Indirect scaling methods for testing quantitative emotion theories

REFERENCES

• Anderson, N. H. (1970). Functional measurement and psychophysical judgment. Psychological Review, 77, 153–170. doi:10.1037/h0029064
   PubMed Web of Science ®Google Scholar
• Anderson, N. H. (1982). Methods of information integration theory. New York, NY: Academic Press.
   Google Scholar
• Anderson, N. H. (1989). Information integration approach to emotions and their measurement. In R. Plutchik & H. Kellerman (Eds.), Emotion: Theory, research and experience (vol. 4, pp. 133–186). New York, NY: Academic Press.
   Google Scholar
• Bagozzi, M. (1980). Causal models in marketing. New York, NY: Wiley.
   Google Scholar
• Bakeman, R. (2005). Recommended effect size statistics for repeated measures designs. Behavior Research Methods, 37, 379–384. doi:10.3758/BF03192707
   PubMed Web of Science ®Google Scholar
• Bates, D. M., & Watts, D. G. (1988). Nonlinear regression analysis and its applications. New York, NY: Wiley.
   Google Scholar
• Bechtel, G. G. (1967). The analysis of variance and pairwise scaling. Psychometrika, 32, 47–65. doi:10.1007/BF02289404
   PubMed Web of Science ®Google Scholar
• Bechtel, G. G., & O'Connor, P. J. (1979). Testing micropreference structures. Journal of Marketing Research, 16, 247–257. doi:10.2307/3150688
   Web of Science ®Google Scholar
• Bell, D. E. (1985). Disappointment in decision making under uncertainty. Operations Research, 33, 1–27. doi:10.1287/opre.33.1.1
   Web of Science ®Google Scholar
• Birnbaum, M. H. (2011). Testing theories of risky decision making via critical tests. Frontiers in Psychology, 2, 315. doi:10.3389/fpsyg.2011.00315.
   PubMed Web of Science ®Google Scholar
• Böckenholt, U. (2001). Thresholds and intransitivities in pairwise judgments: A multilevel analysis. Journal of Educational and Behavioral Statistics, 26, 269–282. doi:10.3102/10769986026003269
   Web of Science ®Google Scholar
• Böckenholt, U. (2003). Thurstonian-based analyses: Past, present, and future utilities. Psychometrika, 71, 615–629. doi:10.1007/s11336-006-1598-5
   Web of Science ®Google Scholar
• Böckenholt, U. (2004). Comparative judgments as an alternative to ratings: Identifying the scale origin. Psychological Methods, 4, 453–465. doi:10.1037/1082-989X.9.4.453
   Web of Science ®Google Scholar
• Bollen, K. A., & Noble, M. D. (2011). Structural equation models and the quantification of behavior. Proceedings of the National Academy of Sciences of the United States of America, 108, 15639–15646. doi:10.1073/pnas.1010661108
   PubMed Web of Science ®Google Scholar
• Borg, I., & Staufenbiel, T. (2007). Theorien und Methoden der Skalierung [Theories and methods of scaling]. Bern: Huber.
   Google Scholar
• Bortz, J., Lienert, G. A., & Boehnke, K. (2008). Verteilungsfreie Methoden in der Biostatistik [Nonparametric methods in biostatistics] (3rd ed.).Heidelberg: Springer.
   Google Scholar
• Boschman, M. C. (2001). DifScal: A tool for analyzing difference ratings on an ordinal category scale. Behavior Research Methods, Instruments, & Computers, 33, 10–20. doi:10.3758/BF03195343
   PubMedGoogle Scholar
• Burton, M. L. (2003). Too many questions? The uses of incomplete cyclic designs for paired comparisons. Field Methods, 15, 115–130. doi:10.1177/1525822X03015002001
   Google Scholar
• Cohen, A. L., Sanborn, A. N., & Shiffrin, R. M. (2008). Model evaluation using grouped or individual data. Psychonomic Bulletin and Review, 15, 692–712. doi:10.3758/PBR.15.4.692
   PubMed Web of Science ®Google Scholar
• Critchlow, D. E., & Fligner, M. A. (1991). Paired comparison, triple comparison, and ranking experiments as generalized linear models, and their implementation on GLIM. Psychometrika, 3, 517–533. doi:10.1007/BF02294488
   Web of Science ®Google Scholar
• Curtis, V., Aunger, R., & Rabie, T. (2004). Evidence that disgust evolved to protect from risk of disease. Proceedings of the Royal Society London B, 271(Suppl.), 131–133. doi:10.1098/rsbl.2003.0144
   Web of Science ®Google Scholar
• Curtis, V., & Biran, A. (2001). Dirt, disgust and disease: Is hygiene in our genes? Perspectives in Biology and Medicine, 44, 17–31. doi:10.1353/pbm.2001.0001
   PubMed Web of Science ®Google Scholar
• Davis, W. (1981). A theory of happiness. Philosophical Studies, 39, 305–317. doi:10.1007/BF00354361
   Web of Science ®Google Scholar
• De Beuckelaer, A., Kampen, J. K., & Van Trijp, H. C. M. (2013). An empirical assessment of the cross-national measurement validity of graded paired comparisons. Quality and Quantity, 47, 1063–1076. doi:10.1007/s11135-011-9583-1
   Web of Science ®Google Scholar
• Forster, K. I., & Forster, J. C. (2003). DMDX: A Windows display program with millisecond accuracy. Behavior Research Methods, Instruments, & Computers, 35, 116–124. doi:10.3758/BF03195503
   PubMedGoogle Scholar
• Fox, C. R., & Poldrack, R. A. (2009). Prospect theory and the brain. In P. W. Glimcher, C. F. Camerer, E. Fehr, & R. A. Poldrack (Eds.), Neuroeconomics: Decision making and the brain pp. 145–174). London: Elsevier/Academic Press.
   Google Scholar
• Frijda, N. H., Ortony, A., Sonnemans, J., & Clore, G. L. (1992). The complexity of intensity: Issues concerning the structure of emotion intensity. In M. S. Clark (Ed.), Review of personality and social psychology. (vol 13, pp. 60–89). Beverley Hills, CA: Sage. 13
   Google Scholar
• Galanter, E. (1990). Utility scales of monetary and nonmonetary events. American Journal of Psychology, 103, 449–470. doi:10.2307/1423318
   Web of Science ®Google Scholar
• Gonzalez, R., & Wu, G. (1999). On the shape of probability weighting function. Cognitive Psychology, 38, 129–166. doi:10.1006/cogp.1998.0710
   PubMed Web of Science ®Google Scholar
• Gratch, J., Marsella, S., Wang, N., & Stankovic, B. (2009). Assessing the validity of appraisal-based models of emotion. International Conference on Affective Computing and Intelligent Interaction. Amsterdam, IEEE, 2009.(Retrieved from: http://www.ict.usc.edu/~marsella/publications/ACII09-appraisal.pdf)
   Google Scholar
• Green, O. H. (1992). The emotions: A philosophical theory. Dordrecht: Kluwer.
   Google Scholar
• Greene, W. H., & Hensher, D. A. (2010). Modeling ordered choices: A primer. Cambridge: Cambridge University Press.
   Google Scholar
• Gridgeman, N. T. (1959). Pair comparison, with and without ties. Biometrics, 15, 382–388. doi:10.2307/2527742
   Web of Science ®Google Scholar
• Guilford, J. P. (1954). Psychometric methods. New York, NY: McGraw-Hill.
   Google Scholar
• Hardin, J. W., & Hilbe, J. M. (2007). Generalized linear models and extensions. College Station, TX: Stata Press.
   Google Scholar
• Junge, M., & Reisenzein, R. (2010). Kleine Wahrscheinlichkeiten—große Emotionen! [Small probabilities—Big emotions!]. Talk presented at the 47th Congress of the German Psychology Association in Bremen.
   Google Scholar
• Junge, M., & Reisenzein R. (2013a). Graded pair comparisons are an equivalent substitute for quadruple comparisons in emotion scaling experiments. Talk presented at the 55th Conference of Experimental Psychologists in Vienna.
   Google Scholar
• Junge, M., & Reisenzein, R. (2013b). Metric scales for emotion measurement. Manuscript in preparation, University of Greifswald.
   Google Scholar
• Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47, 263–291. doi:10.2307/1914185
   Web of Science ®Google Scholar
• Karabatsos, G. (2005). The exchangeable multinomial model as an approach to testing deterministic axioms of choice and measurement. Journal of Mathematical Psychology, 49, 51–69. doi:10.1016/j.jmp.2004.11.001
   Web of Science ®Google Scholar
• Keppel, G. (1991). Design and analysis: A researcher's handbook. Englewood Cliffs, NJ: Prentice Hall.
   Google Scholar
• Knoblauch, K., & Maloney, L. T. (2008). MLDS: Maximum likelihood difference scaling in R. Journal of Statistical Software, 25, 1–28.
   Web of Science ®Google Scholar
• Krantz, D., Luce, R., Suppes, P., & Tversky, A. (1971). Foundations of measurement. Vol. 1: Additive and polynomial representations. New York: Academic Press.
   Google Scholar
• Lazarus, R. S. (1991). Emotion and adaptation. New York, NY: Oxford University Press.
   Google Scholar
• Loomes, G., & Sudgen, R. (1986). Disappointment and dynamic consistency in choice under uncertainty. Review of Economic Studies, 53, 271–282. doi:10.2307/2297651
   Web of Science ®Google Scholar
• MacKay, D. B., & Zinnes, J. L. (1986). A probabilistic model for the multidimensional scaling of proximity and preference data. Marketing Science, 5, 325–344. doi:10.1287/mksc.5.4.325
   Google Scholar
• Maloney, L. T., & Yang, J. N. (2003). Maximum likelihood difference scaling. Journal of Vision, 3, 573–585. doi:10.1167/3.8.5
   PubMed Web of Science ®Google Scholar
• Mauss, I. B., & Robinson, M. D. (2009). Measures of emotion: A review. Cognition and Emotion, 23, 209–237. doi:10.1080/02699930802204677
   PubMed Web of Science ®Google Scholar
• Maydeu-Olivares, A., & Böckenholt, U. (2008). Modeling subjective health outcomes: Top 10 reasons to use Thurstone's method. Medical Care, 46, 346–348. doi:10.1097/MLR.0b013e31816dd8d9
   PubMed Web of Science ®Google Scholar
• Mellers, B. A., Schwartz, A., Ho, K., & Ritov, I. (1997). Decision affect theory: Emotional reactions to the outcomes of risky options. Psychological Science, 8, 423–429. doi:10.1111/j.1467-9280.1997.tb00455.x
   Web of Science ®Google Scholar
• Michell, J. (1990). An introduction to the logic of psychological measurement. Hillsdale, NJ: Erlbaum.
   Google Scholar
• Michell, J. (2006). Psychophysics, intensive magnitudes, and the psychometrician's fallacy. Studies in History and Philosophy of Biological and Biomedical Sciences, 17, 414–432. doi:10.1016/j.shpsc.2006.06.011
   Google Scholar
• Nagengast, B., Marsh, H. W., Scalas, L. F., Xu, M. K., Hau, K.-T., & Trautwein, U. (2011). Who took the “X” out of expectancy-value theory? A psychological mystery, a substantive-methodological synergy, and a cross-national generalization. Psychological Science, 22, 1058–1066. doi:10.1177/0956797611415540
   PubMed Web of Science ®Google Scholar
• Nesse, G., & Williams, C. (1995). Evolution and healing. London: Weidenfeld & Nicolson.
   Google Scholar
• O'Brien, R. (1985). The relationship between ordinal measures and their underlying values: Why all the disagreement?. Quality and Quantity, 19, 265–277.
   Web of Science ®Google Scholar
• Oishi, S., Schimmack, U., Diener, E., & Suh, E. M. (1998). The measurement of values and individualism–collectivism. Personality and Social Psychology Bulletin, 24, 1177–1198. doi:10.1177/01461672982411005
   Web of Science ®Google Scholar
• Olejnik, S., & Algina, J. (2003). Generalized eta and omega squared statistics: Measures of effect size for some common research designs. Psychological Methods, 8, 434–444. doi:10.1037/1082-989X.8.4.434
   PubMed Web of Science ®Google Scholar
• Orth, B. (1982). A theoretical and empirical study of scale properties of magnitude-estimation and category-rating scales. In B. Wegener (Ed.), Social attitudes and psychophysical measurement. pp. 351–377). Hillsdale, NJ: Erlbaum.
   Google Scholar
• Ortony, A., Clore, G. L., & Collins, A. (1988). The cognitive structure of emotions. Cambridge: Cambridge University Press.
   Google Scholar
• Prelec, H. (1998). The probability weighting function. Econometrica, 66, 497–528. doi:10.2307/2998573
   Web of Science ®Google Scholar
• R Development Core Team. (2011). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. (ISBN 3-900051-07-0, URL http://www.R-project.org/)
   Google Scholar
• Reips, U.-D., & Neuhaus, C. (2002). WEXTOR: A web-based tool for generating and visualizing experimental designs and procedures. Behavior Research Methods, Instruments, and Computers, 34, 234–240.
   PubMedGoogle Scholar
• Reisenzein, R. (1994). Pleasure-arousal theory and the intensity of emotions. Journal of Personality and Social Psychology, 67, 525–539. doi:10.1037/0022-3514.67.3.525
   Web of Science ®Google Scholar
• Reisenzein, R. (2000). Exploring the strength of association between the components of emotion syndromes: The case of surprise. Cognition and Emotion, 14, 1–38. doi:10.1080/026999300378978
   Web of Science ®Google Scholar
• Reisenzein, R. (2009a). Emotions as metarepresentational states of mind: Naturalizing the belief–desire theory of emotion. Cognitive Systems Research, 10, 6–20. doi:10.1016/j.cogsys.2008.03.001
   Web of Science ®Google Scholar
• Reisenzein, R. (2009b). Emotional experience in the computational belief desire theory of emotion. Emotion Review, 1, 214–222. doi:10.1177/1754073909103589
   Web of Science ®Google Scholar
• Reisenzein, R. (2010). Is disgust an emotion? [Abstract]. Review of Psychology, 17, 144–145. (Abstract of a talk presented at the 9th Alps Adria Psychology Conference, Klagenfurt, Austria)
   Google Scholar
• Reisenzein, R., & Junge, M. (2006). Überraschung, Enttäuschung und Erleichterung: Emotionsintensität als Funktion von subjektiver Wahrscheinlichkeit und Erwünschtheit [Surprise, disappointment and relief: Emotion intensity as function of subjective probability and desirability]. Talk presented at the 45th Congress of the German Psychological Association in Nuremburg.
   Google Scholar
• Reisenzein, R., & Junge, M. (2012). Language and emotion from the perspective of the computational belief–desire theory of emotion. In P. A. Wilson (Ed.), Dynamicity in emotion concepts ( Lodz Studies in Language, Vol. 27, pp. 37–59). Frankfurt am Main: Peter Lang.
   Google Scholar
• Reisenzein, R., & Junge, M. (2013a). Testing the quantitative belief–desire theory of emotion using mixed nonlinear models. Manuscript in preparation, University of Greifswald.
   Google Scholar
• Reisenzein, R., & Junge, M. (2013b). Disgust as a sensory emotion. Manuscript in preparation, University of Greifswald.
   Google Scholar
• Reisenzein, R., & Schimmack, U. (1999). Similarity judgments and covariations of affects: Findings and implications for affect structure research. Personality and Social Psychology Bulletin, 25, 539–555. doi:10.1177/0146167299025005001
   Web of Science ®Google Scholar
• Reisenzein, R., Studtmann, M., & Horstmann, G. (2013). Coherence between emotion and facial expression: Evidence from laboratory experiments. Emotion Review, 5, 16–23. doi:10.1177/1754073912457228
   Web of Science ®Google Scholar
• Ritz, C., & Streibig, J. C. (2008). Nonlinear regression with R. Berlin: Springer.
   Google Scholar
• Roberts, F. S. (1979). Measurement theory: With applications to decision making, utility, and the social sciences. Reading, MA: Addison-Wesley.
   Google Scholar
• Royzman, E. B., & Sabini, J. (2001). Something it takes to be an emotion: The interesting case of disgust. Journal for the Theory of Social Behaviour, 31, 29–59. doi:10.1111/1468-5914.00145
   Web of Science ®Google Scholar
• Rozin, P., Haidt, J., & McCauley, C. R. (2008). Disgust. In M. Lewis J. M. Haviland-Jones, & L. F. Barrett (Eds.), (3rd ed.). Handbook of emotions. pp. 757–776). New York, NY: Guilford Press.
   Google Scholar
• Scherer, K. R. (2001). Appraisal considered as a process of multi-level sequential checking. In K. R. Scherer A. Schorr, & T. Johnstone (Eds.), Appraisal processes in emotion: Theory, methods, research. pp. 92–120). New York, NY: Oxford University Press.
   Google Scholar
• Stevens, S. S. (1975). Psychophysics. New York, NY: Wiley.
   Google Scholar
• Stott, H. (2006). Cumulative prospect theory's functional menagerie. Journal of Risk and Uncertainty, 32, 101–130. doi:10.1007/s11166-006-8289-6
   Web of Science ®Google Scholar
• Thurstone, L. L. (1927). A law of comparative judgment. Psychological Review, 34, 273–286. doi:10.1037/h0070288
   Google Scholar
• Titchener, E. B. (1905). Experimental psychology: A manual of laboratory practice. Vol. II. Quantitative experiments. Part II. Instructor's manual. London: Macmillan.
   Google Scholar
• Torgerson, W. S. (1958). Theory and methods of scaling. New York, NY: Wiley.
   Google Scholar
• Tukey, J. (1949). One degree of freedom for non-additivity. Biometrics, 5, 232–242. doi:10.2307/3001938
   Web of Science ®Google Scholar
• Tversky, A. (1977). Features of similarity. Psychological Review, 84, 327–352. doi:10.1037/0033-295X.84.4.327
   Web of Science ®Google Scholar
• Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5, 297–323. doi:10.1007/BF00122574
   Web of Science ®Google Scholar
• Westermann, R. (1994). Measurement-theoretical idealizations and empirical research practice. In M. Kuokkanen (Ed.), Idealization VII: Structuralism, idealization and approximation ( Poznan studies in the philosophy of the sciences and the humanities, Vol. 42, pp. 271–284). Amsterdam: Rodopi.
   Google Scholar
• Zheng, B., & Agresti, A. (2000). Summarizing the predictive power of a generalized linear model. Statistics in Medicine, 19, 1771–1781. doi:10.1002/1097-0258(20000715)19:13%3C1771::AID-SIM485%3E3.0.CO;2-P
   PubMed Web of Science ®Google Scholar