Natural sentences as valid units for coded political texts

British Journal of Political Science

Volumn 42, Issue 4, 2012, Pages 937-951

  Däubler, Thomas ^a   Benoit, Kenneth ^b   Mikhaylov, Slava ^c   Laver, Michael ^d  

^a UNIVERSITY OF MANNHEIM   (Germany)

^b LONDON SCHOOL OF ECONOMICS   (United Kingdom)

^c UNIVERSITY COLLEGE LONDON   (United Kingdom)

^d NEW YORK UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84869398082     PISSN: 00071234     EISSN: 14692112     Source Type: Journal    
DOI: 10.1017/S0007123412000105     Document Type: Review

References (36)

1. Michael Laver, Kenneth Benoit and John Garry, 'Extracting Policy Positions from Texts Using Words as Data', American Political Science Review, 97 (2003), 311-31;
2. Jonathan Slapin and Sven-Oliver Proksch, 'A Scaling Model for Estimating Time-Series Party Positions from Texts', American Journal of Political Science, 52 (2008), 705-22.
3. Ian Budge, Hans-Dieter Klingemann, Andrea Volkens, Judith Bara, Eric Tanenbaum, Richard C. Fording, Derek J. Hearl, Hee Min Kim, Michael D. McDonald and Silvia M. Mendes, Mapping Policy Preferences: Estimates for Parties, Electors, and Governments, 1945-1998 (Oxford: Oxford University Press, 2001);
4. Hans-Dieter Klingemann, Andrea Volkens, Judith Bara, Ian Budge and Michael McDonald, Mapping Policy Preferences II : Estimates for Parties, Electors, and Governments in Eastern Europe, European Union, and OECD 1990-2003 (Oxford: Oxford University Press, 2006);
5. Frank R. Baumgartner, Christoffer Green-Pedersen and Bryan D. Jones, Comparative Studies of Policy Agendas (London: Routledge, 2007).
6. Klaus Krippendorff, Content Analysis: An Introduction to its Methodology (Thousand Oaks, Calif.: Sage, 2004).
7. Laver, Benoit and Garry, 'Extracting Policy Positions from Texts Using Words as Data';
8. Burt L. Monroe and Ko Maeda, Rhetorical Ideal Point Estimation: Mapping Legislative Speech (Palo Alto, Calif.: Stanford University: Society for Political Methodology, 2004);
9. Burt Monroe, Michael Colaresi and Kevin M. Quinn, 'Fightin' Words: Lexical Feature Selection and Evaluation for Identifying the Content of Political Conflict', Political Analysis, 16 (2008), 372-403;
10. Slapin and Proksch, 'A Scaling Model for Estimating Time-Series Party Positions from Texts';
11. Daniel J. Hopkins and Gary King, 'A Method of Automated Nonparametric Content Analysis for Social Science', American Journal of Political Science, 54 (2009), 229-47.
12. Watson and DeepQA, can be found at http://www-03.ibm.com/innovation/us/ watson/.
13. Krippendorff, Content Analysis: An Introduction to its Methodology, p. 212.
14. Michael Laver and John Garry, 'Estimating Policy Positions from Political Texts', American Journal of Political Science, 44 (2000), 619-34;
15. Laver, Benoit and Garry, 'Extracting Policy Positions from Texts Using Words as Data';
16. Slapin and Proksch, 'A Scaling Model for Estimating Time-Series Party Positions from Texts'.
17. Klingemann et al., Mapping Policy Preferences II, chaps 4-6.
18. Budge et al., Mapping Policy Preferences;
19. Baumgartner, Green-Pedersen and Jones, Comparative Studies of Policy Agendas.
20. Andrea Volkens, Manifesto Coding Instructions (2nd revised edn), Wissenschaftszentrum Berlin, Discussion Paper FS III 02-201 (2001), p. 96.
21. Krippendorff, Content Analysis.
22. Klingemann et al., Mapping Policy Preferences II, Appendix II.
23. Slava Mikhaylov, Michael Laver and Kenneth Benoit, 'Coder Reliability and Misclassification in the Human Coding of Party Manifestos', Political Analysis, 20 (2012), 78-91
24. Will Lowe, Kenneth Benoit, Slava Mikhaylov and Michael Laver, 'Scaling Policy Preferences from Coded Political Texts', Legislative Studies Quarterly, 36 (2011), 123-55.
25. Daniela Braun, Slava Mikhaylov and Hermann Schmitt, 'Computer-Assisted Human Coding: Experimental Tests of Reliability' (paper presented at 'Political Parties and Comparative Policy Agendas', an ESF Workshop on Political Parties and their Positions, and Policy Agendas, University of Manchester, 2010).
26. Slava Mikhaylov, Michael Laver and Kenneth Benoit, 'Coder Reliability and Misclassification in the Human Coding of Party Manifestos', Political Analysis, 20 (2012), 78-91.
27. Budge et al., Mapping Policy Preferences.
28. Braun, Mikhaylov and Schmitt, 'Computer-Assisted Human Coding'.
29. Joseph L. Fleiss, 'Measuring Nominal Scale Agreement among Many Raters', Psychological Bulletin, 76 (1971), 378-83;
30. Joseph L. Fleiss, Bruce A. Levin and Myunghee Cho Paik, Statistical Methods for Rates and Proportions (Hoboken, N.J.: Wiley, 2003).
31. Chris Roberts, 'Modelling Patterns of Agreement for Nominal Scales', Statistics in Medicine, 27 (2008), 810-30, p. 811.
32. Jacob Cohen ('A Coefficient of Agreement for Nominal Scales', Educational and Psychological Measurement, 20 (1960), 37-46)
33. Andrew F. Hayes and Klaus Krippendorff, 'Answering the Call for a Standard Reliability Measure for Coding Data', Communication Methods and Measures, 1 (2007), 77-89.
34. J. Richard Landis and Gary G. Koch, 'The Measurement of Observer Agreement for Categorical Data', Biometrics, 33 (1977), 159-74;
35. Fleiss, Levin and Paik, Statistical Methods for Rates and Proportions, p. 604.
36. Mikhaylov, Laver and Benoit, 'Coder Reliability and Misclassification in the Human Coding of Party Manifestos'.