 31-40 (n=195 & 132) and those with age group between 41-50

and 51-60 represent 19 percent and 9 percent (n=88 & 41) respectively. 1.5 percent (n

= 7) respondent were belong to more than 60 age group. Of the total sample, almost

64 percent (n=297) were married and 36 percent (n = 164) were belong to unmarried

category.

On the other hand, respondents with higher qualification such as professional and

doctorate represent 13.2 and 1.7 (n=61 and 8), high educational level (postgraduates)

represent 42 percent (n=195) of the sample, while medium education level (graduate)

represent 33.7 percent (n=156) and lower education level (undergraduate and high

school) represent 8.6 percent (n=40) of the sample.

With respect to family size of sample, it can be seen that highest proportion of

respondent n = 245 (53 percent) belong to moderate family size including 4-5 persons.

About 30 percent respondent belong to family size including 2-3 persons in family

(n=137). More than 5 persons in family members is represented by 16 percent (n =

73).




It is evident from table 6.2 with respect to employment status that highest proportion

of the respondent were salaried employee and is represented by 56.6 percent (n=262)

followed by businessman/self-employed 27.4 percent (n=127). Housewife, student,

retired employee and unemployed respondent were represented by 12 percent (n=2.6),

7.8 percent (n=36), 3.7 percent (n=17) and 1.9 percent (n=9) respectively. Of the total

respondents 89 percent (n=412) were staying in their own house while remaining 11

percent (n=51) were staying in rented house.

While distributing respondents on the basis of monthly income, it is found that 17.5

percent of the respondents (n=81) earn less than Rs. 10000, 14.9 percent of

respondents (n=69) earn between Rs. 10001 to Rs. 15000, 21.4 percent respondents

(n=99) earn between Rs. 15001 to Rs. 20000 in a month, 18.4 percent of respondents

(n=85) earn between Rs. 20001 to Rs. 25000 while 27.9 percent of the respondents

(n=129) earn more than Rs. 25000 per month. Distribution of respondents based on

monthly investment in mutual funds shows that 55.5 percent (n=257) respondents

invest less than Rs. 5000 in mutual funds. 25.5 percent of respondents (n=118) invest

between Rs. 5001 to Rs. 10000, 13.4 percent respondents (n=62) invest between Rs.

10001 to Rs. 15000, 2.4 percent respondents (n=11) invest between Rs. 15001 to Rs.

20000 and 3.2 percent of respondents (n=15) invest more than Rs. 20000 per month in

mutual funds.

It is found from table 6.2 that 21.8 percent of the respondents (n=101) prefer online

mode of investment, 42.6 percent of respondents (n=197) prefer offline mode of

investment while 165 respondents (35.6 percent) prefer both mode for investment in

mutual funds.

While distributing respondents based on type of scheme they prefer, it is found that

146 respondents (31.6 percent) prefer open ended schemes, 96 respondents (20.7

percent) respondents prefers close ended schemes and both schemes are preferred by

47.7 percent respondents (n=221).




High risk is preferred by 14 percent respondents (n=65), medium risk is preferred by

53.8 percent respondents while low risk is preferred by 149 respondents (32.2

percent).

Table 6.2 also shows that, while distributing respondents based on preference of

duration of investment 53 respondents (11.5 percent) prefer to invest for less than one

year, 37.6 percent respondents (n=174) prefer to invest for 1-3 years, 30 percent of

respondents (n=139) prefer to invest for 3-5 years, 12.3 percent of respondents (n=57)

prefer to invest for 5-7 years while 40 respondents (8.6 percent) prefer to invest for

more than 7 years.

On the whole, sample respondents were more dominated by male, almost 90 percent

belong to middle and upper middle age group; between 21-50, have higher levels of

education (graduate and postgraduate), with moderate family size, and with 64

percent married (n=297) and 36 percent unmarried (n=164). Majority of the

respondent 56.6 percent (n=262) were salaried employee followed by

businessman/self-employed 27.4 percent (n=127). Of the total respondent almost 90

percent were having their own house and almost 30 percent of the respondents

monthly income was more than 25000 while majority respondent; almost 80 percent

(n= 375) were investing less than Rs. 10000 per month in mutual funds.

Respondent tends to choose online and offline method of investment. Respondents are

equality distributed as far as preference of method of investment is concern. For

preference of scheme, higher proportion of respondent prefers to invest in both open

ended and close ended schemes. It is also evident from the table 6.2 that majority of

respondents (86 percent) are medium or low risk takers and higher proportion of

respondents (67 percent) prefers to invest for short to medium term (1-5 years) in

mutual funds.




6.4 Cross Tabulation

Bivariate analysis examines the relationship between two variables. Different methods

for analyzing bivariate analysis are available, of which cross tabulation is one of the

important and frequently used methods. A cross tabulation counts the number of

observations in each cross category of two variables. The descriptive result of a cross

tabulation is a frequency count for each cell in the analysis. Following section deals

with analysis of bivariate data using cross tabulation method.

Table – 6.3 – Cross Tabulation for Gender * Age of Respondents

Age Total

21-30 31-40 41-50 51-60 More than 60

Gender Male 132 98 76 35 7 348

Female 63 34 12 6 0 115

Total 195 132 88 41 7 463

Table 6.3 shows cross tabulation between gender and age of the respondents. It can be

seen from table 6.3 that majority of the respondents were male and were belong to the

age of 21 to 50 (88 percent) , while in case of female majority of respondents belong

to the age of 21 to 40 (85 percent). From the total respondents (n=463), 75 percent

(n=348) were male and 25 percent (n=115) were female.

Table 6.4 – Cross Tabulation for Gender * Education of Respondents

Education

Total

School Graduate

Post-

Graduate Professional Doctorate Others

Gender Male 33 125 134 47 6 3 348

Female 7 31 61 14 2 0 115

Total 40 156 195 61 8 3 463

Cross tabulation of respondents based on gender and education status reveals that,

from the total male respondents 38 percent (n=134) were post graduate, 36 percent




(n=125) were graduate and 14 percent respondents possess professional qualifications.

Majority of female respondents 53 percent (n=61) were post graduate followed by

graduate n=31 (27 percent) and professional qualification n=14 (12 percent). It can be

concluded that from the total respondents, majority of them were literate and possess

more than graduation qualification.

Table – 6.5 Cross Tabulation for Gender * Family Size of Respondents

Family Size

Total

1 Person 2-3 Persons 4-5 Persons

More than

5 Persons

Gender Male 8 100 187 53 348

Female 0 37 58 20 115

Total 8 137 245 73 463

Based on table 6.5 for cross tabulation, it is found that majority of male respondents

(n=187) belong to moderate family size consisting 4-5 members and 53 male were

belong to family size consisting more than 5 members. In case of female, higher

proportion were belong to the family size of 4-5 members (n=58). Overall it can be

said from table 6.5 that from the total respondents 68 percent (n=318), were belongs

to the typical Indian moderate family size which consists more than 4 members.

Table – 6.6 – Cross Tabulation for Gender * Employment Status of Respondents

Employment Status

Total

Salaried

Businessman/

Self-

employed Housewife Student Retired Unemployed

Gender Male 177 115 1 31 17 7 348

Female 85 12 11 5 0 2 115

Total 262 127 12 36 17 9 463




While distributing respondents based on gender and their employment status, it was

found that higher proportion in male and female both belongs to salaried employee

category. In male category, almost 84 percent (n=292) were belong to salaried and

businessman/self-employed category. Besides, almost 84 percent respondents

(n=389) in male and female category both belong to salaried and businessman

category. In all it can be said that majority of respondents belong to salaried and self-

employed category.

Table – 6.7 – Cross Tabulation for Gender * Monthly Income of Respondents

Monthly Income

Total

Less than

10000

10001 to

15000

15001 to

20000

20001 to

25000

More than

25000

Gender Male 51 51 76 64 106 348

Female 30 18 23 21 23 115

Total 81 69 99 85 129 463

When classifying respondents based on monthly income with respect to their gender

category, it was found that in male category respondents earning more than 25000

income per month were high in numbers (n=106), while other income group is almost

equally distributed with their respective income. In female category there is a not vast

difference between different income categories. Overall in male and female category

both respondents who have earned more than 15000 in a month were high in numbers.

Table – 6.8 – Cross Tabulation for Gender * Monthly Investment of Respondents

Monthly Investment

Total

Less than

5000

5001 to

10000

10001 to

15000

15001 to

20000

More than

20000

Gender Male 186 92 47 10 13 348

Female 71 26 15 1 2 115

Total 257 118 62 11 15 463




Though the majority respondents belongs to the income group of more than 25000 as

seen in table no 6.7, the investment in mutual funds is less than 5000 in almost 50

percent cases. Further of the total male respondents almost 80 percent (n=278)

respondents invest less than Rs. 10000 in mutual funds. In case of female also; almost

85 percent (n=97) invest less than Rs. 10000 in mutual funds.

Table – 6.9 – Cross Tabulation for Age * Employment Status of Respondents

Employment Status

Total

Salaried

Businessman/

Self-

employed Housewife Student Retired Unemployed

Age 21-30 128 22 4 34 0 7 195

31-40 76 49 4 1 1 1 132

41-50 36 45 3 1 2 1 88

51-60 22 10 1 0 8 0 41

More than 60 0 1 0 0 6 0 7

Total 262 127 12 36 17 9 463

From the table 6.9 it can be seen that high proportion of respondents consists salaried

employee. Further, almost 50 percent of salaried employee belong to the age category

of 21-30 years, while remaining 50 percent salaried employee belong to the age

category between 31-60 years. Second category employees are businessman which

includes 27 percent of respondent. The portion of housewife, student, retired

employee and unemployed category includes less than 10 percent respondent in each

category and their portion in each category is very nominal.




Table – 6.10 - Cross Tabulation for Age * Monthly Income of Respondents

Monthly Income

Total

Less than

10000

10001 to

15000

15001 to

20000

20001 to

25000

More than

25000

Age 21-30 61 38 47 24 25 195

31-40 9 14 24 33 52 132

41-50 7 9 20 15 37 88

51-60 2 6 6 12 15 41

More

than 60 2 2 2 1 0 7

Total 81 69 99 85 129 463

Cross tabulation of monthly income with age reveals that 61 respondents earn less

than 10000 in a month belong to the age category of 21-30. In the same age category

respondents who belong 10001 to 15000, 15001 to 20000, 20001 to 25000 and more

than 25000 income groups were 38, 47, 24 and 25 respectively in numbers. In the age

category of 31-40 the respondents whose income was less than 10000, 10001 to

15000, 15001 to 20000, 20001 to 25000 and more than 25000 income group were 9,

14, 24, 33 and 52 respectively in numbers. Overall it can be said that majority of

respondents were belong to the age group of 21-40.

Table – 6.11 – Cross Tabulation for Age * Monthly Investment of Respondents

Monthly Investment

Total

Less than

5000

5001 to

10000

10001 to

15000

15001 to

20000

More than

20000

Age 21-30 136 39 15 2 3 195

31-40 60 43 20 1 8 132

41-50 37 23 20 6 2 88

51-60 19 11 7 2 2 41

More

than 60 5 2 0 0 0 7

Total 257 118 62 11 15 463




Though the income of majority of respondents was more than 15000 per month; the

investment in mutual funds is less than 5000 in 55 percent cases. Further, those who

belong to age group between 21-30, invest less than 5000 in 70 percent cases (n=136).

Respondents in the age group of 21-30 consist 136, 39, 15, 2 and 3 respondents who

invest less than 5000, 5001 to 10000, 10001 to 15000, 15001 to 20000 and more than

20000 in a month respectively. In the age group of 31-40, total 132 respondents are

included. From the total respondents in the same group 60 respondents invest less

than 5000, 43 invest between 5001 to 10000, 20 invest 10001 to 15000, 01 invest

15001 to 20000 and 8 respondents invest more than 20000 in a month in mutual

funds. The proportion of age group 41-50, 51-60 and more than 60 includes 88, 41

and 7 respondents respectively who belong to different investment category.

6.5 Chi– Square Test

The purpose of chi-square test is to show the relationship or lack of relationship

between two variables. It is used to test the statistical significance of the observed

association in a cross tabulation (Malhotra, 2009). It assists in determining whether a

systematic association exists between the two variables. The test is conducted by

computing the cell frequencies that would be expected if no association were present

between the variables, given the existing row and column totals. A number of tests are

available to determine if the relationship between two cross-tabulated variables is

significant. One of the common tests is chi-square test. In present research to study

the association between two variables chi-square statistics has been used. For below;

all hypotheses test were performed at 5 percent level of significance.

6.5.1 Association between Gender of Respondents and Method of Investment in

Mutual Funds

Hypothesis

H0 There is no significant association between gender of respondents and method

of investment in mutual funds.

H1 There is significant association between gender of respondents and method of

investment in mutual funds.




Table – 6.12 – Association between Gender of Respondents * Method of

Investment

Chi-Square Tests

Value Df Sig.

Pearson Chi-Square 3.928 2 .140

Likelihood Ratio 3.904 2 .142

Linear-by-Linear Association .017 1 .897

N of Valid Cases 463

Table 6.12 present the output of chi-square test. The person chi square value is 0.140

at 2 degrees of freedom, which is more than cut of value 0.05 at 95 percent confidence

level. Therefore, null hypothesis is accepted and hence it can be said that there is no

association between gender of the respondents and their method of investment. It can

be also said that gender and method of investment are independent.

6.5.2 Association between Age of Respondents and Method of Investment in

Mutual Funds

Hypotheses

H0 There is no significant association between age of respondents and method

of investment in mutual funds.

H1 There is significant association between age of respondents and method of


Table – 6.13 – Association between Age of Respondents * Method of Investment

Chi-Square Tests

Value Df Sig.








Table 6.13 present the output of chi-square test. The Pearson Chi Square value is

0.148 at 8 degrees of freedom, which is more than cut of value 0.05 at 95 percent

confidence level. Therefore, null hypothesis accepted and hence it can be said that

there is no association between age of the respondents and their method of

investment. It can be also said that age and method of investment, both are

independent of each other and are not significantly related.

6.5.3 Association between Education Level of Respondents and Method of

Investment in Mutual Funds

Hypotheses

H0 There is no significant association between education level of respondents and

method of investment in mutual funds.

H1 There is significant association between education level of respondents and


Table – 6.14 – Association between Education Level of Respondents * Method of

Investment

Chi-Square Tests

Value df Sig.








there is no association between education level of the respondents and their method of

investment. It can be also said that education level and method of investment are

independent.




6.5.4 Association between Employment Status of Respondents and Method of

Investment in Mutual Funds

Hypotheses

H0 There is no significant association between employment status of respondents

and method of investment in mutual funds.

H1 There is significant association between employment status of respondents and


Table – 6.15 – Association between Employment Status of Respondents * Method

of Investment

Chi-Square Tests

Value df Sig.



Linear-by-Linear Association 2.082 1 .149


Table – 6.16 – Cramer’s V for Employment Status of Respondents * Method of

Investment

Value Sig.

Nominal by

Nominal

Phi .257 .001

Cramer’s V .182 .001

Contingency Coefficient .249 .001



0.001 at 10 degrees of freedom, which is less than cut of value 0.05 at 95 percent

confidence level. Therefore, null hypothesis is rejected and hence it can be said that

there is an association between employment status of the respondents and their

method of investment. It can be also said that employment status and method of

investment are significantly related.




Though in case of employment status and method of investment association is

statistically significant, the chi square test does not tell the strength or degree of

association. Strength of association or degree of association is of interest when the

association is statistically significant. The strength of association can be measured by

phi correlation coefficient, Cramers V, Contingency coefficient and the Lamda

Coefficient (Malhotra, 2009). It is used based on the properties of the variables. As

the row and column size are more than 2*2 Cramer’s V value has been considered for

further interpretation. The Cramer’s V value varies between 0 to +1. If it takes the

value of 0 when there is no association while +1 shows perfect positive association. A

large value of V merely indicates a high degree of association, but does not indicate

how the variables are associated. Here in this case the value for Cramer’s V is 18.2

(.182) which shows that the association is not very strong.

6.5.5 Association between Gender of Respondents and Type of Mutual Funds

Preferred

Hypotheses

H0 There is no significant association between gender of the respondents and type

of mutual funds preferred.

H1 There is significant association between gender of the respondents and type

of mutual funds preferred.

Table – 6.17 – Association between Gender of Respondents * Preference for

Mutual Funds Type

Chi-Square Tests

Value df Sig.

Pearson Chi-Square .060 2 .970

Likelihood Ratio .060 2 .970









there is no association between gender of the respondents and preference for mutual

funds type. It can be also said that gender and preference of mutual funds type are

independent.

6.5.6 Association between Age of Respondents and Type of Mutual Funds

Preferred

Hypotheses

H0 There is no significant association between age of the respondents and type of

mutual funds preferred.

H1 There is significant association between age of the respondents and type of

mutual funds preferred.

Table – 6.18 – Association between Age of Respondents * Preference for Mutual

Funds Type

Chi-Square Tests

Value df Sig.








there is no association between age of the respondents and preference for mutual

funds type. It can be also said that age and preference for mutual funds type are

independent.




6.5.7 Association between Education Level of Respondents and Type of Mutual

Funds Preferred

Hypotheses

H0 There is no significant association between education level of the respondents

and type of mutual funds preferred.

H1 There is significant association between education level of the respondents


Table – 6.19 – Association between Education Level of Respondents * Preference

for Mutual Funds Type

Chi-Square Tests

Value df Sig.








there is no association between education level of the respondents and preference for

mutual funds type. It can be also said that education level and preference for mutual

funds type are independent.

6.5.8 Association between Employment Status of Respondents and Type of

Mutual Funds Preferred

Hypotheses

H0 There is no significant association between employment status of the

respondents and type of mutual funds preferred.

H1 There is significant association between employment status of the respondents





Table – 6.20 – Association between Employment Status of Respondents *


Chi-Square Tests

Value Df Sig.








there is no association between employment status of the respondents and preference

for mutual funds type. It can be also said that employment status and preference for

mutual funds type are independent.

6.5.9 Association between Monthly Income of Respondents and Type of Mutual

Funds Preferred

Hypotheses

H0 There is no significant association between monthly income of the respondents


H1 There is significant association between monthly income of the respondents


Table – 6.21 – Association between Monthly Income of Respondents * Preference

for Mutual Funds Type

Chi-Square Tests

Value Df Sig.











there is no association between monthly income of the respondents and preference for

mutual funds type. It can be also said that monthly income and preference for mutual

funds type are independent.

6.5.10 Association between Monthly Investment of Respondents and Type of

Mutual Funds Preferred

Hypotheses

H0 There is no significant association between monthly investment of the


H1 There is significant association between monthly investment of the


Table – 6.22 – Association between Monthly Investment of Respondents *


Chi-Square Tests

Value Df Sig.








there is no association between monthly investment of the respondents and preference

for mutual funds type. It can be also said that monthly investment and preference for

mutual funds type are independent.




6.5.11 Association between Gender of Respondents and Level of Risk Preferred

Hypotheses

H0 There is no significant association between gender of the respondents and

level of risk preferred.

H1 There is significant association between gender of the respondents and level

of risk preferred.

Table – 6.23 – Association between Gender of Respondents * Preference for Risk

Chi-Square Tests

Value Df Sig.








there is no association between gender of the respondents and level of risk preferred.

It can be also said that gender and preference for risk level are independent.

6.5.12 Association between Age of Respondents and Level of Risk Preferred

Hypotheses

H0 There is no significant association between age of the respondents and level of

risk preferred.

H1 There is significant association between age of the respondents and level

of risk preferred.




Table – 6.24 – Association between Age of Respondents * Preference for Risk

Chi-Square Tests

Value Df Sig.








there is no association between age of the respondents and level of risk preferred. It

can be also said that age and preference for risk level are independent.

6.5.13 Association between Education Level of Respondents and Level of Risk

Preferred

Hypotheses

H0 There is no significant association between education level of the respondents

and level of risk preferred.

H1 There is significant association between education level of the respondents and

level of risk preferred.

Table – 6.25 – Association between Education Level of Respondents * Preference

for Risk

Chi-Square Tests

Value Df Sig.











there is no association between education level of the respondents and level of risk

preferred. It can be also said that education level and preference for risk level are

independent.

6.5.14 Association between Employment Status of Respondents and Level of Risk

Preferred

Hypotheses


respondents and level of risk preferred.




Preference for Risk

Chi-Square Tests

Value Df Sig.








there is no association between employment status of the respondents and level of risk

preferred. It can be also said that employment status and preference for risk level are

independent.




6.5.15 Association between Monthly Income of Respondents and Level of Risk

Preferred

Hypotheses





Table – 6.27 – Association between Monthly Income * Preference for Risk

Chi-Square Tests

Value df Sig.





Symmetric Measures

Value Sig.

Nominal by Nominal Phi .224 .003






confidence level. Therefore, null hypothesis rejected and hence it can be said that

there is association between monthly income of the respondents and level of risk

preferred. It can be also said that monthly income and preference for risk level are

significantly related.

As the row and column size are more than 2*2, Cramer’s V value has been consider

to measure the degree or strength of association for further interpretation. Here in this

case the value for Cramer’s V is 15.8 (.158) which shows that the association is not

very strong.




6.5.16 Association between Monthly Investment of Respondents and Level of

Risk Preferred

Hypotheses






Preference for Risk

Chi-Square Tests

Value Df Sig.








there is no association between monthly investment of the respondents and level of

risk preferred. It can be also said that monthly investment and preference for risk level

are independent.

6.5.17 Association between Gender of Respondents and Duration of Investment

Hypotheses

H0 There is no significant association between gender of the respondents and

duration of investment in mutual funds.

H1 There is significant association between gender of the respondents and





Table – 6.29 – Association between Gender of Respondents * Duration of

Investment

Chi-Square Tests

Value Df Sig.








there is no association between gender of the respondents and preference for duration

of investment in mutual funds. It can be also said that gender and investment duration

preference are independent.

6.5.18 Association between Age of Respondents and Duration of Investment

Hypotheses

H0 There is no significant association between age of the respondents and


H1 There is significant association between age of the respondents and duration of


Table – 6.30 – Association between Age of Respondents * Duration of Investment

Chi-Square Tests

Value df Sig.





Symmetric Measures

Value Sig.











there is association between age of the respondents and duration of investment in

mutual funds. It can be also said that age and duration preference are significantly

related.




very strong.

6.5.19 Association between Employment Status of Respondents and Duration of

Investment

Hypotheses


respondents and duration of investment in mutual funds.


and duration of investment in mutual funds.



Chi-Square Tests

Value df Sig.





Symmetric Measures

Value Sig.









0.005 at 20 degrees of freedom, which is equal to the cut of value 0.05 at 95 percent


there is association between employment status of the respondents and duration of

investment in mutual funds. It can be also said that employment status and duration

preference are significantly related.




very strong.

6.5.20 Association between Monthly Income of Respondents and Duration of

Investment

Hypotheses





Table – 6.32 – Association between Monthly Income of Respondents * Duration

of Investment

Chi-Square Tests

Value Df Sig.





Symmetric Measures

Value Sig.











there is association between monthly income of the respondents and duration of

investment in mutual funds. It can be also said that monthly income and duration





very strong.

6.5.21 Association between Monthly Investment of Respondents and Duration of

Investment

Hypotheses







Chi-Square Tests

Value df Sig.





Symmetric Measures

Value Sig.






0.001 at 16 degrees of freedom, which is less than the cut of value 0.05 at 95 percent


there is association between monthly investment of the respondents and duration of




investment in mutual funds. It can be also said that monthly investment and duration





very strong.

6.6 Friedman ANOVA Test

Friedman test is a nonparametric version of the randomized block design ANOVA. It

is used when the observations are more than paired; such each block or person is

assigned to all treatments (Malhotra, 2009). Since the Friedman test is based on ranks,

it is especially useful for testing treatment effects when the observations are in the

form of ranks; where ANOVA test cannot be used. When the assumption of normality

may not hold, Friedman test instead of the parametric ANOVA test is used.

6.6.1 Preference of Respondents for Source of Information about Mutual Funds

Hypotheses

H0 There is no significant difference in mean score of preferences given by

respondents for different source of information about mutual funds.

H1 There is significant difference in mean score of preferences given by

respondents for different source of information about mutual funds.

Table – 6.34 – Cross Tabulation for Source of Information * Gender of

Respondents

Gender

Total Source of information Male Female

Newspaper Advertisement 52 17 69

Agent 51 21 72

Bank 36 13 49

Financial Advisor 67 24 91

Stock Broker 40 11 51




Internet 38 8 46

Friends 33 8 41

Relatives 11 5 16

Television 20 8 28

Count 348 115 463

Table 6.34 reveals that from the total sources of information available to retail

investor first preference given by both male and female respondents (n=91) is

financial advisor. In male category, second preference was given to newspaper

advertisement (n=52) followed by agent, stock broker, internet, bank, friends,

television and relatives respectively. While in female category second preference is

given to agent (n=21) followed by newspaper advertisement bank, stock broker and

relatives. There are equal preferences of female category in case of internet, friends

and television. In both male and female categories; first preference was given to

financial advisor while seeking information about mutual funds.

Table – 6.35 – Mean Rank for Preference for Source of Information

Source of Information Mean Rank

Newspaper Advertisement 4.92

Agent 4.26

Bank 4.62

Financial Advisor 4.17

Stock Broker 4.80

Internet 5.19

Friends 5.15

Relatives 6.02

Television 5.86

Table – 6.36 – Friedman Test for Preference of Source of Information

N 463

Chi-Square 201.437

Df 8

Sig. .000




Table 6.35 and 6.36 presents the output of Friedman ANOVA test. The Friedman test

value is 0.000 at 8 degrees of freedom, which is less than cut of value 0.05 at 95

percent confidence level. Therefore, null hypothesis is rejected and hence it can be

said that there is significance difference in mean score of preferences given by male

and female. Further from mean value analysis it is indicative that financial advisor

(mean rank = 4.17) is the most important and preferred source for collecting

information regarding mutual funds from the respondent view point followed by

Agent (mean rank = 4.26). The mean rank (6.02) of the source relatives is highest, so

it can be said that relatives are the least reliable and preferred source of information

from the respondent point of view.

6.6.2 Preference of Respondents for Different Mutual Funds Schemes

Hypotheses


respondents for different schemes of mutual funds.


respondents for different schemes of mutual funds.

Table – 6.37 – Cross Tabulation for Preference of Schemes * Education Level of

Respondents

Education

Different

mutual funds

schemes School Graduate

Post-

Graduate Professional Doctorate Others Total

Regular / Debt

Schemes 7 26 49 16 2 2 102

Growth

Schemes 13 66 61 15 3 1 159

Balanced

Schemes 8 14 33 9 1 0 65

Liquid Schemes 4 22 23 5 0 0 54

Tax Savings

Schemes 8 28 30 16 2 0 84

Total 40 156 195 61 8 3 463




Table 6.37 shows respondents preferences for different mutual funds schemes. It can

be seen that 34 percent respondents (n=159) preferred to opt growth schemes

followed by regular and debt schemes 22 percent (n=102). It can be further seen that

regular/debt and growth schemes are more preferred by graduate and post graduate

respondents compare to school level education and professional or doctorate level

education. Further it can be seen that balanced schemes and liquid schemes have been

given least preference by respondents 14 percent and 12 percent respectively. Further

it can be said that graduate and post graduate respondents prefer to opt for different

types of schemes.

Table – 6.38 – Mean Rank for Preference of Mutual Funds Schemes

Ranks

Different Mutual funds Schemes Mean Rank

Regular / Debt Schemes 2.88

Growth Schemes 2.56

Balanced Schemes 3.00

Liquid Schemes 3.49

Tax Savings Schemes 3.06

Table – 6.39 – Friedman Test for Preference of Mutual Funds Schemes

N 463

Chi-Square 84.528

Df 4

Sig. .000

Table 6.38 and 6.39 presents the output of Friedman ANOVA test for the preference

of schemes. The Friedman test value is 0.000 at 4 degrees of freedom, which is less

than cut of value 0.05 at 95 percent confidence level. Therefore, null hypothesis is

rejected and hence it can be said that there is significance difference in mean score of

preferences given by respondents. Further from mean value analysis it is indicative

that growth schemes (mean rank = 2.56) are the most important and preferred option

from different mutual funds schemes available from the respondent view point;




followed by regular/debt schemes (mean rank = 2.88). The mean rank (3.49) of the

liquid schemes is highest, so it can be said that liquid schemes are the least preferred

source of information from the respondents point of view.

6.6.3 Preference of Respondents for Different Sponsors Mutual Funds.

Hypotheses


respondents for different sponsors of mutual funds.


respondents for different sponsors of mutual funds.

Table – 6.40 – Cross Tabulation for Sponsorship * Gender of Respondents

Gender

Sponsorship of mutual funds Male Female Total

Corporate House 77 20 97

Financial Institutions 87 26 113

Public Sector Banks 115 53 168

Private Sector Banks 69 16 85

Total 348 115 463

Table 6.40 shows respondents preferences for sponsorship of different mutual funds.

It can be seen that both the category of respondents; (male and female) (n=168) (36

percent) first priorities mutual funds sponsored by public sector banks. Second

preference (n=113) (24 percent) for sponsorship of mutual funds in both male and

female category is financial institutions. Private sector banks are the least preferred

option among respondents of both the category. Overall it can be said that almost 61

percent of respondents (n=281) prefer to invest in mutual funds schemes sponsored by

public sector banks and financial institutions.




Table – 6.41 – Mean Rank for Preference of Sponsors of Mutual Funds

Ranks

Mean Rank

SP1 2.65

SP2 2.40

SP3 2.23

SP4 2.72

Table – 6.42 – Friedman Test for Preference of Sponsors of Mutual Funds

N 463

Chi-Square 43.276

Df 3

Sig. .000

Table 6.41 and 6.42 presents the output of Friedman ANOVA test for the preference

of mutual funds sponsors. The Friedman test value is 0.000 at 3 degrees of freedom,

which is less than cut of value 0.05 at 95 percent confidence level. Therefore, null

hypothesis is rejected and hence it can be said that there is significance difference in

mean score of preferences given by respondents. Further from mean value analysis it

is indicative that public sector banks (mean rank = 2.23) are the most important and

preferred sponsors from different mutual funds sponsors available; followed by

financial institutions (mean rank = 2.40). The mean rank (2.72) of the private sector

banks sponsor is highest, so it can be said that private sector banks mutual funds are

the least preferred sponsors from the respondents point of view.

6.6.4 Preference of Respondents for Different Investment Avenues

Hypotheses


respondents for different investment avenues.


respondents for different investment avenues.




Table – 6.43 – Cross Tabulation for Preference of Investment * Gender of

Respondents

Gender

Source of Information Male Female Total

Shares 70 24 94

Debentures and Bonds 20 6 26

Government Bills and Bonds 25 4 29

Bank Deposits 59 21 80

Post Office Deposits 29 10 39

Provident Fund 15 11 26

Insurance Schemes 22 3 25

Mutual funds 34 12 46

Commodity and FOREX Market 10 1 11

Real Estate 33 6 39

Gold and Silver 33 18 51

Total 348 115 463

Table 6.43 shows respondents preferences for different investment avenues available

for investment. It can be seen that both the categories of respondents (male and

female) (n=94) choose shares as first preference for investment. Second preference

(n=80) for preference of investment in both male and female category is bank

deposits. Mutual funds have been given fourth rank among different investment

avenues. From the analysis, it can be said that after investment in shares, respondents

prefers to invest in traditional investment avenues. But from the overall analysis it can

also be said that different investors have different priorities based on investment

objectives.




Table – 6.44 – Mean Rank for Preference of Investment Avenues

Ranks

Mean Rank

Shares 4.60

Debentures and Bonds 6.30

Government Bills and Bonds 6.02

Bank Deposits 5.13

Post Office Deposits 5.73

Provident Fund 6.05

Insurance Schemes 6.47

Mutual funds 5.56

Commodity and FOREX Market 7.81

Real Estate 6.85

Gold and Silver 5.49

Table – 6.45 – Friedman Test for Preference for Investment Avenues

N 463

Chi-Square 317.885

Df 10

Sig. .000

Table 6.44 and 6.45 presents the output of Friedman ANOVA test for different

investment avenues. The Friedman test value is 0.000 at 10 degrees of freedom, which

is less than cut of value 0.05 at 95 percent confidence level. Therefore, null

hypothesis is rejected and hence it can be said that there is significance difference in

mean score of preferences given by respondents. Further, from mean value analysis it

is indicative that shares (mean rank = 4.60) are the most preferred investment avenues

from different avenues available; followed by bank deposits and gold and silver

(mean rank = 5.13 and 5.49) respectively. Mutual funds stand fourth in a queue of

preference of investment. The mean rank (7.81) for commodity and FOREX market is

highest, so it can be said that it is a least preferred investment avenues from the

respondent point of view.




6.7 Factor Analysis

The major objective of research is to study the factors affecting awareness,

opportunities and problems for investment in mutual funds. To fulfill the objectives,

in-depth literature review was conducted and sixty three variables were identified.

Since it is not possible for companies to concentrate on all sixty three variables; data

are to be presented in summarized format; so decisions can be taken to increase

awareness, identify opportunities of and threats to investment in mutual funds. To

fulfill the objective and to reduce and summarized data, researcher found factor

analysis techniques as most suitable technique.

Factor analysis is an interdependence technique whose primary purpose is to define

the underlying structure among the variables in the analysis (Hair et. al. 2009).

According to Hair et. al. (2009), factor analysis is a multivariate statistical technique

that is used to summarize the information contained in a large number of variables

into a smaller number of subsets or factors. Factor analysis provides the tools for

analyzing the structure of the interrelationships or correlations among a large number

of variables by defining sets of variables that are highly interrelated, known as factors.

According to Luck et. al. (2007), factor analysis seeks to identify a set of dimensions

that is not readily observed in a large set of variables. According to Chawla et. al.

(2011), factor analysis is a multivariate statistical technique in which no distinction

between dependent and independent variables is considered. In factor analysis, all

variables under investigation are analyzed together to extract the underlined factors.

According to Malhotra (2009), factor analysis can be used to identify underlying

dimensions, or factor, that explain the correlations among a set of variables, to

identify a new, smaller set of uncorrelated variables to replace the original set of

correlated variables in subsequent multivariate analysis and to identify a smaller set of

salient variables from a larger set for use in subsequent multivariate analysis. For

present study factor analysis is used to reduce the number of variables that (1) Help to

increase awareness of mutual funds among retail investors (2) Provide key variables

preferred by respondents as opportunities for investing in mutual funds and (3)

Extract key factors that prevent respondents for investing in mutual funds.




6.8 Factor Analysis for Awareness about Mutual Funds.

6.8.1 Bartlett Test of Sphericity

To ensure that the data matrix has sufficient correlations to justify the application of

factor analysis and in order to establish the strength of the factor analysis solution it is

essential to establish the reliability and validity of the obtained reduction. It is done by

KMO and Bartlett test of sphericity (Hair et. al. 2009). Bartlett test of sphericity is a

test statistic used to examine the hypothesis that the variables are uncorrelated in the

population. In other words, the population correlation matrix is an identify matrix

where each variable correlates perfectly with itself but has no correlation with other

variables (Malhotra, 2009).

Table – 6.46 – KMO and Bartlett’s Test for Awareness Variable

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .917

Bartlett's Test of Sphericity Approx. Chi-Square 3430

Df 231

Sig. .000

Table 6.46 shows significant value of Bartlett’s Test of Sphericity 0.000 that satisfies

necessary condition to reject null hypothesis. It indicates that variables are correlated

with itself but has no correlation with other variables. A statistically significant

Bartlett’s Test of Sphericity (0.000) indicates that sufficient correlations exist among

the variables to proceed for factor analysis.

6.8.2 Measures of Sampling Adequacy (MSA)

Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy is an index used to

examine the appropriateness of factor analysis. It compares the magnitudes of the

observed correlation coefficients to the magnitudes of the partial correlation

coefficients (Malhotra, 2009). The value of KMO varies between 0 to 1. Small values

of KMO indicate that correlation between pairs of variables cannot explained by other

variables. Its value must exceed .50. The measure can be interpreted as meritorious if

it carries a value of .80 or above. In table 6.46 the value of KMO stands 0.917 which

shows the appropriateness of factor analysis for present study.




6.8.3 Anti-Image Correlation Matrix

The analytical process is based on a matrix of correlations between the variables. For

factor analysis to be appropriate, the variables must be correlated. If the correlations

between all the variables are small, factor analysis may not be appropriate. The

variables with the value less that 0.5 should be omitted from the analysis one by one,

with the smallest being omitted first.

Table – 6.47 - Anti Image Correlation Matrix for Awareness

A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 A14 A15 A16 A17 A18 A19 A20 A21 A22

A1 .854 -.519 -.223 .021 .027 -.164 .056 .023 -.034 -.135 .025 .081 -.076 -.131 -.077 .116 -.099 -.064 .098 -.013 .009 -.062

A2 -.519 .874 .028 -.104 -.035 -.016 -.086 -.112 .022 .048 -.054 -.027 .059 .036 -.003 -.047 .099 -.091 -.099 .008 -.042 -.007

A3 -.223 .028 .891 -.337 -.150 .136 -.065 .006 -.107 .002 -.021 -.047 .070 .021 -.090 -.100 .026 -.025 -.054 -.007 .036 -.028

A4 .021 -.104 -.337 .895 -.143 -.053 -.042 .001 .081 .000 -.095 -.059 -.040 -.060 .041 -.092 .037 .082 -.088 .008 -.019 .056

A5 .027 -.035 -.150 -.143 .924 -.174 -.103 .059 -.005 -.110 -.048 .011 -.091 .032 .017 -.069 -.052 -.059 -.006 .039 .083 .031

A6 -.164 -.016 .136 -.053 -.174 .882 -.179 -.172 -.105 .140 .004 -.123 .033 .008 -.019 -.002 .065 -.241 .042 -.127 -.041 .091

A7 .056 -.086 -.065 -.042 -.103 -.179 .939 -.222 -.069 -.062 -.060 .052 -.029 .000 -.021 .007 -.040 .004 .010 -.020 -.159 .003

A8 .023 -.112 .006 .001 .059 -.172 -.222 .898 -.174 -.089 -.044 -.059 .026 -.051 .009 -.004 .040 .071 -.229 -.051 .131 .030

A9 -.034 .022 -.107 .081 -.005 -.105 -.069 -.174 .936 -.162 -.044 -.043 .094 .072 -.082 -.049 .030 -.019 -.053 -.051 -.085 -.081

A10 -.135 .048 .002 .000 -.110 .140 -.062 -.089 -.162 .933 -.214 -.124 .038 -.057 -.011 -.023 -.021 -.029 -.018 -.031 -.110 -.008

A11 .025 -.054 -.021 -.095 -.048 .004 -.060 -.044 -.044 -.214 .954 -.133 -.093 -.021 -.135 -.029 -.037 .033 -.008 -.035 .003 -.043

A12 .081 -.027 -.047 -.059 .011 -.123 .052 -.059 -.043 -.124 -.133 .936 -.180 -.122 -.037 .041 -.036 -.161 .028 .042 -.024 -.055

A13 -.076 .059 .070 -.040 -.091 .033 -.029 .026 .094 .038 -.093 -.180 .923 -.159 -.098 -.009 -.048 .022 -.079 -.085 -.063 -.116

A14 -.131 .036 .021 -.060 .032 .008 .000 -.051 .072 -.057 -.021 -.122 -.159 .935 -.061 -.170 -.116 -.094 .019 .043 -.116 .007

A15 -.077 -.003 -.090 .041 .017 -.019 -.021 .009 -.082 -.011 -.135 -.037 -.098 -.061 .956 -.159 -.056 -.056 .019 -.063 -.018 .050

A16 .116 -.047 -.100 -.092 -.069 -.002 .007 -.004 -.049 -.023 -.029 .041 -.009 -.170 -.159 .916 -.249 .050 -.058 -.068 -.007 -.022

A17 -.099 .099 .026 .037 -.052 .065 -.040 .040 .030 -.021 -.037 -.036 -.048 -.116 -.056 -.249 .914 .026 -.079 -.104 -.066 -.141

A18 -.064 -.091 -.025 .082 -.059 -.241 .004 .071 -.019 -.029 .033 -.161 .022 -.094 -.056 .050 .026 .922 -.220 -.119 -.003 -.060

A19 .098 -.099 -.054 -.088 -.006 .042 .010 -.229 -.053 -.018 -.008 .028 -.079 .019 .019 -.058 -.079 -.220 .925 -.098 -.007 -.103

A20 -.013 .008 -.007 .008 .039 -.127 -.020 -.051 -.051 -.031 -.035 .042 -.085 .043 -.063 -.068 -.104 -.119 -.098 .955 -.091 -.107

A21 .009 -.042 .036 -.019 .083 -.041 -.159 .131 -.085 -.110 .003 -.024 -.063 -.116 -.018 -.007 -.066 -.003 -.007 -.091 .918 -.229

A22 -.062 -.007 -.028 .056 .031 .091 .003 .030 -.081 -.008 -.043 -.055 -.116 .007 .050 -.022 -.141 -.060 -.103 -.107 -.229 .921

Table 6.47 indicates that all the variables have diagonal value more than 0.5. Thus,

factor analysis may be considered an appropriate technique for analyzing the

correlation matrix.




6.8.4 Method of Factor Analysis

Once it has been determined that factor analysis is an appropriate technique for

analyzing the data, an appropriate method must be selected. The approach used to

derive the weight or factor score coefficients differentiates the various methods of

factor analysis. Common factor analysis is most appropriate when the primary

objective is to identify the latent dimensions or constructs represented in the original

variables while principle component is most appropriate when data reduction is a

primary concern, focusing on the minimum number of factors needed to account for

the maximum portion of the total variance represented in the original set of variables

(Hair et. al. 2009). As the primary purpose of this study is to reduce data, principle

component analysis method has been selected for further analysis.

6.8.5 Method of Factor Rotation

Unrotated factor solutions achieve the objective of data reduction but it does not

provide information that offers the most adequate interpretation of the variables under

examination. Therefore rotational method requires to achieve simpler and

theoretically more meaningful factor solutions.

The most important tool in interpreting factors is factor rotation. The term rotation

means, the reference axes of the factors are turned about the origin until some other

position has been reached. As indicated above, unrotated factor solutions extract

factors in the order of their variance extracted. The first factor tends to be a general

factor with almost every variable loading significantly, and it accounts for the largest

amount of variance. The second and subsequent factors are portions of variance. The

ultimate effect of rotating the factor matrix is to redistribute the variance from earlier

factors to later ones to achieve a simpler, theoretically more meaningful factor pattern.

Rotation of factors improves the interpretation by reducing some of the ambiguities

that often accompany initial unrotated factor solutions. The ultimate goal of any

rotation is to obtain some theoretically meaningful factors and, if possible, the

simplest factor structure. The major option available for rotation method is to choose

an orthogonal or oblique rotation method.




Orthogonal rotational approaches are more widely used because all computer

packages with factor analysis contain orthogonal rotation options, whereas the oblique

methods are not as widespread. Orthogonal rotations are also utilized more frequently

because the analytical procedures for performing oblique rotations are not as well

developed and are still subject to some controversy. So, in this research researcher has

used orthogonal rotation method for further analysis.

Three different approaches Quartimax, Equimax and Varimax are available for

performing orthogonal rotations. The ultimate goal of a quartimax rotation is to

simplify the rows of a factor matrix, that is, quartimax focuses on rotation of the

initial factor so that a variable loads high on one factor and as low as possible on all

other factors. The quartimax method has not proved especially successful in

producing simpler structures. Its difficulty is that it tends to produce a general factor

as the first factor on which most of the variables have high loadings.

In contrast of quartimax, the varimax criterion centers on simplifying the columns of

the factor matrix. With the varimax rotational approach, the maximum possible

simplification is reached if there are only 1 and 0 in a column. That is, the varimax

method maximizes the sum of variances of required loadings of the factor matrix. In

quartimax, many variables can load high or near high on the same factor because the

technique centers on simplifying the rows, while in varimax, some high loadings are

close to -1 to +1, as some loading near 0 in each column of the matrix. The logic is

that interpretation is easiest when the variable factor correlations are close to +1 or -1

indicating a clear positive or negative association between the variable and the factor,

where 0 indicating clear lack of association. This structure is fundamentally simple. In

general, Kaiser’s experiment indicates that the factor pattern obtained by varimax

rotation tends to be more invariant than that obtained by the quartimax method when

different subsets of variables are analyzed. The varimax method has proved successful

as an analytic approach to obtaining an orthogonal rotation of factors. So, in this study

researcher has used varimax method for factor rotation.




The equimax approach is a compromise between the quartimax and varimax

approaches. Rather than concentrating either on simplification of the rows or on

simplification of the columns, it tries to accomplish some of each. Equimax has not

gained wide spreads acceptance and is used infrequently.

6.8.6 Communalities

Common variance is defined as that variance in a variable that is shared with all other

variables in the analysis. This variance is accounted for, based on variable’s

correlations with all other variables in the analysis. A variable’s communality is the

estimate of its shared or common variance among the variables as represented by the

derived factors (Hair et. al .2009). The size of the communality is a useful index for

assessing how much variance in a particular variable is accounted for by the factor

solution. Higher communality values indicate that a large amount of the variance in a

variable has been extracted by the factor solution. Low communality figure indicates

that the variable is statistically independent and cannot be combined with other

variables. Although no statistical guidelines indicate exactly what is large or small,

practical considerations dictate a lower level of .50 for communalities in this analysis

(Hair et. al .2009).

Table – 6.48 – Communality Statistics for Awareness Variable

Variables Initial Extraction

A1 1.000 .695

A2 1.000 .663

A3 1.000 .589

A4 1.000 .610

A5 1.000 .451

A6 1.000 .569

A7 1.000 .503

A8 1.000 .603

A9 1.000 .557

A10 1.000 .442

A11 1.000 .466

A12 1.000 .392

A13 1.000 .471




A14 1.000 .499

A15 1.000 .377

A16 1.000 .531

A17 1.000 .521

A18 1.000 .551

A19 1.000 .410

A20 1.000 .453

A21 1.000 .460

A22 1.000 .490

Table 6.48 shows the value of communality for each variable. It can be analyzed from

above table that the extracted communality value (0.377) of A15 variable is below

than acceptable value of 0.5. Hence the variable is omitted from the list and the

process is developed once again. New value of Bartlett’s Test of Sphericity, KMO

(MSA) and Anti Image matrix is observed and revised communalities is extracted.

Table – 6.49 – Communality Statistics for Awareness Variable When A12

Variable is deleted

Variable Initial Extraction

A1 1.000 .695

A2 1.000 .663

A3 1.000 .590

A4 1.000 .627

A5 1.000 .458

A6 1.000 .571

A7 1.000 .503

A8 1.000 .602

A9 1.000 .558

A10 1.000 .445

A11 1.000 .462

A12 1.000 .395

A13 1.000 .473

A14 1.000 .500

A16 1.000 .521

A17 1.000 .526

A18 1.000 .551




A19 1.000 .412

A20 1.000 .453

A21 1.000 .465

A22 1.000 .500







Variable is deleted


A1 1.000 .723

A2 1.000 .683

A3 1.000 .589

A4 1.000 .626

A5 1.000 .462

A6 1.000 .566

A7 1.000 .506

A8 1.000 .605

A9 1.000 .553

A10 1.000 .446

A11 1.000 .463

A13 1.000 .448

A14 1.000 .487

A16 1.000 .522

A17 1.000 .536

A18 1.000 .538

A19 1.000 .414

A20 1.000 .464

A21 1.000 .477

A22 1.000 .515










Variable is deleted


A1 1.000 .706

A2 1.000 .679

A3 1.000 .591

A4 1.000 .632

A5 1.000 .462

A6 1.000 .578

A7 1.000 .531

A8 1.000 .581

A9 1.000 .580

A10 1.000 .474

A11 1.000 .478

A13 1.000 .450

A14 1.000 .484

A16 1.000 .517

A17 1.000 .535

A18 1.000 .539

A20 1.000 .457

A21 1.000 .489

A22 1.000 .512










Variable is deleted


A1 1.000 .733

A2 1.000 .695

A3 1.000 .599

A4 1.000 .631

A5 1.000 .471

A6 1.000 .588

A7 1.000 .543

A8 1.000 .596

A9 1.000 .542

A10 1.000 .468

A11 1.000 .478

A14 1.000 .461

A16 1.000 .537

A17 1.000 .566

A18 1.000 .533

A20 1.000 .458

A21 1.000 .507

A22 1.000 .534







Variable is deleted


A1 1.000 .732

A2 1.000 .694

A3 1.000 .602

A4 1.000 .643




A5 1.000 .477

A6 1.000 .579

A7 1.000 .550

A8 1.000 .600

A9 1.000 .557

A10 1.000 .484

A11 1.000 .481

A14 1.000 .472

A16 1.000 .539

A17 1.000 .563

A18 1.000 .524

A21 1.000 .530

A22 1.000 .541







Variable is deleted


A1 1.000 .506

A2 1.000 .538

A3 1.000 .603

A4 1.000 .642

A5 1.000 .467

A6 1.000 .588

A7 1.000 .463

A8 1.000 .460

A9 1.000 .436

A10 1.000 .441

A11 1.000 .441

A16 1.000 .517




A17 1.000 .562

A18 1.000 .463

A21 1.000 .533

A22 1.000 .557







Variable is deleted


A1 1.000 .533

A2 1.000 .568

A3 1.000 .599

A4 1.000 .628

A5 1.000 .467

A6 1.000 .597

A7 1.000 .457

A8 1.000 .427

A10 1.000 .431

A11 1.000 .445

A16 1.000 .521

A17 1.000 .565

A18 1.000 .499

A21 1.000 .550

A22 1.000 .574










Variable is deleted

Variances Initial Extraction

A1 1.000 .591

A2 1.000 .605

A3 1.000 .591

A4 1.000 .624

A5 1.000 .477

A6 1.000 .578

A7 1.000 .424

A10 1.000 .432

A11 1.000 .450

A16 1.000 .537

A17 1.000 .565

A18 1.000 .537

A21 1.000 .551

A22 1.000 .570







Variable is deleted


A1 1.000 .632

A2 1.000 .629

A3 1.000 .593

A4 1.000 .626

A5 1.000 .478

A6 1.000 .551

A10 1.000 .432

A11 1.000 .451




A16 1.000 .537

A17 1.000 .565

A18 1.000 .556

A21 1.000 .545

A22 1.000 .576







Variable is deleted


A1 1.000 .632

A2 1.000 .631

A3 1.000 .598

A4 1.000 .633

A5 1.000 .478

A6 1.000 .552

A11 1.000 .425

A16 1.000 .564

A17 1.000 .595

A18 1.000 .558

A21 1.000 .546

A22 1.000 .596










Variable is deleted


A1 1.000 .635

A2 1.000 .632

A3 1.000 .616

A4 1.000 .638

A5 1.000 .481

A6 1.000 .553

A16 1.000 .583

A17 1.000 .611

A18 1.000 .560

A21 1.000 .550

A22 1.000 .600






Table – 6.60 - KMO and Bartlett's Test for Awareness after Deleting Variables



df 45

Sig. .000

Table – 6.61 - Anti-image Matrices for Awareness Variable

A1 A2 A3 A4 A6 A16 A17 A18 A21 A22

Anti-image Correlation A1 .780 -.516 -.233 .028 -.155 .076 -.132 -.074 -.037 -.069

A2 -.516 .789 -.006 -.138 -.096 -.063 .094 -.126 -.041 -.021

A3 -.233 -.006 .797 -.392 .064 -.155 .002 -.076 .015 -.047

A4 .028 -.138 -.392 .791 -.105 -.135 -.011 .037 -.039 .032

A6 -.155 -.096 .064 -.105 .819 -.051 .034 -.339 -.081 .067

A16 .076 -.063 -.155 -.135 -.051 .807 -.340 -.018 -.065 -.055

A17 -.132 .094 .002 -.011 .034 -.340 .775 -.050 -.129 -.194

A18 -.074 -.126 -.076 .037 -.339 -.018 -.050 .850 -.061 -.134

A21 -.037 -.041 .015 -.039 -.081 -.065 -.129 -.061 .852 -.298

A22 -.069 -.021 -.047 .032 .067 -.055 -.194 -.134 -.298 .818




Table – 6.62 – Communality Statistics for Awareness Variable after Deleting

Variables


A1 1.000 .652

A2 1.000 .652

A3 1.000 .673

A4 1.000 .679

A6 1.000 .562

A16 1.000 .582

A17 1.000 .613

A18 1.000 .576

A21 1.000 .541

A22 1.000 .589


above table that the extracted communality value of all variables are more than

acceptable value of 0.5. It can be further analyzed that all variables holds diagonal

value more than 0.5. Hence, researcher can proceed further for analysis.

6.8.7 Eigenvalue and Total Variance Explained

Factor analysis aims at using small number of variables that still adequately represent

the entire set of variables. Thus the key question arise is how many factors to extract

or retain? In deciding how many factors to extract or how many factors should be in

the analysis, there is not any exact quantitative base has been developed till today.

However, following stopping criteria for the number of factors to extract are currently

being utilized (Hair et. al .2009).

6.8.7.1 Latent Root Criterion / Eigenvalue

The most commonly used technique is the latent root criterion. The rational for the

latent root criterion is that any individual factor should account for the variance of at

least a single variable if it is to be retained for interpretation. With principle

component analysis each variable contributes a value of 1 to the total eigenvalue.

Thus, only the factors having latent roots or eigenvalues greater than 1 is considered

significant. All factors with latent roots less than 1 are considered insignificant and

are disregarded.




6.8.7.2 Total Variance Explained

The percentage of variance criterion is an approach based on achieving a specified

cumulative percentage of total variance extracted by successive factors. The purpose

is to ensure practical significance for the derived factors by ensuring that they explain

at least a specified amount of variance. No absolute threshold has been adopted for all

applications. However, in social sciences, where information is often less precise, it is

not uncommon to consider a solution that accounts for 60 percent of the total variance

as satisfactory when number of variables are between 20 to 50 (Hair et. al .2009).

Table – 6.63 - Total Variance Explained for Awareness Variable

Component

Initial Eigenvalues

Extraction Sums of

Squared Loadings

Rotation Sums of Squared

Loadings

Total

percent

of

Variance

Cumulative

percent Total

percent

of

Variance

Cumulative

percent Total

percent

of

Variance

Cumulative

percent

1 3.699 36.985 36.985 3.699 36.985 36.985 2.358 23.582 23.582

2 1.310 13.099 50.085 1.310 13.099 50.085 1.966 19.657 43.239

3 1.110 11.103 61.188 1.110 11.103 61.188 1.795 17.949 61.188

4 .786 7.861 69.049

5 .702 7.021 76.070

6 .609 6.088 82.158

7 .523 5.228 87.386

8 .501 5.007 92.393

9 .459 4.586 96.979

10 .302 3.021 100.000

Table 6.63 contains information regarding 10 possible factors and their relative

explanatory power as expressed by their eigenvalues. There are total three factors

having eigenvalues more than 1. Hence, researcher has retained these three factors for

further study. Total variance explained by the three factors is 61.188 percent. This is a

fair percentage of variance to be explained and assumes appropriateness of the factor

analysis.




6.8.8 Factor Loading

Factor loading represents the correlation between factors and variables. In

determining a significance level for the interpretation of loadings, an approach similar

to determining a statistical significance of correlation coefficients could be used. In

this research as per above discussion researcher has used varimax method of rotation

to find factor loadings. The larger the absolute size of the factor loading, more

important the loading in interpreting the factor matrix. Following generally accepted

guidelines has been used for identifying significant factor loading based on sample

size (Hair et. al .2009).

Table – 6.64 – Factor Loading for Awareness Variable

Factor

Loading

Sample Size Needed

for Significance

.30 350

.35 250

.40 200

.45 150

.50 120

.55 100

.60 85

.65 70

.70 60

.75 50

Table – 6.65 - Component Matrix for Awareness Variable

Component

Variables 1 2 3

A1 .740 -.310 -.089

A2 .701 -.392 -.077

A18 .639 -.186 -.365

A3 .632 -.113 .511

A6 .581 -.366 -.301

A16 .557 .405 .329

A21 .554 .376 -.304

A22 .545 .462 -.280

A17 .523 .582 .028

A4 .571 -.146 .576




The unrotated matrix shown in table 6.65 indicates the relationship between the

factors and individual variables but in seldom it results factors that may be correlated

with many variables. In this situation, it is difficult to interpret the factors. Therefore,

using rotation method, the factor matrix is transformed into simpler one that is easier

to interpret as mentioned in below table.

Table – 6.66 - Rotated Component Matrix for Awareness Variable

Component

Variables 1 2 3

A6 .742 .083 .070

A2 .735 .071 .325

A1 .720 .161 .329

A18 .706 .275 .031

A17 .005 .743 .247

A22 .230 .732 .004

A21 .297 .673 -.005

A16 -.008 .544 .535

A4 .205 .043 .797

A3 .258 .118 .770

It is clear from the table 6.66 that rotation improves the structure considerably in two

ways. First, the loadings are improved for almost every variable, with loadings more

closely aligned to the objectives of having a high loading on only a single factor.

Second, now only one variable A16 has a cross loading on two factors. Hence, it has

been omitted from the list and again the whole process of factor rotation has been

developed.




6.8.9 Revised Factor Extraction for Awareness Variable

Table – 6.67 - Revised KMO and Bartlett's Test for Awareness Variable



Df 36

Sig. .000

Table – 6.68 - Revised Anti-image Matrices for Awareness Variable

A1 A2 A3 A4 A6 A17 A18 A21 A22

Anti-image

Correlation

A1 .782 -.514 -.225 .038 -.151 -.113 -.072 -.032 -.065

A2 -.514 .783 -.016 -.148 -.100 .077 -.127 -.045 -.025

A3 -.225 -.016 .776 -.422 .057 -.055 -.079 .005 -.056

A4 .038 -.148 -.422 .754 -.113 -.062 .035 -.048 .025

A6 -.151 -.100 .057 -.113 .813 .018 -.340 -.085 .065

A17 -.113 .077 -.055 -.062 .018 .822 -.060 -.161 -.227

A18 -.072 -.127 -.079 .035 -.340 -.060 .841 -.062 -.136

A21 -.032 -.045 .005 -.048 -.085 -.161 -.062 .829 -.303

A22 -.065 -.025 -.056 .025 .065 -.227 -.136 -.303 .788

Table – 6.69 - Revised Communality Statistics for Awareness Variable


A1 1.000 .644

A2 1.000 .642

A3 1.000 .724

A4 1.000 .708

A6 1.000 .635

A17 1.000 .567

A18 1.000 .603

A21 1.000 .567

A22 1.000 .642




Table – 6.70 - Revised Total Variance Explained for Awareness Variable

Component

Initial Eigenvalues

Extraction Sums of

Squared Loadings


Loadings

Total

percent

of

Variance

Cumulative

percent Total

percent

of

Variance

Cumulative

percent Total

percent

of

Variance

Cumulative

percent

1 3.455 38.386 38.386 3.455 38.386 38.386 2.234 24.824 24.824

2 1.242 13.799 52.185 1.242 13.799 52.185 1.804 20.040 44.864

3 1.036 11.508 63.693 1.036 11.508 63.693 1.695 18.829 63.693

4 .721 8.008 71.701

5 .677 7.525 79.226

6 .601 6.675 85.901

7 .501 5.564 91.465

8 .459 5.098 96.563

9 .309 3.437 100.000

Table – 6.71 - Revised Component Matrix for Awareness Variable

Component

Variables 1 2 3

A1 .770 -.206 -.095

A2 .728 -.303 -.143

A18 .659 -.031 -.409

A3 .625 -.236 .527

A6 .603 -.242 -.460

A4 .562 -.299 .550

A21 .552 .510 -.051

A22 .540 .592 .026

A17 .481 .541 .208




Table – 6.72 - Revised Rotated Component Matrix for Awareness Variable

Statements Factor I Factor II Factor III

A6 .794

A18 .729

A2 .700

A1 .666

A22 .783

A17 .727

A21 .711

A4 .825

A3 .815

As shown in revised tables, the values of KMO and Bartlett’s test (0.796, 0.000), Anti

Image Matrix (>0.5), Communalities (>0.5), Eigenvalues (>1), Percentage of

Cumulative Variance Explained (>60 percent) and Factor Loadings (>0.5) are greater

than cut off values. Hence, post detailed analysis three factors have been identified.

First factor consists four variables (A1, A2, A6, A18), second factor consists three

variables (A17, A21, A22) and third factor consists two variables (A3, A4) based on

significant loadings.

6.8.10 Naming of Factors for Awareness Variable

Based on factors extracted from above process; naming of factors has been done in

following sections.

1. First factor refers to four variables. It includes 1) Awareness about the concept of

mutual funds (0.666) 2) Awareness about mutual funds that it is an indirect route

of investment in stock market, bond market etc. (0.700) 3) Awareness about

mutual funds that it provides tax benefits also (0.794) and 4) Awareness about

mutual funds that investment in mutual funds is subject to market risk (0.729).

The groups of variables are concerned with basic awareness about the concept and

working of mutual funds, so this factor has been given a name of “Conceptual

Awareness.” The extracted factor explains 24.82 percent of variance.




2. Second factor consists of three variables 1) Awareness about mutual funds that it

can be taken over and managed by another company (0.727) 2) Awareness about

mutual funds that though the income on mutual funds is tax free in some cases,

dividend distribution is to be paid by investors (0.711) and 3) Awareness about

mutual funds that several banks provides loans against some specific mutual funds

units (0.783). The groups of variables are concerned with operational benefits of

mutual funds. Hence, this factor has been given a name “Operational

Awareness.” The extracted factor explains 20.04 percent of variance.

3. Third factor consists of two variables 1) Awareness about availability of different

types of mutual funds (0.815) and 2) Awareness about different terminology used

in mutual funds industry (0.825). The groups of variables are concerned with

awareness regarding types and terminology used in mutual funds industry. Hence,

this factor has been given a name “Technical Awareness.” The extracted factor

explains 18.83 percent of variance.

Based on factor analysis, researcher has extracted important factors, that has been

given priority by respondents or that plays vital role to create awareness about mutual

funds among investors. Out of twenty two variables affecting awareness, factor

analysis extracted three factors namely conceptual awareness, operational awareness

and technical awareness consisting nine variables which has been given priority by

respondent and which explain highest share among all twenty two variables. Results

reveal that retail investors are not aware about the basic terms and terminology used

in mutual funds. Investors are not conceptually aware about the working and concept

about the mutual funds. There is a lack of awareness among retail investors about

mutual funds.

As it is evident from above analysis, factor analysis is only data reduction technique

that reduces the number of factors or variables to small number of factors; but it does

not provide relative importance given to different factors by different categories of

respondents. To fulfill this objective researcher has applied ANOVA test to compare

relative importance given by different categories of respondent to key factors

extracted through factor analysis.




6. 9 ANOVA Analysis for Awareness Variable

Analysis of variance is used for examining the difference in the mean values of the

dependent variable associated with the effect of the controlled independent variables,

after taking into account the influence of the uncontrolled independent variables.

Essentially, analysis of variance (ANOVA) is used as a test of means for two or more

populations (Malhotra, 2009). In ANOVA dependent variable should be metric while

independent variables must be categorical or non-metric. The basic principle

underlying the technique is that the total variation in the dependent variable is broken

into two parts – one which can be attributed to some specific causes and the other that

may be attributed to chance. The one which is attributed to the specific causes is

called the variation between samples of independent categorical variable and the one

which is attributed chance is termed as the variation within samples of independent

categorical variable. Therefore, in ANOVA, the total variance may be decomposed

into various components corresponding to the sources of the variation (Chawla et. al.,

2011)

The assumption for conducting ANOVA test is to check the homogeneity of

variances. According to this, the variance between the groups of independent variable

should be homogeneous or equal variance assumed. The null hypothesis is that the

group variances are equal and vis-a-vis. To meet the criteria null hypothesis shall be

accepted.

Researcher has used Levene’s test for equality of variances. Levene’s test reveals that

two groups have approximately equal variance on the dependent variable. If the

Levene's test value is significant that is less than 0.05, then the two variances are

significantly different. If it is not significant (Significant value is greater than 0.05),

then these two group variances are equal. If Levene's test is not significant, the

assumption for equality of variances has been met. If null hypothesis is not accepted

or the equality of variances does not exists, in such situations Welch ANOVA needs

to be found out.

The null hypothesis of ANOVA is that “all means are equal”. So, if null hypothesis is

rejected, then it can be said that, there is a significant difference in mean value of

dependent variable for different categories of the independent variables. But if null




hypothesis is not rejected, then it can be said that the independent variable does not

have significant effect on the dependent variable (Malhotra, 2009).

For ANOVA analysis all the hypothesis are formulated based on factors extracted

through factor analysis and divided into three sections for Awareness, Opportunities

and Problems for investing in mutual funds. Hypotheses have been developed for

following factors extracted through factor analysis to test the significance.

1. Hypothesis for Awareness, based on factor extracted through factor analysis.

2. Hypothesis for Opportunities, based on factor extracted through factor analysis.

3. Hypothesis for Problems, based on factor extracted through factor analysis.

6.10 ANOVA Analysis for Awareness and Demographic Variables

6.10.1 ANOVA Analysis for Awareness * Gender of Respondents

Hypothesis

H0 Awareness is not significantly different across gender of the respondents.

H1 Awareness is significantly different across gender of the respondents.

Table - 6.73 - Test of Homogeneity of Variances for Awareness and Gender of

Respondents

Factor Levene

Statistic df1 df2 Sig.

Operational Awareness 3.918 1 461 .048

Conceptual Awareness .005 1 461 .943

Technical Awareness .018 1 461 .894

Table - 6.74 - ANOVA Analysis for Awareness * Gender of Respondents

Sum of

Squares df

Mean

Square F Sig.

Conceptual

Awareness

Between Groups .002 1 .002 .002 .963

Within Groups 369.415 461 .801

Total 369.417 462

Technical

Awareness

Between Groups .128 1 .128 .141 .708


Total 420.117 462




Table – 6.75 - Robust Tests of Equality of Means for Awareness and Gender of

Respondents


Operational Awareness – Welch 1.010 1 215.258 .316

Table 6.73 represents the significant value of homogeneity of variances. The

significant values of homogeneity of variances for conceptual awareness and technical

awareness are 0.943 and 0.894 respectively, which is more than cut off value 0.05 at

95 percent confidence level. Therefore, null hypothesis is accepted and hence it can

be said that the homogeneity of variances does exists so, researcher can proceed

further to conduct ANOVA analysis. While in case of operational awareness

significant value is found to be 0.048, which is less than cut of value 0.05 at 95

percent confidence level. Therefore, null hypothesis is rejected and hence it can be

said that the homogeneity of variances does not exists so, further analysis has been

conducted based on Welch ANOVA; not assuming equal variance.

For ANOVA analysis (Table 6.74), the significant value for conceptual awareness,

operational awareness (Table 6.75, Welch ANOVA) and technical awareness is found

to be 0.963, 0.316 and 0.708 which is more than cut off value 0.05 at 95 percent


there is no significant difference in the level of conceptual, technical and operational

awareness across male and female.

6.10.2 ANOVA Analysis for Awareness * Age of Respondents

Hypothesis

H0 Awareness is not significantly different across age of the respondents.

H1 Awareness is significantly different across age of the respondents.

Table - 6.76 - Test of Homogeneity of Variances for Awareness and Age of

Respondents

Levene Statistic df1 df2 Sig.

Operational Awareness .204 4 458 .936

Conceptual Awareness 3.798 4 458 .005

Technical Awareness 3.005 4 458 .018




Table - 6.77 - ANOVA Analysis for Awareness * Age of Respondents

Sum of

Squares Df

Mean

Square F Sig.

Operational

Awareness

Between Groups 3.266 4 .817 .999 .408


Total 377.762 462

Table - 6.78 - Robust Tests of Equality of Means for Awareness and Age of

Respondents


Conceptual Awareness Welch 2.215 4 42.654 .083

Technical Awareness Welch 1.281 4 42.552 .292


significant value of homogeneity of variances for operational awareness is 0.936,

which is more than cut off value 0.05 at 95 percent confidence level. Therefore, null

hypothesis is accepted and hence it can be said that the homogeneity of variances does

exists so, researcher can proceed further to conduct ANOVA analysis. In case of

conceptual awareness and technical awareness significant values are found to be

0.005 and 0.018, which is less than cut of value 0.05 at 95 percent confidence level.

Therefore, null hypothesis is rejected and hence it can be said that the homogeneity of

variances does not exists so, further analysis has been conducted based on Welch

ANOVA; not assuming equal variance.

For ANOVA analysis (Table 6.77), the significant values for operational awareness,

conceptual awareness (Table 6.78, Welch ANOVA) and technical awareness (Table

6.78, Welch ANOVA) are found to be 0.408, 0.083 and 0.292 which is more than cut

off value 0.05 at 95 percent confidence level. Therefore, null hypothesis accepted and

hence it can be said that there is no significant difference in the level of conceptual,

technical and operational awareness across different age category of respondents.




6.10.3 ANOVA Analysis for Awareness * Education Level of Respondents

Hypothesis

H0 Awareness is not significantly different across education level of the

respondents.

H1 Awareness is significantly different across education level of the respondents.

Table – 6.79 - Test of Homogeneity of Variances for Awareness and Education

Level of Respondents


Conceptual Awareness 1.125 5 457 .346



Table – 6.80 - ANOVA Analysis for Awareness * Education Level of

Respondents

Sum of

Squares df

Mean

Square F Sig.

Conceptual

Awareness

Between Groups 10.116 5 2.023 2.573 .026


Total 369.417 462

Technical

Awareness

Between Groups 4.748 5 .950 1.045 .391


Total 420.117 462

Table – 6.81 - Robust Tests of Equality of Means for Awareness and Education



Operational Awareness – Welch .582 5 17.353 .714


significant value of homogeneity of variances for operational awareness and technical




awareness is 0.346 and 0.711 respectively, which is more than cut off value 0.05 at 95

percent confidence level. Therefore, null hypothesis is accepted and hence it can be

said that the homogeneity of variances does exists so, researcher can proceed further

to conduct ANOVA analysis. In case of operational awareness significant value is

found to be 0.003, which is less than cut of value 0.05 at 95 percent confidence level.

Therefore, null hypothesis is rejected and hence it can be said that the homogeneity of

variances does not exists so, further analysis has been conducted based on Welch

ANOVA; not assuming equal variance.

For ANOVA analysis (Table 6.80), the significant values for technical awareness and

conceptual awareness are found to be 0.391 and 0.714 respectively which is more

than cut off value 0.05 at 95 percent confidence level. Therefore, null hypothesis

accepted and hence it can be said that there is no significant difference in the level of

technical and operational awareness across different education level of the

respondents.

In case of conceptual awareness, the value of ANOVA (F test) is found to be

significant (0.026), which is less than cut of value 0.05 at 95 percent confidence level.

Therefore, null hypothesis rejected and hence it can be said that there is significant

difference in the level of conceptual awareness across different education level of the

respondents.

As the value of ANOVA (F test) turnout to be significant for conceptual awareness,

one additional test is required to check pair wise differences in the means

(Nargundkar R. 2010). It is known as Post-hoc analysis or Pair Wise Multiple

Comparison Test or Range Tests. As in case of education level, differences in level of

conceptual awareness exists; ANOVA test does not tell whether any one of the pairs

(school or post graduate) are significantly different from each other or if the

remaining pairs are also significantly different. To find out; tests such as Tukey’s test,

Duncan’s test or Scheffe’s test are available. For present study researcher has used

Tukey’s test to find out significant difference between pairs.




Table – 6.82 - Post Hoc Test for Conceptual Awareness and Education Level of

Respondents

Multiple Comparisons

Conceptual Awareness - Tukey

(I) Education (J) Education

Mean

Difference

(I-J)

Sig.

School

Graduate -.43333 .066

Post-Graduate -.54872 .005

Professional -.45246 .124

Doctorate -.47500 .737

Others -.60000 .869

Graduate

Post-Graduate -.11538 .831

Professional -.01913 1.000

Doctorate -.04167 1.000

Others -.16667 1.000

Post-Graduate

Professional .09626 .977

Doctorate .07372 1.000

Others -.05128 1.000

Professional Doctorate -.02254 1.000

Others -.14754 1.000

Doctorate Others -.12500 1.000

Post hoc analysis gives pair wise test result of significant difference among means of

different categories of respondents. Based on above table it is found that, at 95 percent

confidence level the cut off value in case of “school and post graduate education” is

found to be significant (0.005). Therefore, it can be said that there is a significant

difference in level of awareness between this pair.




Further from mean difference it is indicative that, for pair wise comparison of school

and post graduate respondents, the value is found to be -0.548. So it can be said that

the respondents having post graduate level education are more conceptually aware

about mutual funds than respondents having school level education.

6.10.4 ANOVA Analysis for Awareness * Employment Status of Respondents

Hypothesis

H0 Awareness is not significantly different across employment status of the

respondents.

H1 Awareness is significantly different across employment status of the

respondents.

Table – 6.83 - Test of Homogeneity of Variances for Awareness and Employment

Status of Respondents

Levene


Conceptual Awareness .542 5 457 .745



Table – 6.84 - ANOVA Analysis for Awareness * Employment status of

Respondents

Sum of

Squares df

Mean

Square F Sig.

Conceptual

Awareness

Between Groups 20.632 5 4.126 5.407 .000


Total 369.417 462

Technical

Awareness

Between Groups 8.783 5 1.757 1.952 .085


Total 420.117 462

Operational

Awareness

Between Groups 6.346 5 1.269 1.562 .170


Total 377.762 462





significant value of homogeneity of variances for conceptual awareness, technical

awareness and operational awareness is 0.745, 0.550 and 0.120 respectively, which is

more than cut off value 0.05 at 95 percent confidence level. Therefore, null hypothesis

is accepted and hence it can be said that the homogeneity of variances does exists so,

researcher can proceed further to conduct ANOVA analysis.

For ANOVA analysis (Table 6.84), the significant values for technical awareness and

operational awareness are found to be .085 and 0.170 respectively which is more than

cut off value 0.05 at 95 percent confidence level. Therefore, null hypothesis is

accepted and hence it can be said that there is no significant difference in the level of

technical and operational awareness across different employment status of the

respondents.

In case of conceptual awareness, the value of ANOVA (F test) is found to be

significant (0.000), which is less than cut of value 0.05 at 95 percent confidence level.

Therefore, null hypothesis rejected and hence it can be said that there is significant

difference in the level of conceptual awareness across different employment status of

the respondents.

To check pair wise differences (salaried or housewife) in the mean value of the

respondents, Tukey’s test was performed.




Table – 6.85 – Post Hoc Test for Conceptual Awareness and Employment Status

of Respondents


Conceptual Awareness – Tukey

(I) Employment

Status

(J) Employment

Status

Mean

Difference

(I-J) Sig.

Salaried Businessman

/Self-employed .02936 1.000

Housewife .69275 .080

Students .66497 .000

Retired .41334 .409

Unemployed .05386 1.000

Businessman/

Self-employed


Students .63561 .002

Retired .38397 .531

Unemployed .02450 1.000

Housewife Students -.02778 1.000

Retired -.27941 .958

Unemployed -.63889 .560

Students Retired -.25163 .925


Retired Unemployed -.35948 .918

Post-hoc analysis gives pair wise test result of significant differences among means of

different category of respondents. Based on above table it is found that, at 95 percent

confidence level the cut off value in case of “salaried employee & students (0.000)”,

and “businessman/self-employed & students (0.002)” is found to be significant (less

than 0.05). Therefore, it can be said that there is a significant difference in level of

conceptual awareness between these pairs.




Further from mean differences it is indicative that, for pair wise comparison of

“salaried employee & students”; the mean difference value is found to be +0.664. So,

it can be said that salaried employee possess more conceptual knowledge about

mutual funds than students.

In case of mean differences between “businessman/self-employed & students”; the

mean difference value is +0.635. So, it can be said that businessman/self-employed

respondents possess more conceptual knowledge about mutual funds than students.

Based on ANOVA analysis it was found that among different demographic variables

only conceptual awareness was found to be significant across education level and

employment status of the respondents. The difference was not found significant in

case of age and gender. It was also found that the respondents having post graduate

level education are conceptually more aware than school level educated respondents.

It was also found that salaried employee and businessman/self-employed were more

conceptually aware than students. However, no significant difference was found

between different demographic variables and operational awareness and technical

awareness.

6.11 Factor Analysis for Opportunities to Invest in Mutual Funds.

Table – 6.86 - KMO and Bartlett’s Test (MSA) for Opportunity Variable



Df 190

Sig. .000

Tables 6.86 reveal values of Bartlett’s Test of Sphericity and KMO of sampling

adequacy 2.637 and 0.921 respectively with significant value of 0.000, which satisfies

necessary conditions for factor analysis. Hence, researcher can proceed for further

analysis.




Table – 6.87 - Anti Image Correlation Matrix for Opportunity Variable

O1 O2 O3 O4 O5 O6 O7 O8 O9 O10 O11 O12 O13 O14 O15 O16 O17 O18 O19 O20

Anti-image

Correlation

O1 .871 -.350 -.176 .066 -.038 -.061 -.042 .048 -.015 -.047 -.027 .067 .028 .060 -.094 -.061 -.019 -.023 -.064 -.003

O2 -.350 .908 -.022 -.166 -.055 -.064 -.014 -.030 -.039 .073 .025 -.084 -.045 -.075 .012 -.044 -.094 -.010 -.075 -.024

O3 -.176 -.022 .933 -.101 -.071 -.019 .073 -.073 -.099 .058 -.045 -.054 -.048 -.088 -.080 -.021 -.010 .014 -.053 .033

O4 .066 -.166 -.101 .917 -.157 -.016 -.156 .037 -.088 -.068 -.018 -.012 .042 -.024 -.074 -.024 -.038 .071 -.019 .030

O5 -.038 -.055 -.071 -.157 .932 -.172 -.093 .046 .008 -.087 -.070 .023 -.143 .011 .032 .017 -.068 .003 -.010 -.103

O6 -.061 -.064 -.019 -.016 -.172 .940 -.158 -.010 .020 -.083 .020 -.040 .013 .002 .068 -.117 -.045 -.085 -.039 -.061

O7 -.042 -.014 .073 -.156 -.093 -.158 .935 -.095 -.099 -.050 -.026 -.040 -.190 -.007 -.065 .011 .010 .028 -.008 -.106

O8 .048 -.030 -.073 .037 .046 -.010 -.095 .935 -.171 .041 .007 -.140 -.047 -.146 -.026 -.044 -.101 -.059 -.052 -.003

O9 -.015 -.039 -.099 -.088 .008 .020 -.099 -.171 .895 -.229 -.076 -.127 .040 .020 .160 -.068 -.136 -.128 .083 .014

O10 -.047 .073 .058 -.068 -.087 -.083 -.050 .041 -.229 .906 -.165 -.101 .058 -.083 -.152 -.005 .082 -.083 -.028 -.002

O11 -.027 .025 -.045 -.018 -.070 .020 -.026 .007 -.076 -.165 .926 -.133 -.148 .018 -.011 -.051 -.192 .064 .053 -.063

O12 .067 -.084 -.054 -.012 .023 -.040 -.040 -.140 -.127 -.101 -.133 .947 -.083 -.049 -.079 -.090 .023 .045 -.051 -.063

O13 .028 -.045 -.048 .042 -.143 .013 -.190 -.047 .040 .058 -.148 -.083 .916 -.131 -.150 -.054 .096 -.131 -.015 .014

O14 .060 -.075 -.088 -.024 .011 .002 -.007 -.146 .020 -.083 .018 -.049 -.131 .941 -.130 -.106 -.088 -.009 .033 -.122

O15 -.094 .012 -.080 -.074 .032 .068 -.065 -.026 .160 -.152 -.011 -.079 -.150 -.130 .914 -.144 -.131 .000 .018 -.122

O16 -.061 -.044 -.021 -.024 .017 -.117 .011 -.044 -.068 -.005 -.051 -.090 -.054 -.106 -.144 .943 -.108 -.199 .022 .063

O17 -.019 -.094 -.010 -.038 -.068 -.045 .010 -.101 -.136 .082 -.192 .023 .096 -.088 -.131 -.108 .929 -.026 -.044 -.039

O18 -.023 -.010 .014 .071 .003 -.085 .028 -.059 -.128 -.083 .064 .045 -.131 -.009 .000 -.199 -.026 .895 -.273 -.137

O19 -.064 -.075 -.053 -.019 -.010 -.039 -.008 -.052 .083 -.028 .053 -.051 -.015 .033 .018 .022 -.044 -.273 .891 -.171

O20 -.003 -.024 .033 .030 -.103 -.061 -.106 -.003 .014 -.002 -.063 -.063 .014 -.122 -.122 .063 -.039 -.137 -.171 .932

Table 6.87 reveals that all the variables have MSA value more than 0.5. Hence,

researcher can proceed further for factor analysis.

Table – 6.88 – Communality Statistics for Opportunity Variable


O1 1.000 .641

O2 1.000 .612

O3 1.000 .480

O4 1.000 .500

O5 1.000 .580

O6 1.000 .469

O7 1.000 .529

O8 1.000 .499

O9 1.000 .434

O10 1.000 .452

O11 1.000 .461

O12 1.000 .486

O13 1.000 .399

O14 1.000 .462

O15 1.000 .398




O16 1.000 .475

O17 1.000 .421

O18 1.000 .594

O19 1.000 .597

O20 1.000 .499

The communality value for variables having value less than 0.5 have been excluded

one by one and revised communalities have been developed till it reaches the value of

0.5 for all remaining variables.

6.11.1 Revised Factor Extraction for Opportunity Variable

Table – 6.89 - Revised KMO and Bartlett’s Test for Opportunity Variable



Df 15

Sig. .000

Table – 6.90 - Revised Anti Image Matrix for Opportunity Variable

O1 O2 O9 O10 O11 O12

Anti-image

Correlation

O1 .678 -.434 -.038 -.090 -.067 .023

O2 -.434 .695 -.121 .008 -.055 -.187

O9 -.038 -.121 .806 -.258 -.136 -.197

O10 -.090 .008 -.258 .789 -.204 -.170

O11 -.067 -.055 -.136 -.204 .818 -.217

O12 .023 -.187 -.197 -.170 -.217 .795

Table – 6.91 - Revised Communality Statistics for Opportunity Variable


O1 1.000 .763

O2 1.000 .732

O9 1.000 .537

O10 1.000 .562

O11 1.000 .512

O12 1.000 .531




Table – 6.92 - Revised Total Variance Explained for Opportunity Variable

Component

Initial Eigenvalues

Extraction Sums of

Squared Loadings

Rotation Sums of

Squared Loadings

Total

percent

of

Variance

Cumulative

percent Total

percent

of

Variance

Cumulative

percent Total

percent

of

Variance

Cumulative

percent

1 2.581 43.023 43.023 2.581 43.023 43.023 2.116 35.274 35.274

2 1.056 17.601 60.624 1.056 17.601 60.624 1.521 25.349 60.624

3 .669 11.149 71.773

4 .654 10.908 82.680

5 .569 9.484 92.165

6 .470 7.835 100.000

Table – 6.93 – Revised Component Matrix for Opportunity Variable

Component

Variables 1 2

O12 .692 -.230

O9 .692 -.243

O10 .667 -.343

O11 .659 -.279

O2 .649 .558

O1 .571 .661

Table – 6.94 - Revised Rotated Component Matrix for Opportunity Variable

Statements Factor I Factor II

O10 .745

O9 .711

O12 .704

O11 .703

O1 .866

O2 .823

As shown in revised tables that values of KMO and Bartlett’s test (0.763, 0.000), Anti



than cut off values. Hence, after detailed analysis two factors have been identified.

First factor consists four variables (09, 010, 011, 012) and second factor consists two

variables (01, 02) based on significant loadings.




6.11.2 Naming of Factors for Opportunity Variable


following sections.

1. First factor refers to four items. It includes 1) Long term investment opportunities

(0.711) 2) Long term tax benefits (0.745) 3) Enable to hold diversified portfolio

with small amount which would otherwise require huge capital (0.703) and 4)

Diversified portfolio reduces risk of high loss (0.704). The groups of variables are

concerned with long term future benefits, so this factor has been given a name of

“Future Perspective.” The extracted factor explains 35.27 percent of variance.

2. While second factor consists of two variables 1) Mutual funds provides better

return (0.866) and 2) Safety of invested amount (0.823). This group of variables

are concerned with yield on investment with security of investment. Hence, this

factor has been given a name “Return & Protection.” The extracted factor explains

25.35 percent of variance.

Based on factor analysis, researcher has extracted important factors, which have been

considered as an opportunities to invest in mutual funds. Out of twenty variables

affecting opportunities, factor analysis extracted two factors namely future

perspective and return & protection consisting six variables which has been given

priority by respondents and which explain highest share among all twenty variables.

Results reveal that retail investors give priority to future benefits and return &

protection on investment. Investors seek for safety of investment and long term

benefits from mutual funds investment.





relative importance given by different categories of respondents to key factors

extracted through factor analysis for perceived opportunities to invest in mutual funds.




6.12 ANOVA Analysis for Opportunity and Demographic Variables

6.12.1 ANOVA Analysis for Opportunity * Gender of Respondents

Hypothesis

H0 Perceived opportunities are not significantly different across gender of the

respondents.

H1 Perceived opportunities are significantly different across gender of the

respondents.

Table – 6.95 - Test of Homogeneity of Variances for Opportunity and Gender of

Respondents

Levene


Future Perspective 3.283 1 461 .071

Return & Protection 6.255 1 461 .013

Table – 6.96 - ANOVA Analysis for Opportunity * Gender of Respondents

Sum of

Squares Df

Mean

Square F Sig.

Future

Perspective

Between Groups .105 1 .105 .163 .687


Total 297.590 462

Table – 6.97 – Robust Tests of Equality of Means for Opportunity and Gender of

Respondents


Return & Protection Welch .018 1 231.676 .893


significant value of homogeneity of variances for future perspective is 0.071, which is



researcher can proceed further to conduct ANOVA analysis. While in case of return

and protection significant value is found to be 0.013, which is less than cut of value

0.05 at 95 percent confidence level. Therefore, null hypothesis is rejected and hence it

can be said that the homogeneity of variances does not exists so, further analysis has

been conducted based on Welch ANOVA; not assuming equal variance.




For ANOVA analysis (Table 6.96), the significant value for future perspective and

return & protection (Table 6.97, Welch ANOVA) is found to be 0.687 and 0.893

respectively which is more than cut off value 0.05 at 95 percent confidence level.

Therefore, null hypothesis accepted and hence it can be said that perceived

opportunities are not significantly different across gender of the respondents.

6.12.2 ANOVA Analysis for Opportunity * Age of Respondents

Hypothesis

H0 Perceived opportunities are not significantly different across age of the

respondents.

H1 Perceived opportunities are significantly different across age of the

respondents.

Table – 6.98 - Test of Homogeneity of Variances for Opportunity and Age of

Respondents

Extracted Factors Levene Statistic df1 df2 Sig.



Table – 6.99 - ANOVA Analysis for Opportunity * Age of Respondents

Extracted

Factor

Sum of

Squares Df Mean Square F Sig.

Future

Perspective

Between Groups 2.217 4 .554 .859 .488


Total 297.590 462

Table - 6.100 - Robust Tests of Equality of Means for Opportunity and Age of

Respondents

Extracted Factor Statistic df1 df2 Sig.

Return & Protection Welch .248 4 40.113 .909





significant value of homogeneity of variances for future perspective is 0.377, which is



researcher can proceed further to conduct ANOVA analysis. While in case of return

and protection significant value is found to be 0.020, which is less than cut of value

0.05 at 95 percent confidence level. Therefore, null hypothesis is rejected and hence it

can be said that the homogeneity of variances does not exists so, further analysis has

been conducted based on Welch ANOVA; not assuming equal variance.


return & protection (Table 6.100, Welch ANOVA) is found to be 0.488 and 0.909

respectively which is more than cut off value 0.05 at 95 percent confidence level.

Therefore, null hypothesis accepted and hence it can be said that perceived

opportunities are not significantly different across age of the respondents.

6.12.3 ANOVA Analysis for Opportunity * Education Level of Respondents

Hypothesis

H0 Perceived opportunities are not significantly different across education level of

the respondents.

H1 Perceived opportunities are significantly different across education level of the

respondents.

Table – 6.101 – Test of Homogeneity of Variances for Opportunity and

Education Level of Respondents

Extracted Factors Levene







Table – 6.102 – ANOVA Analysis for Opportunity * Education Level of

Respondents

Extracted Factors Sum of

Squares df

Mean

Square F Sig.

Future Perspective Between Groups 5.983 5 1.197 1.875 .097


Total 297.590 462

Return & Protection Between Groups 7.624 5 1.525 1.669 .141


Total 425.011 462


significant value of homogeneity of variances for future perspective and return &

protection is 0.275 and 0.122 respectively, which is more than cut off value 0.05 at 95



to conduct ANOVA analysis.


return & protection is found to be 0.097 and 0.141 respectively which is more than cut

off value 0.05 at 95 percent confidence level. Therefore, null hypothesis accepted and

hence it can be said that perceived opportunities are not significantly different across

education level of the respondents.

6.12.4 ANOVA Analysis for Opportunity * Employment Status of Respondents

Hypothesis

H0 Perceived opportunities are not significantly different across employment

status of the respondents.

H1 Perceived opportunities are significantly different across employment status of

the respondents.




Table – 6.103 - Test of Homogeneity of Variances for Opportunity and

Employment Status of Respondents





Table – 6.104 - ANOVA Analysis for Opportunity * Employment Status of

Respondents

Extracted Factors Sum of

Squares Df

Mean

Square F Sig.

Future Perspective Between Groups 7.679 5 1.536 2.421 .035


Total 297.590 462

Return & Protection Between Groups 12.387 5 2.477 2.744 .019


Total 425.011 462


significant value of homogeneity of variances for future perspective and return &

protection is 0.368 and 0.393 respectively, which is more than cut off value 0.05 at 95



to conduct ANOVA analysis.


return & protection is found to be 0.035 and 0.019 respectively which is less than cut

off value 0.05 at 95 percent confidence level. Therefore, null hypothesis rejected and

hence it can be said that perceived opportunities are significantly different across

employment status of the respondents. As the value of ANOVA (F test) turnout to be

significant for future perspective and return & protection factor, Tukey’s test was

performed to check pair wise differences in mean value.




Table – 6.105 - Post Hoc Test for Future Perspective and Employment Status of

Respondents


Tukey – Future Perspective

(I) Employment

Status

(J) Employment

Status

Mean Difference

(I-J) Sig.

Salaried Businessman/

Self-employed .05875 .984


Student .39843 .057

Retired .23013 .858

Unemployed .31510 .852

Businessman/

Self-employed


Student .33968 .213

Retired .17138 .961


Housewife Student -.02778 1.000

Retired -.19608 .987

Unemployed -.11111 1.000

Student Retired -.16830 .980

Unemployed -.08333 1.000

Retired Unemployed .08497 1.000


different categories of respondents. Based on above table it is found that, at 95 percent

confidence level the cut off value in case of “salaried employee & students (0.057)” is

found to be significant for future perspective factor. Therefore, it can be said that

there is a significant difference for future perspective factor as an opportunity to

invest in mutual funds among salaried employee and students.





“salaried employee & students”; the mean difference value is found to be +0.398. So,

it can be said that salaried employee put more emphasis on future perspective factor

as an opportunity while investing in mutual funds compare to students.

Table – 6.106 - Post Hoc Test for Return and Protection and Employment Status

of Respondents


Tukey – Return and Protection

(I) Employment

Status

(J) Employment

Status

Mean

Difference

(I-J) Sig.

Salaried Businessman/

Self-employed -.06528 .988


Student .07252 .998

Retired .51370 .259


Businessman/

Self-employed


Student .13780 .973

Retired .57897 .173


Housewife Student -.66667 .287

Retired -.22549 .989


Student Retired .44118 .614


Retired Unemployed -.16340 .998





different categories of respondent. Based on above table it is found that, at 95 percent

confidence level the cut off value in case of “businessman/self-employed &

housewife (0.059)” is found to be significant. Therefore, it can be said that there is a

significant difference for return and protection factor as an opportunity to invest in

mutual funds among businessman/self-employed & housewife.


“businessman/self-employed & housewife”; the mean difference value is found to be

+0.804. So, it can be said that businessman/self-employed put more emphasis on

return and protection factor as an opportunity while investing in mutual funds

compare to housewife.

Based on ANOVA analysis it was found that among different demographic variables

only employment status was found to be significant in case of both the factors. It

means that the perceived opportunities to invest in mutual funds were significantly

different for both the factors among different employment status of the respondents. It

was also found that the salaried employee perceived more opportunity to invest in

mutual funds compare to students. It was also found that businessman/self-employed

also perceived more opportunity to invest in mutual funds compare to house wife.

However, no significant difference was found for gender, age and education level and

both the factors for opportunities.




6.13 Factor Analysis for Problems of Investment in Mutual

Funds

Table – 6.107 - KMO and Bartlett’s Test (MSA) for Problem Variable



Df 210

Sig. .000

Table 6.107 shows the values of Bartlett’s Test of Sphericity and KMO of sampling

adequacy 3.230 and 0.913 respectively at significant value of 0.000, which satisfies

necessary conditions for factor analysis. Hence, researcher can proceed for further

analysis.

Table – 6.108 - Anti Image Correlation Matrix for Problem Variable

P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13 P14 P15 P16 P17 P18 P19 P20 P21

Anti-image

Correlation

P1 .861 -.416 -.213 -.089 .001 -.120 -.009 .021 .002 -.101 .017 -.088 .061 .057 -.133 .069 -.019 .028 .039 -.025 -.011

P2 -.416 .863 -.062 -.130 -.068 .015 -.015 -.035 -.019 .087 .056 -.045 -.027 -.114 .148 -.099 -.053 .060 -.055 -.052 -.045

P3 -.213 -.062 .916 -.049 -.118 -.067 -.108 -.022 -.055 .053 -.096 .012 -.056 -.021 -.128 .061 -.054 -.066 .005 .164 -.072

P4 -.089 -.130 -.049 .913 -.163 -.008 -.113 -.042 .078 .013 -.083 .084 .041 -.047 -.021 .092 -.084 -.153 -.009 -.112 .015

P5 .001 -.068 -.118 -.163 .898 -.206 -.005 .076 .008 -.218 -.023 .012 -.057 .033 -.149 -.067 .005 -.125 .120 -.063 .080

P6 -.120 .015 -.067 -.008 -.206 .922 -.202 .028 -.027 .038 .042 -.117 -.062 -.013 .088 -.070 .085 -.099 -.058 -.052 -.112

P7 -.009 -.015 -.108 -.113 -.005 -.202 .892 -.316 -.095 .067 -.123 .067 .080 -.035 .005 -.042 -.145 .104 -.093 .001 .014

P8 .021 -.035 -.022 -.042 .076 .028 -.316 .916 -.108 -.168 .002 -.049 -.061 -.143 -.011 -.008 .030 .017 -.013 -.092 .014

P9 .002 -.019 -.055 .078 .008 -.027 -.095 -.108 .943 -.161 -.117 -.098 -.013 -.002 -.085 .024 -.071 -.080 .099 -.089 -.025

P10 -.101 .087 .053 .013 -.218 .038 .067 -.168 -.161 .910 -.169 -.021 -.044 .019 -.036 -.081 .046 -.003 -.141 .022 -.099

P11 .017 .056 -.096 -.083 -.023 .042 -.123 .002 -.117 -.169 .923 -.265 -.130 -.041 .055 -.032 -.049 -.038 -.025 .084 -.074

P12 -.088 -.045 .012 .084 .012 -.117 .067 -.049 -.098 -.021 -.265 .924 -.165 -.157 -.093 .015 .014 -.048 -.143 .012 .028

P13 .061 -.027 -.056 .041 -.057 -.062 .080 -.061 -.013 -.044 -.130 -.165 .937 -.051 -.143 -.054 -.070 .117 .011 -.112 -.058

P14 .057 -.114 -.021 -.047 .033 -.013 -.035 -.143 -.002 .019 -.041 -.157 -.051 .938 -.189 -.199 .008 -.055 -.024 .003 .017

P15 -.133 .148 -.128 -.021 -.149 .088 .005 -.011 -.085 -.036 .055 -.093 -.143 -.189 .918 -.138 -.038 .018 -.109 .020 -.030

P16 .069 -.099 .061 .092 -.067 -.070 -.042 -.008 .024 -.081 -.032 .015 -.054 -.199 -.138 .933 -.155 -.122 -.067 -.096 .010

P17 -.019 -.053 -.054 -.084 .005 .085 -.145 .030 -.071 .046 -.049 .014 -.070 .008 -.038 -.155 .944 -.114 -.085 -.049 -.055

P18 .028 .060 -.066 -.153 -.125 -.099 .104 .017 -.080 -.003 -.038 -.048 .117 -.055 .018 -.122 -.114 .908 -.068 -.106 .035

P19 .039 -.055 .005 -.009 .120 -.058 -.093 -.013 .099 -.141 -.025 -.143 .011 -.024 -.109 -.067 -.085 -.068 .931 -.205 -.035

P20 -.025 -.052 .164 -.112 -.063 -.052 .001 -.092 -.089 .022 .084 .012 -.112 .003 .020 -.096 -.049 -.106 -.205 .878 -.430

P21 -.011 -.045 -.072 .015 .080 -.112 .014 .014 -.025 -.099 -.074 .028 -.058 .017 -.030 .010 -.055 .035 -.035 -.430 .891

Table 6.108 reveals that all the variables have MSA value more than 0.5. Hence,

researcher can proceed further.




Table – 6.109 – Communality Statistics for Problem Variable


P1 1.000 .619

P2 1.000 .588

P3 1.000 .567

P4 1.000 .493

P5 1.000 .614

P6 1.000 .402

P7 1.000 .512

P8 1.000 .543

P9 1.000 .419

P10 1.000 .441

P11 1.000 .517

P12 1.000 .539

P13 1.000 .447

P14 1.000 .432

P15 1.000 .485

P16 1.000 .483

P17 1.000 .361

P18 1.000 .586

P19 1.000 .493

P20 1.000 .726

P21 1.000 .561

The communality value for variables having less than 0.5 have been excluded one by

one and revised communalities have been developed till it reaches the value of >0.5

for all remaining variables.




6.13.1 Revised Factor Extraction for Problem Variable

Table – 6.110 - Revised KMO and Bartlett’s Test for Problem Variable


Bartlett's Test of Sphericity Approx. Chi-Square 678.391

Df 15

Sig. .000

Table – 6.111 - Revised Anti Image Correlation Matrix for Problem Variable

P1 P2 P3 P19 P20 P21

Anti-image

Correlation

P1 .704 -.438 -.306 -.019 -.054 -.040

P2 -.438 .750 -.101 -.078 -.121 -.035

P3 -.306 -.101 .762 -.097 .081 -.120

P19 -.019 -.078 -.097 .814 -.309 -.094

P20 -.054 -.121 .081 -.309 .682 -.490

P21 -.040 -.035 -.120 -.094 -.490 .722

Table – 6.112 - Revised Communality Statistics for Problem Variable


P1 1.000 .725

P2 1.000 .618

P3 1.000 .558

P19 1.000 .530

P20 1.000 .761

P21 1.000 .663




Table – 6.113 - Revised Total Variance Explained for Problem Variable

Component

Initial Eigenvalues Extraction Sums of

Squared Loadings


Loadings

Total

percent

of

Variance

Cumulative

percent Total

percent

of

Variance

Cumulative

percent Total

percent

of

Variance

Cumulative

percent

1 2.677 44.613 44.613 2.677 44.613 44.613 1.976 32.941 32.941

2 1.178 19.637 64.251 1.178 19.637 64.251 1.879 31.310 64.251

3 .690 11.503 75.754

4 .650 10.830 86.584

5 .428 7.133 93.717

6 .377 6.283 100.000

Table – 6.114- Revised Component Matrix for Problem Variable

Variables Component

1 2

P20 .719 -.494

P21 .697 -.421

P2 .695 .368

P1 .688 .502

P19 .632 -.362

P3 .565 .489

Table – 6.115 - Revised Rotated Component Matrix for Problem Variable

Statements Factor I Factor II

P20 .862

P21 .796

P19 .708

P1 .836

P2 .744

P3 .743

As shown in revised tables values of KMO and Bartlett’s test (0.730, 0.000), Anti



than cut off values. Hence, after detailed analysis two factors have been identified.

First factor consists three variables (P19, P20, P21) and second factor consists three

variables (P1, P2, P3) based on significant loadings.




6.13.2 Naming of Factors for Problem Variable


following sections.

1. First factor refers to 03 items. It includes 1) Difficulty for getting clarity about

fund investment portfolio (0.708) 2) Difficulty in getting expert advisor for

selecting ideal mutual funds (0.862) and 3) Difficulty for getting accurate advice

to invest in mutual funds (0.796). The groups of variables are concerned with

seeking clarity and advice for investment in mutual funds, so this factor has been

given a name of “Expert Recommendations.” The extracted factor explains 32.94

percent of variance.

2. While second factor consists of three variables 1) Mutual funds selection is

complex process (0.836) 2) Mutual funds selection is time consuming (0.744) and

3) Difficulty in identifying mutual funds that provide significant return (0.743).

The groups of variables are concerned with facing difficulty in selecting ideal

mutual funds. Hence, this factor has been given a name “Selection Complexity.

The extracted factor explains 31.31 percent of variance.

Based on factor analysis, researcher has extracted important factors, which are

perceived as problems for investing in mutual funds. Out of twenty one variables

posing problems, factor analysis extracted two factors namely expert

recommendations and selection complexity consisting six variables which are

perceived as problems while investing in mutual funds. Result reveals that investors

face difficulties for getting expert advice and selecting right kind of mutual funds for

investment purpose.





relative importance given by different categories of respondents to key factors

extracted through factor analysis for perceived problems to invest in mutual funds.




6.14 ANOVA Analysis for Problems and Demographic Variables

6.14.1 ANOVA Analysis for Problems * Gender of Respondents

Hypothesis

H0 Perceived problems are not significantly different across gender of the

respondents.

H1 Perceived problems are significantly different across gender of the

respondents.

Table – 6.116 - Test of Homogeneity of Variances for Problems and Gender of

Respondents

Extracted Factors Levene Statistic df1 df2 Sig.

Expert Recommendation .402 1 461 .526

Selection Complexity .417 1 461 .519

Table – 6.117 - ANOVA Analysis for Problems * Gender of Respondents

Sum of

Squares Df

Mean

Square F Sig.

Expert Recommendation Between Groups .002 1 .002 .003 .959


Total 406.324 462

Selection Complexity Between Groups .431 1 .431 .535 .465


Total 371.296 462


significant value of homogeneity of variances for expert recommendation and

selection complexity is 0.526 and 0.519 respectively, which is more than cut off value

0.05 at 95 percent confidence level. Therefore, null hypothesis is accepted and hence

it can be said that the homogeneity of variances does exists so, researcher can proceed

further to conduct ANOVA analysis.




For ANOVA analysis (Table 6.117), the significant value for expert recommendation

and selection complexity is found to be 0.959 and 0.465 respectively which is more


accepted and hence it can be said that perceived problems are not significantly

different across gender of the respondents.

6.14.2 ANOVA Analysis for Problems * Age of Respondents

Hypothesis

H0 Perceived problems are not significantly different across age of the

respondents.

H1 Perceived problems are significantly different across age of the respondents.

Table – 6.118 - Test of Homogeneity of Variances for Problems and Age of

Respondents




Selection Complexity 1.273 4 458 .280

Table – 6.119 - ANOVA Analysis for Problems * Age of Respondents

Sum of

Squares df

Mean

Square F Sig.

Expert

Recommendation

Between Groups 2.029 4 .507 .575 .681


Total 406.324 462

Selection

Complexity

Between Groups 2.016 4 .504 .625 .645


Total 371.296 462


significant value of homogeneity of variances for expert recommendation and

selection complexity is 0.837 and 0.280 respectively, which is more than cut off value

0.05 at 95 percent confidence level. Therefore, null hypothesis is accepted and hence

it can be said that the homogeneity of variances does exists so, researcher can proceed

further to conduct ANOVA analysis.





and selection complexity is found to be 0.681 and 0.645 respectively which is more


accepted and hence it can be said that perceived problems are not significantly

different across age of the respondents.

6.14.3 ANOVA Analysis for Problems * Education Level of Respondents

Hypothesis

H0 Perceived problems are not significantly different across education level of the

respondents.

H1 Perceived problems are significantly different across education level of the

respondents.

Table – 6.120 – Test of Homogeneity of Variances for Problems and Education


Levene




Table – 6.121 - ANOVA Analysis for Problems * Education Level of

Respondents

Sum of

Squares df

Mean

Square F Sig.

Expert

Recommendation

Between Groups 3.321 5 .664 .753 .584


Total 406.324 462

Table 6.122 - Robust Tests of Equality of Means for Problems and Education



Selection Complexity Welch .938 5 17.339 .481





significant value of homogeneity of variances for expert recommendation is 0.846,



exists so, researcher can proceed further to conduct ANOVA analysis. While in case

of selection complexity significant value is found to be 0.006, which is less than cut

of value 0.05 at 95 percent confidence level. Therefore, null hypothesis is rejected and

hence it can be said that the homogeneity of variances does not exists so, further

analysis has been conducted based on Welch ANOVA; not assuming equal variance.


and selection complexity (Table 6.122, Welch ANOVA) is found to be 0.584 and

0.481 respectively which is more than cut off value 0.05 at 95 percent confidence

level. Therefore, null hypothesis accepted and hence it can be said that perceived

problems are not significantly different across education level of the respondents.

6.14.4 ANOVA Analysis for Problems * Employment Status of Respondents

Hypothesis

H0 Perceived problems are not significantly different across employment status of

the respondents.

H1 Perceived problems are significantly different across employment status of the

respondents.

Table – 6.123 - Test of Homogeneity of Variances for Problems and Employment

Status of Respondents


Expert Recommendation 2.660 5 457 .022





Table – 6.124 - ANOVA Analysis for Problems * Employment Status of

Respondents

Sum of

Squares df

Mean

Square F Sig.

Selection Complexity Between Groups 3.057 5 .611 .759 .580


Total 371.296 462

Table – 6.125 - Robust Tests of Equality of Means for Problems and

Employment Status of Respondents


Expert Recommendation Welch 1.185 5 39.085 .334


significant value of homogeneity of variances for selection complexity is 0.250,



exists so, researcher can proceed further to conduct ANOVA analysis. While in case

of selection expert recommendation value is found to be 0.022, which is less than cut

of value 0.05 at 95 percent confidence level. Therefore, null hypothesis is rejected and

hence it can be said that the homogeneity of variances does not exists so, further

analysis has been conducted based on Welch ANOVA; not assuming equal variance.


(Table 6.125, Welch ANOVA) and selection complexity is found to be 0.334 and

0.580 respectively which is more than cut off value 0.05 at 95 percent confidence

level. Therefore, null hypothesis accepted and hence it can be said that perceived

problems are not significantly different across employment status of the respondents.

Based on ANOVA analysis, no significant different was found between different

demographic variables of the respondent and factors extracted through factor analysis.

It means that the perceived problems to invest in mutual funds are not significantly

different across the different category of the respondents.




6.15 Summary

Collected data were analyzed using SPSS software package. Factor analysis was first

applied to awareness construct followed by opportunities and problems to assess

unidimensionality (Conway and Huffcutt, 2003). Post that ANOVA analysis was

applied to study the significant differences in mean score in demographic variables

and three factors extracted through factor analysis. Factor analysis result shows that

22 items were grouped in to three factors: Conceptual, Technical and Operational

Awareness. ANOVA analysis reveals that conceptual awareness was significantly

different across education level and employment status of respondents. Further it was

found that graduate level respondents were more conceptually aware about mutual

funds. It was also found that salaried and businessman/self-employed were more

conceptually aware about mutual funds.

After thorough analysis and interpretation of gathered data next step is to draw the

findings and conclusion of the research study. Next section of the thesis discusses on

major findings, conclusion and implications of the research.

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