From b702ff861137f93d506c8bc586d2fe7e7b499917 Mon Sep 17 00:00:00 2001 From: Samuel Tobler <47925314+samueltobler@users.noreply.github.com> Date: Tue, 30 Jan 2024 16:00:59 +0100 Subject: [PATCH] Update README.md --- README.md | 38 +++++++++++++++++++++++--------------- 1 file changed, 23 insertions(+), 15 deletions(-) diff --git a/README.md b/README.md index 6233a99..9841134 100644 --- a/README.md +++ b/README.md @@ -9,7 +9,7 @@ ## Introduction
-The **`mcc`** package allows calculating and visualizing metacognitive performance judgement data. For a theoretical elaboration of the methodological approach and for the results from an empirical application, please read the following paper: +The **`mcc`** package allows calculating and visualizing metacognitive performance judgment data. For a theoretical elaboration of the methodological approach and for the results from an empirical application, please read the following paper: **Tobler, S. & Kapur, M. (2023). Metacognitive calibration: a methodological expansion and empirical application. https://doi.org/10.3929/ethz-b-000600979** @@ -20,7 +20,7 @@ In this paper, the following functions are described: - Calibration accuracy - Miscalibration -These functions can be applied on performance-judgement-based on 4-point Likert scale data (yes / rather yes / rather no / no) or on performance-judgement-based on binary data (yes / no). +These functions can be applied to performance judgment based on 4-point Likert scale data (yes / rather yes / rather no / no) or on performance judgment based on binary data (yes / no). Additionally, commonly used functions in the literature for calibration accuracy are available as well. These correspond to: @@ -33,7 +33,7 @@ Additionally, commonly used functions in the literature for calibration accuracy To use the functions, the data need to be prepared. Therefore, the functions require 1. a data frame with the performance data (participants x questions), in which questions are rated 1 if correct and 0 if incorrect -2. a data frame with the performance-judgement values, either numerically designated (e.g., 0-3) or alphabetical with the already correctly assigned letters (see Table 1 in the paper). +2. a data frame with the performance-judgment values, either numerically designated (e.g., 0-3) or alphabetically with the already correctly assigned letters (see Table 1 in the paper). The assigned letters in the data preparation correspond to those depicted in Table 1 of the paper mentioned above. A step-by-step instruction in R is shown below. @@ -42,15 +42,15 @@ The assigned letters in the data preparation correspond to those depicted in Tab #### Data Preparation -Ideally, the performance-judgement is assessed by using a 4-point Likert scale. In case the performance judgement has been assessed on a binary scale, you need to first transform them by using the `btof`-function. This function only works if the confidence judgements are already transformed to the letters _a_ to _d_. +Ideally, the performance judgment is assessed using a 4-point Likert scale. In case the performance judgment has been assessed on a binary scale, you need to first transform them by using the `btof`-function. This function only works if the confidence judgments are already transformed to the letters _a_ to _d_. -- `btof`: To transform the **binary** judgement data that they can be used in the various functions. This step is necessary for all functions described in here beside _d'_, _gamma_, or _G_-index. +- `btof`: To transform the **binary** judgment data so that they can be used in the various functions. This step is necessary for all functions described here beside _d'_, _gamma_, or _G_-index. -In case the judgement data is assessed on a 4-point Likert scale and the values are numerical, step 1 and 2 have to be performed. If the values are already transformed to letters according to table 1 in the paper, step 1 can be skipped. +In case the judgment data is assessed on a 4-point Likert scale, and the values are numerical, steps 1 and 2 have to be performed. If the values are already transformed into letters according to Table 1 in the paper, step 1 can be skipped. **Step 1**: Transforming Likert data to letters a-h -- `letterassignment`: requires the input of performance and judgement data +- `letterassignment`: requires the input of performance and judgment data **Step 2**: Count different letters per participant @@ -66,24 +66,32 @@ In case you need to use the functions _d'_, _gamma_, or _G_-index, but you colle To analyze overconfidence, underconfidence, calibration accuracy, and miscalibration, the following functions can be used: -- `overconfidence`: requires a data with the counted letters per participant (i.e., the result from the `participant_summary`-function) -- `underconfidence`: requires a data with the counted letters per participant (i.e., the result from the `participant_summary`-function) -- `calibrationaccuracy`: requires a data with the counted letters per participant (i.e., the result from the `participant_summary`-function) -- `miscalibration`: requires a data with the counted letters per participant (i.e., the result from the `participant_summary`-function) +- `overconfidence`: requires data with the counted letters per participant (i.e., the result from the `participant_summary`-function) +- `underconfidence`: requires data with the counted letters per participant (i.e., the result from the `participant_summary`-function) +- `calibrationaccuracy`: requires data with the counted letters per participant (i.e., the result from the `participant_summary`-function) +- `miscalibration`: requires data with the counted letters per participant (i.e., the result from the `participant_summary`-function) -Alternatively, one can directly use the function `conf.stats` to skip all these steps and directly get the summary. This function works only when the judgement data has been assessed on a 4-point Likert scale. +#### Shortcut Function +Alternatively, one can use the function `conf.stats` to skip all these steps and get the summary directly. This function works only when the judgment data has been assessed on a 4-point Likert scale. -- `conf.stats`: requires performance values (0 / 1), and judgement values (on a numerical scale) +- `conf.stats`: requires performance values (0 / 1), and judgment values (on a numerical scale)
#### Visualization -To visualize the findings, one can either look at the confidence accuracy ratings and the miscalibration individually, or directly visualize both in one plot. The functions to do so are. +To visualize the findings, one can either look at the confidence accuracy ratings and the miscalibration individually or directly visualize both in one plot. The functions to do so are. - `confidence_plot`: requires the calibration accuracy values - `miscalibration_plot`: requires the miscalibration values -- `combined_plot`: requires both, calibration and miscalibration values +- `combined_plot`: requires both calibration and miscalibration values + +Additionally, the following functions can be used to compare two or more groups visually: + +- `confidence_plot.groups`: requires additionally a group value +- `miscalibration_plot.group`: requires additionally a group value +- `overconfidence_plot.groups`: to visualize overconfidence values in different groups +- `underconfidence_plot.groups`: to visualize underconfidence values in different groups