one_way_ancova: One-way ANCOVA

In HelikarLab/DBERlibR: Automated Assessment Data Analysis for Discipline-Based Education Research

One-way ANCOVA

Description

This function automatically merges pre-post data sets, binds treatment-control data sets, checks assumptions of one-way ANCOVA, and then runs the main One-way ANCOVA all at once. Please make sure to name data files accurately (i.e., "data_treat_pre.csv", "data_treat_post.csv", "data_ctrl_pre.csv"," data_ctrl_post.csv") and have them saved in the working directory.

Usage

one_way_ancova(
  treat_pre_csv_data,
  treat_post_csv_data,
  ctrl_pre_csv_data,
  ctrl_post_csv_data,
  m_cutoff = 0.15,
  m_choice = FALSE,
  key_csv_data
)

Arguments

treat_pre_csv_data	This function requires a csv file with treatment group's 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.
treat_post_csv_data	This function requires a csv file with treatment group's 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.
ctrl_pre_csv_data	This function requires a csv file with control group's pre-test. Its name (e.g., "data_ctrl_pre.csv") can be passed as an argument. Make sure to set the folder with the data file(s) as the working directory.
ctrl_post_csv_data	This function requires a csv file with control group's post-test 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).

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

• pre_descriptive_statistics: Pre-test data descriptive statistics

• post_descriptive_statistics: Post-test data descriptive statistics

• boxplots: Boxplots - visual presentation of the descriptive statistics

• scatter_plot: Scatter plots to test the linearity statistics

• shapiro_wilk_test: Shapiro-Wilk test results to determine normality of residuals

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

• levene_test: Test homogeneity of variances

• independent_samples_t_test_equal: Results of the independent samples t-test with equal variances assumed

• one_way_ancova: Results of the one-way ANCOVA

• estimated_marginal_means: Estimated marginal means

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.
one_way_ancova(treat_pre_csv_data =
         system.file("extdata", "data_treat_pre.csv", package = "DBERlibR"),
         treat_post_csv_data =
         system.file("extdata", "data_treat_post.csv", package = "DBERlibR"),
         ctrl_pre_csv_data =
         system.file("extdata", "data_ctrl_pre.csv", package = "DBERlibR"),
         ctrl_post_csv_data =
         system.file("extdata", "data_ctrl_post.csv", package = "DBERlibR"),
         m_cutoff = 0.15, m_choice = FALSE, key_csv_data = NULL)

HelikarLab/DBERlibR documentation built on Sept. 20, 2023, 12:37 p.m.