Browsing by Author "Tefera, Helen"
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Item Application of Data Mining Technology to Identify Significant Patterns in Census or Survey Data : The Case of 2001 Child Labor Survey in Ethiopia(Addis Ababa University, 2003-07) Tefera, Helen; Kebede, Gashaw (PhD); Anagaw, Shegaw (PhD)Knowledge and understa nding of a problem is always the first step in identifying effective solutions . Child labor is both a sign and cause of poverty that should b eliminated as soon as possible. In Ethiopia, there is no much statistical data on chi ld labor practice. To fill this data gap, the FORE, CSA carried out country wide child labor survey in 200 I . This organization uses very simple statistical tools to show summary figures of different variables involved in 2001 child labor survey database. However traditional statistical method s are not good enough to discover complex relationships from large volume databases. The inefficiency of these tools necessitated the development of more powerful methods and techniques that can be used to study relationships and patters through the large volumes of data collected for example for census and survey purposes. In developed world, govemmrnt non-govemment organizations which have access to censuses and surveys are making use of the relatively new a nd modern technology, data mining, to identify important patterns and relationships within the data that is accumulated in large database. The application of data mining techniques to official data such as the 200 I child labor survey has great potential • in supporting good public policy. This research focused on identifying relationships between attributes within the 200 I child labor survey database that can be used to clearly understand the nature of child labor problem in Ethiopia . So the goal of the data mining process in this research was identifying interesting pattems and relationships in the 2001 child labor database. After the identification and understanding of the problem domain and the research objectives, the remaining stages of the research project focused on the following three major phases in data mining process. During the first phase, selection of the appropriate data mining tool which can be used to attain the defined data mining goal and the target dataset used in model building were the major tasks. The next phase, data cleaning and preparation, involved identifying and correcting mis-transmitted information, consolidating and combining records, transforming data from one form to another suitable for the selected data mining tool, handling missing attributes and selecting relevant attributes for generating meaningful association rules. As a final step for data preparation, the selected dataset was categorized into five classes using expectation maximization clustering algorithm implemented in knowledge studio version 3.0. A dataset of 2398 records with 63 attributes were used for clustering purpose. Apriori is an association rule algorithm which is implemented in Weka software. in the third phase, model building and evaluation, the apriori algorithm was used to generate association rules from the clustered as well as non-clustered selected dataset. Different attributes were given to apriori in an effort to generate meaningful rules. The results from this study were encouraging, which strengthened the hypothesis that interesting pattems can be generated from census and survey database by applying one of the data mining techniques: association rule mining. Key words: Data mining, knowledge discovery, association rule, apriori algorithmItem Application of Data Mining Technology to Identify Significant Patterns in Census or Survey Data: the Case of 2001 Child Labor Survey in Ethiopia(Addis Ababa University, 2003-06) Tefera, Helen; Kebede, Gashaw(PhD); Anagaw, ShegawKnowledge and understanding of a problem is always the first step in identifying effective solutions. Child labor is both a sign and cause of poverty that should be eliminated as soon as possible. In Ethiopia, there is no much statistical data on child labor practice. To fill this data gap, the FDRE, CSA carried out country wide child labor survey in 2001. This organization uses very simple statistical tools to show summary figures of different variables involved in 2001 child labor survey database. However traditional statistical methods are not good enough to discover complex relationships from large volume databases. The inefficiency of these tools necessitated the development of more powerful methods and techniques that can be used to study relationships and patterns through the large volumes of data collected for example for census and survey purposes. In developed world, government and non-government organizations which have access to censuses and surveys are making use of the relatively new and modern technology, data mining, to identify important patterns and relationships within the data that is accumulated in large database. The application of data mining techniques to official data such as the 2001 child labor survey has great potential in supporting good public policy. This research focused on identifying relationships between attributes within the 2001 child labor survey database that can be used to clearly understand the nature of child labor problem in Ethiopia. So the goal of the data mining process in this research was identifying interesting patterns and relationships in the 2001 child labor database. After the identification and understanding of the problem domain and the research objectives, the remaining stages of the research project focused on the following three major phases in data mining process. During the first phase, selection of the appropriate data mining tool which can be used to attain the defined data mining goal and the target dataset used in model building were the major tasks. The next phase, data cleaning and preparation, involved identifying and correcting mis-transmitted information, consolidating and combining records, transforming data from one form to another suitable for the selected data mining tool, handling missing attributes and selecting relevant attributes for generating meaningful association rules. As a final step for data preparation, the selected dataset was categorized into five classes using expectation maximization clustering algorithm implemented in knowledge studio version 3.0. A dataset of 2398 records with 63 attributes were used for clustering purpose. Apriori is an association rule algorithm which is implemented in Weka software. In the third phase, model building and evaluation, the apriori algorithm was used to generate association rules from the clustered as well as non-clustered selected dataset. Different attributes were given to apriori in an effort to generate meaningful rules. The results from this study were encouraging, which strengthened the hypothesis that interesting patterns can be generated from census and survey database by applying one of the data mining techniques: association rule mining. Key words:Data mining , knowledge discovery, association rule, apriori algorithm.Item Crisis Communication of Ethiopian Revenue and Customs Authority (ERCA): Addressing Public Grievances Following the 2017 Income Tax Collection Process(Addis Ababa University, 2018-11) Tefera, Helen; Dinku, Getachew (PhD)The purpose of this study is to assess the crisis communication effort of Ethiopian Revenue and Customs Authority (ERCA) in addressing public grievances in response to the authority’s recent assessment of average daily and annual incomes of the business community in Addis Ababa and levy taxes accordingly. Situational Crisis Communication Theory of W. T Coombs is used to examine the crisis communication activity, response strategy of ERCA and the role played by the PR department of the authority in mitigating the crisis. Both qualitative and quantitative research approaches were employed to collect data from ERCA managers, public relations professionals and category “C” income tax payers to study ERCA’s effort to handle the crisis. Eight respondents were interviewed and 383 category “C” income tax payers were asked to fulfil questionnaire regarding the view of the tax payers toward ERCA’s crisis communication effort. The study found out that ERCA’s crisis communications was generally weak and lacked sufficient preparation and prevention at the pre-crisis stage. The role played by PR in handling the crisis appears to be limited. The public relations professionals were only drawn into the crisis management process after the crisis had reached its peak and was fully realized. During the crisis stage, communication problems created unfavorable situation between the tax payers and ERCA. Timely getting accurate information concerning the newly introduced tax collection system appeared to be a problem and created confusion. At times conflicting messages were transmitted by different people in the organization. The poor implementation of crisis communication in the preparation stage affected the process of handling the crisis situation throughout crisis and post crisis stages. Absence of proper preparation seemed to have resulted in fragmented communication techniques that were less effective. Based on study findings, recommendations are forwarded to ERCA as well as the business community to prevent the crisis from escalating and to improve the organizational performance of ERCA towards building better public trust.Item Crisis Communication of Ethiopian Revenue and Customs Authority(ERCA): Addressing Public Grievances Following the 2017 Income Tax Collection Process(Addis Ababa University, 2018-11) Tefera, Helen; Dinku, GetacbewThe purpose of this study is to assess the crisis communication effort of Ethiopian Revenue and Customs Authority (ERCA) in addressing public grievances in response to the authority's recent assessment of average daily and annual incomes of the business community in Addis Ababa and levy taxes accordingly. Situational Crisis Communication Theory of W. T Coombs is used to examine the crisis communication activity, response strategy of ERCA and the role played by the PR department of the authority in mitigating the crisis. Both qualitative and quantitative research approaches were employed to collect data from ERCA managers, public relations professionals and category "c" income tax payers to study ERCA's effort to handle the crisis. Eight respondents were interviewed and 383 category "c" income tax payers were asked to fulfil questionnaire regarding the view of the tax payers toward ERCA's crisis communication effort. The study found out that ERCA's crisis communications was generally weak and lacked sufficient preparation and prevention at the pre-crisis stage. The role played by PR in handling the crisis appears to be limited. The public relations professionals were only drawn into the crisis management process after the crisis had reached its peak and was fully realized. During the crisis stage, communication problems created unfavorable situation between the tax payers and ERCA. Timely getting accurate information concerning the newly introduced tax collection system appeared to be a problem and created confusion. At times conflicting messages were transmitted by different people in the organization. The poor implementation of crisis communication in the preparation stage affected the process of handling the crisis situation throughout crisis and post crisis stages. Absence of proper preparation seemed to have resulted in fragmented communication techniques that were less effective. Based on study findings, recommendations are forwarded to ERCA as well as the business community to prevent the crisis from escalating and to improve the organizational performance of ERCA towards building better public trust.Item The Gender Dimensions of Povertyt: The Case of Intra Household in Addis Ababa (Addis Ketema Sub City)(Addis Ababa University, 2007-07) Tefera, Helen; Adal, Yigremew (Assistant Professor)Gender dimension of poverty largely examined in relation to head of the households, comparing female headed with that of male-headed households. Intra household poverty issues largely have gone unexamined. Owing to this gap, the thesis has objectives to asses the Gender dimension of poverty in the case of intra household arena, examine wives’ and husbands’ access to and control over resources, power relation, roles and responsibilities, vulnerability to risks and other related matters, so as to investigate whether women are disadvantaged or poorer than their male counterparts. The research is done in Addis Ababa, Addis Ketema sub city ‘Amanueal Sefer’. It is one of the disadvantaged areas in the city and large numbers of poor residents are living. The research design involves the use of both primary and secondary data. In this study more of qualitative methods are utilized. Added to this simple percentages were also used. Primary data is generated through interview, focus group discussions and to some extent observations. Married couples with children both sons and daughters were selected considering their age differential and living conditions. A total of thirty-three households were selected for interview, couples from each household were interviewed. A total of thirty-two women and men were also involved in the Focus Group Discussions. Secondary data were generated from books and other related researches. Findings of the study show that in most of the cases women have disadvantaged position comparing with their male counterparts. They are the one who carried out major household responsibilities and very much responsible for the sustenance of the households. Yet they have low access to and control over households’ resources. Among the workingwomen most of them have low controlling power over the income they generated. Even if women have greater responsibility for household matters, it is their husbands who are major decision-makers. Women are also very much vulnerable in times of difficulty than their husbands. Most of the time women are disadvantaged and poorer than their husbands. The societal norm, women’s self concept and the culture play significant roles to keep women in disadvantaged position