In two previous posts I put together a data set and presented some exploratory data analysis on school achievement for national standards. After those posts I exchanged emails with a few people about the sources of data and Jeremy Greenbrook-Held pointed out Education Counts as a good source of additional variables, including number of teachers per school and proportions for different ethnic groups.

The code below call three files: Directory-Schools-Current.csv, teacher-numbers.csv and SchoolReport_data_distributable.csv, which you can download from the links.

options(stringsAsFactors = FALSE) library(ggplot2) # Reading School directory and dropping some address information # for 2012 obtained from here: # http://www.educationcounts.govt.nz/directories/list-of-nz-schools directory <- read.csv('Directory-Schools-Current.csv') directory <- subset(directory, select = -1*c(3:9, 11:13)) # Reading teacher numbers for 2011 obtained from here: # http://www.educationcounts.govt.nz/statistics/schooling/teaching_staff teachers <- read.csv('teacher-numbers.csv') # Reading file obtained from stuff.co.nz obtained from here: # http://schoolreport.stuff.co.nz/index.html fairfax <- read.csv('SchoolReport_data_distributable.csv') # Merging directory and teachers info standards <- merge(directory, teachers, by = 'school.id', all.x = TRUE) # Checking that the school names match # This shows ten cases, some of them obviously the same school # e.g. Epsom Girls Grammar School vs Epsom Girls' Grammar School tocheck <- subset(standards, school.name.x != school.name.y) tocheck[, c(1:2, 29)] #school.id school.name.x school.name.y #61 64 Epsom Girls Grammar School Epsom Girls' Grammar School #125 135 Fraser High School Hamilton's Fraser High School #362 402 Waiau Area School Tuatapere Community College #442 559 Te Wainui a Rua Whanganui Awa School #920 1506 St Michael's Catholic School (Remuera) St Michael's School (Remuera) #929 1515 Sunnyhills School Sunny Hills School #985 1573 Willow Park School Willowpark School #1212 1865 Te Wharekura o Maniapoto Te Wharekura o Oparure #1926 2985 Sacred Heart Cathedral School Sacred Heart School (Thorndon) #2266 3554 Waitaha School Waitaha Learning Centre # Merging now with fairfax data standards <- merge(standards, fairfax, by = 'school.id', all.x = TRUE) # Four schools have a different name tocheck2 <- subset(standards, school.name.x != school.name) tocheck2[, c(1:2, 32)] #school.id school.name.x school.name #920 1506 St Michael's Catholic School (Remuera) St Michael's School (Remuera) #929 1515 Sunnyhills School Sunny Hills School #985 1573 Willow Park School Willowpark School #2266 3554 Waitaha School Waitaha Learning Centre # Checking the original spreadsheets it seems that, despite # the different name they are the same school (at least same # city) # Now start dropping a few observations that are of no use # for any analysis; e.g. schools without data standards <- subset(standards, !is.na(reading.WB)) # Dropping school 498 Te Aho o Te Kura Pounamu Wellington # http://www.tekura.school.nz/ # It is a correspondence school without decile information standards <- subset(standards, school.id != 498) # Converting variables to factors standards$decile <- factor(standards$decile) standards$school.type <- factor(standards$school.type) standards$education.region <- factor(standards$education.region, levels = c('Northern', 'Central North', 'Central South', 'Southern')) # Removing 8 special schools standards <- subset(standards, as.character(school.type) != 'Special School') standards$school.type <- standards$school.type[drop = TRUE] # Saving file for Eric # write.csv(standards, 'standardsUpdated.csv', # row.names = FALSE, quote = FALSE) # Create performance groups standards$math.OK <- with(standards, math.At + math.A) standards$reading.OK <- with(standards, reading.At + reading.A) standards$writing.OK <- with(standards, writing.At + writing.A) # Creating a few proportions standards$prop.euro <- with(standards, european/total.roll) standards$prop.maori <- with(standards, maori/total.roll) standards$prop.pacific <- with(standards, pacific.island/total.roll) standards$prop.asian <- with(standards, asian/total.roll) |

This updated data set is more comprehensive but it doesn’t change the general picture presented in my previous post beyond the headlines. Now we can get some cool graphs to point out the obvious, for example the large proportion of Maori and Pacific Island students in low decile schools:

qplot(prop.maori, prop.pacific, data = standards, color = decile, xlab = 'Proportion of Maori', ylab = 'Proportion of Pacific Island') |

I have avoided ‘proper’ statistical modeling because i- there is substantial uncertainty in the data and ii- the national standards for all schools (as opposed to only 1,000 schools) will be released soon; we do’t know if the published data are a random sample. In any case, a quick linear model fitting the proportion of students that meet reading standards (reading.OK) as a function of decile and weighted by total school rollâ€”to account for the varying school sizesâ€”will explain roughly 45% of the observed variability on reading achievement.

m1 <- lm(reading.OK ~ decile, data = standards, weights = total.roll) summary(m1) # Coefficients: # Estimate Std. Error t value Pr(>|t|) # (Intercept) 0.56164 0.01240 45.278 < 2e-16 *** # decile2 0.06238 0.01769 3.527 0.000439 *** # decile3 0.10890 0.01749 6.225 7.07e-10 *** # decile4 0.16434 0.01681 9.774 < 2e-16 *** # decile5 0.18579 0.01588 11.697 < 2e-16 *** # decile6 0.22849 0.01557 14.675 < 2e-16 *** # decile7 0.25138 0.01578 15.931 < 2e-16 *** # decile8 0.24849 0.01488 16.701 < 2e-16 *** # decile9 0.28861 0.01492 19.347 < 2e-16 *** # decile10 0.31074 0.01439 21.597 < 2e-16 *** # # Residual standard error: 1.594 on 999 degrees of freedom # (1 observation deleted due to missingness) # Multiple R-squared: 0.4548, Adjusted R-squared: 0.4499 # F-statistic: 92.58 on 9 and 999 DF, p-value: < 2.2e-16 |

Model fit has a few issues with distribution of residuals, we should probably use a power transformation for the response variable, but I wouldn’t spend much more time before getting the full data for national standards.

par(mfrow = c(2, 2)) plot(m1) par(mfrow = c(1, 1)) |

**Bonus plot:** map of New Zealand based on school locations, colors depicting proportion of students meeting reading national standards.

[sourcecode lang=”R”]

qplot(longitude, latitude,

data = standards, color = reading.OK)

[/sourcecode]

**P.S. 2012-09-26 16:01**. The simple model above could be fitted taking into account the order of the decile factor (using `ordered()`

) or just fitting linear and quadratic terms for a numeric expression of decile. Anyway, that would account for 45% of the observed variability.

m1d <- lm(reading.OK ~ as.numeric(decile) + I(as.numeric(decile)^2), data = standards, weights = total.roll) summary(m1d) #Coefficients: # Estimate Std. Error t value Pr(>|t|) #(Intercept) 0.5154954 0.0136715 37.706 < 2e-16 *** #as.numeric(decile) 0.0583659 0.0051233 11.392 < 2e-16 *** #I(as.numeric(decile)^2) -0.0023453 0.0004251 -5.517 4.39e-08 *** #--- #Residual standard error: 1.598 on 1006 degrees of freedom # (1 observation deleted due to missingness) #Multiple R-squared: 0.4483, Adjusted R-squared: 0.4472 #F-statistic: 408.8 on 2 and 1006 DF, p-value: < 2.2e-16 |

**P.S. 2012-09-26 18:17**. Eric Crampton has posted preliminary analyses based on this dataset in Offsetting Behaviour.