demo_group_diff | R Documentation |
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.
demo_group_diff(
score_csv_data,
group_csv_data,
m_cutoff = 0.15,
group_name,
m_choice = FALSE,
key_csv_data
)
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") |
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 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
# 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", m_choice = FALSE, key_csv_data = NULL)
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