As mentioned before, the present study has two different approaches, the conventional quantitative demographic study and the micro approach. For the first approach, two techniques of analysis are employed, multiple regression and path analysis. Regression analysis is a statistical method of analysis designed to express the response variable as a function of predictor variables, so this technique specifically addresses how the predictor variables influence, describe or control the response variable. The selection of regression analysis also fulfils another purpose of the chapter, to predict the expected birth control acceptance rate in a particular year, since predicting is one of the major uses of regression analysis (Gunst and Mason, 1980: 6-9).
To follow the framework for the analysis of fertility change (Figure 1.2 in Chapter 1), the analysis in Chapter 4 uses percentage of contraceptive acceptance at the kabupaten level as the response variable, while demographic and ecological variables, socio economic and related variables, and family planning program input variables are the predictor variables. The relationship between the response variable and the predictor variables can be formulated as follows:
Y= bo + baXa + bfrXb + bcXc + R where
Y represents proportion o f contraceptive acceptance; Xa represents demographic and ecological variables; Xb represents socio-economic and related variables; Xc represents family planning program input variables; bi represents regression coefficient for variable i;
R is the eerror or disturbance term representing all unmeasured influences o f contraceptive acceptance.
In this system acceptance of contraceptive methods is treated as completely determined by a set of variables in the model, together with, where necessary, the unmeasured
residuals ( R ) . The residuals or disturbance in the regression analysis is an expression of
the inexactness or imprecision due to lack of information. It therefore can reasonably be defined as being independent of the explanatory variables in the same equation (Kendall and O ’Muircheartaigh, 1977: 12).
Before the multiple regression analysis, the variables are examined to select predictors useful in explaining the variation of contraceptive acceptance rates at the district level. Some variables are initially selected within each group, through applying stepwise regression, forward entry and backward elimination4, then followed by a multicollinear examination among the selected variables in every group of variables.
A further step is to run a block stepwise regression with all the chosen variables put together in one equation, and the current users as the dependent variable. The latter step is meant to explore the joint effect of those variables on the current users, because the first selection is carried out within each group of variables. Predictors which are useful in predicting current users are chosen, then the combination of stepwise and hierarchical regression is run to see the contribution of each group of variables to the variation of the current users, and the hierarchical regression is applied to see each variable’s contribution. Using SPSSX’s subcommand allowing a combination of stepwise and hierarchical regression, the researcher can control the entry of blocks of variables according to the theory or framework, and at the same time also consider the statistical criteria within each block (Tabachnick and Fidell, 1983: 105). The predicted contraceptive prevalence rate is calculated using the beta coefficients from the last setwise regression.
^ Forward entry, backward elimination and stepwise selection are variable selection method subcommands which are provided by the SPSSX package program. B y applying the forward entry method, variables are added to the equation one at a time. At each step, the variable with the highest probability to enter, that is the variable with the smallest probability o f F value and which is smaller than the entry criterion, is entered. With backward elimination, all variables in the block are first put in the equation and considered for removal. At each step, the variable with the largest probability o f F, if it is larger than the remove criterion, is excluded from the equation. Stepwise selection is a method which uses both entry and remove criteria.
The second technique used, which is applied in Chapter 5, is path analysis; this is a method o f decomposing and interpreting linear additive relationships of a set of variables by assuming causal sequences o f these variables. Path analysis is a method which bears close affinities to multiple regression analysis but with the diagrammatic presentation it helps make explicit the underlying assumptions and interrelationships (Hermalin, 1979: 102). The path coefficient, which is simply the standardized regression coefficient or the beta coefficient, represents the direct effect o f one variable on the other (Kendall and O ’Muircheartaigh, 1977: 9; Hermalin, 1979: 103).
To follow the analytical framework, demographic and ecological variables (Xa), socio economic and related variables (Xb), and family planning program input variables (Xc) are exogenous variables, which the theory does not seek to explain, whereas age at first marriage (Y2), contraceptive acceptance rate (Y3) and fertility change (Yi) are considered endogenous variables, which the model seeks to account for. The relationships o f the endogenous and exogenous variables appear in the path diagram in
accord with conventions o f path analysis, as follows (Hermalin, 1979: 103):
Yl = P12Y2 + P13Y3 + PlaXa + PlbXb + PlcXc + PluRu Y2 = P2aXa + P2bXb + P2wR\v
Y3 = P3aXa + P3bXb + P3cXc + P3vRv where
Pij are path coefficients which represent the direct effect. Ri are residual effects o f unmeasured variables, which are uncorrelated
with the other determining variables in each equation.
For the second approach, two groups o f respondents were selected in each observed region, first, the implementers and persons potentially influential to family planning implementation in the area, and second married women at reproductive ages. It was mentioned before that the main purpose o f the survey was to find out what lay behind
the implementation and differences, if any, between those in each selected region. Therefore, we tried to stimulate respondents to give as much information as they could about the family planning program in their areas and its implementation.
In order to record all information, the first technique brought into use was to use a portable cassette recorder and a set of question guidelines which contained a list of possible questions. These questions serve as a basis for the conversation between researcher and respondents. During the interview researchers tried to extend the discussion to other subjects related to family planning and fertility which were specific in the area, such as arranged marriage and intercourse-taboo in lactation period. Field notebooks we kept to record the facts and basic demographic information of respondents, such as age of women and husbands, their educational attainments, their age at first marriages and number of children bom alive and living children.
2.5 Summary
The thesis, which applies two different approaches, has quantitative and qualitative sources of data. Censuses and surveys are used in the study, but the main quantitative source of data is the 1980 Indonesian Population Census. For the quantitative approach, two techniques of analysis, multiple regression and path analysis, are employed. The results of the interviews in villages in five selected kabupaten in Java provide the qualitative source of data in this study.
Earlier studies have found that region is a more important factor than demographic and socio-economic background variables in influencing the family planning adoption. Therefore this study employs the regional unit of kabupaten as units of analysis.