AlbersS. (1979), “An Extended Algorithm for Optimal Product Positioning,”European Journal of Operations Research, 3, 222–31.
2.
AlbersS. (1982), “PROPOPP: A Program Package for Optimal Positioning in an Attribute Space,”Journal of Marketing Research, 19(November), 606–8.
3.
AlbersS., and BrockhoffK. (1977), “A Procedure for New Product Positioning in an Attribute Space,”European Journal of Operations Research, 1, 230–38.
4.
ArabiePhipps (1991), “Was Euclid an Unnecessarily Sophisticated Psychologist?”Psychometrika, 45, 211–35.
5.
ArabiePhipps, and Douglas CarrollJ. (1980), “MAPCLUS: A Mathematical Programming Approach to Fitting the ADCLUS Model,”Psychometrika, 45, 211–35.
6.
ArabiePhipps, and Douglas CarrollJ., and DeSarboWayne S. (1987), Three-Way Scaling and Clustering.Newbury Park, CA: Sage Publications. [Translated into Japanese by A. Okada and T. Imaizumi, 1990. Tokyo: Kyoritsu Shuppan.]
7.
ArabiePhipps, and HubertLawrence (1994), “Cluster Analysis in Marketing Research,” in Advanced Methods of Marketing Research, BagozziR., ed. Oxford: Blackwell,160–89.
8.
ArabiePhipps, and SoliSigfrid D. (1982), “The Interface Between the Types of Regression and Methods of Collecting Proximity Data,” in Proximity and Preference: Problems in the Multidimensional Analysis of Large Data Sets, GolledgeR. G., and RaynerJ. N., eds. Minneapolis: University of Minnesota Press,90–115.
9.
BachemA., and SimonH. (1981), “A Product Positioning Model with Costs and Prices,”European Journal of Operations Research, 1, 362–70.
10.
BentlerP. M., and WeeksD. G. (1978), “Restricted Multidimensional Scaling Models,”Journal of Mathematical Psychology, 17, 138–51.
11.
BlattbergRobert C., and SenSubrata K. (1974), “Market Segmentation Using Models of Multidimensional Purchasing Behavior,”Journal of Marketing, 38(October), 17–28.
12.
BöckenholtUlf (1992), “Multivariate Models of Preference and Choice,” in Multidimensional Models of Perception and Cognition, AshbyF. G., ed. Hillsdale, NJ: Lawrence Erlbaum and Associates,89–113.
13.
CarrollJ. Douglas (1972), “Individual Differences and Multidimensional Scaling,” in Multidimensional Scaling: Theory and Applications in the Behavioral Sciences, Vol. 1, ShepardR. N., RomneyA. K., and NerloveS. B., eds. New York and London: Seminar Press, 105–55. [Reprinted in P. Davies and A. P. M. Coxon, eds. (1984), Key Texts on Multidimensional Scaling. Exeter, NH: Heinemann, 267–301.]
14.
CarrollJ. Douglas (1973), “Models and Algorithms for Multidimensional Scaling, Conjoint Measurement and Related Techniques,” in Multiattribute Decisions in Marketing, GreenP. E., and WindY., eds. Hinsdale, IL: Dryden Press, 299–387. [Partially reprinted in P. E. Green, F. J. Carmone, and S. Smith, eds. (1989), Multidimensional Scaling: Concepts and Applications. Newton, MA: Allyn and Bacon, 332–37.]
15.
CarrollJ. Douglas (1976), “Spatial, Non-Spatial and Hybrid Models for Scaling” (Presidential Address for Psychometric Society),Psychometrika, 41, 439–63.
16.
CarrollJ. Douglas (1980), “Models and Methods for Multidimensional Analysis of Preferential Choice (or Other Dominance) Data,” in Similarity and Choice, LantermannE. D., and FergerH., eds. Bern: Hans Huber,234–89.
17.
CarrollJ. Douglas, and ArabiePhipps (1980), “Multidimensional Scaling,” in Annual Review of Psychology, Vol. 31, RosenzweigM. R., and PorterL. W., eds. Palo Alto, CA: Annual Reviews, 607–649. [Reprinted in P. E. Green, F. J. Carmone, and S. M. Smith, eds. (1989), Multidimensional Scaling: Concepts and Applications. Newton, MA: Allyn and Bacon, 168–204.]
18.
CarrollJ. Douglas, and ArabiePhipps (1983), “INDCLUS: An Individual Differences Generalization of the ADCLUS Model and the MAPCLUS Algorithm,”Psychometrika, 48, 157–169. [Reprinted in H. G. Law, W. Snyder, J. Hattie, and R. P. McDonald, eds. (1984), Research Methods for Multimode Data Analysis. New York: Praeger, 372–402.]
19.
CarrollJ. Douglas, and ArabiePhipps (in press), “Theory and Methods of Multidimensional Scaling,” in Handbook of Perception and Cognition: Volume 3: Measurement, Judgment and Decision Making, BirnbaumM. H., ed. New York: Academic Press.
20.
CarrollJ. Douglas, and ChangJih-Jie (1964), “A General Index of Nonlinear Correlation and Its Application to the Problem of Relating Physical and Psychological Dimensions (Abstract),”American Psychologist, 19, 540.
21.
CarrollJ. Douglas, and ChangJih-Jie (1970), “Analysis of Individual Differences in Multidimensional Scaling Via an N-Way Generalization of ‘Eckart-Young’ Decomposition,”Psychometrika, 35, 283–319. [Reprinted in P. Davies and A. P. M. Coxon, eds. (1984), Key Texts in Multidimensional Scaling. Exeter, NH: Heinemann, 229–52].
22.
CarrollJ. Douglas, and ChangJih-Jie (1973), “A Method for Fitting a Class of Hierarchical Tree Structure Models to Dissimilarities Data and Its Application to Some ‘Body Parts’ Data of Miller's,”Proceedings of the 81st Annual Convention of the American Psychological Association, 8, 1097–98.
23.
CarrollJ. Douglas, and ChaturvediAnil (1995), “A General Approach to Clustering and Multidimensional Scaling of Two-Way, Three-Way, or Higher-Way Data,” in Geometric Representations of Perceptual Phenomena, LuceR. D., D'ZmuraM., HoffmanD. D., IversonG., and RomneyA. K., eds. Mahwah, NJ: Lawrence Erlbaum and Associates,295–318.
24.
CarrollJ. Douglas, and SoeteGeert De (1991), “Toward a New Paradigm for the Study of Multiattribute Choice Behavior: Spatial and Discrete Modeling of Pairwise Preference,”American Psychologist, 46, 342–51.
25.
CarrollJ. Douglas, SoeteGeert De, and DeSarboWayne S. (1990), “Two Stochastic Multidimensional Choice Models for Marketing Research,”Decision Sciences, 21, 337–56.
26.
CarrollJ. Douglas, and GreenPaul E. (1995), “Psychometric Methods in Marketing Research: Part I, Conjoint Analysis,”Journal of Marketing Research, 32(November), 385–91.
27.
CarrollJ. Douglas, GreenPaul E., and CarmoneFrank J. (1976), “CANDELINC (CANonical DEcomposition with LINear Constraints): A New Method for Multidimensional Analysis with Constrained Solutions,” in Proceedings of the 21st International Congress of Psychology, Paris, France: Presses Universitaires de France, 322.
28.
CarrollJ. Douglas, and PruzanskySandra (1975), “Fitting of Hierarchical Tree Structure (HTS) Models, Mixtures of HTS Models and Hybrid Models, Via Mathematical Programming and Alternating Least Squares,” in Proceedings of the U. S.-Japan Seminar on Multidimensional Scaling, IndowT., ed. Tokyo: Japan Society for the Promotion of Science,9–19.
29.
CarrollJ. Douglas, and PruzanskySandra (1980), “Discrete and Hybrid Scaling Models,” in Similarity and Choice, LantermannE. D., and FegerH., eds. Bern: Hans Huber,108–39.
30.
CarrollJ. Douglas, and PruzanskySandra (1983), “Representing Proximities Data by Discrete, Continuous or ‘Hybrid’ Models,” in Numerical Taxonomy, FelsensteinJ., ed. Berlin: Springer-Verlag,229–48.
31.
CarrollJ. Douglas, and PruzanskySandra (1984), “The CANDECOMP-CANDELINC Family of Models and Methods for Multidimensional Data Analysis,” in Research Methods for Multimode Data Analysis, LawG., SnyderW., HattieJ., and McDonaldR. P., eds. New York: Praeger,372–402.
32.
CarrollJ. Douglas, and PruzanskySandra (1986), “Discrete and Hybrid Models for Proximity Data,” in Classification as a Tool of Research, GaulW., and SchraderM., eds. Amsterdam: North-Holland,47–59.
33.
CarrollJ. Douglas, PruzanskySandra, and KruskalJoseph B. (1980), “CANDELINC: A General Approach to Multidimensional Analysis of Many-Way Arrays with Linear Constraints on Parameters,”Psychometrika, 45, 3–24.
34.
CarrollJ. Douglas, and WinsbergSuzanne (1995), “Fitting an Extended INDSCAL Model to Three-Way Proximity Data,”Journal of Classification, 12, 57–71.
35.
CarrollJ. Douglas, and WishMyron (1974a), “Models and Methods for Three-Way Multidimensional Scaling,” in Contemporary Developments in Mathematical Psychology, Vol. 2, KrantzD. H., AtkinsonR. C., LuceR. D., and SuppesP., eds. San Francisco: W. H. Freeman,57–105.
36.
CarrollJ. Douglas, and WishMyron (1974b), “Multidimensional Perceptual Models and Measurement Methods,” in Handbook of Perception, Vol. 2, CarteretteE. C., and FriedmanM. P., eds. New York: Academic Press, 391–447. [Reprinted in P. Davies and A. P. M. Coxon eds. (1984), Key Texts on Multidimensional Scaling. Exeter, NH: Heinemann, 43–58.]
37.
ChangJih-Jie, and Douglas CarrollJ. (1968), “How to Use MDPREF: A Computer Program for Multidimensional Analysis of Preference Data,” unpublished manuscript, AT&T Bell Laboratories, Murray Hill, NJ.
38.
ChangJih-Jie, and Douglas CarrollJ. (1969), “How to Use INDSCAL: A Computer Program for Multidimensional Analysis of N-Way Tables and Individual Differences in Multidimensional Scaling (Long Version),” unpublished manuscript, AT&T Bell Laboratories, Murray Hill, NJ.
39.
ChangJih-Jie, and Douglas CarrollJ. (1972a), “How to Use IDIOSCAL: A Computer Program for Individual Differences in Orientation Scaling,” unpublished manuscript, AT&T Bell Laboratories, Murray Hill, NJ.
40.
ChangJih-Jie, and Douglas CarrollJ. (1972b), “How to Use PREFMAP and PREFMAP2: Programs which Relate Preference Data to Multidimensional Scaling Solutions,” unpublished manuscript, AT&T Bell Laboratories, Murray Hill, NJ.
41.
ChangJih-Jie, and Douglas CarrollJ. (1972c), “How to Use PROFIT: A Computer Program for PROPerty FITing by Optimizing Nonlinear or Linear Correlation (Long Version),” unpublished manuscript, AT&T Bell Laboratories, Murray Hill, NJ.
42.
ChangJih-Jie, and Douglas CarrollJ. (1989a), “A Short Guide to MDPREF: Multidimensional Analysts of Preference Data,” in Multidimensional Scaling: Concepts and Applications, GreenP. E., CarmoneF. J., and SmithS. M., eds. Newton, MA: Allyn and Bacon,279–86.
43.
ChangJih-Jie, and Douglas CarrollJ. (1989b), “How to Use INDSCAL—A Computer Program for Canonical Decomposition of N-Way Tables and Individual Differences in Multidimensional Scaling,” in Multidimensional Scaling: Concepts and Applications, GreenP. E., CarmoneF. J., and SmithS. M., eds. Newton, MA: Allyn and Bacon,287–302.
44.
ChangJih-Jie, and Douglas CarrollJ. (1989c), “How to Use PREFMAP—A Program that Relates Preference Data to Multidimensional Scaling Solutions,” in Multidimensional Scaling: Concepts and Applications, GreenP. E., CarmoneF. J., and SmithS. M., eds. Newton, MA: Allyn and Bacon,303–17.
45.
ChangJih-Jie, and Douglas CarrollJ. (1989d), “How to Use PROFIT—A Computer Program for Property Fitting by Optimizing Nonlinear or Linear Correlation,” in Multidimensional Scaling: Concepts and Applications, GreenP. E., CarmoneF. J., and SmithS. M., eds. Newton, MA: Allyn and Bacon,318–31.
46.
ChaturvediAnil, and Douglas CarrollJ. (1994), “An Alternating Combinatorial Optimization Approach to Fitting the INDCLUS and Generalized INDCLUS Models,”Journal of Classification, 11, 155–70.
47.
ChintaguntaPreadeep K. (1994), “Heterogeneous Logit Model Implications for Brand Positioning,”Journal of Marketing Research, 31(May), 304–11.
48.
ChoiS. Chan, DeSarboWayne S., and HarkerP. T. (1990), “Product Positioning Under Price Competition,”Management Science, 36(2), 175–99.
49.
ChoiS. Chan, DeSarboWayne S., and HarkerP. T. (1992), “A Numerical Approach to Deriving Long-Run Equilibrium Solutions in Spatial Positioning Models,”Management Science, 38(1), 75–86.
50.
CoombsClyde H. (1964), A Theory of Data.New York: John Wiley & Sons.
51.
CooperLee G. (1983), “A Review of Multidimensional Scaling in Marketing Research,”Applied Psychological Measurement,1(Fall), 427–50.
52.
CooperLee G., and NakanihiMaseo (1988), Market Share Analysis.Boston: Kluwer.
53.
DayGeorge S., ShockerAllan D., and SrivastavaRajendra K. (1979), “Customer Oriented Approaches to Identifying Product-Markets,”Journal of Marketing, 43(4), 8–19.
54.
De LeeuwJan (1988), “Convergence of the Majorization Method for Multidimensional Scaling,”Journal of Classification, 5, 163–80.
55.
De LeeuwJan, and HeiserWillem (1980), “Multidimensional Scaling with Restrictions on the Configuration,” in Multivariate Analysis-V, KrishnaiahP. R., ed. Amsterdam: North-Holland,501–22.
56.
De SoeteGeert (1983a), “A Least Squares Algorithm for Fitting Additive Trees to Proximity Data,”Psychometrika, 48, 621–26.
57.
De SoeteGeert (1983b), “Are Nonmetric Additive Tree Representations of Numerical Proximity Data Meaningful?”Quality & Quantity, 17, 475–78.
58.
De SoeteGeert (1984a), “A Least Squares Algorithm for Fitting an Ultrametric Tree to a Dissimilarity Matrix,”Pattern Recognition Letters, 2, 133–37.
59.
De SoeteGeert (1984b), “Ultrametric Tree Representations of Incomplete Dissimilarity Data,”Journal of Classification, 1, 235–42.
60.
De SoeteGeert (1984c), “Additive Tree Representations of Incomplete Dissimilarity Data,”Quality & Quantity, 18, 387–93
61.
De SoeteGeert (1984d), “Computer Programs for Fitting Ultrametric and Additive Trees to Proximity Data by Least Squares Methods,”Behavior Research Methods, Instruments & Computers, 16, 551–52.
62.
De SoeteGeert (1988), “Tree Representations of Proximity Data by Least Squares Methods,” in Classification and Related Methods of Data Analysis, BockH. H., ed. Amsterdam: North-Holland,147–56.
63.
De SoeteGeert, and Douglas CarrollJ. (1983), “A Maximum Likelihood Method for Fitting the Wandering Vector Model,”Psychometrika, 48, 553–66.
64.
De SoeteGeert, and Douglas CarrollJ. (1992) “Probabilistic Multidimensional Models of Pairwise Choice Data,” in Multidimensional Models of Perception and Cognition.Hillsdale, NJ: Lawrence Erlbaum and Associates,61–88.
65.
De SoeteGeert, and Douglas CarrollJ. (1996), “Tree and Other Network Models for Representing Proximity Data,” in Clustering and Classification, ArabieP., HubertL. J., and De SoeteG., eds. River Edge, NJ: World Scientific,157–97.
66.
De SoeteGeert, Douglas CarrollJ., and ChaturvediAnil (1993), “A Modified CANDECOMP Method for Fitting the Extended INDSCAL Model,”Journal of Classification, 10, 75–92.
67.
De SoeteGeert, Douglas CarrollJ., and DeSarboWayne S. (1986), “The Wandering Ideal Point Model: A Probabilistic Multidimensional Unfolding Model for Paired Comparisons Data,”Journal of Mathematical Psychology, 30, 28–41.
68.
De Soete, and HeiserWillem J. (1993), “A Latent Class Unfolding Model for Analyzing Single Stimulus Preference Ratings,”Psychometrika, 58, 545–65.
69.
De Soete, and WinsbergSuzanne (1993), “Latent Class Vector Models for Preference Ratings,”Journal of Classification, 10, 195–218.
70.
DeSarboWayne S. (1982), “GENNCLUS: New Models for General Nonhierarchical Clustering Analysis,”Psychometrika, 47, 449–75.
71.
DeSarboWayne S., and Douglas CarrollJ. (1981), “Three-Way Unfolding and Consumer Preference Analysis,”Proceedings of the Educators’ Conference of the American Marketing Association.Chicago: American Marketing Association.
72.
DeSarboWayne S., and Douglas CarrollJ. (1981), “Three-Way Metric Unfolding,” in Marketing, Measurement and Analysis, KeanJ.W., ed. Providence, RI: TIMS College of Marketing.
73.
DeSarboWayne S., and Douglas CarrollJ. (1985), “Three-Way Metric Unfolding via Alternating Weighted Least Squares,”Psychometrika, 50, 275–300.
74.
DeSarboWayne S., Douglas CarrollJ., LehmannDonald R., and O'ShaughnessyJohn (1982), “Three-Way Multivariate Conjoint Analysis,”Marketing Science, 1, 323–50.
75.
DeSarboWayne S., and ChoJ. (1989), “A Stochastic Multidimensional Scaling Vector Threshold Model for the Spatial Representation of ‘Pick Any/N’ Data,”Psychometrika, 54, 105–29.
76.
DeSarboWayne S., HowardDaniel J., and JedidiKamel (1991), “MULTICLUS: An Approach for Performing Simultaneous Multidimensional Scaling and Cluster Analysis,”Psychometrika, 56, 121–36.
77.
DeSarboWayne S., JohnsonMichael D., ManraiAjay K., ManraiLalita A., and EdwardsElizabeth A. (1992), “TSCALE: A New Multidimensional Scaling Procedure Based on Tversky's Contrast Model,”Psychometrika, 57, 43–69.
78.
DeSarboWayne S., and ManraiAjay K. (1992), “A New Multidimensional Scaling Methodology for the Analysis of Asymmetric Proximity Data in Marketing Research,”Marketing Science, 11, 1–20.
79.
DeSarboWayne S., ManraiAjay K., and ManraiLalita A. (1993), “Non-Spatial Tree Models for the Assessment of Competitive Market Structure: An Integrated Review of the Marketing and Psychometric Literature,” in Handbooks in Operations Research and Management Science, Vol. 5, EliashbergJ., and LilienG. L., eds. Amsterdam: North-Holland,193–257.
80.
DeSarboWayne S., ManraiAjay K., and ManraiLalita A. (1994), “Latent Class Multidimensional Scaling: A Review of Recent Developments in the Marketing and Psychometric Literature,” in Advanced Methods of Marketing Research, BagozziR., ed. Oxford: Blackwell,190–222.
81.
DeSarboWayne S., and RaoVithala R. (1984), “GENFOLD2: A Set of Models and Algorithms for the General Unfolding Analysis of Preference/Dominance Data,”Journal of Classification, 1, 147–86.
82.
DeSarboWayne S., and RaoVithala R. (1986), “GENFOLD2: A New Constrained Unfolding Model for Product Positioning,”Marketing Science, 5(1), 1–19.
83.
DeSarboWayne S., WedelM., VriensK., and RamaswamyV. (1992), “Latent Class Metric Conjoint Analysis,”Marketing Letters, 3, 273–88.
84.
EliashbergJehoshua, and ManraiAjay (1989), “Optimal Positioning of New Products: Some Analytical Implications and Empirical Results,” working paper, The Wharton School, University of Pennsylvania.
85.
ElrodTerry (1988), “Choice Map: Inferring a Product-Market Map form Panel Data,”Marketing Science, 7(Winter), 21–40.
86.
GavishB., HorskyD., and SrikanthK. (1983), “An Approach to the Optimal Positioning of a New Product,”Management Science, 29(11), 1277–97.
87.
GowerJohn C. (1978), “Unfolding: Some Technical Problems and Novel Uses,” presented at European Meeting on Psychometrics and Mathematical Psychology, Uppsala, Sweden.
88.
GowerJohn C., and GreenacreMichael J. (1996), “Unfolding a Symmetric Matrix,”Journal of Classification, 13(1), 81–105.
89.
GreenPaul E. (1975), “Marketing Applications of MDS: Assessment and Outlook,”Journal of Marketing, 39(January), 24–31.
90.
GreenPaul E., and CarmoneFrank J. (1970), Multidimensional Scaling and Related Techniques in Marketing Analysis.Boston: Allyn and Bacon.
91.
GreenPaul E., and KriegerAbba M. (1989), “Recent Contributions to Optimal Product Positioning and Buyer Segmentation,”European Journal of Operational Research41, 127–41.
92.
GreenPaul E., KriegerAbba M., and Douglas CarrollJ. (1987), “Conjoint Analysis and Multidimensional Scaling: A Complementary Approach,”Journal of Advertising Research, 27(October/November), 21–27.
93.
GreenPaul E., and RaoVithala R. (1972), Applied Multidimensional Scaling: A Comparison of Approaches and Algorithms.New York: Holt, Rinehart, and Winston.
94.
GreenPaul E., and RaoVithala R. (1977), “Nonmetric Approaches to Multivariate Analysis in Marketing,” in Multivariate Methods in Market and Survey Research, ShethJ.N., ed. Chicago: American Marketing Association,237–54.
95.
GreenPaul E., and WindYoram (1973), Multiattribute Decision in Marketing: A Measurement Approach.Hinsdale, IL: Dryden Press.
96.
GuttmanLouis (1968), “A General Nonmetric Technique for Finding the Smallest Coordinate Space for a Configuration of Points,”Psychometrika, 33, 469–506.
97.
HartiganJohn A. (1967), “Representation of Similarity Matrices by Trees,”Journal of the American Statistical Association, 62, 1140–58.
98.
HauserJohn R., and SimmieP. (1981), “Profit Maximizing Perceptual Positions: An Integrated Theory for the Selection of Product Features and Price,”Management Science, 27(1), 33–56.
99.
HefnerR. (1958), “Extension of the Law of Comparative Judgment to Discriminable and Multidimensional Stimuli,” doctoral dissertation, Department of Psychology, University of Michigan.
100.
HeiserWillem J. (1989), “The City-Block Model for Three-Way Multidimensional Scaling,” in Multiway Data Analysis, Cop-piR., and BolascoS., eds. Amsterdam: North-Holland,395–404.
101.
HoffmanDonna L., LeeuwJan De, and ArjunjiRamesh V. (1994), “Multiple Correspondence Analysis,” in Advanced Methods of Marketing Research, BagozziR., ed. Oxford: Blackwell,260–94.
102.
HolmanEric W. (1979), “Monotonic Models for Asymmetric Proximities,”Journal of Mathematical Psychology, 20, 1–15.
103.
HubertLawrence, ArabiePhipps, and Hesson-McinnisMatthew (1992), “Multidimensional Scaling in the City-Block Metric: A Combinatorial Approach,”Journal of Classification, 9, 211–36.
104.
JedidiKamel, and DeSarboWayne S. (1991), “A Stochastic Multidimensional Scaling Procedure for the Spatial Representation of Three-Mode, Three-Way Pick Any/J Data,”Psychometrika, 56, 471–94.
105.
JohnsonRichard M. (1987), Adaptive Perceptual Mapping Systems.Ketchum, ID: Sawtooth Software.
106.
JohnsonS. C. (1967), “Hierarchical Clustering Schemes,”Psychometrika, 32, 241–54.
107.
KaulAnil, and RaoVithala R. (1994), “Research for Product Positioning and Design Decisions: An Integrative Review,”International Journal of Research in Marketing, 12, 293–320.
108.
KrumhanslCarol L. (1978), “Concerning the Applicability of Geometric Models to Similarity Data: The Interrelationship Between Similarity and Spatial Density,”Cognitive Psychology, 11, 346–74.
109.
KruskalJoseph B. (1964a), “Multidimensional Scaling by Optimizing Goodness of Fit to a Nonmetric Hypothesis,”Psychometrika, 29, 1–27. Also included in Bell System Monograph 4821. See Kruskal (1964b).
110.
KruskalJoseph B. (1964b), “Nonmetric Multidimensional Scaling: A Numerical Method,”Psychometrika, 29, 115–29. Also included in Bell System Monograph 4821. See Kruskal (1964a).
111.
KruskalJoseph B., and CarmoneFrank (1972), “How to Use M-D-Scal (Version 5M) and Other Useful Information,” unpublished manuscript, AT&T Bell Laboratories, Murray Hill, NJ.
112.
KruskalJoseph B., and Douglas CarrollJ. (1969), “Geometric Models and Badness-of-Fit Functions,” in Multivariate Analysis, Vol. 2, KrishnaiahP. R., ed. New York: Academic Press,639–71.
113.
KruskalJoseph B., and WishMyron (1978), Multidimensional Scaling.Newbury Park, CA: Sage Publications.
114.
KruskalJoseph B., YoungForrest W., and SeeryJudith B. (1977), “How to Use KYST2-A: A Very Flexible Program to Do Multidimensional Scaling and Unfolding,” unpublished manuscript, AT&T Bell Laboratories, Murray Hill, NJ.
115.
LehmannDonald R. (1972), “Judged Similarity and Brand-Switching Data as Similarity Measures,”Journal of Marketing Research, 9(August), 331–34.
116.
MacKayDavid B., and ZinnesJoseph L. (1986), “A Probabilistic Model for the Multidimensional Scaling of Proximity and Preference Data,”Marketing Science, 5, 325–44.
117.
MacKayDavid B., and ZinnesJoseph L. (1995), “Probabilistic Multidimensional Unfolding: An Anisotropic Model for Preference Ratio Judgments,”Journal of Mathematical Psychology, 39, 99–111.
118.
MeulmanJacqueline, HeiserWillem J., and Douglas CarrollJ. (1986), “How to Use PREFMAP3,” unpublished manuscript, AT&T Bell Laboratories, Murray Hill, NJ.
119.
MillerG. A., and NicelyP. E. (1955), “An Analysis of Perceptual Confusion Among Some English Consonants,”Journal of the Acoustical Society of America, 27, 338–52.
120.
MooreWilliam L., and WinerRussell S. (1987), “A Panel-Data Based Method for Merging Joint Space and Market Response Function Estimation,”Marketing Science, 6(1), 25–47.
121.
MorganN., and PurnellJ. (1969), “Isolating Openings for New Products in a Multidimensional Space,”Journal of the Market Research Society, 11(July), 245–66.
122.
PruzanskySandra (1975), “How to Use SINDSCAL,” unpublished manuscript, AT&T Bell Laboratories, Murray Hill, NJ.
123.
RamaswamyVenkat, and DeSarboWayne S. (1990), “SCULPTRE: A New Methodology for Deriving and Analyzing Hierarchical Product-Market Structures from Panel Data,”Journal of Marketing Research, 27(November) 418–27.
124.
RamsayJames O. (1977), “Maximum Likelihood Estimation in Multidimensional Scaling,”Psychometrika, 42, 241–66.
125.
RamsayJames O. (1980), “Some Small Sample Results for Maximum Likelihood Estimation in Multidimensional Scaling,”Psychometrika, 45, 139–44.
126.
RamsayJames O. (1982), MULTISCALE II Manual.Mooresville, IN: International Education Services.
RaoVithala R., and SabavalaDarine J. (1981), “Inference of Hierarchical Choice Processes from Panel Data,”Journal of Consumer Research, 8(June), 85–96.
129.
RichardsonM. W. (1938), “Multidimensional Psychophysics,”Psychological Bulletin, 35, 659–60.
130.
SattathShmuel, and TverskyAmos (1977), “Additive Similarity Trees,”Psychometrika, 42, 319–45.
131.
SchonemannPeter H., and WangMing Mei (1972), “An Individual Differences Model for the Multidimensional Analysis of Preference Data,”Psychometrika, 37, 275–309.
132.
ShepardRoger N. (1962a), “The Analysis of Proximities: Multidimensional Scaling with an Unknown Distance Function, Part I,”Psychometrika, 27, 125–39.
133.
ShepardRoger N. (1962b), “The Analysis of Proximities: Multidimensional Scaling with an Unknown Distance Function, Part II,”Psychometrika, 27, 219–46.
134.
ShepardRoger N., and ArabiePhipps (1979), “Additive Clustering: Representation of Similarities as Combination of Discrete Overlapping Properties,”Psychological Review, 86, 87–123.
135.
ShockerAllan D., and SrinivasanV. (1974), “A Consumer-Based Methodology for the Identification of New Product Ideas,”Management Science, 20, 921–37.
136.
ShockerAllan D., and SrinivasanV. (1979), “Multiattribute Approaches for Product Concept Evaluation and Generation: A Critical Review,”Journal of Marketing Research, 16(May), 159–80.
137.
ShuganSteven M. (1987), “Estimating Brand Positioning Maps Using Supermarket Scanning Data,”Journal of Marketing Research, 24(February), 1–19.
138.
SmithScott M. (1990), PC-MDS 5.1 Users Manual.Provo, UT: Brigham Young University.
139.
SrinivasanV., and ShockerAllan D. (1973), “Linear Programming Techniques for Multidimensional Analysis of Preferences,”Psychometrika, 38, 337–67.
140.
SrivastavaRajendra K., LeoneRobert P., and ShockerAllan D. (1981), “Market Structure Analysis: Hierarchical Clustering of Products Based on Substitution in Use,”Journal of Marketing, 45(September), 38–48.
141.
StefflreVolney J. (1969), “Market Structure Studies: New Products for Old Markets and New Markets (Foreign) for Old Products,” in Application of the Sciences in Marketing, BassF. M., KingC. W., and PessimerE. A., eds. New York: John Wiley & Sons,251–68.
142.
SudharshanD., Ravi KumarK., and GrucaThomas S. (1975), “NICHER: An Approach to Identifying Defensible Product Positions,”European Journal of Operational Research, 84, 292–309.
143.
SudharshanD., MayJ. M., and ShockerAllan D. (1987), “A Simulation Comparison of Methods for New Product Location,”Marketing Science, 6(2), 182–207.
144.
TakaneYoshio, and ShibayamaTadashi (1991), “Principal Component Analysis with External Information on Both Subjects and Variables,”Psychometrika, 56, 97–120.
145.
TakaneYoshio, YoungForrest W., and LeeuwJan De (1977), “Nonmetric Individual Differences Multidimensional Scaling: An Alternating Least Squares Method with Optimal Scaling Features,”Psychometrika, 42, 7–67.
146.
TorgersonWarren S. (1958), Theory and Methods of Scaling.New York: John Wiley & Sons.
147.
TuckerLeyard R., and MessickSamuel J. (1963), “Individual Differences Model for Multidimensional Scaling,”Psychometrika, 28, 333–67.
148.
TverskyAmos (1977), “Features of Similarity,”Psychological Review, 84, 327–52.
149.
UrbanGlenn L., JohnsonPhilip L., and HauserJohn R. (1984), “Testing Competitive Market Structures,”Marketing Science, 3(Spring), 83–112.
150.
WinsbergSuzanne, and Douglas CarrollJ. (1989), “A Quasi-Nonmetric Method for Multidimensional Scaling Via an Extended Euclidean Model,”Psychometrika, 54, 217–29
151.
WinsbergSuzanne, and SoeteGeert De (1993), “A Latent Class Approach to Fitting the Weighted Euclidean Model, CLASCAL,”Psychometrika, 58, 315–30.
152.
WinsbergSuzanne, and SoeteGeert De (1996), “Multidimensional Scaling with Constrained Dimensions: CONSCAL,” working paper, British Journal of Mathematical and Statistical Psychology.
153.
WishMyron (1970), “Comparisons Among Multidimensional Structures of Nations Based on Different Measures of Subjective Similarity,” in General Systems, Vol. 15, von BertalanffyL., and RapoportA., eds. Ann Arbor, MI: Society for General Systems Research,55–65.
154.
WishMyron, and Douglas CarrollJ. (1974), “Applications of Individual Differences Scaling to Studies of Human Perception and Judgment,” in Handbook of Perception: Psychophysical Judgment and Measurement, Vol. 2, CarteretteE. C., and FriedmanM. P., eds. New York: Academic Press,449–91.
155.
WishMyron, DeutschMorton, and BienerLois (1970), “Differences in Conceptual Structures of Nations: An Exploratory Study,”Journal of Personality & Social Psychology, 16, 361–73.
156.
WishMyron, DeutschMorton, and BienerLois (1972), “Differences in Perceived Similarity of Nations,” in Multidimensional Scaling: Theory and Applications in the Behavioral Science, Vol. 2, Applicants, RomneyA. K., ShepardR. N., and NerloveS., eds. New York: Seminar Press,289–313.
157.
YoungForrest W., and LewyckyjR. (1981), “ALSCAL.4 User's Guide,” L. L. Thurstone Psychometric Laboratory, unpublished manuscript, University of North Carolina, Chapel Hill.
158.
YoungGale, and HouseholderA. S. (1938), “Discussion of a Set of Points Terms of their Mutual Distances,”Psychometrika, 3, 19–22.
159.
ZielmanBerne, and HeiserWillem J. (1993), “Analysis of Asymmetry by a Slide-Vector,”Psychometrika, 58, 101–14.
160.
ZielmanBerne, and HeiserWillem J. (1994), “Models for Asymmetric Proximities,”Internal Report RR-94-04.Leiden: Department of Data Theory, University of Leiden.
161.
ZinnesJoseph L., and GriggsRichard A. (1974), “Probabilistic, Multidimensional Unfolding Analysis,”Psychometrika, 39, 327–50.
162.
ZinnesJoseph L., and MacKayDavid B. (1989), “Probabilistic Multidimensional Analysis of Preference Ratio Judgments,” in New Developments in Psychological Choice Modeling, De SoeteG., FegerH., and KlauerK. C., eds. Amsterdam: North Holland,177–205.
163.
ZufrydenF. S. (1979), “ZIPMAP—A Zero-One Integer Programming Model for Market Segmentation and Product Positioning,”Journal of Operational Research Society, 30, 63–70.