DBERlibR (version 0.1.3)

demo_group_diff: Demographic Group Differences

Description

This function automatically combines demographic variables to a dataset, and runs the analysis of variance (ANOVA) with assumptions check to examine demographic sub-group differences all at once. Please make sure to name data files accurately and have them saved in the working directory.

Usage

demo_group_diff(score_csv_data, group_csv_data, m_cutoff = 0.15, group_name)

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

• descriptive_statistics: Descriptive statistics

• boxplots: Boxplots - visual presentation of the descriptive 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

• one_way_anova: Results of the one-way anova with equal variances assumed

• one_way_anova_pwc: Pairwise t-test results for the one-way ANOVA with equal variances assumed

• welch_anova_test: Results of the one-way ANOVA with unequal variance

• games_howell_test: Pairwise t-test results for the one-way ANOVA with unequal variances assumed

• kruskal_wallis_test: Results of the Kruskal-Wallis test (non- parametric version of the one-way ANOVA)

• kruskal_wallis_test_pwc: Pairwise t-test results for the Kruskal-Wallis test

Arguments

score_csv_data

This function requires a csv data file. 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.

group_csv_data

This function requires a csv data file. Its name (e.g., "demographic_data.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.

group_name

This function requires a group name as indicated in the csv data file (e.g., "gender", "grade")

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.
demo_group_diff(score_csv_data =
        system.file("extdata", "data_treat_pre.csv", package = "DBERlibR"),
        group_csv_data =
        system.file("extdata", "demographic_data.csv", package = "DBERlibR"),
        m_cutoff = 0.15,
        group_name = "grade")