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A multinomial logit analysis of post-conflict justice mechanisms : publishing with undergraduate co-authors

Author: Steve B Lem
Publisher: London : SAGE Publications Ltd, 2019.
Series: SAGE Research Methods. Cases.
Edition/Format:   eBook : Document : English
Summary:
Publishing quantitative research with undergraduate students presents unique challenges and opportunities. In this case study, I discuss how a team of students and I worked together to develop a research question on post-conflict justice mechanisms in the aftermath of civil war. Then, I highlight some of the data and methodological challenges we encountered along the way. In particular, the case study looks at the  Read more...
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Genre/Form: Case studies
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Steve B Lem
ISBN: 9781526475527 1526475529
OCLC Number: 1084514396
Description: 1 online resource : illustrations.
Series Title: SAGE Research Methods. Cases.
Responsibility: Steve B. Lem.

Abstract:

Publishing quantitative research with undergraduate students presents unique challenges and opportunities. In this case study, I discuss how a team of students and I worked together to develop a research question on post-conflict justice mechanisms in the aftermath of civil war. Then, I highlight some of the data and methodological challenges we encountered along the way. In particular, the case study looks at the complexity of analyzing six types of post-conflict justicétruth commissions, trials, amnesty, reparations, purges, and exileśin a single analysis. Given that 64 post-conflict justice combinations are possible, our biggest challenge was to collapse the data down to a statistically and substantively meaningful number of outcomes. We accomplished this by recoding our six types into two categories: restorative justice and retributive justice. Combined with the absence of a post-conflict justice, we analyzed our outcomes using a multinomial logistic regression model. We also explored a linear combination in an ordinal logistic regression model. Last, I discuss other data reduction techniques, including principal components analysis and factor analysis.

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