narcc: Score the Cognitive Causation (CC) and Negative Affect in...

In PROscorer: Functions to Score Commonly-Used Patient-Reported Outcome (PRO) Measures and Other Psychometric Instruments

Score the Cognitive Causation (CC) and Negative Affect in Risk (NAR) scales

Description

Scores the Cognitive Causation (CC) and Negative Affect in Risk (NAR) scales, two scales measuring intuitive elements of cancer risk perception (see references).

Usage

narcc(
  df,
  items = NULL,
  whichScale,
  minmax = c(0, 3),
  okmiss = 0.5,
  keepNvalid = FALSE
)

Arguments

df	A data frame containing responses to the CC and/or NAR items, and possibly other variables.
items	(optional) A character vector with the CC or NAR item names, or a numeric vector indicating the column numbers of the CC or NAR items in df. If items is omitted, then narcc will assume that df contains ONLY the items to be scored (either CC or NAR items) and no non-scored variables.
whichScale	(required) Either "CC" or "NAR", the scale you wish to score.
minmax	A vector of 2 integers with format c(itemMin, itemMax), indicating the minimum and maximum possible item responses. The default value is c(0, 3), and assumes that the item responses are coded from 0 to 3. If, instead, your item responses are coded from 1 to 4, then enter c(1, 4) for this argument.
okmiss	(optional) The maximum proportion of items on whichScale that a respondent is allowed to have missing and still have their non-missing items scored (and prorated). If the proportion of missing items for a respondent is greater than okmiss, then the respondent will be assigned a value of NA for their scale score. The default value is 0.50, and this generally should not be changed.
keepNvalid	(optional) Logical value indicating whether a variable containing the number of valid, non-missing items for each respondent should be returned in a data frame with the scale score. The default is FALSE. Set to TRUE to return this variable, which will be named "whichScale_N" (with whatever name you gave to the whichScale argument). Most users should omit this argument entirely. This argument might be removed from future versions of the package, so please let me know if you think this argument useful and would rather it remain a part of the function.

Details

The CC scale originally contained 10 items (Hay et al., 2014). Later, evidence that 3 of the items might be measurement non-invariant across important subgroups led to the recommendation to omit these 3 items and score a 7-item version of the CC scale (Baser et al., 2016). When whichScale = "CC" the narcc function will accept and score either 7 or 10 CC items, although the 7-item version is recommended. The NAR scale has 6 items, and the narcc function will accept only 6 NAR items when whichScale = "NAR".

If you want to score both the CC and NAR scales, then you need to run the narcc function twice, once for CC and again for NAR.

Value

A data frame containing a variable containing the scored scale, named either "CC" or "NAR". Scores are scales to have range 0 to 100.

Optionally, the data frame can additionally have a variable containing the number of valid item responses on the scale for each respondent (if keepNvalid = TRUE, but this option might be removed in future package updates).

Note

The narcc function assumes that your item data are numerically coded from 0 to 3 (i.e., with 0 = "Strongly Disagree" and 3 = "Strongly Agree"). However, your item data might instead be coded from 1 to 4. If this is the case, you MUST let the narcc function know this by using the minmax argument, specifically, minmax = c(1, 4).

References

Hay, JL, Baser, R, Weinstein, ND, Li, Y, Primavera, L, & Kemeny, MM. (2014). Examining intuitive risk perceptions for cancer in diverse populations. Health, Risk & Society, 16(3), 227-242.

Baser, RE, Li, Y, Brennessel, D, Kemeny, MM, & Hay, JL. (2016). Measurement Invariance of Intuitive Cancer Risk Perceptions Across Diverse Populations: The Cognitive Causation and Negative Affect in Risk Scales. Journal of Health Psychology; In Submission.

Examples

# Make fake data for the example
nardat <- PROscorerTools::makeFakeData(nitems = 6, values = 0:3,
                                       propmiss = 0.40, prefix = "nar")
ccdat <- PROscorerTools::makeFakeData(nitems = 7, values = 0:3,
                                      propmiss = 0.40, prefix = "cc",
                                      id = TRUE)

# The nardat data frame contains ONLY NAR items, so can omit "items" argument
narcc(nardat, whichScale = "NAR")

# The ccdat data frame contains an "ID" variable, so need to use "items" arg
names(ccdat)

# The "items" argument can be either:
#     (1) the numeric vector indexing the location of the items in df, or
#     (2) a character vector of the item names
narcc(ccdat, items = 2:8, whichScale = "CC")

cc_names <- c("cc1", "cc2", "cc3", "cc4", "cc5", "cc6", "cc7")
narcc(ccdat, items = cc_names, whichScale = "CC")

PROscorer documentation built on Oct. 17, 2023, 1:06 a.m.