nepal presentation 03-06-11

8/2/2019 Nepal Presentation 03-06-11

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Factors Affecting the Number of Spousefor a Man in Chitwan


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Number of spouse lines (for Male only)

Frequency PercentCumulative

Percent1 1476 76.9 76.92 332 17.3 94.23 81 4.2 98.4

4 19 1.0 99.45 4 .2 99.66 1 .1 99.77 2 .1 99.8

8 2 .1 99.99 1 .1 99.912 1 .1 100.0Total 1919 100.0


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R esearch Question

What factors explain the number of spouse for a man?


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Q. A man should not have mare than one wife . (Full Sample)

Frequency PercentValid

PercentCumulative

PercentStronglyagree

3369 63.9 64.3 64.3

Agree 687 13.0 13.1 77.4

Disagree 1143 21.7 21.8 99.2

Stronglydisagree

42 .8 .8 100.0

Total 5241 99.4 100.0


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number of wife

Totalone multiple

agree Count 1172 296 1468

Percent 79.8% 20.2% 100.0%

Q. A man should not have more than one wife . (married man class)


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O ne-Sample Statistics

N Mean

Std.

Deviation

Std. Error

MeanNumber of spouse lines

1919 1.32 .748 .017

Test Value = 1

t df Sig. (2-tailed)Number of

spouse lines

18.834 1918 .000

H1: Chitwan mean no . of spouse is different fromworld average


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G roup Statistics: Age and no . of spouse

number of wife N Mean

Std.Deviation

Std. Error Mean

R espondentage

One 1476 36.90 11.699 .305

multiple 443 45.35 12.804 .608

H1: mean age is different between two spouse groups


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L evene'sTest for

Equality of Variances t-test for Equality of Means

F Sig. t df

Sig.(2-

tailed)R espondent age

Equalvariancesassumed

5.144

.023 -13.050 1917 .000

Equalvariances notassumed

-12.432 678.494 .000

H1: mean age is different between two spouse groups


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R espondent age

Number of spouse lines Pearson Correlation .556 **

Sig. (2-tailed) .000

Correlation between

No . of spouse and Age


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L inear Regression Model of

No.

of Spouse and AgeCoefficients a

Model

UnstandardizedCoefficients

StandardizedCoefficients

t Sig.B Std.Error Beta

(Constant) -.057 .022 -2.652 .008

R espondent age .030 .001 .556 48.576 .000

a. Dependent Variable: Number of spouse lines


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Model R

R Square

AdjustedR

Square

Std.Error of the

Estimate

1 .556 a .309 .309 .594

ModelSum of

Squares df Mean

Square F Sig.R egression 833.180 1 833.180 2359.666 .000

R esidual 1860.444 5269 .353

Total 2693.625 5270

L inear Regression Model of No . of spouse and Age

ANO VA


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Model


StandardizedCoefficients

t Sig.BStd.Error Beta

(Constant) -.001 .041 -.017 .986

R espondent age .028 .001 .524 40.868 .000

Education -.017 .002 -.105 -8.052 .000

Watch movie ever .152 .026 .072 5.721 .000

Watch TV ever -.065 .027 -.030 -2.386 .017

a. Dependent Variable: Number of spouse lines

Multiple Regression Model of No . of Spouseand O ther Variables


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Model R R Square Adjusted R

SquareStd. Error of the

Estimate

1 .567 a .322 .321 .590

a. Predictors: (Constant), Watch TV ever, R espondent age,Watch movie ever, Education

ModelSum of

Squares df Mean

Square F Sig.R egression 866.111 4 216.528 622.769 .000 a

R esidual 1827.435 5256 .348

Total 2693.546 5260

a. Predictors: (Constant), Watch TV ever, R espondent age, Watch movieever, Educationb. Dependent Variable: Number of spouse lines

Multiple Regression Model of No . of Spouseand O ther Variables

ANO VA


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Model


StandardizedCoeffic

ients

t Sig.

CollinearityStatistics

BStd.Error Beta Tolerance VIF

(Constant) -.001 .041 -.017 .986

R espondent age .028 .001 .524 40.868

.000 .785 1.275

Education -.017 .002 -.105 -8.052 .000 .755 1.325

Watch movieever

.152 .026 .072 5.721 .000 .820 1.219

Watch TV ever -.065 .027 -.030 -2.386 .017 .843 1.186

Test for Collinearity in Multiple Regression Modelof No . of Spouse and O ther Variables


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B S.E. Wald df Sig. Exp(B)

R ESPAGE .064 .004 284.227 1 .000 1.066Constant -4.301 .169 647.078 1 .000 .014

L ogistic Regression Model of No . of Spouse and Age


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THANK YOU

nepal presentation 03-06-11

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