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This study examines the SENSE-TRI program's effectiveness in improving the fundamental literacy skills of grade 3 learners in insurgency-challenged Gombe and Adamawa states of Nigeria during the COVID-19 lockdown. A quasi-experimental design was employed, with 400 participants equally divided between randomly selected schools and pupils from SENSE intervention schools (the treatment group) and a counterfactual group from schools and pupils not participating in the SENSE-TRI program (the comparison group). The learners' performance in both groups was assessed by the abbreviated Early Grade Reading Assessment (EGRA), focusing on tasks such as letter-sound identification, syllable sound identification, familiar word reading, invented word reading, and reading comprehension. The TRI program compared learners' scores in the treatment group with those of learners in the comparison group using Tobit regression models. The results revealed that sociodemographic variables had no significant independent influence on the observed outcomes. However, the scores showed a statistically significant improvement in the literacy abilities of the treatment group on all parameters and tasks compared to the control group. This improvement exceeded the initial SENSE baseline reading proficiency aggregate values, indicating the program's effectiveness in both challenging and normal circumstances as a possible way out when no schools exist. [ABSTRACT FROM AUTHOR]

This paper introduced a Quasi-Negative Binomial Regression as an extension of Quasi-Negative Binomial to handle response count datasets modulated with covariates. In some literature, Poisson regression is assumed to model the count data appropriately with exemption of excess-zero and over-dispersion more than other identical models. Nevertheless, in the presence of excess-zero and over-dispersion, the Negative Binomial regression and Generalized Poisson regression and their zero-inflation provided some respite. If the data were highly skewed, with long tail, they fit the data incorrectly. Therefore, Quasi-Negative Binomial is recommended even though it cannot accommodate data modulated with covariates, hence the proposed Quasi-Negative Binomial Regression model and its zero-inflated model. The estimates of the model parameters were derived using maximum likelihood method and the performance of the model was examined using simulation study. Also, the adequacy of the model was established by comparison with other competing models' information criteria. The results showed that the proposed models outperformed the competing models. [ABSTRACT FROM AUTHOR]