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- W2787218634 abstract "Logistic regression is widely used because it is a less restrictive than other techniques such as the discriminant analysis, multiple regression, and multiway frequency analysis. Because of it, many researchers do think that LR has no an assumption at all. The objective of this paper was to perform a complete LR assumptions testing and check whether the PS were improved. Agriculture data collected from 878 corn farmers were used. Six assumptions of LR were tested which included a sample size; expected cell frequencies; linearity in the logit; multicollinearity; outliers and influential cases; as well as the independence of residuals. A comparison of models was performed by comparing a model with the constant plus predictors with a model that had only the constant. Later, a model with only some predictors against the model with all predictors (full model) was compared to find out whether they were statistically significant. Findings have revealed that the two methods (significance of a variable testing and odds ratio) gave different variables to be included in the model. A statistically significant variable method gave better propensity scores than odds ratio method. An inclusion of only variables with an odds ratio >= 1 in the model, increased the dispersion of propensity scores when compared to the scores generated by the significant variables. Based on findings it is therefore recommended that all logistic regression assumptions, should be tested to ensure they hold in order to achieve better propensity scores for matching. Moreover, it is also recommended that variables which are to be included in the model should be based on significance testing." @default.
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- W2787218634 date "2018-01-01" @default.
- W2787218634 modified "2023-09-23" @default.
- W2787218634 title "Effect of Testing Logistic Regression Assumptions on the Improvement of the Propensity Scores" @default.
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