EdSurvey (version 3.0.2)

percentile: EdSurvey Percentiles

Description

Calculates the percentiles of a numeric variable in an edsurvey.data.frame, a light.edsurvey.data.frame, or an edsurvey.data.frame.list.

Usage

percentile(
  variable,
  percentiles,
  data,
  weightVar = NULL,
  jrrIMax = 1,
  varMethod = c("jackknife", "Taylor"),
  alpha = 0.05,
  omittedLevels = TRUE,
  defaultConditions = TRUE,
  recode = NULL,
  returnVarEstInputs = FALSE,
  returnNumberOfPSU = FALSE,
  pctMethod = c("symmetric", "unbiased", "simple"),
  confInt = TRUE,
  dofMethod = c("JR", "WS")
)

Value

The return type depends on whether the class of the data argument is an edsurvey.data.frame or an edsurvey.data.frame.list.

The data argument is an edsurvey.data.frame

When the data argument is an edsurvey.data.frame,

percentile returns an S3 object of class percentile. This is a data.frame with typical attributes (names,

row.names, and class) and additional attributes as follows:

n0

number of rows on edsurvey.data.frame before any conditions were applied

nUsed

number of observations with valid data and weights larger than zero

nPSU

number of PSUs used in the calculation

call

the call used to generate these results

The columns of the data.frame are as follows:

percentile

the percentile of this row

estimate

the estimated value of the percentile

se

the jackknife standard error of the estimated percentile

df

degrees of freedom

confInt.ci_lower

the lower bound of the confidence interval

confInt.ci_upper

the upper bound of the confidence interval

nsmall

the number of units with more extreme results, averaged across plausible values

When the confInt argument is set to FALSE, the confidence intervals are not returned.

The data argument is an edsurvey.data.frame.list

When the data argument is an edsurvey.data.frame.list,

percentile returns an S3 object of class percentileList. This is a data.frame with a call attribute. The columns in the data.frame are identical to those in the previous section, but there also are columns from the edsurvey.data.frame.list.

covs

a column for each column in the covs value of the edsurvey.data.frame.list. See Examples.

When returnVarEstInputs is TRUE, an attribute varEstInputs also is returned that includes the variance estimate inputs used for calculating covariances with varEstToCov.

Arguments

variable

the character name of the variable to percentiles computed, typically a subject scale or subscale

percentiles

a numeric vector of percentiles in the range of 0 to 100 (inclusive)

data

an edsurvey.data.frame or an edsurvey.data.frame.list

weightVar

a character indicating the weight variable to use.

jrrIMax

a numeric value; when using the jackknife variance estimation method, the default estimation option, jrrIMax=1, uses the sampling variance from the first plausible value as the component for sampling variance estimation. The \(V_{jrr}\) term (see Statistical Methods Used in EdSurvey) can be estimated with any number of plausible values, and values larger than the number of plausible values on the survey (including Inf) will result in all plausible values being used. Higher values of jrrIMax lead to longer computing times and more accurate variance estimates.

varMethod

a character set to jackknife or Taylor that indicates the variance estimation method used when constructing the confidence intervals. The jackknife variance estimation method is always used to calculate the standard error.

alpha

a numeric value between 0 and 1 indicating the confidence level. An alpha value of 0.05 would indicate a 95% confidence interval and is the default.

omittedLevels

a logical value. When set to the default value of TRUE, drops those levels of all factor variables that are specified in achievementVars and aggregatBy. Use print on an edsurvey.data.frame to see the omitted levels.

defaultConditions

a logical value. When set to the default value of TRUE, uses the default conditions stored in an edsurvey.data.frame to subset the data. Use print on an edsurvey.data.frame to see the default conditions.

recode

a list of lists to recode variables. Defaults to NULL. Can be set as recode=list(var1= list(from= c("a", "b", "c"), to= "d")).

returnVarEstInputs

a logical value set to TRUE to return the inputs to the jackknife and imputation variance estimates which allows for the computation of covariances between estimates.

returnNumberOfPSU

a logical value set to TRUE to return the number of primary sampling units (PSUs)

pctMethod

one of “unbiased”, “symmetric”, “simple”; unbiased produces a weighted median unbiased percentile estimate, whereas simple uses a basic formula that matches previously published results. Symmetric uses a more basic formula but requires that the percentile is symetric to multiplying the quantity by negative one.

confInt

a Boolean indicating if the confidence interval should be returned

dofMethod

passed to DoFCorrection as the method argument

Author

Paul Bailey

Details

Percentiles, their standard errors, and confidence intervals are calculated according to the vignette titled Statistical Methods Used in EdSurvey. The standard errors and confidence intervals are based on separate formulas and assumptions.

The Taylor series variance estimation procedure is not relevant to percentiles because percentiles are not continuously differentiable.

References

Hyndman, R. J., & Fan, Y. (1996). Sample quantiles in statistical packages. American Statistician, 50, 361--365.

Examples

if (FALSE) {
# read in the example data (generated, not real student data)
sdf <- readNAEP(system.file("extdata/data", "M36NT2PM.dat", package="NAEPprimer"))

# get the median of the composite
percentile("composite", 50, sdf)

# get several percentiles
percentile("composite", c(0,1,25,50,75,99,100), sdf)
# build an edsurvey.data.frame.list
sdfA <- subset(sdf, scrpsu %in% c(5,45,56))
sdfB <- subset(sdf, scrpsu %in% c(75,76,78))
sdfC <- subset(sdf, scrpsu %in% 100:200)
sdfD <- subset(sdf, scrpsu %in% 201:300)

sdfl <- edsurvey.data.frame.list(list(sdfA, sdfB, sdfC, sdfD),
                                 labels=c("A locations",
                                           "B locations",
                                           "C locations",
                                           "D locations"))
# this shows how these datasets will be described:
sdfl$covs

percentile("composite", 50, sdfl)
percentile("composite", c(25, 50, 75), sdfl)
}