Modelling association between two Multiple Response Categorical Variables (MRCV) using generalized log-linear models: the case of urban agriculture in Mutare City

Abstract

Purpose: This research aimed to apply log-linear modelling to model association between multiple response categorical variables (MRCV) on urban agriculture and enhance data analysis of the paper by Basera, Chakaipa, & Dube (2020) impetus of urban agriculture on open spaces of Mutare City. Research methodology: The research data was obtained from households and farmers in Mutare City - urban and peri-urban (inclusive of plots in Weirmouth Park and Fern Valley area in December 2020. A total of one hundred and fifteen (115) household farmers were surveyed. Results: Simultaneous Pairwise Marginal Independence (SPMI) tests revealed the presence of associations. Log-linear tests revealed a perfect fit based on small standardized Pearson residuals and a strong positive association based on observed and model-predicted odds ratios on-field agricultural activities and use of herbicides. Log-linear and further application of heterogeneity tests revealed partial and near no perfect fit in other pairs of MRCVs with a strong negative association between municipality vacant places and field agricultural activities. Limitations: The research could not carry out log-linear model associations of three or more MRCVs because files exceeded 2GB in memory on both MI.test () function for SPMI tests and genloglin regressions. Contribution: The study contributes to urban agriculture planning especially in enactment of urban agriculture laws, agriculture one stop shop business centers housing farm input supply shops, farm produce shops, and determining fit support that can be rendered to urban farmers. Keywords: Multiple Response Categorical Variables (MRCV), Association, Urban agriculture