Install R/CTTvis from Github using
devtools::install_github("TaridWong/CTTvis")library(CTTvis)To demonstrate the difficulty_plot and
point_biserial_plot functions, we will first load a
built-in dataset called dichotomous_response.
In some context, item difficulty flag thresholds may change. This can
be adjusted using the easyFlag and hardFlag
arguments. The following use the easy flag threshold of .8, meaning that
items that gets answered correctly 80% of the total test takers or
greater are considered easy. On the other hand, items that gets answered
correctly 60% of the total test takers or less are considered
difficult.
data(dichotomous_response)
difficulty_plot(responses = dichotomous_response, title = "Item Difficulty Plot", easyFlag = .80, hardFlag = .60)For the point_biserial_plot function, you could adjust
your point-biserial correlation (pBis) threshold as well. For example,
if you want the pBis threshold to be .3, you could configure the
pBis_threshold as follows:
point_biserial_plot(responses = dichotomous_response, title = "Item Discrimination Plot", pBis_threshold = 0.30)To demonstrate the coefficient_alpha_plot function, we
need to load another built-in dataset called
reliability_df. This dataset was simulated to test the
capability of this function.
The influence of an item when dropped to the overall unidimensional coefficient alpha could vary, hence the option to configure the rounding of overall coefficient alpha. For example, if dropping an item increases the overall coefficient alpha by 0.001, then rounding the alpha by three decimal places could allow researchers to see the increase compared to rounding the alpha by two decimal points.
The following demonstration rounds the overall alpha by four decimal points. This argument can be adjusted based on the researchers’ needs.
data(reliability_df)
coefficient_alpha_plot(responses = reliability_df, title = "Coefficient Alpha Plot", alpha_round = 4)The R/CTTvis package as a whole is distributed under GPL-3 (GNU General Public License version 3).