lawsheCVR | R Documentation |
Lawshe (1975) proposed an index to quantify content validity of items as
assessed by experts. He coined the index as content validity ratio
(CVR
) that can be calculated using the following formula.
\displaystyle CVR=\frac{n_e - \frac{N}{2}}{\frac{N}{2}}
where n_e
is the number of experts identifying an item as essential.
In this function, we define a cutoff value using two methods. The first is
"max" calculated by searching the maximum value of the used rating scale
("max_cat") and then divide it by two. The second method is "min" by using
the minimum value of the used rating scale ("min_cat") as the cutoff
criteria.
lawsheCVR(data, max_cat = 4, min_cat = 1, method = "max")
data |
a dataframe of categorical value from expert judgment to the item content validity. |
max_cat |
a maximum category point of used rating scale (the default value is 4). |
min_cat |
a minimum category point of used rating scale (the default value is 1). |
method |
a method to determine cutoff value between essential and non-essential items |
a dataframe of CVR of each item as calculated using the Lawshe's formula
Gilbert, G.E. & Prion, S. Making Sense of Methods and Measurement: Lawshe's Content Validity Index. Clinical Simulation in Nursing 12, 530-531 (2016).
Lawshe, C.H. A Quantitative Approach of Content Validity. Personnel Psychology 28, 563-575 (1975).
# In this example, we define a dataframe describing the rating of ten
# imaginary items as assessed by seven artificial experts. The minimum point
# of the rating scale is 1, and the maximum point that could be given by
# those experts is 4.
df <- data.frame(item1 = c(3,3,3,4,4,4,3),
item2 = c(2,4,3,2,4,4,4),
item3 = c(4,3,3,2,4,4,3),
item4 = c(3,2,3,3,4,3,3),
item5 = c(4,4,4,3,3,3,3),
item6 = c(3,3,3,4,3,3,4),
item7 = c(4,4,4,3,4,4,4),
item8 = c(3,3,4,4,4,4,4),
item9 = c(4,4,4,3,4,4,4),
item10 = c(4,3,4,4,3,3,4))
# Compute the Lawshe's CVR
lawsheCVR(df)
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