README.md

In samueltobler/behavdata: Behavioral Data Preprocessing and Analysis

behavdata

Introduction

The behavdata package allows easy pre-processing and analysis of behavioral data. This package includes the following functions:

Scale Analysis Related

• likert_transform: for fastly transforming text inputs like from Likert scale answers into numerical values
• likert_switch: to invert numerical values Likert scales
• AlphaCI_Bounds: to determine the confidence interval for Cronbach's Alpha values
• combined_scaleanalysis: To quickly determine standard values for scale analyses
• scales: An extension of the function combined_scaleanalysis
• likert_means_4p: Average Likert score for 4-point Likert scales
• likert_means_5p: Average Likert score for 5-point Likert scales
• likert_means_7p: Average Likert score for 7-point Likert scales
• in.numeric: for transforming data on any scale (i.e., non-numeric Likert scale) into numeric valuess

Qualitative Data Related

• answer_rating: for facilitated and unbiased rating of student answers on qualitative or open-ended questions

Correlation Analyses

• correlation_table: to calculate all pairwise correlations of a big data set and directly obtain a CSV table
• single_correlation_table: to calculate pairwise correlations of a single vector with many others and directly obtain a CSV table

Effect Size Analyses

• eta_to_d: to calculate Cohen's d values from eta-squared scores
• r_to_d: to calculate Cohen's d values from correlation values
• f_to_d: to calculate Cohen's d values from ANOVA F scores
• gse: to determine the standard error of Hedge's g effect sizes
• finding_d: to determine the lowest Cohen's d value with which two group means are statistically equivalent
• finding_d_from_df: to determine the lowest Cohen's d value with which two group means are statistically equivalent from a data frame

Outlier Analysis

• outliers: to determine statistical outliers
• truefalsecounter: compare two vectors to make a vector with true/false values to indicate where the values in vector 1 are present in vector 2

Other Functions

• se_propagation: to propagate standard errors
• ci_to_sd: to find standard deviation values from confidence intervals
• pathback: to go one folder up in the working directory
• stat.info: to get descriptive test statistics of numerical data
• stat.info_chr: to get descriptive test information of non-numerical data
• count_if: to count how many times a certain number or element is present in the data
• p.signs: to assign symbols to p-values

More functions will be added over time.

Installation

library(devtools)
devtools::install_github("samueltobler/behavdata", force = TRUE)
library(behavdata)

Citation

To cite the repository behavdata in publications, please use:

Tobler, S. (2022). behavdata: R Package for Behavioral Data Preprocessing and Analysis (Version 0.1.1) [Computer software]. https://github.com/samueltobler/behavdata

If you used the finding_d function, please cite additionally:

Tobler, S. (2022, October). Finding equivalence: a novel tool to investigate the effect size at which two groups are statistically equivalent. In 7th Annual Learning Sciences Graduate Student Conference (LSGSC 2022). https://doi.org/10.3929/ethz-b-000575508

References

Some of the functions require previously published R packages. These are the references of these packages (in alphabetical order).

• Hmisc: Harrell Jr F (2022). Hmisc: Harrell Miscellaneous. R package version 4.7-0, https://CRAN.R-project.org/package=Hmisc.
• psych: Revelle, W. (2022) psych: Procedures for Personality and Psychological Research, Northwestern University, Evanston, Illinois, USA. https://CRAN.R-project.org/package=psych
• sjmisc: Lüdecke D (2018). “sjmisc: Data and Variable Transformation Functions.” Journal of Open Source Software, 3 (26), 754. doi:10.21105/joss.00754
• TOSTER: Lakens, D. (2017). Equivalence tests: A practical primer for t-tests, correlations, and meta-analyses. Social Psychological and Personality Science, 8(4), 355-362. doi:10.1177/1948550617697177

samueltobler/behavdata documentation built on July 9, 2024, 11:33 a.m.