Skip to main content
SpringerLink
Log in

Mixture Models

  • Chapter
  • First Online:
Advanced Methods for Modeling Markets

Part of the book series: International Series in Quantitative Marketing ((ISQM))

Abstract

Marketing research literature, and most quantitative literature in other fields, addresses two main topics:

  1. 1.

    categorization, and

  2. 2.

    prediction.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 299.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 379.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 379.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Compare Sect. 5.6, Vol. I.

References

  • Bakk, Z., Tekle, F.B., Vermunt, J.K.: Estimating the association between latent class membership and external variables using bias adjusted three-step approaches. Sociol. Methodol. 43, 272–311 (2013)

    Article  Google Scholar 

  • Bhatnagar, A., Ghose, S.: Segmenting consumers based on the benefits and risks of internet shopping. J. Bus. Res. 57, 1352–1360 (2004)

    Article  Google Scholar 

  • Bijmolt, T.H., Paas, L.J., Vermunt, J.K.: Country and consumer segmentation: multi-level latent class analysis of financial product ownership. Int. J. Res. Mark. 21, 323–340 (2004)

    Article  Google Scholar 

  • Bolck, A., Croon, M.A., Hagenaars, J.A.: Estimating latent structure models with categorical variables: one-step versus three-step estimators. Polit. Anal. 12, 3–27 (2004)

    Article  Google Scholar 

  • Croon, M.A.: Latent class analysis with ordered latent classes. Br. J. Math. Stat. Psychol. 43, 171–192 (1990)

    Article  Google Scholar 

  • Dayton, C.M.: Latent Class Scaling Analysis. Sage, Thousand Oaks, CA (1999)

    Google Scholar 

  • Dayton, C.M., Macready, G.B.: Concomitant-variable latent class models. J. Am. Stat. Assoc. 83, 173–178 (1988)

    Article  Google Scholar 

  • De Keyser, A., Konüs, U., Schepers, J.: Multichannel customer segmentation: does the after-sales channel matter? A replication and extensions. Int. J. Res. Mark. 32, 453–456 (2015)

    Article  Google Scholar 

  • Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm (with discussion). J. R. Stat. Soc. Ser. B. 39, 1–38 (1977)

    Google Scholar 

  • Dickenson, J.R., Kirzner, E.: Priority patterns of acquisition of financial assets. J. Acad. Mark. Sci. 14, 43–49 (1986)

    Article  Google Scholar 

  • Feick, L.F.: Latent class models for the analysis of behavioral hierarchies. J. Mark. Res. 24, 174–186 (1987)

    Article  Google Scholar 

  • Goodman, L.A.: Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika. 61, 215–231 (1974)

    Article  Google Scholar 

  • Green, P.E., Carmone, F.J., Wachspress, D.P.: Consumer segmentation via latent class analysis. J. Consum. Res. 3, 170–174 (1976)

    Article  Google Scholar 

  • Grover, R., Srinivasan, V.: A simultaneous approach to market segmentation and market structuring. J. Mark. Res. 24, 139–153 (1987)

    Article  Google Scholar 

  • Gupta, S., Chintagunta, P.K.: On using demographic variables to determine segment membership in logit mixture models. J. Mark. Res. 31, 128–136 (1994)

    Article  Google Scholar 

  • Gutman, J.: A means-end model based on consumer categorization processes. J. Mark. 46(Spring), 60–72 (1982)

    Article  Google Scholar 

  • Hagenaars, J.A.: Latent structure models with direct effects between indicators: local dependence models. Sociol. Methods Res. 16, 379–405 (1988)

    Article  Google Scholar 

  • Hambleton, R.K., Swaminathan, H.: Item Response Theory: Principles and Applications. Sage, Thousand Oaks, CA (1985)

    Book  Google Scholar 

  • Heinen, T.: Latent Class and Discrete Latent Trait Models: Similarities and Differences. Sage, Thousand Oaks, CA (1996)

    Google Scholar 

  • Kamakura, W.A., Mazzon, J.A.: Socioeconomic status and consumption in an emerging economy. Int. J. Res. Mark. 30, 4–18 (2013)

    Article  Google Scholar 

  • Kamakura, W.A., Novak, T.P.: Value-system segmentation: exploring the meaning of LCV. J. Consum. Res. 19, 119–132 (1992)

    Article  Google Scholar 

  • Kamakura, W.A., Ramaswami, S.N., Srivastava, R.K.: Applying latent trait analysis in the evaluation of prospects for cross-selling financial products. Int. J. Res. Mark. 8, 329–349 (1991)

    Article  Google Scholar 

  • Kamakura, W.A., Wedel, M., Agrawal, J.: Concomitant variable latent class models for the external analysis of choice data. Int. J. Mark. Res. 11, 541–464 (1994)

    Article  Google Scholar 

  • Lanza, S.T., Collins, L.M., Lemmon, D.R., Schafer, J.L.: PROC LCA: a SAS procedure for latent class analysis. Struct. Equ. Model. 14, 671–694 (2007)

    Article  Google Scholar 

  • Lazarsfeld, P.F.: The logical and mathematical foundation of latent structure analysis & the interpretation and mathematical foundation of latent structure analysis. In: Stouffer, S.A., et al. (eds.) Measurement and Prediction, pp. 362–472. Princeton University Press, Princeton, NJ (1950)

    Google Scholar 

  • Leeflang, P.S.H., Wittink, D.R., Wedel, M., Naert, P.A.: Building Models for Marketing Decisions. Kluwer Academic Publishers, Boston (2000)

    Book  Google Scholar 

  • Lindsay, B., Clogg, C.C., Grego, J.: Semiparametric estimation in the Rasch model and related models, including a simple latent class model for item analysis. J. Am. Stat. Assoc. 86, 96–107 (1991)

    Article  Google Scholar 

  • Magidson, J., Vermunt, J.K.: Latent class factor and cluster models, bi-plots and related graphical displays. Sociol. Methodol. 31, 223–264 (2001)

    Article  Google Scholar 

  • McLachlan, G.J., Peel, D.: Finite Mixture Models. John Wiley & Sons, New York (2000)

    Book  Google Scholar 

  • Nagelkerke, E. Oberski, D.L., Vermunt, J.K.: Goodness-of-fit measures for multilevel latent class models. Sociol. Methodol. 46, 252--282 (2016)

    Google Scholar 

  • Oberski, D.L., van Kollenburg, G.H., Vermunt, J.K.: A Monte Carlo evaluation of three methods to detect local dependence in binary data latent class models. Adv. Classif. Data Anal. 7, 267–279 (2013)

    Article  Google Scholar 

  • Paas, L.J., Bijmolt, T.H.A., Vermunt, J.K.: Long-term developments of respondent financial product portfolios in the EU: a multilevel latent class analysis. Metron. 73, 249–262 (2015)

    Article  Google Scholar 

  • Paas, L.J., Molenaar, I.W.: Analysis of acquisition patterns: a theoretical and empirical evaluation of alternative methods. Int. J. Res. Mark. 22, 87–100 (2005)

    Article  Google Scholar 

  • Paas, L.J., Sijtsma, K.: Nonparametric item response theory for evaluating the dimensionality of marketing measurement scales: a SERVQUAL application. Mark. Lett. 19, 157–170 (2008)

    Article  Google Scholar 

  • Proctor, C.H.: A probabilistic formulation and statistical analysis of Guttman scaling. Psychometrika. 35, 73–78 (1970)

    Article  Google Scholar 

  • Rossiter, J.R., Bellman, S.: Marketing Communications: Theory and Applications. Prentice Hall, Frenchs Forest (2005)

    Google Scholar 

  • Rost, J.: Rasch models in latent classes. An integration of two approaches to item analysis. Appl. Psychol. Meas. 14, 271–282 (1990)

    Article  Google Scholar 

  • Sijtsma, K., Molenaar, I.W.: Introduction to Nonparametric Item Response Theory. Sage, Thousand Oaks, CA (2002)

    Book  Google Scholar 

  • Skrondal, A., Rabe-Hesketh, S.: Generalized Latent Variable Modeling: Multilevel, Longitudinal and Structural Equation Models. Chapman & Hall/CRC, London (2004)

    Book  Google Scholar 

  • Smith, W.R.: Product differentiation and market segmentation as alternative marketing strategies. J. Mark. 21, 3–8 (1956)

    Article  Google Scholar 

  • Ter Hofstede, F., Steenkamp, J-B.E.M., Wedel, M.: International segmentation based on consumer-product relations. J. Mark. Res. 36, 1–17 (1999)

    Article  Google Scholar 

  • Vermunt, J.K.: LEM: A general program for the analysis of categorical data. Department of methodology and statistics, Tilburg University, The Netherlands (1997)

    Google Scholar 

  • Vermunt, J.K.: The use restricted latent class models for defining and testing nonparametric and parametric IRT models. Appl. Psychol. Meas. 25, 283–294 (2001)

    Article  Google Scholar 

  • Vermunt, J.K.: Multilevel latent class models. Sociol. Methodol. 33, 213–239 (2003)

    Article  Google Scholar 

  • Vermunt, J.K.: Latent class modeling with covariates: two improved three-step approaches. Polit. Anal. 18, 450–469 (2010)

    Article  Google Scholar 

  • Vermunt, J.K., Magidson, J.: Technical Guide for Latent GOLD 5.0: Basic, Advanced, and Syntax. Statistical Innovations Inc., Belmont, MA (2000–2016)

    Google Scholar 

  • Vermunt, J.K., Magidson, J.: Latent class cluster analysis. In: Hagenaars, J., McCutcheon, A. (eds.) Applied Latent Class Analysis, pp. 89–106. Cambridge University Press, Cambridge (2002)

    Chapter  Google Scholar 

  • Wedel, M., Kamakura, W.A.: Market Segmentation: Conceptual and Methodological Foundations, 2nd edn. Kluwer, Dordrecht (2000)

    Book  Google Scholar 

  • Yip, G.S.: Total Global Strategy. Prentice Hall, Englewood Cliffs, NJ (1995)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Methodology and Statistics, Tilburg University, Warandelaan 2, Tilburg, 5037 AB, The Netherlands

    Jeroen K. Vermunt

  2. Department of Marketing, School of Communication, Journalism and Marketing, Massey University, Private Bag 102904, North Shore, Auckland, 0745, New Zealand

    Leo J. Paas

Authors
  1. Jeroen K. Vermunt
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Leo J. Paas
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jeroen K. Vermunt .

Editor information

Editors and Affiliations

  1. Department of Marketing, University of Groningen, Groningen, The Netherlands

    Peter S. H. Leeflang

  2. Department of Marketing, University of Groningen, Groningen, The Netherlands

    Jaap E. Wieringa

  3. Department of Marketing, University of Groningen, Groningen, The Netherlands

    Tammo H.A Bijmolt

  4. Department of Marketing, D’Amore-McKim School of Business, Northeastern University, Boston, USA

    Koen H. Pauwels

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this chapter

Cite this chapter

Vermunt, J.K., Paas, L.J. (2017). Mixture Models. In: Leeflang, P., Wieringa, J., Bijmolt, T., Pauwels, K. (eds) Advanced Methods for Modeling Markets. International Series in Quantitative Marketing. Springer, Cham. https://doi.org/10.1007/978-3-319-53469-5_13

Download citation

Publish with us

Policies and ethics

Search

Navigation