Abstract
The main purpose of this study is to analyze the correlation between the status of female and educational indicators. The study uses the seats in parliament held by women (%), female administrators and managers (%), female professional and technical workers (%), the ratio of female adult literacy (%), and a combination of the ratio of first-, secondary-, and third-level education (%) for analysis. The raw data are collected from the UNDP (1995/96/97). There are five null hypotheses in the study to test. The results are as follows: Using Cluster Analysis, five indicators are used to categorize 101 countries into four groups: the status of higher-level females (33 countries), the status of middle-upper- level females (34 countries), the status of middle-level females (23 countries), and the status of lower-level females (11 countries). In order to test the consistency of the clustering, Discriminant Analysis is used to reclassify the clustered countries. Above 95% of countries are correctly classified by the five indicators (hypothesis 1). Hypotheses 2, 3, and 4 are tested by multi-regression analysis which uses the seats in parliament held by women (%), female administrators and managers (%), female professional and technical workers (%) as dependent variables, respectively, and use the ratio of female adult literacy (%), women’s share of earned income, and a combination of the ratio of first-, secondary-, and third-level education (%) as independent variables in the model for analysis. All the independent variables are significant in the three models.
To understand the representative of the female indicators causality model, the research employed the LISREL for this study. It tested female indicators with 101 countries included in the model, and found that there are two latent variables in this model, such as educational and the status of female latent variables. The results showed that the c2 value is not significant, that is, the model is fitted better, and other indices, GFI, AGFI, and RMR, are also better. Also, the status of female latent variables is influenced by the educational latent variables, that is, it is significant (p < .01), too.
| Keywords: | the seats in parliament held by women (%); female administrators and managers (%); female professional and technical workers (%); the ratio of female adult literacy (%); combining the ratio of first-, secondary-, and third-level education (%); women’s share of earned income |
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