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dropR: Dropout Analysis by Condition

Description

Analysis and visualization of dropout between conditions in surveys and (online) experiments. Features include computation of dropout statistics, comparing dropout between conditions (e.g. Chi square), analyzing survival (e.g. Kaplan-Meier estimation), comparing conditions with the most different rates of dropout (Kolmogorov-Smirnov) and visualizing the result of each in designated plotting functions. Sources: Andrea Frick, Marie-Terese Baechtiger & Ulf-Dietrich Reips (2001)https://www.researchgate.net/publication/223956222_Financial_incentives_personal_information_and_drop-out_in_online_studies; Ulf-Dietrich Reips (2002) "Standards for Internet-Based Experimenting"doi:10.1027//1618-3169.49.4.243.

Author(s)

Maintainer: Annika Tave Overlanderannika-tave.overlander@uni.kn

Authors:

• Matthias Bannertbannert@kof.ethz.ch

Other contributors:

• Ulf-Dietrich Reips [contributor]

See Also

Useful links:

• https://iscience-kn.github.io/dropR/

• https://github.com/iscience-kn/dropR

Add Dropout Index to a Data.Frame

Description

Find drop out positions in a data.frame that contains multiplequestions that had been asked sequentially.This function adds the Dropout Index variabledo_idx to the data.frame which is necessaryfor further analyses of dropout.

Use this functionfirst to prepare your dropout analysis. Then, keep going by creatingthe dropout statistics usingcompute_stats().

Usage

add_dropout_idx(df, q_pos)

Arguments

Details

Importantly, this function will start counting missing data at the end of thedata frame. Any missing data which is somewhere in between, i.e.a single item that was skipped or forgotten will not be counted as dropout.The function will identify sequences of missing data that go until the end of thedata frame and add the number of the last answered question indo_idx.

Therefore, the variables must be in the order that they were asked, otherwise analyseswill not be valid.

Value

Returns original data frame with columndo_idx added.

Source

R/add_dropout_idx.R

See Also

compute_stats() which is usually the next step for dropout analysis.

Examples

dropout <- add_dropout_idx(dropRdemo, 3:54)

Compute Dropout Statistics

Description

This is thesecond step in conducting dropout analysis withdropR.Outputs all necessary statistics to analyze and visualize dropout, such asthe sample size N of the data (and in each condition if selected), cumulativedropout and remaining participants in absolute numbers and percent.If no experimental condition is added, the stats are only calculated for thewhole data in total.

Usage

compute_stats(df, by_cond = "None", no_of_vars)

Arguments

Value

A data frame with 6 columns (q_idx, condition, cs, N, remain, pct_remain)and as many rows as questions in original data (for overall data and if conditions selectedagain for each condition).

Examples

do_stats <- compute_stats(df = add_dropout_idx(dropRdemo, 3:54),by_cond = "experimental_condition",no_of_vars = 52)

Compute Chisq-Test Given a Question Position

Description

This function performs a chi-squared contingency table test on dropout fora given question in the data. Note that the input data should be in the format ascomputed bycompute_stats().The test can be performed on either all conditions (excluding total) or on select conditions.

Usage

do_chisq(do_stats, chisq_question, sel_cond_chisq, p_sim = TRUE)

Arguments

Value

Returns test results from chisq.test between experimental conditions at defined question.

See Also

add_dropout_idx() andcompute_stats() which are necessary for the proper data structure.

Examples

do_stats <- compute_stats(add_dropout_idx(dropRdemo, 3:54),by_cond = "experimental_condition",no_of_vars = 52)do_chisq(do_stats, 47, c(12, 22), TRUE)

Kaplan-Meier Survival Estimation

Description

This function needs a data set with a dropout index added byadd_dropout_idx().Thedo_kpm function performs survival analysis with Kaplan-Meier Estimationand returns a list containing survival steps, the original data frame, and the model fit type.The function can fit the survival model either for the entire data set or separately by a specified condition column.

Usage

do_kpm(df, condition_col = "experimental_condition", model_fit = "total")

Arguments

Value

Returns a list containingsteps (survival steps extracted from the fitted models),d (the original data frame), andmodel_fit (the model fit type).

See Also

survival::Surv() used to fit survival object.

Examples

demo_kpm <- do_kpm(df = add_dropout_idx(dropRdemo, 3:54),condition_col = "experimental_condition",model_fit = "total")head(demo_kpm$steps)

Compute Kolmogorov-Smirnov Test for most extreme conditions

Description

This test is used for survival analysis between the most extreme conditions,so the ones with the most different rates of dropout.This function automatically prepares your data and runsstats::ks.test() on it.

Usage

do_ks(do_stats, question)

Arguments

Value

Returns result of Kolmogorov-Smirnoff test including which conditions have the most different dropout rates.

Examples

do_stats <- compute_stats(df = add_dropout_idx(dropRdemo, 3:54),by_cond = "experimental_condition",no_of_vars = 52)do_ks(do_stats, 52)

Dropout Odds Ratio Table

Description

This function calculates an Odds Ratio table at a given question for selected experimentalconditions. It needs data in the format as created bycompute_stats() as input.

Usage

do_or_table(do_stats, chisq_question, sel_cond_chisq)

Arguments

Value

Returns a Matrix containing the Odds Ratios of dropout between all selected conditions.

See Also

compute_stats()

Examples

do_stats <- compute_stats(df = add_dropout_idx(dropRdemo, 3:54),by_cond = "experimental_condition",no_of_vars = 52)do_or_table(do_stats, chisq_question = 51, sel_cond_chisq = c("11", "12", "21", "22"))

Calculate Steps for Uneven Data Points

Description

Thedo_steps function calculates steps for data points represented by numbers of questions from the originalexperimental or survey data inx and remaining percent of participants iny.

Usage

do_steps(x, y, return_df = TRUE)

Arguments

Details

Due to the nature of dropout/ survival data, step functions are necessary to accurately depict participants remaining.Dropout data includes the time until the event (a.k.a. dropout at a certain question or time), so that changes in remainingparticipants are discrete rather than continuous. This means that changes in survival probability occur at specific pointsand are better represented as steps than as a continuum.

Value

Returns a data frame or a list containing the modifiedx andy values.

Examples

x <- c(1, 2, 3, 4, 5)y <- c(100, 100, 95, 90, 85)do_steps(x, y)# Using the example dataset dropRdemodo_stats <- compute_stats(df = add_dropout_idx(dropRdemo, 3:54),by_cond = "experimental_condition",no_of_vars = 52)tot_stats <- do_stats[do_stats$condition == "total", ]do_steps(tot_stats$q_idx, tot_stats$pct_remain)

Demo Dataset for Dropout in an Online Survey

Description

Simulated demo data set for dropout in a survey.

Format

A data frame with 246 rows and 54 variables (in the order they were presented in the fictional survey).

obs_id

Observation ID

experimental_condition

experimental condition

vi_1

item 1

vi_2

item 2

vi_3

item 3

vi_4

item 4

vi_5

item 5

vi_6

item 6

vi_7

item 7

vi_8

item 8

vi_9

item 9

vi_10

item 10

vi_11

item 11

vi_12

item 12

vi_13

item 13

vi_14

item 14

vi_15

item 15

vi_16

item 16

vi_17

item 17

vi_18

item 18

vi_19

item 19

vi_20

item 20

vi_21

item 21

vi_22

item 22

vi_23

item 23

vi_24

item 24

vi_25

item 25

vi_26

item 26

vi_27

item 27

vi_28

item 28

vi_29

item 29

vi_30

item 30

vi_31

item 31

vi_32

item 32

vi_33

item 33

vi_34

item 34

vi_35

item 35

vi_36

item 36

vi_37

item 37

vi_38

item 38

vi_39

item 39

vi_40

item 40

vi_41

item 41

vi_42

item 42

vi_43

item 43

vi_44

item 44

vi_45

item 45

vi_46

item 46

vi_47

item 47

vi_48

item 48

vi_49

item 49

vi_50

item 50

vi_51

item 51

vi_52

item 52

Source

dropRdemo Demo data for dropout.

Compute Odds From Probabilities

Description

Compute odds from probabilities. The function is vectorized andcan handle a vector of probabilities, e.g. remaining percent of participantsas calculated bycompute_stats().

Usage

get_odds(p)

Arguments

Value

Returns numerical vector of the same length as original input reflecting the odds.

Examples

get_odds(0.7)get_odds(c(0.7, 0.2))

Compute Odds Ratio

Description

Computes odds ratio given two probabilities.In this package, the function can be used to compare the percentages of remainingparticipants between two conditions at a time.

Usage

get_odds_ratio(a, b)

Arguments

Value

Returns numerical vector of the same length as original input reflecting the Odds Ratio (OR).

See Also

get_odds(), as this is the basis for calculation.

Examples

get_odds_ratio(0.7, 0.6)

Get Steps Data by Condition

Description

Theget_steps_by_cond function calculates steps data based on survival model results.This utility function is used inside thedo_kpm() function ofdropR.

Usage

get_steps_by_cond(sfit, condition = NULL)

Arguments

Value

Returns a data frame containing the steps data, including time, survival estimates, upper confidence bounds, and lower confidence bounds.

See Also

do_kpm()

Test Survival Curve Differences

Description

This function compares survival curves as modeled withdo_kpm().It outputs a contingency table and a Chisq measure of difference.

Usage

get_survdiff(kds, cond, test_type)

Arguments

Value

Returns survival test results as called fromsurvival::survdiff().

Examples

kpm_est <- do_kpm(add_dropout_idx(dropRdemo, 3:54))get_survdiff(kpm_est$d, "experimental_condition", 0)get_survdiff(kpm_est$d, "experimental_condition", 1)

Plot Dropout Curves

Description

This functions usesggplot2to create drop out curves.Please note that you should useadd_dropout_idx() andcompute_stats() on yourdata before running this function as it needs a certain data structure and variables towork properly.

Usage

plot_do_curve(  do_stats,  linetypes = TRUE,  stroke_width = 1,  full_scale = TRUE,  show_points = FALSE,  show_confbands = FALSE,  color_palette = "color_blind")

Arguments

Value

Returns aggplot object containing the dropout curve plot. Using the Shiny App version ofdropR, this plot can easily be downloaded in different formats.

See Also

add_dropout_idx() andcompute_stats() which are necessary for the proper data structure.

Examples

do_stats <- compute_stats(add_dropout_idx(dropRdemo, 3:54),by_cond = "experimental_condition",no_of_vars = 52)plot_do_curve(do_stats)

Plot a Kaplan Meier Survival Estimation

Description

Theplot_do_kpm function generates a Kaplan-Meier survival plot based on theoutput from thedo_kpm() function. It allows for customization of conditionsto display, confidence intervals, color palettes, and y-axis scaling.

Usage

plot_do_kpm(  kds,  sel_conds = c("11", "12", "21", "22"),  kpm_ci = TRUE,  full_scale_kpm = FALSE,  color_palette_kp = "color_blind")

Arguments

Value

Returns aggplot object containing the Kaplan-Meier survival plot. Using the Shiny App version ofdropR, this plot can easily be downloaded in different formats.

Examples

plot_do_kpm(do_kpm(d = add_dropout_idx(dropRdemo, 3:54),condition_col = "experimental_condition",model_fit = "total"))plot_do_kpm(do_kpm(d = add_dropout_idx(dropRdemo, 3:54),condition_col = "experimental_condition",model_fit = "conditions"), sel_conds = c("11", "12", "21", "22"))

Plot Most Extreme Conditions to Visualize Kolmogorov-Smirnov Test Results

Description

With this function, you can easily plot the most extreme conditions, a.k.a. those with the mostdifferent dropout rates at a certain question. You need to define that question in the function call ofdo_ks() already, or just call that function directly inside the plot function.

Usage

plot_do_ks(  do_stats,  ks,  linetypes = FALSE,  show_confbands = FALSE,  color_palette = c("#E69F00", "#CC79A7"))

Arguments

Value

Returns aggplot object containing the survival curve plot of the most extremedropout conditions. Using the Shiny App version of dropR, this plot can easily be downloaded in different formats.

See Also

compute_stats(),do_ks()

Examples

do_stats <- compute_stats(add_dropout_idx(dropRdemo, 3:54), by_cond = "experimental_condition",no_of_vars = 52)ks <- do_ks(do_stats, 52)plot_do_ks(do_stats, ks, color_palette = "gray")# ... or call the do_ks() function directly inside the plotting functionplot_do_ks(do_stats, do_ks(do_stats, 30))plot_do_ks(do_stats, ks, linetypes = TRUE, show_confbands = TRUE, color_palette = c("red", "violet"))

Start the dropR Shiny App

Description

Starts the interactive web application to use dropR in your web browser.Make sure to use Google Chrome or Firefox for best experience.

Usage

start_app()

Details

The app will give less experienced R users or statisticians a good overview ofhow to conduct dropout analysis. For more experienced analysts, it can still be very helpfulin guiding how to use the package as there are some steps that should be taken in order,which is outlined in the app (as well as function documentation).

Value

No return value; starts the shiny app as a helper to get started with dropout analysis. All app procedures are available as functions.