THORNTON Simón y James (ambos de ocho años de edad) están jugando en el patio Simulan

In the first examination, variables are selected in each group separately, so that it is not possible to examine the joint effect of variables from different groups on the current users. To develop a subset of predictors that is useful in predicting the current users, and to eliminate those which do not provide additional predictive power, the selected variables in each group of variables from the first examination are then put together in stepwise as well as block stepwise regressions.

Table 4.5 shows that only two out of 12 variables, LDLESS and STUDENT, are excluded from the equation. All of those which are in the equation significantly affect the use of contraception. The table also shows the statistical criteria on which in stepwise regression, the order of the entry of the variables is based. The variables FLPR and NONFWKR, for example, entered the equation after the variables DOCTOR and CLINIC.

This means that FLPR and NONFWKR have less predictive power than DOCTOR or CLINIC. Demographic variables, moreover, are found to be the most important variables

This finding confirms the generally reported findings on the importance of demographic variables in explaining variation in contraceptive practice (Freedman, et al.,1981; Soeradji and Hatmadji,1982). The two family planning program input variables are also found to influence significantly contraceptive practice.

Table 4.5: Results of the stepwise multiple regression on proportion using contraception

Variable C oefficien t S T

D em o g ra p h ic va ria b les

Infant m ortality rate -.10C .03

D ependency ratio -.29b .12

Proportion urban residents -.30b .13

E c o lo g ic a l va ria b les

Proportion households non-nuclear .68b .28

Proportion irrigated paddy field .12C .03

E du cation al v a ria b le s

Proportion fem ales finished primary school .16b .07

F am ily plan n in g p ro g ra m input va ria b les

Num ber fam ily planning doctors -3.65C 1.25 Num ber fam ily planning clinics* 2.94C 1.10

W om en s econ om ic a c tiv ity va ria b les

Fem ale labour force participation rate .4 ic .12 Proportion fem ales non-fam ily workers -.35b .14 Notes: a Significant at 10% level of significance

b Significant at 5% level of significance c Significant at 1% level o f significance * Per 10,000 married women aged 15-44 years

Two other variables, LDLESS and STUDENT, are excluded from the equation.

Once again, the 12 selected variables from the first examination are put together in block stepwise regression, which means hierarchical over blocks, but stepwise within blocks. So that the order of block entry is arranged according to the analytical framework, however, the order of variables within each block follows the statistical criteria (Tabachnick and Fidell, 1983: 105). Table 4.6 shows that all predictors from the first two groups of variables, that is demographic and ecological variables , are included

in the equation: IMR, DEPTIO, URBAN, NONCLF, IRRFLD, and LDLESS. The proportion of females at reproductive ages who finished elementary school (FELMNT)ris one of the variables in the educational group which fulfils the criteria to enter the equation. However, the next group of variables set to enter the equation after the educational variables, does not have a strong predictive power. Hence, none of the variables in that group remains in the equation. Among the predictors in the equation, LDLESS, the proportion of landless farmers, insignificantly influences the current users.

Table 4.6: Results of the block stepwise multiple regression of proportion using contraception

Variable Coefficient SJE

Demographic variables

Infant mortality rate -.10C .04

Dependency ratio -.25b .12

Proportion urban residents -.43c .12

Ecological variables

Proportion households non-nuclear .6ia .31

Proportion irrigated paddy fields . l ie .03

Proportion landless fanners -.15 .14

Educational variables

Proportion females finished primary school .16b .07

Women’s economic activity variables

Female labour force participation rate .18a .09

Family planning program input variables

Number family planning doctors -3.65C 1.25

Number family planning clinics* 2.94C 1.10

Notes: a Significant at 10% level of significance b Significant at 5% level of significance c Significant at 1% level of significance * Per 10,000 married women aged 15-44 years

On the basis of the results of stepwise and block stepwise regression it was decided to keep the predictors which exist in both equations and also those which are only found in one of the equations but which show significant association with current users. Hence,

IMR, DEPTIO, URBAN, NONCLF, IRRFLD, FELMNT, FLPR, NONFWKR, DOCTOR and CLINIC are included in the final regression to predict the current users in each Jcabupaten in Java.

4.2.2 Determinants of contraceptive use

First, to see the contribution of each group I applied the combination of stepwise and hierarchical regression; the result of this regression is presented in Table 4.7. The model explains 73 per cent of the variation of the adoption of family planning in the districts in Java.

Table 4.7: Result of stepwise and hierarchical regression of proportion using contraception

Variable B Beta R2 R^chg

D e m o g r a p h ic v a r ia b le s IMR

Dependency ratio

Proportion urban residents

-.10C -.29b -.30b -.27C -.19b -.18b .57 E c o lo g ic a l v a r ia b le s

Proportion households non-nuclear Proportion irrigated paddy field

.68b .12C .18b .29C .64 .07 E d u c a tio n a l v a r ia b le

Proportion females finish primary school .16b .17b

.66 .02

W o m en e c o n o m ic - a c tiv ity v a r ia b le s Female labour force participation rate Proportion females non-family workers

.4 ic -.35b .38c -.27b .69 .04 F a m ily p la n n in g p r o g r a m in p u t variables Number family planning doctors* Number family planning clinics*

-3.65C 2.94C

-.22C