Illustrating correlations and causal inferences : synthesizing global indices on conscription policies and regime types / Christopher Harrison.

Format:

Book

Author/Creator:

Harrison, Christopher, author.

Series:

SAGE research methods: doing research online.

SAGE research methods: doing research online

Language:

English

Subjects (All):

Research--Methodology--Case studies.

Research.

Draft--Research--Case studies.

Draft.

Physical Description:

1 online resource.

Place of Publication:

London : SAGE Publications Ltd, 2024.

Summary:

This case study examines my experiences and decisions when confronted with too much information pertaining to multiple ongoing events across the world but not enough data points to utilize an analytical program. The original research project from which this case study stems explored the role of conscription as a more common foundational aspect of sociopolitical relationships in authoritarian territories rather than, as has been previously proposed, in democracies. In this case study, I emphasize the research process of synthesizing quantitative datasets to inform a causal inference analysis. The specific methodological approach identifies correlations present in the quantitative statistics from two global indices. Given that statistical programs are built to handle many datapoints and my study consisted of nominal observations to generate simple counts, which illustrated correlations between conscription and regime types, I began with what turned out to be too few a number of variables for both of the two criteria. The datasets focused on 177 regimes and initially only the presence or absence of conscription policies, a limited design that I soon needed to change. The practical challenges addressed in the case study include my recognition of both the benefits and limits of quantitative research as well as predicaments that arise when I attempted to research the moving target of an ongoing contemporary issue; in this case, Russia's war in Ukraine. Readers will learn how to categorize datasets too large for small N methodologies but with too few variables to accurately utilize data analysis software into manageable and practical formats. Learning how and why it is useful to synthesize two related indices allows readers to apply similar comparative methodological approaches in their own work on contemporary global issues.

Notes:

Description based on XML content.

ISBN:

1-5296-8433-1

9781529684339

OCLC:

1418722730