IDEAS home Printed from https://ideas.repec.org/a/spr/qualqt/v51y2017i2d10.1007_s11135-016-0429-8.html
   My bibliography  Save this article

Weighted log ratio analysis by means of Poisson factor models: a case study to evaluate the quality of the public services offered to the citizens

Author

Listed:
  • Antonello D’Ambra

    (Second University of Naples)

  • Anna Crisci

    (Pegaso Telematic University)

  • Luigi D’Ambra

    (University of Naples, Federico II)

Abstract

In this paper we analyse the degree of dissatisfaction expressed by citizens regarding the quality of public services offered by municipalities in an average size city in the south of Italy. A previous study, carried out by multiple correspondence analysis, showed that the degree of dissatisfaction was closely linked to the age and education level of interviewed subjects, and the urban area (neighbourhood) in which they lived. On the basis of this result, we created two contingency tables. The first contingency, $${\mathbf{N}}$$ N , represents a cross classification of n dissatisfied individuals based upon three variables: Urban area (residential neighbourhood), age, and education. Taking population distribution (based on Age and Education) within each neighbourhood into account, we considered another table, $${\mathbf{S}}$$ S , which intersects the same variable and represents the target population. We started with a Goodman RC(M) association model, and obtained a weighted log ratio analysis. In particular, we propose a weighted log ratio analysis using Poisson factor models that explicitly considers count nature and automatically incorporates an offset. Moreover, we have compared the results of the log ratio analysis with and without offset by means of the $$R_{V}$$ R V coefficient.

Suggested Citation

  • Antonello D’Ambra & Anna Crisci & Luigi D’Ambra, 2017. "Weighted log ratio analysis by means of Poisson factor models: a case study to evaluate the quality of the public services offered to the citizens," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(2), pages 629-639, March.
    • Handle: RePEc:spr:qualqt:v:51:y:2017:i:2:d:10.1007_s11135-016-0429-8
    DOI: 10.1007/s11135-016-0429-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11135-016-0429-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11135-016-0429-8?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Becker, Mark P., 1992. "Exploratory analysis of association models using loglinear models and singular value decompositions," Computational Statistics & Data Analysis, Elsevier, vol. 13(3), pages 253-267, April.
    2. Greenacre, Michael, 2009. "Power transformations in correspondence analysis," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 3107-3116, 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. Blasius, J. & Greenacre, M. & Groenen, P.J.F. & van de Velden, M., 2009. "Special issue on correspondence analysis and related methods," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 3103-3106, June.
    2. Tsagris, Michail & Preston, Simon & T.A. Wood, Andrew, 2016. "Improved classi cation for compositional data using the $\alpha$-transformation," MPRA Paper 67657, University Library of Munich, Germany.
    3. Ida Camminatiello & Antonello D’Ambra & Luigi D’Ambra, 2022. "The association in two-way ordinal contingency tables through global odds ratios," METRON, Springer;Sapienza Università di Roma, vol. 80(1), pages 9-22, April.
    4. Michael Greenacre, 2023. "The chi-square standardization, combined with Box-Cox transformation, is a valid alternative to transforming to logratios in compositional data analysis," Economics Working Papers 1857, Department of Economics and Business, Universitat Pompeu Fabra.
    5. Michail Tsagris & Simon Preston & Andrew T. A. Wood, 2016. "Improved Classification for Compositional Data Using the α-transformation," Journal of Classification, Springer;The Classification Society, vol. 33(2), pages 243-261, July.
    6. Eric J. Beh & Rosaria Lombardo, 2024. "Correspondence Analysis Using the Cressie–Read Family of Divergence Statistics," International Statistical Review, International Statistical Institute, vol. 92(1), pages 17-42, April.
    7. Michael Greenacre & Paul Lewi, 2009. "Distributional Equivalence and Subcompositional Coherence in the Analysis of Compositional Data, Contingency Tables and Ratio-Scale Measurements," Journal of Classification, Springer;The Classification Society, vol. 26(1), pages 29-54, April.
    8. Lombardo, Rosaria & Camminatiello, Ida & D'Ambra, Antonello & Beh, Eric J., 2021. "Assessing the Italian tax courts system by weighted three-way log-ratio analysis," Socio-Economic Planning Sciences, Elsevier, vol. 73(C).
    9. Raymond Sin-Kwok Wong, 2001. "Multidimensional Association Models," Sociological Methods & Research, , vol. 30(2), pages 197-240, November.
    10. Tsai, Arthur C. & Liou, Michelle & Simak, Maria & Cheng, Philip E., 2017. "On hyperbolic transformations to normality," Computational Statistics & Data Analysis, Elsevier, vol. 115(C), pages 250-266.
    11. Siciliano, Roberta & Mooijaart, Ab, 1997. "Three-factor association models for three-way contingency tables," Computational Statistics & Data Analysis, Elsevier, vol. 24(3), pages 337-356, May.
    12. J. L. Scealy & Patrice de Caritat & Eric C. Grunsky & Michail T. Tsagris & A. H. Welsh, 2015. "Robust Principal Component Analysis for Power Transformed Compositional Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 136-148, March.

    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:spr:qualqt:v:51:y:2017:i:2:d:10.1007_s11135-016-0429-8. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.