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AGE RANGE GENDE R QUALI FICAT ION POSIT ION M. STATU S EXPER IENCE Job Clari ty and Job Secur ity Q17 Q9 Q13 Q16 Q3 Total JC and JS N Valid 54 54 54 54 54 54 54 54 54 54 54 54 Missi ng 0 0 0 0 0 0 0 0 0 0 0 0 Correlations Total JC and JS Total T&D Total H&S Total R&R Final Total Total H&C Team Tot P& Total JC and JS Pearson Correlation 1 .188 -.080 .349(**) .444(**) .149 Sig. (2-tailed) . .173 .564 .010 .001 .282 N 54 54 54 54 54 54 Total T&D Pearson Correlation .188 1 .269(*) .524(**) .809(**) .653(**) .441 Sig. (2-tailed) .173 . .049 .000 .000 .000 N 54 54 54 54 54 54 Total H&S Pearson Correlation -.080 .269(*) 1 .217 .363(**) .161 - Sig. (2-tailed) .564 .049 . .115 .007 .245 N 54 54 54 54 54 54 Total R&R Pearson Correlation .349(**) .524(**) .217 1 .786(**) .555(**) .29 Sig. (2-tailed) .010 .000 .115 . .000 .000 N 54 54 54 54 54 54 Final Total Pearson Correlation .444(**) .809(**) .363(**) .786(**) 1 .812(**) .573 Sig. (2-tailed) .001 .000 .007 .000 . .000 N 54 54 54 54 54 54 Total H&C Team Pearson Correlation .149 .653(**) .161 .555(**) .812(**) 1 .411 Sig. (2-tailed) .282 .000 .245 .000 .000 . N 54 54 54 54 54 54 Total P&P Pearson Correlation .197 .441(**) -.108 .298(*) .573(**) .411(**) Sig. (2-tailed) .153 .001 .436 .029 .000 .002 N 54 54 54 54 54 54 ** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed). Regression

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Page 1: Correlations Ee

AGE RANG

EGEND

ER

QUALIFICATI

ONPOSIT

ION

M. STAT

US

EXPERIENC

E

Job Clarity

and Job

Security Q17 Q9 Q13 Q16 Q3

Total JC and

JSN Valid 54 54 54 54 54 54 54 54 54 54 54 54

Missing

0 0 0 0 0 0 0 0 0 0 0 0

Correlations

Total JC and JS Total T&D Total H&S Total R&R Final Total

Total H&C Team Total P&P

Total JC and JS Pearson Correlation 1 .188 -.080 .349(**) .444(**) .149Sig. (2-tailed) . .173 .564 .010 .001 .282N 54 54 54 54 54 54

Total T&D Pearson Correlation .188 1 .269(*) .524(**) .809(**) .653(**) .441(**)Sig. (2-tailed) .173 . .049 .000 .000 .000N 54 54 54 54 54 54

Total H&S Pearson Correlation -.080 .269(*) 1 .217 .363(**) .161Sig. (2-tailed) .564 .049 . .115 .007 .245N 54 54 54 54 54 54

Total R&R Pearson Correlation .349(**) .524(**) .217 1 .786(**) .555(**)Sig. (2-tailed) .010 .000 .115 . .000 .000N 54 54 54 54 54 54

Final Total Pearson Correlation .444(**) .809(**) .363(**) .786(**) 1 .812(**) .573(**)Sig. (2-tailed) .001 .000 .007 .000 . .000N 54 54 54 54 54 54

Total H&C Team Pearson Correlation .149 .653(**) .161 .555(**) .812(**) 1 .411(**)Sig. (2-tailed) .282 .000 .245 .000 .000 .N 54 54 54 54 54 54

Total P&P Pearson Correlation .197 .441(**) -.108 .298(*) .573(**) .411(**)

Sig. (2-tailed) .153 .001 .436 .029 .000 .002N 54 54 54 54 54 54

** Correlation is significant at the 0.01 level (2-tailed).* Correlation is significant at the 0.05 level (2-tailed).

Regression

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Variables Entered/Removed(b)

ModelVariables Entered

Variables Removed Method

1 Total JC and JS,

Total H&S, Total P&P, Total H&C

Team, Total R&R, Total

T&D(a)

. Enter

a All requested variables entered.b Dependent Variable: Final Total

Model Summary

Model R R SquareAdjusted R

SquareStd. Error of the Estimate

Change Statistics

R Square Change F Change df1 df2 Sig. F Change

1 .999(a) .999 .999 .396 .999 6629.408 6 47

a Predictors: (Constant), Total JC and JS, Total H&S, Total P&P, Total H&C Team, Total R&R, Total T&D

ANOVA(b)

Model Sum of

Squares df Mean Square F Sig.1 Regressio

n6227.974 6 1037.996 6629.408 .000(a)

Residual 7.359 47 .157Total 6235.333 53

a Predictors: (Constant), Total JC and JS, Total H&S, Total P&P, Total H&C Team, Total R&R, Total T&Db Dependent Variable: Final Total

Coefficients(a)

Model

Unstandardized Coefficients

Standardized Coefficients

t Sig.B Std. Error Beta1 (Constant) 1.421 .597 2.381 .021

Total JC and JS

.957 .023 .226 41.292 .000

Total P&P 1.005 .026 .226 38.147 .000Total H&C Team

.987 .021 .338 47.397 .000

Total T&D .984 .030 .240 32.923 .000Total H&S .992 .024 .227 41.262 .000Total R&R .991 .024 .278 41.933 .000

a Dependent Variable: Final Total

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Reliability****** Method 1 (space saver) will be used for this analysis ******_

R E L I A B I L I T Y A N A L Y S I S - S C A L E (A L P H A)

Reliability Coefficients

N of Cases = 54.0 N of Items = 34

Alpha = .8365

Factor AnalysisCommunalities

Initial ExtractionJob Clarity and Job Security 1.000 .746

Q17 1.000 .827Q9 1.000 .835Q13 1.000 .816Q16 1.000 .840Q3 1.000 .773Pride and Passion Q26 1.000 .632Q19 1.000 .647Q27 1.000 .634Q10 1.000 .729Helping & Caring Team spirit Q7 1.000 .680

Q14 1.000 .782Q15 1.000 .733Q11 1.000 .728Q2 1.000 .695Alliance with Work T&D Q6 1.000 .712

Q25 1.000 .768Q12 1.000 .790Q4 1.000 .783Health & Safety Q8 1.000 .818

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Q24 1.000 .766Q22 1.000 .677Q21 1.000 .794Reward & Recog Q5 1.000 .762Q18 1.000 .755Q20 1.000 .846Q23 1.000 .732

Extraction Method: Principal Component Analysis.

Total Variance Explained

Component

Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings

Total % of Variance Cumulative % Total % of Variance Cumulative % Total % of Variance1 5.790 21.444 21.444 5.790 21.444 21.444 3.3452 2.969 10.995 32.439 2.969 10.995 32.439 2.9433 2.421 8.966 41.404 2.421 8.966 41.404 2.8034 2.247 8.323 49.727 2.247 8.323 49.727 2.3295 1.763 6.529 56.256 1.763 6.529 56.256 1.9216 1.629 6.033 62.289 1.629 6.033 62.289 1.9157 1.253 4.642 66.932 1.253 4.642 66.932 1.7668 1.128 4.178 71.110 1.128 4.178 71.110 1.6629 1.102 4.082 75.191 1.102 4.082 75.191 1.61710 .862 3.193 78.38511 .809 2.997 81.38112 .686 2.540 83.92113 .616 2.282 86.20314 .525 1.946 88.14915 .504 1.868 90.01716 .434 1.606 91.62317 .373 1.382 93.00418 .365 1.352 94.35719 .325 1.202 95.55920 .257 .950 96.50921 .223 .826 97.33522 .198 .735 98.07023 .152 .565 98.63524 .137 .509 99.14425 .099 .368 99.51226 .093 .346 99.85827 .038 .142 100.000

Extraction Method: Principal Component Analysis.

Component Matrix(a)

Component

1 2 3 4 5 6 7 8Job Clarity and Job Security .530 -.184 -.324 .040 -.073 -.039 .204 .504

Q17 -.175 .499 .387 -.335 .072 .106 .184 .273

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Q9 .430 .581 -.056 -.016 -.050 -.540 -.105 .060Q13 .150 .032 -.609 -.258 .181 .346 .248 .000Q16 -.190 -.682 .131 -.055 -.297 -.302 .085 .286Q3 .130 .055 .116 .324 .150 -.609 .436 -.219Pride and Passion Q26 .629 -.069 -.312 -.138 .144 .106 .054 -.009Q19 .349 .284 .172 -.371 -.022 .256 .343 -.267Q27 .623 -.026 .006 -.360 -.231 -.069 .173 -.136Q10 -.491 .160 .131 .384 -.076 .282 .148 -.415Helping & Caring Team spirit Q7 .434 -.487 -.035 .219 .106 .275 .168 .037

Q14 .575 .540 .005 -.017 -.183 .180 -.288 -.055Q15 .380 .245 .605 -.394 .052 .017 -.005 .020Q11 .476 -.366 .345 .106 -.155 .244 -.218 .019Q2 .661 -.402 -.077 .093 .257 .119 -.008 -.019Alliance with Work T&D Q6 .633 -.160 .208 -.376 .123 .062 -.088 -.053

Q25 .297 .286 .379 .430 .066 .375 -.152 .320Q12 .532 -.429 -.110 .034 -.369 .137 -.021 -.228Q4 .401 .490 -.131 .352 -.087 .085 .355 .224Health & Safety Q8 .402 .294 -.088 .657 -.176 -.044 .009 -.218Q24 .164 -.179 .657 .369 .343 .089 -.098 -.032Q22 -.527 .074 -.254 .260 .396 .059 -.180 .252Q21 .390 .002 -.133 .259 -.673 -.108 -.255 .034Reward & Recog Q5 .672 .250 -.046 -.156 .145 -.265 -.306 .069Q18 .616 .194 -.368 .264 .353 .068 .040 -.022Q20 .513 -.269 .479 .226 .138 -.220 .337 .110Q23 .311 -.233 -.142 -.049 .549 -.285 -.323 -.249

Undefined error #11401 - Cannot open text file "C:\Program Files\SPSS\en\windows\spss.err": No sucha 9 components extracted.

Rotated Component Matrix(a)

Component

1 2 3 4 5 6 7 8Job Clarity and Job Security .134 .005 .580 -.057 .571 -.049 .093 -.207

Q17 .100 .040 -.178 -.024 .020 .833 -.172 -.219Q9 .684 .172 -.241 -.151 .367 .051 -.069 .104Q13 -.074 .072 .127 -.253 .129 -.092 .825 -.033Q16 -.575 -.043 .236 -.075 .269 -.159 -.568 -.139Q3 .078 .034 -.032 .007 -.047 -.033 -.068 .086Pride and Passion Q26 .260 .229 .591 -.116 .220 .080 .202 .227Q19 .155 .586 .134 -.051 -.176 .430 .200 -.050Q27 .133 .704 .189 -.071 .242 -.062 .105 .012Q10 -.027 -.225 -.101 -.005 -.790 .052 -.058 -.189Helping & Caring Team spirit Q7 -.091 .049 .778 .241 -.009 -.053 .013 .040

Q14 .773 .328 .041 .071 .049 .054 .052 -.010Q15 .159 .556 -.132 .339 .150 .465 -.139 .077

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Q11 -.021 .370 .173 .610 .109 -.378 -.028 -.019Q2 .053 .189 .615 .302 .195 -.158 .174 .303Alliance with Work T&D Q6 .016 .602 .102 .329 .325 -.032 .199 .278

Q25 .452 -.133 .102 .655 -.007 .145 -.003 -.257Q12 .071 .410 .671 -.065 -.059 -.311 -.221 .033Q4 .537 .016 .076 .124 .115 .003 .391 -.464Health & Safety Q8 .692 -.060 .311 .068 -.286 -.215 -.107 -.106Q24 -.030 -.026 .051 .805 -.159 .072 -.161 .179Q22 -.075 -.782 -.123 -.044 -.043 .104 .107 .088Q21 .415 .233 .072 -.027 .158 -.627 -.228 -.294Reward & Recog Q5 .574 .248 .146 .021 .431 .101 -.063 .387Q18 .521 -.046 .395 .091 .133 -.058 .464 .217Q20 -.068 .294 .221 .590 .226 -.031 -.048 -.034Q23 .062 -.033 .185 .052 .143 -.079 .045 .805

Undefined error #11401 - Cannot open text file "C:\Program Files\SPSS\en\windows\spss.err": No such Undefined error #11408 - Cannot open text file "C:\Program Files\SPSS\en\windows\spss.err": No sucha Rotation converged in 19 iterations.

Component Transformation Matrix

Component 1 2 3 4 5 6 7 8 91 .488 .524 .486 .259 .344 -.108 .144 .146 .1132 .719 -.022 -.462 -.149 -.103 .407 .189 -.185 .0273 -.128 .309 -.261 .685 -.144 .333 -.453 -.069 .0954 .326 -.537 .212 .366 -.326 -.382 -.098 -.205 .3515 -.102 -.366 .042 .288 .051 .408 .417 .628 .1846 -.044 .021 .292 .278 -.384 .139 .410 -.283 -.6487 -.293 .183 .238 -.150 -.119 .314 .312 -.468 .6098 -.037 -.389 .059 .186 .743 .206 -.056 -.436 -.1469 .153 -.151 .539 -.301 -.165 .489 -.535 .115 -.088

Undefined error #11401 - Cannot open text file "C:\Program Files\SPSS\en\windows\spss.err": No such

6.5 RELIABILITY ASSESSMENT

The concept of reliability has been used to cover several aspects of score consistency. Test reliability

indicates the extent to which individual differences in test scores are attributable to “true” differences in

the characteristics under consideration and the extent to which they are attributable to chance errors.

These errors cannot be avoided or corrected through improved methodology. A youth version (EQ-i YV)

(Bar-On and Parker, 2000a) for children from six to twelve years of age and for adolescents from thirteen

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to seventeen years of age, was normed with a sample of 9172 students from United States and Canada

(Bar –On and Parker 2002).

Table 6.2: Reliability of the Factors of EI

Factors of EI Cronbach Alpha

Intrapersonal EQ-i 0.86

Interpersonal EQ-i 0.81

Adaptability EQ-i 0.82

Stress management EQ-i 0.83

General mood EQ-i 0.80

UC 0.69

RC 0.75

In this study, reliability for EQ-i YV and UC and RC in the form of Cronbach Alpha was found to be 0.69 to

0.86 for the 7 factors and an overall average internal consistency of 0.78. The values of Cronbach alpha

for all the factors of EI are presented in the Table 6.2. The differences in the Cronbach alpha values may

be due to the cultural differences as Bar- On and Parker’s (2002) study had samples from USA and

Canada and this study is in the Indian context.

6.6 ASSESSMENT OF THE DEGREE OF ASSOCIATION OF FACTORS

In order to test the association of EI and its factors a detailed set of statistical analysis was conducted

first being a confirmatory Pearson's Correlation as seen in Table 6.3.

Table 6.3: Correlations of EI and its factors

Page 8: Correlations Ee

Factors 2 3 4 5 6 7 8

1 0.604(**) 0.656(**) 0.542(**) 0.712(**) 0.532(**) 0.008 0.036

2 0.203(**) 0.368(**) 0.262(**) 0.245(**) -0.093(**) -0.062

3 0.159(**) 0.601(**) 0.639(**) 0.060 0.087(**)

4 0.234(**) 0.124(**) 0.011 0.006

5 0.554(**) 0.012 0.060

6 0.007 0.031

7 0.420(**)

** Correlation is significant at 0.01 level (2-tailed).

1 = Total EQ-i 2 = Intrapersonal ability EQ-i 3 = Interpersonal EQ-i 4 = Stress management EQ-i

5 = Adaptability EQ-i 6 = General mood EQ-i

7 = UC 8 = RC

There is a range of correlation coefficients between the factors as described below:

Adaptability EQ-i has the highest correlation with EI followed by interpersonal EQ-i compared to the

study conducted by Parker et al (2004b) where r = 0.712 for adaptability EQ-i and 0.656 for

interpersonal EQ-i. In that study the former correlates highest with total EQ-i.

Intra personal EQ-i correlates moderately with total EQ-i and the extent to which stress

management EQ-i and general mood EQ-i correlate is nearly the same similar to the study

conducted by Parker et al (2004b).

There is no correlation between the UC and RC with EI along with subscales. At 0.01 levels, UC and

RC do not correlate with intra personal EQ-i, interpersonal EQ-i, stress management EQ-i,

adaptability EQ-i and general mood. EQ-i

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Interpersonal EQ-i is positively associated with RC. Significant correlation exists between

interpersonal ability and RC, which signifies that an individual's responsibility and sensitivity in

communication is associated with one's ability to interact with others. Excellence and reasoning in

communication is difficult to be identified at such a tender age and would probably increase sharply

with age.

Adaptability EQ-i, stress management EQ-i and general mood EQ-i do not denote any significant

correlation with UC and RC.

Intrapersonal EQ-i is negatively associated with UC (-0.093). Significant correlation exists between

intrapersonal EQ-i and UC which signifies that if an individual's excellence and reasoning in

communication is high his intra personal EQ is low. There is no significant association of

intrapersonal ability and RC.

6.7 TESTS OF REGRESSION

6.7.1 Testing the overall significance of regression

Regression is the determination of a statistical relationship between two or more variables. In simple

regression, there are only two variables; one variable (defined as independent) is the cause of the

behavior of another one (defined as dependent variable). Regression interprets what exists physically

i.e. there must be a physical way in which independent variable can affect dependent variable. As the

objective of this study is to identify and assess the effect of factors on total EQ, the method of multiple

regression analysis has been chosen, as it helps in assessing the individual and the combined effect of

independent variables (interpersonal EQ-i, intrapersonal EQ-i, adaptability EQ-i, stress management EQ-i,

general mood EQ-i, UC and RC) on the dependent variable (EI) measured as total EQ-i. A Levene’s test of

heteroscedasticity was conducted to test the homogeneity of the sample. The results showed no different

in sample variances and hence the entire sample was found to be homogenous (p= 0.01).

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In forward stepwise regression the algorithm adds one independent variable at a time – which explains

most of the variation in the dependent variable ‘Y’. The next step is of one more variable X 2, then

rechecking the model to see that both variables form a good model. The process continues with addition

of a third and more variables if it still adds up to the explanation of ‘Y’ (Nargundkar, 2002). The steps

used in conducting the regression analysis on the above sample are as follows:

Firstly, School wise analysis with 7 factors (as explanatory variables) of total EQ-i was done. The

regression equation for school wise analysis with 7 factors is as follows:

Y = A + B1X1 + B2 X2 + B3 X3+ B4 X4 + B5 X5+ B6 X6 + B7 X7 .......... (1)

Y = dependent variable representing the emotional intelligence

B1, B2, B3, B4, B5, B6 and B7 are the coefficients of the regression equation

X1 = Intrapersonal EQ-i X2 = Interpersonal EQ-i

X3 = Stress management EQ-i X4 = Adaptability EQ-i

X5 = General mood EQ-i X6 = UC X7 = RC A = Constant term

The regression was then tested for its significance using F-test for the regression as a whole, (i.e. to test

whether the EI is dependent on the intrapersonal EQ-i, interpersonal EQ-i, stress management EQ-i,

adaptability EQ-i, general mood EQ-i, communication ability and communication potential at 5% level of

significance. This was followed by t-test to test the significance of each of the factors at 5% level of

significance. The F-test results showed that the regression as a whole was significant for the first 4

factors. Hence, in order to improve and get more significant results it was essential to omit the factors that

were not significant.

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In this study it is sought to assess whether individual factor predicts variance in EI, over and above the

variance predicted by other factors. Given the number of inter correlated factors present in the current

analyses it is not feasible to force a large number of factors into the equation. Such an analysis would

tend to mask the true effects, due to co linearity issues between the independent factors. Thus a

stepwise regression analysis was conducted.

The first step of the analysis involves entering the factors into the model. The next step involved using a

stepwise procedure to evaluate whether any of the factors should enter the model. The final step

involved using a stepwise procedure to evaluate if any of the factors should enter into the model. All

variables were standardized in order to reduce co linearity problems (Aiken and West, 1991). If a factor

entered the model in step 2 then the competence that formed a part of that interaction was forced into

the model in step 3, as it is necessary for a well-structured model. The F-test results showed that the

regression as a whole was significant for the first 4 factors. Hence, in order to improve and get more

significant results it was essential to omit the factors that were not significant.

Table 6.4: Summary of Regression Analysis for four Factors of EI

Model R R Square Adjusted R Square Std. Error of the Estimate

1 .712(a) .507 .507 10.581

2 .834(b) .695 .695 8.321

3 .878(c) .770 .770 7.224

4 .914(d) .835 .835 6.120

a. Model 1 consists of one independent variable - Adaptability EQ-ib. Model 2 consists of two independent variables - Adaptability EQ-i, Intrapersonal EQ-ic. Model 3 consists of three independent variables - Adaptability EQ-i, Intrapersonal EQ-i, and

Interpersonal EQ-i. d. Model 4 consists of four independent variables - Adaptability EQ-i, Intrapersonal EQ-i,

Interpersonal EQ-i, Stress management EQ-i

Page 12: Correlations Ee

Table 6.5: ANOVA for four Factors of EI

Model Sum of Squares Degrees of freedom Mean Square F P

1 Regression 629613.786 1 629613.786 5623.252 .000(a)

Residual 611782.926 5463 111.966

Total 1241396.713 5464

2 Regression 863172.355 2 431586.177 6233.748 .000(b)

Residual 378224.358 5462 69.234

Total 1241396.713 5464

3 Regression 956336.648 3 318778.883 6108.082 .000(c)

Residual 285060.065 5461 52.190

Total 1241396.713 5464

4 Regression 1036846.795 4 259211.699 6920.341 .000(d)

Residual 204549.918 5460 37.456

Total 1241396.713 5464

Dependent Variable: Total EQ-i

a. Model 1 Predictors: (Constant), adaptability EQ-ib. Model 2 Predictors: (Constant), adaptability EQ-i, intrapersonal EQ-ic. Model 3 Predictors: (Constant), adaptability EQ-i, intrapersonal EQ-i, interpersonal EQ-i. d. Model Predictors: (Constant), adaptability EQ-i, intra personal EQ-i, interpersonal EQ-i, stress

management EQ-i

As seen in table 6.4 there is no difference in the R square and adjusted R square value. This is due to the

sample ‘n’ being large – 5464 students in the age group of 9 – 14 years (Koutsoyiannis, 1977). From the

t-ratios in the above regressions, it could be seen that general mood EQ-i, UC and RC were not significant

factors of EI.

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Further, the overall impact of general mood EQ-i; UC and RC can be overcome by using and calculating

EI as a function of the intrapersonal EQ-i, Interpersonal EQ-i, stress management EQ-i and adaptability EQ-

i by omitting general mood EQ-i, UC and RC. So, regression analysis with 4 factors (as explanatory

variables) of EI was carried out with the following equation.

Y = A + B1X1 + B2 X2 + B3 X3+ B4X4………….. (2)

Adding the values of beta and the constant term from the regression Table 6.6, we get:

Y = -28.151 + 0.344(X1) + 0.334 (X2) + 0.277 (X3) + 0.363 (X4)…………………… (3)

Table 6.6: Coefficients of Regression Analysis for four Factors of EI

Model Factors Unstandardized Coefficients

Standardized Coefficients

T p

B Std. Error Beta

1 (Constant) 27.720 .852 32.537 .000

Adaptability EQ-i .662 .009 .712 74.988 .000

2 (Constant) -8.893 .920 -9.668 .000

Adaptability EQ-i .553 .007 .595 76.959 .000

Intrapersonal EQ-i .482 .008 .449 58.082 .000

3 (Constant) -17.624 .825 -21.364 .000

Adaptability EQ-i .373 .008 .402 49.377 .000

Intrapersonal EQ-i .467 .007 .435 64.717 .000

Interpersonal EQ-i .304 .007 .338 42.251 .000

4 (Constant) -32.137 .766 -41.966 .000

Adaptability EQ-i .337 .006 .363 52.237 .000

Intrapersonal EQ-i .369 .006 .344 57.062 .000

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Interpersonal EQ-i .301 .006 .334 49.329 .000

Stress management EQ-i

.316 .007 .277 46.362 .000

As the regression equation has R square value 0.835 we tried to increase the value by adding dummy

gender variables. As shown in Table 6.7, there is no difference in the R square value.

Table 6.7 Coefficients of Regression Analysis with Gender

Factors Unstandardized Coefficients

Standardized Coefficients

T Sig. R R Square

Std. Error of the

Estimate

B Std. Error

Beta .835 .835 6.113

(Constant) -32.91 .828 -39.75 .000

Intra personal EQ-i .366 .007 .342 56.143 .000

Interpersonal EQ-i .290 .007 .323 42.337 .000

Stress Management EQ-i .317 .007 .278 46.494 .000

Adaptability EQ-i .332 .007 .357 49.848 .000

General mood EQ-i .023 .007 -.023 -3.180 .601

UC .130 .046 -.017 -2.800 .505

RC -.012 .037 -.002 -.311 .756

Gender -.206 .170 -.007 -1.211 .226

Dependent Variable: Total EQ-i

Experiments with age are not conducted as it was considered in groups (9 -12 years and 13 to 14 years)

and considered for cluster analysis. Table 6.4, 6.5 and 6.6 presents the results of regression analysis for 4

factors of EI. The following points are worth mentioning:

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The results were found to be significant in the data of 5464 students in the age group of 9 – 14

years.

Four explanatory variables - intrapersonal EQ-i, interpersonal EQ-i, stress management EQ-i and

adaptability EQ-i are significant factors affecting EI. General Mood EQ-i, UC and RC do not seem

to impact EI.

General mood EQ-i along with UC and RC are not significantly affecting EI and hence cannot be

considered as a factor.

6.7.2 Testing the improvement of fit by additional regressions

* The Logarithmic Exponential

As the regression equation has R square value 0.835 we tried to increase the value by considering the

logarithmic exponential of independent and dependent factors. As shown in Table 6.8, there is no

difference in the R square value.

Table 6.8: Coefficients of Regression Analysis with Logarithms of Factors

Unstandardized Coefficients

Standardized Coefficients

T P R Square

Adjusted R Square

Std. Error of

the

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Estimate

B Std. Error

Beta

(Constant) -468.737 4.156 -112.7 .00 .835 .834 6.103

Log intra personal EQ-i 35.702 .715 .347 49.92 .00

Log interpersonal EQ-i 26.335 .709 .336 37.14 .00

Log stress management EQ-i 28.574 .690 .284 41.41 .00

Log adaptability EQ-i 30.482 .718 .354 42.45 .00

Log general mood EQ-i 2.422 .750 .028 3.22 .56

Log UC .584 .229 .017 2.55 .61

Log RC .308 .294 .007 1.04 .29

Dependent Variable: Total EQ-i

6.7.3 Testing of significance of the difference between a single prediction and actual observation

The tests of significance between single prediction and the actual observation are this test of testing the

predictive power of the equation (Koutsoyiannis, 1977). This test is frequently used as the basis for

evaluation of the forecasting power of the model. In this test observed ‘T’ is compared with its

theoretical value and decide whether the observed difference is significant. Sampling of 375 students

was done for testing the predictive power of the equation in June / July 2008. In our test, actual value of

‘T’ is less than ‘T’, that is predicted value. The observation is compatible with the estimated relationship.

Table 6.9: Values of T actual and T Predicted for Four Significant Factors of EI.

Model Factors T (actual) T (predicted)

1. Adaptability EQ-i 74.988 73.88

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2. Adaptability EQ-i 76.959 72.1

Intrapersonal EQ-i 58.082 58.02

3. Adaptability EQ-i 49.377 49.21

Intrapersonal EQ-i 64.717 62.5

Interpersonal EQ-i 42.251 41.11

4. Adaptability EQ-i 52.237 51.12

Intrapersonal EQ-i 57.062 57.00

Interpersonal EQ-i 49.329 49.21

Stress management EQ-i 46.362 46.21

Dependent Variable: Total EQ-i

a. Model 1 Predictors: adaptability EQ-ib. Model 2 Predictors: adaptability EQ-i, intrapersonal EQ-ic. Model 3 Predictors: adaptability EQ-i, intrapersonal EQ-i, interpersonal EQ-i. d. Model Predictors: adaptability EQ-i, intra personal EQ-i, interpersonal EQ-i, stress management

EQ-i

In this case we accept that the predicted power of our equation is good. With reference to Table 6.6 we

obtained the values of T actual on the basis of which the regression equation was framed. In Table 6.9

below, both values of T – actual and predicted based on the equation are presented.

In our test the difference between the actual and forecasted value may be due to abnormal conditions

in the period of forecast (Koutsoyiannis, 1977). In this case our equation is still valid and we do not need

to modify it.

Initial Cluster Centers

Cluster

1 2Total R&R 19 9Final Total 116 73

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Total H&S 14 13Total T&D 19 9Total H&C Team 25 9Total P&P 16 8Total JC and JS 26 25EXPERIENCE 4 1M. STATUS 1 1GENDER 1 1AGE RANGE 4 2QUALIFICATION 1 1POSITION 2 1

Final Cluster Centers

Cluster

1 2Total R&R 15 10Final Total 99 81Total H&S 13 12Total T&D 16 12Total H&C Team 20 15Total P&P 15 12Total JC and JS 21 19EXPERIENCE 3 2M. STATUS 1 1GENDER 1 1AGE RANGE 3 3QUALIFICATION 1 1POSITION 2 2

Number of Cases in each Cluster

Cluster 1 28.0002 26.000

Valid 54.000Missing .000

CLUSTER ANALYSIS

Cluster Analysis is a multi-variate procedure (Nargundkar, 2002) is a group of similar objects. Cluster

analysis is an exploratory data analysis tool for solving classification problems. Its object is to sort cases

(people, things, events, etc) into groups, or clusters, so that the degree of association is strong between

members of the same cluster and weak between members of different clusters. Each cluster thus

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describes, in terms of the data collected, the class to which its members belong; and this description

may be abstracted through use from the particular to the general class or type.

Cooper and Schindler (2007) have identified five basic steps:

a. Selection of sample to be clustered. b. Definition of the variables on which to measure the

objects. c. Computation of the similarities through correlation. d. Selection of mutually exclusive

clusters. e. Cluster comparison

Based on these steps EQ-i and its factor scores of students in the age group of 9 – 14 years were

classified as presented in table 6.13

Table 6.13: Categories of EQi and its factors (Bar-On and Parkar 2000a)

Student scores Total EQi Intrapersonal EQi Interpersonal EQi Adaptability EQi

Stress managemen

t EQi

General mood EQi

65 – 89 Low Low Low Low Low Low

90 – 110 High High High High High High

Above 111 Very high Very high Very high Very high Very high Very high

The understanding communication (UC) and responsibility in communication (RC) scores were

classified as:

1. 0 – 4: low UC / RC

2. 5 – 7: high UC / RC

3. 8 - 10: very high UC / RC

The basic clustering methods (Nargundkar, 2002) used in computer packages are

Hierarchical clustering or Linkage methods and Non - hierarchical clustering or Nodal methods

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In this study the second type including the K- means approach is considered where the number of

clusters is specified in advance. The specified number of nodes and points closest to them are used to

form initial clusters and through an iterative rearrangement the final K clusters are determined by SPSS®

11.5 for MS Windows®. K–means procedure generally gives more stable cluster, since it is an interactive

procedure compared with the single – pass hierarchical methods.

Table 6.14 depicts the number of cases in each cluster and signifies that each cluster is determined by

significant number of cases.

Table 6.14: Number of Cases in each Cluster

Cluster 1 1127.000

2 1887.000

3 979.000

4 1411.000

Valid 5404.000

Missing 60.000

Final cluster centers describe the mean value of each variable for each of the 4 clusters. The brief

description of each of the 4 clusters as presented in Table 6.15 is given below:

Cluster 1

Students belonging to this cluster are males in the age group - 13 to 14 years. They have low EQ-i, and

low scores of intrapersonal EQ-i, interpersonal EQ-i, stress management EQ-i, adaptability EQ-i, general

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mood EQ-i and UC. They have high scores of RC. Unfortunately their father has expired but mother

having undergone professional education results in family income below Rs.100, 000 per annum.

Cluster 2

Students belonging to this cluster are males in the age group of 9- 12 years. They have low EQ -i score

and low scores of intrapersonal EQ-i, interpersonal EQ-i, stress management EQ-i, adaptability EQ-i,

general mood EQ-i, UC. They have high scores of RC. Their father’s manage a business and mothers are

housewives. Both parents are graduates with total family income above Rs. 500,000 per annum

Table 6.15: Final Cluster Centers

Cluster

1 2 3 4

Categories of total EQ –i 1 1 2 2

Age group 2 1 2 2

Category of intrapersonal EQ-i 1.64 1.64 2.27 2.12

Category of interpersonal EQ-i 1.26 1.31 2.12 2.10

Category of stress management EQ-i 1.35 1.34 1.70 1.65

Category of adaptability EQ-i 1.39 1.42 2.31 2.34

Category of general mood EQ-i 1.24 1.24 1.95 1.90

Category of UC 1.37 1.41 1.33 1.49

Category of RC 2.13 2.13 2.13 2.29

Father's Occupation 0 3 2 3

Mother's Occupation 4 1 1 3

Father's Education 2 2 1 2

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Mother's Education 3 2 1 2

Income 1 3 1 3

Gender 1 1 2 1

Cluster 3

Students belonging to this cluster are adolescent females in the age group of 13- 14 years. They have

high EQ-i scores and high scores of intrapersonal EQ-i, interpersonal EQ-i, adaptability EQ-i, and RC. The

scores of stress management EQ-i, general mood EQ-i and UC are low. Their father is in service and

mothers are housewives. Both parents are educated till the 10 th standard with their family income below

Rs.100, 000 per annum.

Cluster 4

Students belonging to this cluster are adolescent males in the age group of 13- 14 years. They have high

EQ-i scores and high scores of intrapersonal EQ-i, interpersonal EQ-i, adaptability EQ-i, and RC. The scores

of stress management EQ-i, general mood EQ-i and UC are low. Both parents are graduates and are

occupied in managing business. Their total family income is above Rs.500, 000 per annum.