knowledge about the dependence structure of test items. When no such information is available a priori, they may lend little utility. The other type, surface local dependence, arises when items are highly similar in contents or locations. When it takes place, it is assumed that endorsing an item is influenced by other items in the test. A salient example is a set of questions that are phrased slightly differently, while in point of fact, the items function as if they are a single question, measuring the same aspect of the construct. Consider a pair of perfectly locally dependent items for instance. In this event the item that comes later has a response identical to the preceding one without regard to the latent trait variable. In other words, the underlying cognitive process implied by the model does not play any role in determining the performance of the second item. The currently available approaches for identifying the existence of surface local dependence are to use general-purpose goodness-of-fit indices, such as Pearson’s χ 2 or the likelihood ratio test statistic (Bishop, Fienberg, & Holland, 1975, p. 57; Chen & Thissen, 1997). Yen’s Q$_{3}$ statistic (Yen, 1984, 1993), score test statistics (Glas & Su´ arez Falc´ on, 2003; Y. Liu & Thissen, 2012), M$_{3}$ statistic (Maydeu-Olivares & Joe, 2005, 2006), etc. These indices, however, are not specifically aimed at detecting surface local dependence. Rather, they are designed to assess overall tenability of local independence or item misfit concerning the model assumptions (e.g., dimensionality, local independence, item characteristic function, distribution of latent variables). Even the indices designed for detecting local item dependence can only identify violations of a weak form of local independence, which deals with bivariate relationships of the items (see Junker, 1993; McDonald, 1979, 1982; Stout, 2002). Aside from the self-imposed limitation, the nature of pairwise comparison in these indices presents problems when only a few observations are available for compiling contingency tables. The purpose of this article is to provide a parametric approach for identifying the presence of surface local dependence within the framework of DCMs. It is useful to note that the aforementioned parametric models, though developed in the item response theory context, can be readily extended to DCM applications by appending extra latent parameters. The major distinction of the current work from the previous efforts is that, while the prior approaches relate local dependence partially or exclusively to unmodeled latent traits, the present study attempts to address local dependence arising from an interplay among the items. Specifically, this study assumes that local dependence occurs as a consequence of an interaction among items but still and all the latent class of interest 3