Scores the Cognitive Causation (CC) and Negative Affect in Risk (NAR) scales, two scales measuring intuitive elements of cancer risk perception (see references).
A data frame containing responses to the CC and/or NAR items, and possibly other variables.
(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
A vector of 2 integers with format
(optional) The maximum proportion of items on
(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
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
= "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.
A data frame containing a variable containing the scored scale, named either
"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
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
minmax argument, specifically,
minmax = c(1, 4).
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
# 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")
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.