Description Usage Arguments Details Value Examples
norm_sim
simulates n.samples
student growth paths using the mean and
standard deviation estimates provided in NWEA's 2011 Student Growth Norms data
tables.
1 2 |
start.grade |
start grade as integer |
end.grade |
end grade as integer |
start.subject |
measurement scale |
start.rit |
starting RIT value as integer for simulation |
yearly.cycle |
season intervals used to identify typical growth and standard deviations. These are passed as
a vector of two digit intergers in the style of the 2011 Student Growth Norms Table where each season
recieves an integer value corresponding to Winter = 1, Spring = 2, Fall = 4. Integer pairs indicate
the appropriate interval to simulate growth. For example, to simulate fall-to-winter followed
by winter-to-spring growth for each grade level the vector |
n.samples |
the number of samples |
This function builds a growth path over specified intervales (e.g.,
spring-to-spring, fall-to-winter followed by winter-spring) as well as
grades (eg. 5-8, K-11) for a given start grade, measurement scale (i.e., subject)
and starting RIT value. All simulations start wiht the same RIT value
and then iterativelydraw the next assessments realization from rnorm
using the means and standard deviations for appropriate intervals as
provided by the norms_student_2011
. The function iterates over
grades and intervals within grades and construct a data.table in
long format.
a data frame with n.sample + (n.sample x grades x
length(yearly.cycle)
rows).
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | # Run simulations with defaul settings
x.t <- norm_sim()
# Create grade-season variable for summary stats and plotting
x.t[,GradeSeason:=ifelse(Season==41, StartGrade-.7, StartGrade)]
# Calculate grade-season means
x.avg<-x.t[,list(Avg=mean(StartRIT), sigma=sd(StartRIT)), by=list(GradeSeason)]
# Calculate grade-season upper and lower confidence levels
x.avg[,ucl:=Avg+1.96*sigma]
x.avg[,lcl:=Avg-1.96*sigma]
# Plot!
p <- ggplot(x.t, aes(x=GradeSeason, y=StartRIT)) + geom_line(aes(group=ID), alpha=.01) + theme_bw()
p + geom_smooth(data=x.avg, aes(x=GradeSeason, y=Avg, ymin=lcl, ymax=ucl), color="orange", fill="orange", size=1.7)
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