narcc | R Documentation |
Scores the Cognitive Causation (CC) and Negative Affect in Risk (NAR) scales, two scales measuring intuitive elements of cancer risk perception (see references).
narcc(
df,
items = NULL,
whichScale,
minmax = c(0, 3),
okmiss = 0.5,
keepNvalid = FALSE
)
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
|
whichScale |
(required) Either |
minmax |
A vector of 2 integers with format |
okmiss |
(optional) The maximum proportion of items on |
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
|
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.
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).
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)
.
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.
# 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")
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