IDEAS home Printed from https://ideas.repec.org/a/sae/somere/v25y1997i3p267-283.html
   My bibliography  Save this article

Adjusting for Overdispersion in an Analysis of Comparative Social Mobility

Author

Listed:
  • GARRETT M. FITZMAURICE

    (Nuffield College, Oxford University)

  • JOHN H. GOLDTHORPE

    (Nuffield College, Oxford University)

Abstract

The authors discuss the problem of overdispersion in large-scale data sets and its potential impact on standard model selection strategies. Overdispersion is considered to be present when the data display more variability than is predicted by the assumed sampling model. In a recent cross-national analysis of social mobility, data were combined from nine national studies that employed somewhat different sampling schemes and related data collection procedures. Ignoring these features of the data is quite likely to introduce excess dispersion. Typically, the presence of overdispersion can be due to design effects, hidden clusters, or the absence of relevant explanatory variables in the model. When there is overdispersion, model selection based on the standard likelihood ratio test, the Akaike information criterion, or the Bayesian information criterion generally would be expected to perform poorly. A very simple adjustment to these model selection criteria, to account for overdispersion, is proposed.

Suggested Citation

  • Garrett M. Fitzmaurice & John H. Goldthorpe, 1997. "Adjusting for Overdispersion in an Analysis of Comparative Social Mobility," Sociological Methods & Research, , vol. 25(3), pages 267-283, February.
    • Handle: RePEc:sae:somere:v:25:y:1997:i:3:p:267-283
    DOI: 10.1177/0049124197025003001
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0049124197025003001
    Download Restriction: no
    File URL: https://libkey.io/10.1177/0049124197025003001?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. N. E. Breslow, 1984. "Extra‐Poisson Variation in Log‐Linear Models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 33(1), pages 38-44, March.
    2. D. A. Williams, 1982. "Extra‐Binomial Variation in Logistic Linear Models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 31(2), pages 144-148, June.
    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. Erni Tri Astuti & Takashi Yanagawa, 2002. "Testing Trend for Count Data with Extra-Poisson Variability," Biometrics, The International Biometric Society, vol. 58(2), pages 398-402, June.
    2. Mabel Morales-Otero & Vicente Núñez-Antón, 2021. "Comparing Bayesian Spatial Conditional Overdispersion and the Besag–York–Mollié Models: Application to Infant Mortality Rates," Mathematics, MDPI, vol. 9(3), pages 1-33, January.
    3. Peter Congdon, 1990. "Issues in the Analysis of Small Area Mortality," Urban Studies, Urban Studies Journal Limited, vol. 27(4), pages 519-536, August.
    4. Richard B. Davies & Robert Crouchley, 1986. "The Mover-Stayer Model," Sociological Methods & Research, , vol. 14(4), pages 356-380, May.
    5. Darcy Steeg Morris & Kimberly F. Sellers, 2022. "A Flexible Mixed Model for Clustered Count Data," Stats, MDPI, vol. 5(1), pages 1-18, January.
    6. Lluís Bermúdez & Dimitris Karlis & Isabel Morillo, 2020. "Modelling Unobserved Heterogeneity in Claim Counts Using Finite Mixture Models," Risks, MDPI, vol. 8(1), pages 1-13, January.
    7. You-Gan Wang, 1999. "Estimating Equations for Removal Data Analysis," Biometrics, The International Biometric Society, vol. 55(4), pages 1263-1268, December.
    8. Nasim Vahabi & Anoshirvan Kazemnejad & Somnath Datta, 2018. "A Marginalized Overdispersed Location Scale Model for Clustered Ordinal Data," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 80(1), pages 103-134, December.
    9. Roberto Patuelli & Daniel A. Griffith & Michael Tiefelsdorf & Peter Nijkamp, 2011. "Spatial Filtering and Eigenvector Stability: Space-Time Models for German Unemployment Data," International Regional Science Review, , vol. 34(2), pages 253-280, April.
    10. Gene A. Pennello & Susan S. Devesa & Mitchell H. Gail, 1999. "Using a Mixed Effects Model to Estimate Geographic Variation in Cancer Rates," Biometrics, The International Biometric Society, vol. 55(3), pages 774-781, September.
    11. Francesca Dominici & Giovanni Parmigiani, 2001. "Bayesian Semiparametric Analysis of Developmental Toxicology Data," Biometrics, The International Biometric Society, vol. 57(1), pages 150-157, March.
    12. Oludare Ariyo & Emmanuel Lesaffre & Geert Verbeke & Adrian Quintero, 2022. "Bayesian Model Selection for Longitudinal Count Data," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(2), pages 516-547, November.
    13. Paul D. Allison, 1987. "Introducing a Disturbance into Logit and Probit Regression Models," Sociological Methods & Research, , vol. 15(4), pages 355-374, May.
    14. Chris J. Lloyd, 2000. "Regression Models for Convex ROC Curves," Biometrics, The International Biometric Society, vol. 56(3), pages 862-867, September.
    15. Lee, Keunbaik & Joo, Yongsung, 2019. "Marginalized models for longitudinal count data," Computational Statistics & Data Analysis, Elsevier, vol. 136(C), pages 47-58.
    16. Mengya Liu & Qi Li & Fukang Zhu, 2020. "Self-excited hysteretic negative binomial autoregression," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(3), pages 385-415, September.
    17. Maryam Aghayerashti & Ehsan Bahrami Samani & Mojtaba Ganjali, 2023. "Bayesian Latent Variable Model of Mixed Correlated Rank and Beta-Binomial Responses with Missing Data for the International Statistical Literacy Project Poster Competition," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 210-250, May.
    18. Peter J. Hannan & David M. Murray, 1996. "Gauss or Bernoulli?," Evaluation Review, , vol. 20(3), pages 338-352, June.
    19. Martin Ridout & John Hinde & Clarice G. B. Demétrio, 2001. "A Score Test for Testing a Zero‐Inflated Poisson Regression Model Against Zero‐Inflated Negative Binomial Alternatives," Biometrics, The International Biometric Society, vol. 57(1), pages 219-223, March.
    20. Jeonghwan Kim & Woojoo Lee, 2019. "On testing the hidden heterogeneity in negative binomial regression models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 82(4), pages 457-470, May.

    More about this item

    Statistics

    Access and download statistics

    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:sae:somere:v:25:y:1997:i:3:p:267-283. 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: SAGE Publications (email available below). General contact details of provider: .

    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.