This paper departs from the observation that empirical and conceptual frameworks describing the intersection of new technology and development studies have begun to embrace the idea of open development. Frameworks for research, however, continue to reflect older notions of technology appropriation and empowerment. In order to start a dialogue about research design appropriate to open development, I provide an overview of key ontological, epistemological, and methodological considerations of significance to this field. An open development approach, I argue, should focus on enhancing cognitive justice rather than productivity or empowerment. This can best be carried out through the application of a constructivist and critical realist epistemology, through positional methodology and through networked research processes.
Received: 10 November 2011
Revised: 20 November 2011
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Maintaining data quality and integrity is important for research studies involving prospective data collection. Data must be entered, erroneous or missing data must be identified and corrected if possible, and an audit trail created.
Using as an example a large prospective study, the Missouri Lower Respiratory Infection (LRI) Project, we present an approach to data management predominantly using SAS software. The Missouri LRI Project was a prospective cohort study of nursing home residents who developed an LRI. Subjects were enrolled, data collected, and follow-ups occurred for over three years. Data were collected on twenty different forms. Forms were inspected visually and sent off-site for data entry. SAS software was used to read the entered data files, check for potential errors, apply corrections to data sets, and combine batches into analytic data sets. The data management procedures are described.
Study data collection resulted in over 20,000 completed forms. Data management was successful, resulting in clean, internally consistent data sets for analysis. The amount of time required for data management was substantially underestimated.
Data management for prospective studies should be planned well in advance of data collection. An ongoing process with data entered and checked as they become available allows timely recovery of errors and missing data.
Article originally published in ‘Welcome to BMC Medical Research Methodology’, 8 (2008), n. 61. – ©2008 Kruse and Mehr. Open Access article distributed under the terms of the Creative Commons Attribution License (//creativecommons.org/licenses/by/2.0)
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It is relatively easy to investigate how to employ a particular research method in the social sciences. It is considerably more difficult to decide which to use. Which method to use is arguably a more important question than how to use that method. ‘Which method?’ is, at least, the necessarily prior question. One cannot look up how to do something until one has decided what that something is. Methodological innovation depends directly on methodological choice. Researchers continuing a tradition, or working within a paradigm can often avoid making difficult methodological choices. Researchers seeking to innovate cannot. The question ‘which method?’ is particularly important for selecting research designs, because design choice importantly shapes most of the other choices researchers make. Designs are most effective and have the greatest potential for innovation when they are dictated by the nature of the research problem.
Paper originally presented at the Social Research Methodology Centre, City University, London, 18 April 2007, and published in “Methodological Innovations Online“ V. 3 (2008), n.1. Reprinted with permission.
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