The RSES is a 10-item psychometric scale that is designed to measure self-esteem. It uses a scale of 0-30 where a score less than 15 may indicate a problematic low self esteem. The RSES includes 10 items rated from 0 to 3 that are then added together. If more than 2 items have not been answered, then the total score is not calculated.

scoring_rses(
  data,
  items = 1:12,
  keep = TRUE,
  nvalid = 8,
  digits = NULL,
  reverse = c(2, 5, 6, 8, 9)
)

Arguments

data

a data.frame containing the RSES items orderd from 1 to 11

items

A character vector with the RSES item names ordered from 1 to 10, or a numeric vector indicating the column numbers of the RSES items in data.

keep

Logical, whether to keep the single items and whether to return variables containing the number of non-missing items on each scale for each respondent. The default is TRUE.

nvalid

A numeric value indicating the number of non-missing items required for score calculations. The default is 8.

digits

Integer of length one: value to round to. No rounding by default.

reverse

items to be scored reversely. These items can be specified either by name or by index. Default: 2, 5, 6, 8, 9

Value

The function returns 2 variables:

  • nvalid.rses: Number of valid values (MAX=10)

  • score.rses: RSES Score

Details

  • Number of items: 10

  • Item range: 0 to 3

  • Reverse items: 2, 5, 6, 8, 9

  • Score range: 0 to 30

  • Cut-off-values: A score of less than 15 suggests low self-esteem may be an issue.

  • Minimal clinically important difference: none

  • Treatment of missing values: If more than 2 items have not been answered, then the total score is not calculated.

References

Link to Questionnaire (http://callhelpline.org.uk/Download/Rosenberg%20Self-Esteem%20Scale.pdf)

Rosenberg M (1965). Society and the adolescent self-image. Princeton, New Jersey: Princeton University Press.

von Collani, Herzberg (2003) (https://doi.org/10.1024//0170-1789.24.1.3)

Examples

if (FALSE) { library(dplyr) items.rses <- paste0("RSES_", seq(1, 10, 1)) scoring_rses(mydata, items = items.rses) }