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**Speakout Elementary Tests.pdf [2022]**

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), we use a generalized linear model (GLM) and examine the impact of individual test questions on the probability of passing the evaluation. Here, for each district, we include the number of students who failed any of the four core subjects and the percent of those who failed questions that were of that district. From this we build a logistic regression model that estimates the probability of passing the evaluation for each district. Our regressions use a multivariate logistic model with binomial errors, which allow the probability to be non-zero, and are analogous to random-effects models. They also implicitly allow for the possibility that, across a district, the number of students who failed may be affected by a number of district-level characteristics. From the GLM we obtain standard errors for each district, using the delta method. This method is valid for any GLM and is straightforward to implement. A formal discussion of its application to the binomial distribution is provided in Appendix A of the Online Supporting Information (OSI). Finally, it is important to note that because districts vary so substantially in their conditions, differences in the likelihood of passing the evaluation may be more or less meaningful depending on the conditions under which they are occurring. In our specification, we control for a set of district-level variables that should be associated with higher probabilities of passing. These include the district’s percentage of black students, the percentage of black students who are in the lowest academic decile, and the proportion of English language learners (ELLs) in the district. We also include a term for the number of students enrolled per teacher, since we think that, in some districts, these students would be more likely to receive additional support in the form of additional resources or services. We report the effects of district-level variables on the likelihood of passing on the following tables. Table 2 contains estimates of the probability of passing the evaluation by students who failed any core subjects. Although the table reports the expected probability of passing among those who failed, this is not very useful for policy. We therefore report the probabilities of passing among those who failed and the estimated probabilities of passing conditional on having passed. Table 3 presents the same results by quintile, which allows us to compare the effect of students’ academic performance on their chances of passing across districts. If all students who passed were equally likely to pass the evaluation, this would mean that, for example, the first quintile is equivalent to the fourth quintile. However, some districts have higher probabilities

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