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Comparing Time-Series-Cross-Section Methods for Cross-National Crime and Justice Research

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Abstract:

A common problem in quantitative, cross-national crime and justice research is small sample sizes, which are a function of a relatively small universe of units of analysis and limitations in quantitative data availability.  Many researchers compensate for this problem by pooling data for available countries across several years and then adjusting for modeling complications using fixed- and/or random-effects.  While this approach is commonly used for the analysis of panel data, time-series-cross-section data present unique methodological challenges because the time-series is usually larger than the number of countries and because the speed of change in variables across time for cross-national research is typically much slower than for individual-level research.  There have been several developments to try to improve the analytical techniques for time-series-cross-section data in other social science disciplines that have not been widely adopted in criminal justice research.  This paper empirically assesses the strengths and limitations of some of these techniques.  Specifically, this paper replicates John Sutton's analysis of "The Political Economy of Imprisonment in Affluent Western Democracies, 1960-1990" using fixed-effects models and then compares the results to analyses using panel-corrected standard errors (PCSE) and fixed-effect vector decomposition (FEVD) models.
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Association:
Name: ASC Annual Meeting
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http://www.asc41.com


Citation:
URL: http://citation.allacademic.com/meta/p516601_index.html
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MLA Citation:

DeMichele, Matthew. and Stamatel, Janet. "Comparing Time-Series-Cross-Section Methods for Cross-National Crime and Justice Research" Paper presented at the annual meeting of the ASC Annual Meeting, Washington Hilton, Washington, DC, <Not Available>. 2014-11-25 <http://citation.allacademic.com/meta/p516601_index.html>

APA Citation:

DeMichele, M. and Stamatel, J. P. "Comparing Time-Series-Cross-Section Methods for Cross-National Crime and Justice Research" Paper presented at the annual meeting of the ASC Annual Meeting, Washington Hilton, Washington, DC <Not Available>. 2014-11-25 from http://citation.allacademic.com/meta/p516601_index.html

Publication Type: Conference Paper/Unpublished Manuscript
Abstract: A common problem in quantitative, cross-national crime and justice research is small sample sizes, which are a function of a relatively small universe of units of analysis and limitations in quantitative data availability.  Many researchers compensate for this problem by pooling data for available countries across several years and then adjusting for modeling complications using fixed- and/or random-effects.  While this approach is commonly used for the analysis of panel data, time-series-cross-section data present unique methodological challenges because the time-series is usually larger than the number of countries and because the speed of change in variables across time for cross-national research is typically much slower than for individual-level research.  There have been several developments to try to improve the analytical techniques for time-series-cross-section data in other social science disciplines that have not been widely adopted in criminal justice research.  This paper empirically assesses the strengths and limitations of some of these techniques.  Specifically, this paper replicates John Sutton's analysis of "The Political Economy of Imprisonment in Affluent Western Democracies, 1960-1990" using fixed-effects models and then compares the results to analyses using panel-corrected standard errors (PCSE) and fixed-effect vector decomposition (FEVD) models.


Similar Titles:
Not-So-Standard Errors: A New and Robust Method for Calculating Standard Errors in Time-Series Cross-Sectional Studies

Decommodification and Homicide Revisited: A Time-Series Cross-Sectional Analysis of IAT in the OECD Nations (1970-2002)

Cross-National Comparative Research: A Synthesis of Mixed Methods Designs


 
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