NetworkToolbox (version 1.4.2)

resp.rep: Repeated Responses Check

Description

Screens data to identify potential cases of repeated responding. The function is based on two criteria: no variance (i.e., a standard deviation of zero for given responses)and frequency proportion of the response values (which is set by freq.prop). Note that these criteria are highly related. Additional criteria will be added in the future.

Usage

resp.rep(data, scale.lens = NULL, max.val, reverse = NULL, freq.prop = 0.8)

Arguments

data

A dataset

scale.lens

The number of items for each scale in the data. A vector indicating the length for each scale to be checked in the data

max.val

Maximum value for data (or scales). If scales have different maximum values, then a vector must be input with each scale's maximum value (see examples)

reverse

Reverse scored responses. If responses have not yet reversed, then do not reverse them. If responses have been reversed, then a vector indicating which responses have been reverse-scored should be input (see examples). Can be TRUE/FALSE or 1/0 (reversed/not reversed)

freq.prop

Frequency proportion of the response values. Allows the researcher to determine the maximum frequency proportion of a certain response value is suspicious. The default is set to .80 (or 80 percent responses are a single value)

Value

Returns a matrix when scale.lens = NULL and a list with elements corresponding to the order of scales. In general, the output contains potential bad cases that should be further inspected by the researcher.

Details

If a case is returned, then it does not mean that it is a bad case. Researchers should thoroughly inspect each case that is returned. A general guideline is that if a participant responded with all middle values (e.g., all 3's on a 5-point Likert scale), then they should be dropped. Note that a participant who responds with all maximum or minimum values may be a real case or a bad case. It is up to the researcher to decide and justify why or why not a case is kept.

Examples

# NOT RUN {
#Re-reverse responses
rev.vec <- c(TRUE,FALSE,TRUE,FALSE,TRUE,TRUE,TRUE,FALSE,TRUE,FALSE,
TRUE,FALSE,FALSE,TRUE,FALSE,TRUE,FALSE,TRUE,TRUE,FALSE,TRUE,FALSE,TRUE,
FALSE,FALSE,TRUE,FALSE,TRUE,FALSE,TRUE,TRUE,FALSE,TRUE,FALSE,TRUE,
FALSE,TRUE,FALSE,FALSE,TRUE,FALSE,TRUE,TRUE,FALSE,FALSE,TRUE,FALSE,TRUE)

#Maximum value (5-point Likert scale)
mv.vec <- 5

#Repeated responses check
resp.rep(neoOpen, reverse = rev.vec, max.val = mv.vec)

#Example with multiple scales

#Facet scale lengths of NEO-PI-3 Openness to Experience
s.len <- c(8, 8, 8, 8, 8, 8)

#Maximum values
mv.vec <- c(5, 5, 5, 5, 5, 5)

#Re-reverse responses
rev.vec <- c(TRUE,FALSE,TRUE,FALSE,TRUE,TRUE,TRUE,FALSE,TRUE,FALSE,
TRUE,FALSE,FALSE,TRUE,FALSE,TRUE,FALSE,TRUE,TRUE,FALSE,TRUE,FALSE,TRUE,
FALSE,FALSE,TRUE,FALSE,TRUE,FALSE,TRUE,TRUE,FALSE,TRUE,FALSE,TRUE,
FALSE,TRUE,FALSE,FALSE,TRUE,FALSE,TRUE,TRUE,FALSE,FALSE,TRUE,FALSE,TRUE)

#Repeated responses check
resp.rep(neoOpen, scale.lens = s.len, max.val = mv.vec, reverse = rev.vec)

# }