check.norms {mokken}

Standard errors for norm statistics

Description

The function presents standard errors for the mean, standard deviation, standard scores, stanine boundaries, and percentiles based on a vector of test scores (Oosterhuis, Van der Ark, & Sijtsma, 2017).

Usage

check.norms(y, nice.output = TRUE)

Arguments

y

numerical vector. Typically a numerical vector of length N, representing the test scores of N respondents. Missing values are not allowed

nice.output

Logical: If TRUE, norm statistics and standard errors are combined in an a single object of class noquote

Value

list of five components:

(1) mean: Sample mean and its standard error (noquote).

(2) sd: Sample standard deviation and its standard error (noquote).

(3) z: For each unique testscore, the test score, its frequency, the corresponding estimated standard score and its standard error (noquote).

(4) sta9: The estimates of the 8 boundaries of the stanines and their standard error (noquote).

(5) z: For each unique testscore, the test score, its frequency, the corresponding estimated percentile rank and its standard error (noquote).

Author(s)

L. A. van der Ark [e-mail] and H. E. M. Oosterhuis

References

Oosterhuis, H. E. M., Van der Ark, L. A., & Sijtsma, K. (2017). Standard errors and confidence intervals of norm statistics for educational and psychological tests. Psychometrika, 82, 559-588. doi:10.1007/s11336-016-9535-8

See Also

DS14, recode, twoway

Examples

  data(DS14)

  # Handle missing data and recode negatively worded items
  X <- DS14[, 3 : 16]
  X <- twoway(X)
  X <- recode(X, c(1, 3))

  # Negative affectivity
  Na <- X[, c(1, 3, 6, 8, 10, 11, 14)] 

  # Social inhibition
  Si <- X[, c(2, 4, 5, 7, 9, 12, 13)] 

  # Norms
  check.norms(rowSums(Na))
  check.norms(rowSums(Si))