Article S7.2 Usefulness of Data Analysis in Social Science Research Montanus C. Milanzi Faculty of Public Administration and Management, Mzumbe University, P.O. Box 2 Mzumbe, Tanzania mcmilanzi[at]hotmail.com useful [adj] 1. able to be used for some practical purpose, producing or able to produce good results; 2. creditable, efficient [The Oxford Popular English Dictionary, 1990] 1. Introduction A pre-data-collection seminar was organized at Mzumbe University in Tanzania in order to equip graduate students who have taken research methods course during the previous semester. The seminar intended to equip students with practical skills and knowledge of various approaches that may be used when performing data analysis. The seminar was organized to deliberate and discuss the usefulness of various approaches to data analysis in social science research. Seven MBA students from St Augustine University of Tanzania (SAUT) in Mwanza and several postgraduate students (in MPA and MSc programmes) from Mzumbe University participated in the seminar, which was facilitated by Montanus C. Milanzi. The seminar was perceived and valued crucial to the participants inasmuch as all of them will be required to be in the field after their proposals have received approvals from their respective research supervisors. One of the central themes discussed in the seminar was “usefulness.” The dictionary meaning of the word useful was put first on the agenda to underline the fact that the data-item collected by the researcher should be able to produce good results, whether intended or unintended ones. In addition, the data collected should be seen and perceived credible and, above all, it should be efficient. 2. Data and Data Analysis in Social Science Research A data-item represents one of the smallest units of facts collected in a known intervention context as a result of experience, observations or an experiment. Data provide pictures or representations of objects, events and images in the real world. A data-item is perceived as one of the lowest or smallest units sliced from the real world. Data show the lowest level of abstraction or conceptualization in social science research from which information and knowledge are derived. There are different methods or techniques through which data can be collected. Data can be gathered by using instruments designed and prepared for the purpose. There are basically two major sources. The first source is the primary data collection instruments which include instruments such as questionnaires, interviews and observations. Second, there are secondary data collection sources such as literature and documents, which require tools for documentary review. The events, images and objects collected by a researcher through various data collection instruments using some established rules and procedures are then to be assigned numbers, images or symbols. The objects, events or images systematically gathered and to which numbers are assigned are able to indicate empirical or observational properties. Researchers claim that any single (non-repetitive), repetitive or multiple events, observations, images in social science research can be measured through the processes of conceptualization and operationalization. Conceptualization means the specification of the research concept, construct and variable. In other words, it denotes the refinement and specification of abstract concepts, constructs and variables and their identified and conceived scientific meanings. Hence, documentary review is one of the methods of refining and fine tuning the concepts, constructs and variables scientifically. On the other hand, operationalization shows the development of specific research procedures that will result in empirical observations. It specifies and determines the indicants of the real world and observable objects, events and images, which give the evidence of the presence or absence of a concept, construct or variable (Milanzi, 2002). Thus, data analysis in social science research involves a process of gathering, modeling, editing, coding and transforming/processing data for the purpose of attaining useful information, suggesting conclusions and supporting decision making, among other things. The process itself entails multiple facets and approaches. The collected data are to be edited, codified, processed, tabulated and classified so as to become amenable to interpretation. The collected data from each of the data collection instruments are to be edited. It is done by the examination of the raw data and critically checking the completed and filled questionnaire and interview notes. The editing is done to check the accuracy of the information given and consistency of information provided by respondents and/or accuracy of the notes written by the researcher. The next step of data analysis is data coding in which numbers are assigned against each respondent’s answers so that individual responses are placed into limited number of selected categories or classes. Coding of data may also imply defining variables for individual responses from every data collection instrument used. Each class defined, selected and identified shall possess the following properties: completeness, exhaustiveness, mutual exclusivity and unidimensionality. The interdependence of codes was not discussed in the seminar. However, it is not always possible to have all data collected used successfully by the researcher. Some data may not be reflected in the results of the study, therefore not used in the final report. 3. Conclusion Data collection involves the operationalization of the concepts and variables in the real world situation. Social scientists are urged to be aware of the significance of data collection and data analysis activities during research interventions. Collecting data in research without making use of them would not be wise in an endeavor of creating and generating knowledge. Improved data collection and analysis techniques and methods should enable researchers in social sciences to come up with new knowledge and facilitate the application of this knowledge. Further Readings Foucault, M. (1980). Power/knowledge: Selected interviews and other writings 1972-1977 (C. Gordon, Ed.). New York: Harvester Wheatsheaf. Milanzi, M. C. (2004). Application of soft systems methodology among farming communities in Tanzania. Unpublished PhD Thesis. University of Lincoln, United Kingdom. Milanzi, M. C. (2002). Research methods in social sciences: Philosophy, methodology and theories. Unpublished manuscript. Mzumbe University, Tanzania.
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