Estimating Support for Extremism and Its Correlates: The Case of Pakistan

• Authors: Tarek Al Baghal
• Languages of publication: EN

Abstracts

EN

The extent of support of extremist ideology is a major area of concern for both policy makers and academic researchers. Identifying the extent and correlates of a difficult to measure concept such as extremist ideology is often limited by the use of a single imperfect indicator. This paper outlines one approach, latent class analysis (LCA), to overcome this issue and uses the example of estimating support for such ideology in Pakistan. Using survey data from Pakistani men, the level of support is estimated using LCA employing several indicators related to extremism. The results suggest that although most Pakistanis are not supportive of extremist ideology, a substantively important portion of men are supportive. LCA also allows for class assignment, which is useful for understanding covariate relationships with the latent variable. Based on the results of the LCA, respondents are assigned to different classifications of extremist support, and a continuation-ratio logistic regression model is employed allowing for more covariates to be examined. The results suggest that there are a number of characteristics important in influencing support within this subset of the population. In particular, younger and less educated men are more likely to support extremism ideology. The results suggest a potentially useful methodology in understanding extremism, as well as a greater understanding of the problem of extremist support.

Keywords

EN

extremist ideology; Pakistan; public opinion; latent class analysis

• Publisher: Institute of Philosophy and Sociology, Polish Academy of Sciences
• Journal: ASK. Research and Methods
• Year: 2014
• Volume: 23
• Pages: 35-56

Contributors

author

Tarek Al Baghal

• Institute for Social and Economic Research, University of Essex, Wivenhoe Park, Colchester, Essex, CO4 3SQ UK

References

• Agresti, Alan. 2012. Categorical Data Analysis, Third Edition. New York, NY: Wiley.
• Asparouhov, Tihomir and Bengt Muthén. 2014a. Auxiliary Variables in Mixture Modeling: 3-Step Approaches Using Mplus. Mplus Web Notes: No. 15 https://www.statmodel.com/download/webnotes/webnote15.pdf, accessed 29 October 2014.
• Asparouhov, Tihomir and Bengt Muthén. 2014b. Response to “Relating Latent Class Assignment to External Variables: Standard Errors for Correct Inference” Z. Bakk, D.L. Oberski, and J.K. Vermunt. Unpublished manuscript, September 24, 2014.
• Bakk, Zsuzsa, Daniel L. Oberski, and Jeroen K. Vermunt. 2014. Relating latent class assignments to external variables: standard errors for correct inference. Political Analysis, 22, 520-540.
• Beissinger, Mark R. 2013. The Semblance of Democratic Revolution: Coalitions In Ukraine’s Orange Revolution. American Political Science Review, 107, 574–92.
• Blair, Graeme, C. Christine Fair, Neil Malhotra, and Jacob N. Shapiro. 2013. Poverty and Support for Militant Politics: Evidence from Pakistan. American Journal of Political Science, 57, 30-48.
• Blaydes, Lisa and Drew A. Linzer. 2008. The Political Economy of Women’s Support for Fundamentalist Islam. World Politics, 60, 576-609.
• Bolck, Annabelle, Marcel Croon and Jacques Hagenaars. 2004. Estimating Latent Structure Models with Categorical Variables: One-Step versus Three-Step Estimators. Political Analysis, 12, 3–27.
• Bullock, Will, Kosuke Imai, and Jacob N. Shapiro. 2011. Statistical Analysis of Endorsement Experiments: Measuring Support for Militant Groups in Pakistan. Political Analysis, 19, 363–84.
• Celeux Gilles and Gilda Soromenho. 1996. An Entropy Criterion for Assessing the Number of Clusters in a Mixture Model. Journal of Classification, 13, 195–212.
• Central Intelligence Agency. 2013. World Fact Book: Pakistan https://www.cia.gov/ library/publications/the-world-factbook/geos/pk.html, accessed 13 October 2013
• Clogg, Clifford C., and Leo A. Goodman. 1984. Latent Structure Analysis of a Set of Multidimensional Contingency Tables. Journal of the American Statistical Association 79, 762-771
• Fair, C. Christine. 2004. Militant Recruitment in Pakistan: Implications for Al Qaeda and Other Organizations Studies in Conflict and Terrorism, 27, 489-504.
• Fair, C. Christine and Bryan Shepherd. 2006. Who Supports Terrorism? Evidence from Fourteen Muslim Countries Studies in Conflict and Terrorism, 29, 51–74
• Fair, C. Christine, Neil Malhotra, and Jacob N. Shapiro. 2012. Faith or Doctrine? Religion and Support for Political Violence in Pakistan Public Opinion Quarterly 76, 688-720
• Finch, W. Holmes, and Kendall C. Bronk. 2011. Conducting Confirmatory Latent Class Analysis Using Mplus. Structural Equation Modeling 18, 132-151.
• Gallup. 2013. Gallup World Poll Methodology. PDF monograph https://worldview.gallup.com/content/methodology.aspx
• Garret-Mayer, Elisabeth and Jeannie-Marie Leoutsakos. 2010. Latent Class Analysis. In Chow, S-C (ed.) Encyclopedia of Biopharmaceutical Statistics, 3rd Edition, 700-707 Informa Healthcare, New York.
• Goodman, Leo A. 2007. On the Assignment of Individuals to Latent Classes Sociological Methodology 37, 1-22.
• Haddad, Simon and Hilal Khashan. 2002. Islam and Terrorism: Lebanese Views on September 11. Journal of Conflict Resolution, 46, 812-828.
• Haqqani, Husain. 2005. The ideologies of South Asian Jihadi groups. Current Trends in Islamist Ideology, 1, 12–26.
• Haqqani, Husain. 2006. “Weeding out the heretics:” Sectarianism in Pakistan. Current Trends in Islamist Ideology 4, 1–12.
• Horgan, John. 2008. Psychology on Radicalization into Terrorism From Profiles to Pathways and Roots to Routes: Perspectives The ANNALS of the American Academy of Political and Social Science, 618, 80-94.
• Huntington, Samuel P. 1996. The Clash of Civilizations and the Remaking of World Order, New York: Simon and Schuster.
• James, Sigrid, Edward S. McField, and Susanne B. Montgomery. 2013. Risk Factor Profiles Among Intravenous Drug Using Young Adults: A Latent Class Analysis (LCA) Approach. Addictive Behaviors, 38, 1804-1811.
• Kaltenthaler, Karl, William J. Miller, Stephen Ceccoli, and Ron Gelleny. 2010. The Sources of Pakistani Attitudes toward Religiously Motivated Terrorism Studies In Conflict and Terrorism, 33, 815–835.
• Kemp, Robert. 2008. Religious Extremism and Militancy in the Pashtun Areas of Afghanistan and Pakistan. BC Journal of International Affairs, 11.
• Khan, Ismail. 2010. “Female Bomber Kills Dozens in Pakistan, Official Says” New York Times, Dec. 26, 2010, http://www.nytimes.com/2010/12/26/world/asia/26pstan.html, accessed 29 October 2014.
• Krueger, Alan B. and Jitka Maleckova. 2003. Education, Poverty and Terrorism: Is There a Causal Connection? Journal of Economic Perspectives, 17, 119-144.
• Lessler, Judith T. and William D. Kalsbeek. 1992. Nonsampling Errors in Surveys. New York: Wiley.
• Linzer, Drew A. and Jeffrey B. Lewis. 2011. poLCA: An R Package for Polytomous Variable Latent Class Analysis Journal of Statistical Software, 42.
• McCutcheon, Allan L. 1985. A Latent Class Analysis of Tolerance for Nonconformity in the American Public Public Opinion Quarterly 49, 474-488.
• McCutcheon, Allan L. 1987. Latent Class Analysis. Newbury Park, CA: Sage
• McCutcheon, Allan L. 2002. Basic Concepts and Procedures in Single- and Multiple-Group
• Latent Class Analysis. In J.A. Hagenaars and A.L. McCutcheon (eds.) Applied Latent Class Analysis 56-88 Cambridge, MA: Cambridge University Press.
• Muthén, Linda K. and Bengt O. Muthén. 1998-2010. Mplus User’s Guide. Sixth Edition. Los Angeles, CA: Muthén & Muthén.
• Oberski, Daniel L., Geert H. van Kollenburg, and Jeroen K. Vermunt. 2013. A Monte Carlo Evaluation of Three Methods to Detect Local Dependence in Binary Data Latent Class Models. Advances in Data Analysis and Classification, 7, 267-279.
• Rashid, Ahmed. 1999. The Taliban: Exporting extremism. Foreign Affairs, 78, 22-35.
• Reidel, Bruce. 2008. Pakistan and Terror: The Eye of the Storm The ANNALS of the American Academy of Political and Social Science, 618, 31-45
• Shapiro, Jacob N., and C. Christine Fair. 2010. Understanding Support for Islamist Militancy in Pakistan. International Security, 34, 79–118.
• Tay, Louis, Daniel A. Newman, and Jeroen K. Vermunt, 2011. Using Mixed-Measurement Item Response Theory With Covariates (MM-IRT-C) To Ascertain Observed and Unobserved Measurement Equivalence. Organizational Research Methods, 14, 147-176.
• Tessler, Mark. and Michael D.H. Robbins. 2007. What Leads Some Ordinary Arab Men and Women to Approve of Terrorist Acts Against the United States? Journal of Conflict Resolution, 51, 305-328.
• Vermunt, Jeroen K. 2010. Latent Class Modeling with Covariates: Two Improved Three-Step Approaches. Political Analysis, 18, 450–469.