sampling, etc., rather than the simple random sampling. Such situations commonly arise in large scale data collection, for example, within several states of a country or even among different countries. Suitable statistical methods are required to analyze these data by taking care of the stratified structure of the data; this is because there often exist several inter and intra-class correlations within such stratification and ignoring them often lead to erroneous inference. Further, in many such complex surveys, stratified observations are collected on some categorical responses having two or more mutually exclusive unordered categories along with some related covariates and inference about their relationship is of up-most interest for insight generation and policy making. Polytomous logistic regression (PLR) model is a useful and popular tool in such situations to model categorical responses with associated covariates. However, most of classical literature deals with the cases of simple random sampling scheme (e.g. McCullagh, 1980; Lesaffre and Albert, 1989; Agresti, 2002; Gupta et al., 2006, 2008). The application of PLR model under complex survey setting can be found, for example, in Binder (1983), Roberts et al. (1987), Morel (1989), Morel and Neerchal (2012) and Castilla et al. (2018); most of them, except the last 