Perc_Mode_cutoff: Identification of Careless Response Using the Percent of...

In manateelab/EMAeval-R-Package: Identification of Careless Responses and Responders in EMA Data

Description

Perc_Mode_cutoff returns dataframe of assessment flagged by the Percent of Items at Mode per assessment cutoff value.

Usage

Perc_Mode_cutoff(data, cutoff, condition, item.colnames, ID.colname)

Arguments

data	dataframe to be analyzed.
cutoff	numeric cutoff value for the Percent of Items at Mode, 'default' value set to 0.7.
condition	character string of condition that is desired for comparison of data to Percent of Items at Mode cutoff value, i.e <, >=, etc. 'Default' logic set to ">=".
item.colnames	vector of column names of all items/questions to be used to calculate item score Standard Deviation and Longstring responses.
ID.colname	character string of column name for ID of assessment.

Details

This function creates the dataframe that includes the ID and index of assessments that met the cutoff criterion for Percent of Items at Mode per assessment. If an assessment has a Percent of Items at Mode greater than or equal to the cutoff value, it will be flagged and placed in the dataframe.

Value

The item "item.colnames" must be the column names of all items to be included in the calculations for the Percent of Items at Mode. The base function colnames can be utilized if user prefers. If columns x through y are to be used for this calculation, the following syntax must be followed: item.colnames = colnames(data[,x:y]) Example of use with column names can bee seen below.

References

Jaso, B.A., Kraus, N.I., Heller, A.S. (2020) Identification of careless responding in ecological momentary assessment: from post-hoc analyses to real-time data monitoring.

See Also

TPI_cutoff for a similar function, using Time per Item rather than Percent of Items at Mode.

SD_cutoff for a similar function, using Standard Deviation rather than Percent of Items at Mode.

See the following functions for more information on Careless Response Identification in EMA: flagging_df, flagging_plots, longstringr, Combined_cutoff, and Combined_cutoff_percent

manateelab/EMAeval-R-Package documentation built on Oct. 13, 2021, 6:48 a.m.