IDEAS home Printed from https://ideas.repec.org/a/gam/jstats/v7y2024i3p54-905d1462211.html
   My bibliography  Save this article

Scoring Individual Moral Inclination for the CNI Test

Author

Listed:
  • Yi Chen

    (Department of Educational Studies in Psychology, Research Methodology and Counseling, The University of Alabama, Tuscaloosa, AL 35401, USA)

  • Benjamin Lugu

    (Department of Educational Studies in Psychology, Research Methodology and Counseling, The University of Alabama, Tuscaloosa, AL 35401, USA)

  • Wenchao Ma

    (Department of Educational Studies in Psychology, Research Methodology and Counseling, The University of Alabama, Tuscaloosa, AL 35401, USA)

  • Hyemin Han

    (Department of Educational Studies in Psychology, Research Methodology and Counseling, The University of Alabama, Tuscaloosa, AL 35401, USA)

Abstract

Item response theory (IRT) is a modern psychometric framework for estimating respondents’ latent traits (e.g., ability, attitude, and personality) based on their responses to a set of questions in psychological tests. The current study adopted an item response tree (IRTree) method, which combines the tree model with IRT models for handling the sequential process of responding to a test item, to score individual moral inclination for the CNI test—a broadly adopted model for examining humans’ moral decision-making with three parameters generated: sensitivity to moral norms, sensitivity to consequences, and inaction preference. Compared to previous models for the CNI test, the resulting EIRTree-CNI Model is able to generate individual scores without increasing the number of items (thus, less subject fatigue or compromised response quality) or employing a post hoc approach that is deemed statistically suboptimal. The model fits the data well, and the subsequent test also supported the concurrent validity and the predictive validity of the model. Limitations are discussed further.

Suggested Citation

  • Yi Chen & Benjamin Lugu & Wenchao Ma & Hyemin Han, 2024. "Scoring Individual Moral Inclination for the CNI Test," Stats, MDPI, vol. 7(3), pages 1-12, August.
    • Handle: RePEc:gam:jstats:v:7:y:2024:i:3:p:54-905:d:1462211
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2571-905X/7/3/54/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2571-905X/7/3/54/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. David Thissen, 1982. "Marginal maximum likelihood estimation for the one-parameter logistic model," Psychometrika, Springer;The Psychometric Society, vol. 47(2), pages 175-186, June.
    2. De Boeck, Paul & Partchev, Ivailo, 2012. "IRTrees: Tree-Based Item Response Models of the GLMM Family," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(c01).
    3. A. Béguin & C. Glas, 2001. "MCMC estimation and some model-fit analysis of multidimensional IRT models," Psychometrika, Springer;The Psychometric Society, vol. 66(4), pages 541-561, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Anne Thissen-Roe & David Thissen, 2013. "A Two-Decision Model for Responses to Likert-Type Items," Journal of Educational and Behavioral Statistics, , vol. 38(5), pages 522-547, October.
    2. Steven Andrew Culpepper & James Joseph Balamuta, 2017. "A Hierarchical Model for Accuracy and Choice on Standardized Tests," Psychometrika, Springer;The Psychometric Society, vol. 82(3), pages 820-845, September.
    3. Andrés López-Sepulcre & Sebastiano De Bona & Janne K. Valkonen & Kate D.L. Umbers & Johanna Mappes, 2015. "Item Response Trees: a recommended method for analyzing categorical data in behavioral studies," Behavioral Ecology, International Society for Behavioral Ecology, vol. 26(5), pages 1268-1273.
    4. Martijn G. de Jong & Jan-Benedict E. M. Steenkamp & Bernard P. Veldkamp, 2009. "A Model for the Construction of Country-Specific Yet Internationally Comparable Short-Form Marketing Scales," Marketing Science, INFORMS, vol. 28(4), pages 674-689, 07-08.
    5. Michael Edwards, 2010. "A Markov Chain Monte Carlo Approach to Confirmatory Item Factor Analysis," Psychometrika, Springer;The Psychometric Society, vol. 75(3), pages 474-497, September.
    6. Cees Glas, 1988. "The derivation of some tests for the rasch model from the multinomial distribution," Psychometrika, Springer;The Psychometric Society, vol. 53(4), pages 525-546, December.
    7. Christian A. Gregory, 2020. "Are We Underestimating Food Insecurity? Partial Identification with a Bayesian 4-Parameter IRT Model," Journal of Classification, Springer;The Classification Society, vol. 37(3), pages 632-655, October.
    8. Gregory Camilli & Jean-Paul Fox, 2015. "An Aggregate IRT Procedure for Exploratory Factor Analysis," Journal of Educational and Behavioral Statistics, , vol. 40(4), pages 377-401, August.
    9. Li Cai, 2010. "A Two-Tier Full-Information Item Factor Analysis Model with Applications," Psychometrika, Springer;The Psychometric Society, vol. 75(4), pages 581-612, December.
    10. Kuan-Yu Jin & Yi-Jhen Wu & Hui-Fang Chen, 2022. "A New Multiprocess IRT Model With Ideal Points for Likert-Type Items," Journal of Educational and Behavioral Statistics, , vol. 47(3), pages 297-321, June.
    11. Sainan Xu & Jing Lu & Jiwei Zhang & Chun Wang & Gongjun Xu, 2024. "Optimizing Large-Scale Educational Assessment with a “Divide-and-Conquer” Strategy: Fast and Efficient Distributed Bayesian Inference in IRT Models," Psychometrika, Springer;The Psychometric Society, vol. 89(4), pages 1119-1147, December.
    12. Francesco Bartolucci & Alessio Farcomeni & Luisa Scaccia, 2017. "A Nonparametric Multidimensional Latent Class IRT Model in a Bayesian Framework," Psychometrika, Springer;The Psychometric Society, vol. 82(4), pages 952-978, December.
    13. Battauz, Michela & Vidoni, Paolo, 2022. "A likelihood-based boosting algorithm for factor analysis models with binary data," Computational Statistics & Data Analysis, Elsevier, vol. 168(C).
    14. Dora Matzke & Conor Dolan & William Batchelder & Eric-Jan Wagenmakers, 2015. "Bayesian Estimation of Multinomial Processing Tree Models with Heterogeneity in Participants and Items," Psychometrika, Springer;The Psychometric Society, vol. 80(1), pages 205-235, March.
    15. Yan Huo & Jimmy de la Torre & Eun-Young Mun & Su-Young Kim & Anne Ray & Yang Jiao & Helene White, 2015. "A Hierarchical Multi-Unidimensional IRT Approach for Analyzing Sparse, Multi-Group Data for Integrative Data Analysis," Psychometrika, Springer;The Psychometric Society, vol. 80(3), pages 834-855, September.
    16. Lara Fontanella & Paola Villano & Marika Di Donato, 2016. "Attitudes towards Roma people and migrants: a comparison through a Bayesian multidimensional IRT model," Quality & Quantity: International Journal of Methodology, Springer, vol. 50(2), pages 471-490, March.
    17. Steven Andrew Culpepper, 2019. "An Exploratory Diagnostic Model for Ordinal Responses with Binary Attributes: Identifiability and Estimation," Psychometrika, Springer;The Psychometric Society, vol. 84(4), pages 921-940, December.
    18. Thorsten Meiser & Fabiola Reiber, 2023. "Item-Specific Factors in IRTree Models: When They Matter and When They Don’t," Psychometrika, Springer;The Psychometric Society, vol. 88(3), pages 739-744, September.
    19. Yingbin Zhang & Zhaoxi Yang & Yehui Wang, 2022. "The Impact of Extreme Response Style on the Mean Comparison of Two Independent Samples," SAGE Open, , vol. 12(2), pages 21582440221, June.
    20. Francesco Bartolucci, 2007. "A class of multidimensional IRT models for testing unidimensionality and clustering items," Psychometrika, Springer;The Psychometric Society, vol. 72(2), pages 141-157, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jstats:v:7:y:2024:i:3:p:54-905:d:1462211. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.