SAGE Journal Articles
SAGE Journal Articles combine cutting-edge academic journal scholarship with the topics in your course for a robust classroom experience.
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Article 1: Newman, D. A. (2014). Missing Data Five Practical Guidelines. Organizational Research Methods, 17(4), 372-411.
Summary: In this article, the authors discuss missing data and the five practical guidelines. All missing data treatments are imperfect and are rooted in particular statistical assumptions, some missing data treatments are worse than others, on average (i.e., they lead to more bias in parameter estimates and less accurate hypothesis tests). Social scientists still routinely choose the more biased and error-prone techniques (listwise and pairwise deletion), likely due to poor familiarity with and misconceptions about the less biased/less error-prone techniques (maximum likelihood and multiple imputation)..
Questions to Consider:
1. What is syntax aimed to do – in terms of missing data?
2. How do the five guidelines assist with the goal of reducing missing data bias and error?
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Article 2: Larry Tan, K. L., Lambert, P. S., Turner, K. J., Blum, J., Gayle, V., Jones, S. B., Sinnott, R. O., & Warner, G. (2009). Enabling Quantitative Data Analysis Through e-Infrastructure. Social Science Computer Review, 27(2), 539-552.
Summary: This article discusses how quantitative data analysis in the social sciences can engage with and exploit an e-Infrastructure. The authors’ highlight how a number of activities that are central to quantitative data analysis, referred to as ‘‘data management,’’ can benefit from e-Infrastructural support. The authors’ conclude by discussing how these issues are relevant to the Data Management through e-Social Science (DAMES) research Node, an ongoing project that aims to develop e-Infrastructural resources for quantitative data analysis in the social sciences.
Questions to Consider:
1. What are some of the common themes of e-social science in quantitative analysis?
2. How can e-science and e-infrastructure help researches with their data and quantitative analyses?
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Article 3: Pryjmachuk, S. & Richards, D. A. (2007). Look before you leap and don’t put all your eggs in one basket: The need for caution and prudence in quantitative data analysis. Journal of Research in Nursing, 12(1), 43-54.
Summary: This paper’s aim is to draw attention to the pitfalls that novice and, sometimes, experienced researchers fall into when undertaking quantitative data analysis in the health and social sciences, and to offer some guidance as to how such pitfalls might be avoided.
Questions to Consider:
1. Why do researchers need to exercise caution when conducting quantitative data analyses?
2. What is prudence?