RESEARCH METHODOLOGY
3.5. DATA ANALYSIS
The statistical package for social sciences (SPSS) software is used to analyse Rwanda Demographic and Health Survey data. Frequencies and cross tabulation are used to describe the association between socio-economic and demographic characteristics and attitudes, communication in couple's towards contraceptive use on desired fertility. In addition, both bivariate and multivariate analyses are used to assess the effect of a number of independent variables on desired fertility.
3.5.1. Dependent Variable
The dependant variable, a measure of desired fertility, is taken directly from a survey question asking fecund women and their husbands whether the respondent would like to have a child (or another child) or would prefer not to have any more children. The variable has six categories of responses, indicating wants more within two years, wants more after two years, and wants unsure timing, undecided, wants no more, declared infecund. Because our concern is with the demand for additional children, we measure the dependant variable as dichotomous, assigning a value of zero for respondents who do not want any more children and a value of one for all other responses. The respondents
who replied "undecided" have been included among "all others", under the assumption that they did not have a clear wish to stop childbearing and could be counted as effectively wanting more children.
For the multivariate analysis, the researcher used logistic regression, an appropriate function form for the analysis of dichotomous variables. The logit analysis provides the natural logarithm of the odds of desiring not to have more children (the dependant variable) as a function of a set predictor or independent variables. The probability of the desire not to have more children is predicted to be dependent on or to be correlated with the characteristics of women and men, and the coefficients represent the magnitude of the increase in the log-odds of desiring no more children when there is a unit increase in the predictor variables.
3.5.2. Independent Variables
In order to assess the socio-economic and demographics factors; current age, type of residence, education of wives and husbands, number of living sons and number of living daughters will be analysed in relation with the desire for additional children. In order to examine gender difference as a factor influencing desired fertility, men and women differ in the degree in which having living sons and living daughters is important in formulating a desire of not having more children. The researcher evaluated the significance of having living sons and living daughters towards the decision of not having more children.
To capture the influence or awareness of couples and the compatibility of their views, a couple's variable has been created in order to compare the responses of husbands and wives. In each case, the reference category in the logistic regression is the category that is predicted to have the largest negative association with the desire to have no more children for instance, both partners disapproved family planning, but neither has discussed this.
Communication was liberally interpreted in the communication about family planning
would like to have. If either family planning or desired family size had been discussed, the communication variable took the value of one.
A number of control variables have been used also in the regression; current age, husband's and wives' education, type of residence, employment, attitudes toward family planning, discussed family planning, number of living children, number of living sons and number of living daughters. In this study, the researcher presented separately for wives and husbands two logistic regression models of the effect of selected predictor variables. The first model includes the control variables only, age, type of residence, number of living sons and daughters, employment and education. The second model adds in variables that serve as proxies for awareness of attitudes toward the family planning program; their approval of family planning, discussion with their spouse about family planning. The researcher also demonstrates the results for urban and rural respondents separately.
3.5.3. Missing Values
Although complete data for all subjects are desired, the possibility that some data items will not be available, will become lost, or will be unusable for other unknown reasons should not be ignored. Data that is not available for any subject will be assigned a
"missing" value in the database. In the treatment of data, after identification of errors, missing values, and true (extreme or normal) values, the researcher must decide what to do with problematic observations. The options are limited to correcting, deleting, or leaving unchanged. There are general rules for which option to choose. Impossible values are never left unchanged, but should be corrected if a correct value can be found, otherwise they should be deleted (Jan Van den Broeck et al, 2005).
(As De Vaus ,2002) warns, the inclusion of missing cases confuses real responses with non responses; can distort results; can inflate or deflate summary statistics or scale scores and can also destroy the ordinal and interval character of any variable. Untreated, missing data can introduce serious error into estimates. Frequently, there is a correlation between
the characteristics of those missing and variables to be estimated, resulting in biased estimates. For this reason, it is often best to employ adjustments and imputation to mitigate this damage. Without weight adjustments or imputation, calculations of totals are underestimated. In this study missing cases were excluded from analysis. This was done by assigning them a code, then declaring them missing to ensure their exclusion from data analysis. While this might have affected data reliability, opting to impute would have also introduced bias to the study. I therefore argue that it is best to exclude the missing cases from analysis than exaggerate the correlation between communication about family planning and desired fertility as well as attitudes towards family planning and desired fertility that might result if missing cases are replaced through data imputation.
3.5.4. Limitation of the study
The fact that this research process will be conducted by means of a quantitative research strategy that is based on secondary data poses the possibility limitations. There is a general lack of information about men in the sexual and reproductive health and this is perceived as a constraint to elaborate on this subject. Therefore, there can be a possibility of capturing lesser variables from this data set, some socio-economic variables which may be used in our analysis were not applicable, and these are religion, income and ethnicity of the respondents.