title: Testing conditional independence in sets of I × J tables by means of moment and correlation score tests with application to HPV vaccine.
abstract: A new testing approach is described for improving statistical tests of independence in sets of tables stratified on one or more relevant factors in case of categorical (nominal or ordinal) variables. Common tests of independence that exploit the ordinality of one of the variables use a restricted-alternative approach. A different, relaxed-null method is presented. Specifically, the M-moment score tests and the correlation tests are introduced. Using multinomial-Poisson homogeneous modeling theory, it is shown that these tests are computationally and conceptually simple, and simulation results suggest that they can perform better than other common tests of conditional independence. To illustrate, the proposed tests are used to better understand the human papillomavirus type-specific infection by exploring the intention to vaccinate. Copyright © 2016 John Wiley & Sons, Ltd.
authors.affiliation: Department of Political Sciences, University of Naples Federico II, Italy. maria.iannario@unina.it.
authors.forename: Maria
authors.initials: M
authors.lastname: Iannario
authors.affiliation: Department of Statistics and Actuarial Science, University of Iowa, Iowa City, U.S.A.
authors.forename: Joseph B
authors.initials: JB
authors.lastname: Lang
mesh_terms: D003627:Data Interpretation, Statistical; D006801:Humans; D053918:Papillomavirus Vaccines
chemical_list: D053918:Papillomavirus Vaccines
keywords: Cochran-Mantel-Haenszel test; M-moment score tests; Pearson's chi-squared test; correlation tests; latent distributions