independent_samples | R Documentation |
This function automatically cleans the datasets (e.g., converting missing values to "0), binds treatment-control group datasets, check assumptions, and then runs the Independent Samples T-test (parametric) and Mann–Whitney U test (nonparametric) to help you examine the difference between the groups. R scripts and their outputs are as follows (just pay attention to the outputs since the codes are automatically run back-end by the function).
independent_samples(
treat_csv_data,
ctrl_csv_data,
m_cutoff = 0.15,
m_choice = FALSE,
key_csv_data
)
treat_csv_data |
This function requires a csv file with treatment group 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. |
ctrl_csv_data |
This function requires a csv file with control group data. Its name (e.g., "data_ctrl_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. |
m_choice |
This package is capable of handling multiple-choice answers for the convenience of users. If users want to use a csv data file with multiple-choice answers, they should put m_choice = TRUE and provide another csv file that contains answer keys using the argument of key_csv_data. |
key_csv_data |
This function requires a csv file that contains answer keys if m_choice = TRUE. The loaded answer keys will change the multiple- choice answers to a binary format of 1 (correct) and 0 (incorrect). |
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
descriptive_statistics
: Descriptive statistics
boxplots
: Boxplots - visual presentation of the descriptive
statistics
shapiro_wilk_test
: Shapiro-Wilk test results to determine
normality
normal_qq_plot
: The normal q-q plot to visually inspect the
normality
independent_samples_t_test_equal
: Results of the independent
samples t-test with equal variances assumed
independent_samples_t_test_unequal
: Results of the independent
samples t-test with unequal variances assumed
mann_whitney_u_test
: Results of the Mann-Whitney U test
# Run the following codes directly in the console panel. The plots
# generated through the link above may be displaced depending on the screen
# resolution.
independent_samples(treat_csv_data =
system.file("extdata", "data_treat_post.csv", package = "DBERlibR"),
ctrl_csv_data =
system.file("extdata", "data_ctrl_post.csv", package = "DBERlibR"),
m_cutoff = 0.15, m_choice = FALSE, key_csv_data = NULL)
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