README.md

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

EMAeval Description

Noah Kraus 11/03/2020

EMAeval R Markdown

The R package EMAeval contains functions created to help researchers identify careless responses as well as responders ine EMA data. An example dataset is included in the package to help the user better understand the uses of each function. This dataset “EMAeval_Data” is used in the example code below.

There are 4 “participants,” each having 50 assessments. Each was asked 8 questions per assessment.

EMAeval\_Data Example ID StartDate EndDate Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 1 1001 2020-01-02 10:01:23 2020-01-02 10:02:07 61 9 46 39 5 64 74 32 56 1002 2020-01-07 10:01:16 2020-01-07 10:01:24 45 50 49 48 54 55 48 52 111 1003 2020-01-12 10:00:46 2020-01-12 10:12:08 72 99 25 68 23 42 21 25 172 1004 2020-01-23 09:58:22 2020-01-23 09:58:38 15 5 12 30 73 83 36 6

Functions

Below are the functions in the R package EMAeval. The functions are:

• flagging_df
• flagging_plots
• TPI_cutoff
• SD_cutoff
• Perc_Mode_cutoff
• Combined_cutoff
• Combined_cutoff_percent

Each section will be dedicated to a particular function, giving an example of the usage with the EMAeval_Data and showing the output.

flagging_df

*This function creates a dataframe that reports Time to Complete (TTC), Time per Item (TPI), Item Standard Deviation (SD), and Longstring. If the Longstring returns NA, then there was no longstring response because all item responses were different. The partial results of the flagging_df function are based from the EMAeval_Data Example above. *

flaggingDF <- flagging_df(EMAeval_Data, 
                          ttc.colnames = c("StartDate", "EndDate"), 
                          item.colnames = colnames(EMAeval_Data[,4:11]))

Flagging Dataframe Example TTC TPI SD Longstring 1 44 5.50 25.21 NA 56 8 1.00 3.36 48 111 682 85.25 29.31 25 172 16 2.00 30.22 NA

flagging_plots

This function creates a histograms of each of the calculations reported in the flagging_df function. This can be used to help users identify the cutoff values for TPI and SD.

flagging_plots(EMAeval_Data, 
               ttc.colnames = c("StartDate", "EndDate"), 
               item.colnames = colnames(EMAeval_Data[,4:11]),
               number.items = 8)

Note: the Longstring histogram has a much smaller scale for the Count. This is due to the lack of longstring values because many assessments do not have a Longstring value because all item responses differ.

TPI_cutoff

This function creates a dataframe of ID and data indices in which the assessment met the cutoff criterion for Time per Item. The user inputs their own cutoff for TPI. The user can also specify what type of comparison they would like to complete with the cutoff value using condition =… If responses to all items are mandatory, then the following response should be included:

mandatory.response = TRUE

Below is the code for the function.

TPI_cutoff(EMAeval_Data, 
           cutoff = 1, 
           condition = "<=",  
           ttc.colnames = c("StartDate", "EndDate"),  
           number.items = 8,
           mandatory.response = TRUE, 
           item.colnames = colnames(EMAeval_Data[,4:11]), 
           ID.colname = "ID")

Assessments Flagged by TPI Cutoff ID Index\_of\_Flagged\_Assessment 1002 51 1002 52 1002 53 1002 54 1002 55 1002 56 1002 57 1002 58 1002 62 1002 63 1002 65 1002 67 1002 68 1002 69 1002 70 1002 71 1002 72 1002 75 1002 76 1002 78 1002 79 1002 80 1002 81 1002 82 1002 83 1002 87 1002 90 1002 91 1002 92 1002 93 1002 96 1002 97 1002 98 1002 99 1003 116

SD_cutoff

This function creates a dataframe of ID and data indices in which the assessment met the cutoff criterion for Item Score Standard Deviation. The user inputs their own cutoff for SD. The user can also specify what type of comparison they would like to complete with the cutoff value using condition =…

Below is the code for the function.

SD_cutoff(EMAeval_Data, 
          cutoff = 5,  
          condition = "<=", 
          item.colnames = colnames(EMAeval_Data[,4:11]), 
          ID.colname = "ID")

Assessments Flagged by SD Cutoff ID Index\_of\_Flagged\_Assessment 1002 51 1002 52 1002 53 1002 54 1002 55 1002 56 1002 57 1002 58 1002 59 1002 60 1002 61 1002 62 1002 63 1002 64 1002 65 1002 66 1002 67 1002 68 1002 69 1002 70 1002 71 1002 72 1002 73 1002 74 1002 75 1002 76 1002 77 1002 78 1002 79 1002 80 1002 81 1002 82 1002 83 1002 84 1002 85 1002 86 1002 87 1002 88 1002 89 1002 90 1002 91 1002 92 1002 93 1002 94 1002 95 1003 142

Perc_Mode_cutoff

This function creates a dataframe of ID and data indices in which the assessment met the cutoff criterion for the Percent of Items at Mode. The user inputs their own cutoff for Percent of Items at Mode. The user can also specify what type of comparison they would like to complete with the cutoff value using condition =…

Below is the code for the function.

Perc_Mode_cutoff(EMAeval_Data, 
                 cutoff = 0.7,  
                 condition = ">=", 
                 item.colnames = colnames(EMAeval_Data[,4:11]), 
                 ID.colname = "ID")

Assessments Flagged by PErcent of Items at Mode Cutoff ID Index\_of\_Flagged\_Assessment 1002 100 1003 138 1003 140 1003 142 1003 150

Combined_cutoff

This function creates a dataframe of ID and data indices in which the assessment met the cutoff criteria for Time per Item OR Item Score Standard Deviation OR Percent of Items at Mode. The user inputs their own cutoff for TPI, SD and Percent of Items at Mode. The user can also specify what type of comparison they would like to complete with each cutoff value using either SD.condition =… and TPI.condition=… and Perc.Mode.condition=… Users can also specify the logical component for the criteria, specifying with Combined.logic = … If responses to all items are mandatory, then the following response should be included:

mandatory.response = TRUE

Below is the code for the function.

Combined_cutoff(EMAeval_Data, 
                SD.cutoff = 5, SD.condition = "<=", 
                TPI.cutoff = 1, TPI.condition = "<=", 
                Perc.Mode.cutoff = 0.7, Perc.Mode.condition = ">=", 
                Combined.logic = "or", 
                ttc.colnames = c("StartDate", "EndDate"), 
                number.items = 8, mandatory.response = TRUE, 
                item.colnames = colnames(EMAeval_Data[,4:11]), 
                ID.colname = "ID")

Assessments Flagged by TPI, SD or Percent of Items at Mode Cutoffs ID Index\_of\_Flagged\_Assessment 1002 51 1002 52 1002 53 1002 54 1002 55 1002 56 1002 57 1002 58 1002 59 1002 60 1002 61 1002 62 1002 63 1002 64 1002 65 1002 66 1002 67 1002 68 1002 69 1002 70 1002 71 1002 72 1002 73 1002 74 1002 75 1002 76 1002 77 1002 78 1002 79 1002 80 1002 81 1002 82 1002 83 1002 84 1002 85 1002 86 1002 87 1002 88 1002 89 1002 90 1002 91 1002 92 1002 93 1002 94 1002 95 1002 96 1002 97 1002 98 1002 99 1003 116 1003 142

Combined_cutoff_percent

This function creates a dataframe of ID and percent of responses in which assessments met the cutoff criteria for Time per Item OR Item Score Standard Deviation OR Percent of Items at Mode. The user inputs their own cutoff for TPI, SD and Percent of Items at Mode. The user can also specify what type of comparison they would like to complete with each cutoff value using either SD.condition =… and TPI.condition=… and Perc.Mode.condition=… Users can also specify the logical component for the criteria, specifying with Combined.logic = … If responses to all items are mandatory, then the following response should be included:

mandatory.response = TRUE

Below is the code for the function.

Combined_cutoff_percent(EMAeval_Data, 
                        SD.cutoff = 5, SD.condition = "<=", 
                        TPI.cutoff = 1, TPI.condition = "<=",  
                        Perc.Mode.cutoff = 0.7, Perc.Mode.condition = ">=", 
                        Combined.logic = "or",
                        ttc.colnames = c("StartDate", "EndDate"),  
                        number.items = 8, mandatory.response = TRUE, 
                        item.colnames = colnames(EMAeval_Data[,4:11]), 
                        ID.colname = "ID")

Percentage of Assessments Flagged by TPI, SD or Percent of Items at Mode Cutoffs ID Percent\_Flagged 1002 100 1003 10

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