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Latent Variable Modelling and Item Response Theory Analyses in Marketing Research

Author

Listed:
  • Brzezińska Justyna

    (University of Economics in Katowice, Faculty of Finance and Insurance, Department of Economic and Financial Analysis, 1 Maja 50, 40-287 Katowice, Poland)

Abstract

Item Response Theory (IRT) is a modern statistical method using latent variables designed to model the interaction between a subject’s ability and the item level stimuli (difficulty, guessing). Item responses are treated as the outcome (dependent) variables, and the examinee’s ability and the items’ characteristics are the latent predictor (independent) variables. IRT models the relationship between a respondent’s trait (ability, attitude) and the pattern of item responses. Thus, the estimation of individual latent traits can differ even for two individuals with the same total scores. IRT scores can yield additional benefits and this will be discussed in detail. In this paper theory and application with R software with the use of packages designed for modelling IRT will be presented.

Suggested Citation

  • Brzezińska Justyna, 2016. "Latent Variable Modelling and Item Response Theory Analyses in Marketing Research," Folia Oeconomica Stetinensia, Sciendo, vol. 16(2), pages 163-174, December.
    • Handle: RePEc:vrs:foeste:v:16:y:2016:i:2:p:163-174:n:12
    DOI: 10.1515/foli-2016-0032
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    References listed on IDEAS

    as
    1. Erling Andersen, 1977. "Sufficient statistics and latent trait models," Psychometrika, Springer;The Psychometric Society, vol. 42(1), pages 69-81, March.
    2. Frank Baker, 1961. "Empirical comparison of item parameters based on the logistic and normal functions," Psychometrika, Springer;The Psychometric Society, vol. 26(2), pages 239-246, June.
    3. David Andrich, 1978. "A rating formulation for ordered response categories," Psychometrika, Springer;The Psychometric Society, vol. 43(4), pages 561-573, December.
    4. Yang, Chih-Chien, 2006. "Evaluating latent class analysis models in qualitative phenotype identification," Computational Statistics & Data Analysis, Elsevier, vol. 50(4), pages 1090-1104, February.
    5. Fox, Jean-Paul, 2007. "Multilevel IRT Modeling in Practice with the Package mlirt," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 20(i05).
    6. Elizabeth S. Garrett & Scott L. Zeger, 2000. "Latent Class Model Diagnosis," Biometrics, The International Biometric Society, vol. 56(4), pages 1055-1067, December.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    latent class analysis; latent variables; item response theory models; survey discrete survey response data; R software;
    All these keywords.

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C59 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Other

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