It has been our constant endeavour to provide healthy, competitive environment to the students. What do you see? Take a moment to reflect on what these differences suggest for the relationship of interest (that between Catholic schooling and student achievement).When it comes to educating a child, particularly at school level, parents, educators and administrators are faced with many issues and challenges because the school education plays a major role in a student’s life during which the qualities related to attitude, aptitude and approach are formed.Īt RK Modern School, we are among the top 10 schools in Noida. # catholic race_white p5hmage w3income p5numpla w3momed_hsb Summarise_all(funs(mean(., na.rm = T))) # Adding missing grouping variables: `catholic` # A tibble: 2 × 6 Let’s calculate the mean for each covariate by the treatment status: ecls_cov % w3momed_hsb: Is the mother’s education level high-school or below (1) or some college or more (0)?.p5numpla: Number of places the student has lived for at least 4 months.race_white: Is the student white (1) or not (0)?.We’ll work with the following covariates for now: # alternative hypothesis: true difference in means is not equal to 0ġ.2 Difference-in-means: pre-treatment covariates The difference-in-means is statistically significant at conventional levels of confidence (as is also evident from the small standard error above): with(ecls, t.test(c5r2mtsc_std ~ catholic)) # Summarise(mean_math = mean(test)) # A tibble: 2 × 2 Mutate(test = (c5r2mtsc - mean(c5r2mtsc)) / sd(c5r2mtsc)) %>% #this is how the math score is standardized This could have been calculated using the non-standardized outcome variable as follows: ecls %>% The summary table above indicates that 3rd grade Catholic school students’ average math score is more than 20% of a standard deviation higher than that of public school students. Note that the outcome variable has been standardized (mean = 0, sd = 1). # catholic n_students mean_math std_error Std_error = sd(c5r2mtsc_std) / sqrt(n_students)) # A tibble: 2 × 4 The independent variable of interest is catholic (1 = student went to catholic school 0 = student went to public school). Note that we’re using students’ standardized math score ( c5r2mtsc_std) – with a mean of 0 and standard deviation of 1 – as the outcome variable of interest. Here is some basic information about public and catholic school students in terms of math achievement. Examine the difference-in-means between Treated and Control for pre-treatment covariates.īefore we start, load a few packages and read in ecls.csv: library(MatchIt)ġ.1 Difference-in-means: outcome variable.Examine the difference-in-means between Treated and Control for the outcome variable.In addition, before we implement a matching method, we’ll conduct the following analyses using the non-matched data: Examine covariate balance after matching.In this tutorial we’ll use nearest neighbor propensity score matching. Choose and execute a matching algorithm.Estimate the propensity score (the probability of being Treated given a set of pre-treatment covariates).To examine the effect of going to Catholic school (“Treated”) versus public school (“Control”) on student achievement using matching we will go through the following steps: To get the dataset used below (ecls.csv), please follow the instructions outlined here. UPDATE: Many people have asked for the data used in this tutorial. Because students who attend Catholic school on average are different from students who attend public school, we will use propensity score matching to get more credible causal estimates of Catholic schooling. In this tutorial we’ll analyze the effect of going to Catholic school, as opposed to public school, on student achievement. 4.3 Average absolute standardized difference.4 Examining covariate balance in the matched sample.2.1 Examining the region of common support.1.2 Difference-in-means: pre-treatment covariates.1.1 Difference-in-means: outcome variable.
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