An Analysis of Sociocultural Barriers on Social Empowerment of Women in Sahiwal Division

The paper sets out to discuss impact of socio-cultural barriers on social empowerment of rural women in term of decision making related to their personal as well as social life in Sahiwal division. Pakistan is a patriarchal society characterized by patri-local residence and exclusion of women from the right of inheritance and succession, which pave ways for male-headed society. In addition, socio-cultural factors strongly favour male-dominance and an inferior status of females in all walks of life. A quantitative approach was adopted for carrying out current study. A survey instrument was designed and employed for data collection from 384 respondents from rural areas of Sahiwal division. The findings of the study revealed that less than one third (31.25%) of the respondents were consulted in decision-making about domestic matters. Likewise, more than half (51.8%) of the respondents were not independent in moving out of home for meeting any social need. Similarly, more than one third (41.4%) of the respondents were not free to visit their friends in the neighborhood. The study concludes that women in the rural areas are less empowered and still suffering socially, economically and psychologically in their day to day life. The study provides an insight for professional social workers, policy makers and stakeholders in public and private sectors for influencing policy-making and planning for revisiting and redesigning existing policies and plans intended for empowerment of rural women in Pakistan

Robust Outlier Detection Techniques for Skewed Distributions and Applications to Real Data

Most of the data sets belonging to the real world contain observations at the extremes that might not be in conformity with the remaining data set. These extreme observations known to be outliers might have positive or negative effect on the data analysis like regression estimates, forecasting and ANOVA etc. Outliers are powerful tools to identify the most interesting events of the world in cross sectional data and historically important events can be picked by detecting outliers in time series data sets. Numerous outlier detection techniques have been proposed in the literature. This study provides a survey of these techniques and their properties. Most of these techniques work well under the assumption that data come from a symmetric distribution and these techniques fail to work in skewed distributions. Because of this limitation, Hubert and Vandervieren (2008) proposed a technique for outlier‟s detection in skewed data sets. Our thesis presents a new technique to measure robust skewness (SSS) and a new outlier detection technique (SSSBB) for skewed data distributions. The study shows that the proposed technique measures skewness more accurately than existing techniques and the proposed technique for outlier‟s detections works better than Hubert‟s technique on a class of theoretically skewed and symmetric distributions. The study also compares the technique with other established outlier detection techniques in the literature. This study uses simulation technique for computer generated distributions and some real data sets for comparison purposes. The study also analyzes real life data sets and compares the baby birth weight data and stock returns, both of which are known to be skewed. These results will help us in making a choice of appropriate outlier detection technique for skewed data sets for different sample sizes which might be helpful in identifying underweight babies.