Preference structures that underlie survey orexperimental responses may systematically varyduring the administration of such measurement.Maturation, learning, fatigue, and responsestrategy shifts may all affect the sequentialelicitation of respondent preferences at differentpoints in the survey or experiment. Theconsequence of this phenomenon is that responsesand effects can vary systematically within the dataset. To capture these structural changes, the authorspresent a maximum likelihood–based change-pointmultiple regression methodology that explicitlydetects discrete structural changes at various pointsin time/sequence in regression coefficients bysimultaneously estimating the number of changepoints, their location and duration in the sequenceof data points, and the respective regressioncoefficients for each subset of the data defined bythe change points. An application involving astated preference or conjoint analyses study ofstudent apartment choices illustratesthat the structure of preferences changessignificantly over the sequence of profileresponses.
Anderson, J. R. (1981). Foundations of informationintegration theory. San Diego: Academic Press.
3.
Anderson, J. R. (1983). The architecture of cognition.Cambridge, MA: Harvard University Press.
4.
Andrews, D. W. K. (1993). Tests for parameterinstability and structural change with unknown changepoint. Econometrica, 62, 1383-1414.
5.
Bettman, J. R. (1979). An information processingtheory of consumer choice. Reading, MA:Addison-Wesley.
6.
Bettman, J. R., & Park, W. C. (1980). Effects ofprior knowledge and experience and phase of thechoice process on consumer decision processes: Aprotocol analysis. Journal of Consumer Research,7, 234-248.
7.
Bettman, J. R., & Zins, M. A. (1979a). Constructiveprocesses in consumer choice. Journal of Consumer Research, 4, 75-85.
8.
Bettman, J. R., & Zins, M. A. (1979b). Informationformat and choice task effects in decision making.Journal of Consumer Research, 6, 141-153.
9.
Bozdogan, H. (1987). Model selection and Akaike’sinformation criterion (AIC): The general theoryand its analytical extensions. Psychometrika, 52,345-370.
10.
Bozdogan, H. (1994). Mixture model clusteranalysis using model selection criteria and a newinformational measure of complexity. In H. Bozdogan(Ed.), Multivariate statistical modeling(Vol. 2,pp. 69-113). Dordrecht, The Netherlands: Kluwer Academic.
11.
Brown, R. L., Durbin, J., & Evans, J. M. (1975).Techniques for testing the constancy of regressionrelationships over time. Journal of the Royal Statistical Society, B37, 149-192.
12.
Burnham, K. P., & Anderson, D. R. (2002). Modelselection and multimodal inference(2nd ed.).New York: Springer-Verlag.
13.
Carlin, B. P. (1992). Hierarchical Bayesian analysisof change point problems. Applied Statistics, 41,389-405.
14.
Carroll, J. D. (1969). Categorical conjointmeasurement. Unpublished manuscript. Murray Hill, N. J.:Bell Laboratories.
15.
Chen, J., & Gupta, A. K. (2000). Parametricstatistical change point analysis. Boston: Birkhäuser.
16.
Chow, G. (1960). Tests of the equality betweentwo sets of coefficients in two linear regressions.Econometrica, 28, 561-605.
17.
Coupey, E., Irwin, J. R., & Payne, J. W. (1998).Product category familiarity and preferenceconstruction. Journal of Consumer Research, 24,459-468.
18.
DeSarbo, W. S., & Cron, W. L. (1988). Aconditional mixture maximum likelihood methodologyfor clusterwise linear regression. Journal of Classification, 5, 249-289.
19.
DeSarbo, W. S., Hollman, F., Fong, D., & Leichty, J.(2001). Evolutionary consumer preference/utilityfunctions: A dynamic perspective. Working paper, Pennsylvania State University.
20.
Diggle, P. J., Heagerty, P., Liang, K., & Zeger, S. L.(2002). Analysis of longitudinal data(2nd ed.).Oxford, UK: Oxford University Press.
21.
Einhorn, H. J., & Hogarth, R. M. (1985).Ambiguity and uncertainty in probabilistic inference. Psychological Review, 92, 433-461.
22.
Farley, J. U.,& Hinich, M. J. (1970). A test for ashifting slope coefficient in a linear model. Journalof the American Statistical Association, 65,1320-1329.
23.
Feldman, J. M., & Lynch, J. G., Jr. (1988).Self-generated validity and other effects ofmeasurement on belief, attitude, intention, andbehavior. Journal of Applied Psychology, 73,421-435.
24.
Fischhoff, B., Slovic, P., & Lichtenstein, S. (1980).Knowing what you want: Measuring labile values.In T. Wallstein (Ed.), Cognitive processes in choiceand decision behavior(pp. 117-141). Hillsdale,NJ: Lawrence Erlbaum.
25.
Gilbert, D. T. (1989). Thinking lightly aboutothers: Automatic components of the social inferenceprocess. In J. S. Uleman & J. A. Bargh (Eds.),Unintended thought(pp. 189-211). New York:Guilford.
26.
Gilbert, D. T., Krull, D. S., & Pelham, B. W. (1988).Of thoughts unspoken: Social inference and theself-regulation of behavior. Journal of Personalityand Social Psychology, 55, 685-694.
27.
Goldfield, S. M., & Quandt, R. E. (1973). A Markovmodel for switching regression. Journal of Econometrics, 1, 3-16.
28.
Green, P. E. (1974). On the design of choiceexperiments involving multifactor alternatives. Journalof Consumer Research, 1, 61-68.
29.
Green, P. E., Helsen, K., & Shandler, B. (1988).Conjoint internal validity under alternative profile presentations. Journal of Consumer Research,15, 392-397.
30.
Green, P. E., & Rao, V. R. (1971). Conjointmeasurement for quantifying judgmental data. Journal of Marketing Research, 8, 355-363.
31.
Greene, W. H. (2003). Econometric analysis(5th ed.).Upper Saddle River, NJ: Prentice Hall.
32.
Gustafsson, F. (2000). Adaptive filtering and changedetection. New York: John Wiley.
33.
Hair, J. E., Anderson, R. E., Tatham, R. L., & Black,W. C. (1995). Multivariate data analysis(4th ed.).Englewood Cliffs, NJ: Prentice Hall.
34.
Hamilton, J. D. (1989). A new approach to theeconomic analysis of nonstationary time series andthe business cycle. Econometrica, 57(2), 357-384.
35.
Hamilton, J. D. (1990). Analysis of time seriessubject to changes in regime. Journal of Econometrics, 45, 39-70.
36.
Hanssens, D. M., Parsons, L. J., & Schultz, R. L.(2001). Market response models: Econometricand time series analysis. Boston: Kluwer.
37.
Hayes-Roth, B. (1977). Evolution of cognitivestructures and processes. Psychological Review, 84,260-278.
38.
Henderson, R.,& Matthews, J. N. S. (1993). Aninvestigation of change points in the annual number ofcases of haemolytic uraemic syndrome. Applied Statistics, 42, 461-471.
39.
Herr, P. M. (1989). Priming price: Prior knowledgeand context effects. Journal of Consumer Research, 16, 67-75.
40.
Huber, J., & Hansen, D. (1986). Testing the impactof dimensional complexity and affectivedifferences of paired concepts in adaptive conjointanalysis. In M. W. Wallendorf & P. Anderson(Eds.), Advances in consumer research(Vol. 14,pp. 159-163). Provo, UT: Association for Consumer Research.
41.
Huber, J., Wittink, D. R., Johnson, R. M., &Miller, R. (1992). Learning effects in preferencetasks: Choice-based versus standard conjoint.In Sawtooth Software conference proceedings.Sequim, WA: Sawtooth Software, Inc.
42.
Jain, A. K., & Pinson, C. (1976). The effects of orderof presentation of similarity judgments onmultidimensional scaling results: An empiricalexamination. Journal of Marketing Research, 13, 435-439.
43.
Johar, G. V., Jedidi, K., & Jacoby, J. (1997). Avarying-parameter averaging model of on-linebrand evaluations. Journal of Consumer Research,24, 232-246.
44.
Johnson, E. J., & Meyer, R. J. (1984).Compensatory choice models of noncompensatoryprocesses: The effect of varying context. Journal of Consumer Research, 11, 528-541.
45.
Johnson, M. D., Lehmann, D. R., & Horne, D. R.(1990). The effects of fatigue on judgments ofinterproduct similarity. International Journal of Research in Marketing, 7(1), 35-43.
46.
Johnson, R. M., & Orme, B. K. (1996). How manyquestions should you ask in choice-basedconjoint studies? Technical report, Sawtooth Software, Inc., Sequim, WA.
47.
Kahneman, D., Slovic, P., & Tversky, A. (Eds.).(1982). Judgment under uncertainty: Heuristicsand biases. Cambridge, UK: Cambridge University Press.
48.
Kardes, F. R., & Kalyanaram, G. (1992). Order-ofentry effects on consumer memory and judgment:An information integration perspective. Journal of Marketing Research, 29, 343-357.
49.
Kardes, F. R., & Snell, J. (1990). Predictingutility. In R. M. Hogarth (Ed.), Insights indecision making: A tribute to Hillel J. Einhorn(pp. 295-310). Chicago: University of Chicago Press.
50.
Kim, C.-J., & Nelson, C. R. (1999). State-spacemodels with regime switching. Cambridge, MA: MIT Press.
51.
Kim, H. J., & Siegmund, D. (1989). The likelihoodratio test for a change point in simple linearregression. Biometrika, 76, 409-423.
52.
Kim, I.-M., & Maddala, G. S. (1991). Multiplestructural breaks and unit roots in the nominal and realexchange rates. Unpublished manuscript,University of Florida, Department of Economics.
53.
Kruskal, J. B. (1965). Analysis of factorialexperiments by estimating monotone transformations ofthe data. Journal of the Royal Statistical Society,Series B, 27, 251-263.
54.
Laird, N., Lange, N., & Stram, D. (1987). Maximumlikelihood computations with repeated measures:Application of the EM algorithm. Journal of the American Statistical Society, 82, 97-105.
55.
Leeflang, P. S. H., Wittink, D. R., Wedel, M.,& Naert,P. (2000). Building models for marketingdecisions. Boston: Kluwer.
56.
Lindsey, J. K. (1999). Models for repeatedmeasurements(2nd ed.). Oxford, UK: Oxford University Press.
57.
Lopes, L. L. (1982). Toward a procedural theoryof judgment. Madison: University of Wisconsin,Department of Psychology, Wisconsin Information Processing Program.
58.
Louviere, J. J., Hensher, D. A., & Swait, J. D.(2000). Stated choice models: Analysis andapplication. Cambridge, UK: Cambridge University Press.
59.
Luce, R. D., & Tukey, J. W. (1964).Simultaneous conjoint measurement: A new type offundamental measurement. Journal of Mathematical Psychology, 1, 1-127.
60.
March, J. G. (1978). Bounded rationality,ambiguity, and the engineering of choice. Bell Journal of Economics, 9, 587-608.
61.
Meyer, R. J. (1987). The learning of multiattributejudgment policies. Journal of Consumer Research,14, 155-173.
62.
Moore, D. A. (1999). Order effects in preferencejudgments: Evidence for context dependence inthe generation of preferences. Organizational Behavior and Human Decision Processes, 78,146-165.
63.
Neftci, S. N. (1984). Are economic time seriesasymmetric over the business cycle? Journal of Political Economy, 92(2), 306-328.
64.
Neter, J., Kutner, M. H., Nachtsheim, C. J., &Wasserman, W. (1996). Applied linear statisticalmodels(4th ed.). New York: McGraw-Hill.
65.
Payne, J. W., Bettman, J. R., & Johnson, E. J.(1992). Behavioral decision research: Aconstructive processing perspective. Annual Review of Psychology, 43, 87-131.
66.
Payne, J. W., Bettman, J. R., & Schkade, D. (1999).Measuring constructed preferences: Towards abuilding code. Journal of Risk and Uncertainty,19(1-3), 243-270.
67.
Ploberger, W., Kramer, W., & Kontrus, K. (1989).A new test for structural stability in the linearregression model. Journal of Econometrics, 40,307-318.
68.
Quandt, R. E. (1958). The estimation of theparameters of a linear regression system obeying twoseparate regimes. Journal of the American Statistical Association, 53, 873-880.
69.
Quandt, R. E. (1960). Tests of the hypothesis that alinear regression system obeys two separateregimes. Journal of the American Statistical Association, 55, 324-330.
70.
Quandt, R. E. (1972). A new approach toestimating switching regressions. Journal of the American Statistical Association, 67, 306-310.
71.
Sclove, S. L. (1983). Time-series segmentation: Amodel and a method. Information Sciences, 29,7-25.
72.
Shank, R., & Abelson, R. (1977). Scripts, plans,goals, and understanding: An inquiry into humanknowledge structures. Hillsdale, NJ: Lawrence Erlbaum.
73.
Simonson, I., & Tversky, A. (1992). Choice incontext: Tradeoff contrast and extremeness aversion.Journal of Marketing Research, 29, 281-295.
74.
Stephens, D. A. (1994). Bayesian retrospectivemultiple change point identification. Applied Statistics,44, 159-178.
75.
Taylor, S. E., & Crocker, J. (1981). Schematic basesof social information processing. In E. T. Higgins,P. Herman, & M. Zanna (Eds.), Social cognition:The Ontario Symposium(Vol. 1). Hillsdale, NJ:Lawrence Erlbaum.
76.
Tversky, A., & Simonson, I. (1993). Contextdependent preferences. Management Science, 39(10),1179-1189.
77.
Verbeke, G.,& Molenberghs, G. (2000). Linear mixedmodels for longitudinal data. New York: Springer-Verlag.
78.
von Eye, A., & Schuster, C. (1998). Regressionanalysis for social science. New York: Academic Press.
79.
von Neumann, J., & Morgenstern, O. (1947). Theoryof games and economic behavior. Princeton, NJ:Princeton University Press.
80.
Wecker, W. E. (1979). Predicting the turning points ofa time series. Journal of Business, 52(1), 35-50.
81.
Wildt, A. R., & Winer, R. S. (1983). Modelingand estimation in changing market environments. Journal of Business, 56(3), 365-388.
82.
Yao, Y. C. (1988). Estimating the number of changepoints by Schwartz’s criterion. Statistics and Probability Letters, 6, 181-187.
83.
Young, F. W. (1969). Polynomial conjoint analysisof similarities: Definitions for a special algorithm(Research Paper No. 76). Chapel Hill: Universityof North Carolina, Psychometric Laboratory.
