DBERlibR (version 0.1.3)

paired_samples: Paired Samples Data Analysis

Description

This function automatically cleans the datasets (e.g., converting missing values to "0), merges pre-post datasets, checks assumptions, and then runs the (parametric) Paired Samples T-test and (nonparametric) Wilcoxon Signed-Rank test to help you examine the difference between pre-post scores.

Usage

paired_samples(pre_csv_data, post_csv_data, m_cutoff = 0.15)

Value

This function returns a tibble() including the following information:

• n_students_deleted: Number of students deleted from the data for analysis based on the percentage obtained via the argument of m_cutoff

• shapiro_wilk_test: Shapiro-Wilk test results to determine normality

• normal_qq_plot: The normal q-q plot to visually inspect the normality

• descriptive_statistics: Descriptive statistics

• boxplots: Boxplots - visual presentation of the descriptive statistics

• paired_samples_t_test: Paired samples t-test results

• wilcoxon_signed_rank_test: Wilcoxon signed rank test results

Arguments

pre_csv_data

This function requires a csv file with pre-test data. Its name (e.g., "data_treat_pre.csv") can be passed as an argument. Make sure to set the folder with the data file(s) as the working directory.

post_csv_data

This function requires a csv file with post-test data. Its name (e.g., "data_treat_post.csv") can be passed as an argument. Make sure to set the folder with the data file(s) as the working directory.

m_cutoff

This package will treat skipped answers as incorrect. However, too many skipped answers may skew the results of the data analysis. User can can provide a cutoff for the proportion of skipped answers. For example, if the user enters 0.1, students who skipped more than 10 percent of the answers will be excluded from the data analysis to prevent skewed results. The default of 0.15 is commonly applied as a rule of thumb.

Examples

# Run the following codes directly in the console panel. The plots
# generated through the link above may be displaced depending on the screen
# resolution.
paired_samples(pre_csv_data =
         system.file("extdata", "data_treat_pre.csv", package = "DBERlibR"),
         post_csv_data =
         system.file("extdata", "data_treat_post.csv", package = "DBERlibR"),
         m_cutoff = 0.15)