validatednamesr

Authors: Jae Yeon Kim and Charles Crabtree

Summary

Researchers have used names to indicate race in various experimental tasks such as surveys, conjoint experiments, and correspondence experiments. When doing so, researchers must consider at least the following three factors:

• Choose names that vary by race but remain constant across other perceived attributes, or choose names that vary by race and other perceived attributes.
• Use not only one but several names to increase reliability.
• Use names that indicate differences in citizenship, education, and income within and across races to explore.

This R package provides functions to perform each task based on a validated dataset of 600 names (100 white, 300 Asian, 100 black, and 100 Hispanic) published in Nature Scientific Data (Crabtree, Kim, Gaddis, Holbein, Guage, and Marx, 2023). It helps researchers choose names in an experimental study that are consistent with their research objectives and underlying assumptions.

Installation

devtools::install_github("jaeyk/validatednamesr", dependencies = TRUE)

Usage

View and load datasets

1. view_data(): This function views the dataset's filename, type, and notes (metadata).

view_data()

#>            filename    type          notes
#> 1         names.rds   Names      Raw names
#> 2 study-1-names.rds Studies Study 1 result
#> 3 study-2-names.rds Studies Study 2 result
#> 4 study-3-names.rds Studies Study 3 result
#> 5      study123.rds  Pooled  Pooled result

2. load_data(): This function loads a particular dataset. Use either the file_name or file_note argument.

# The following two commands will provide the same output.

#file name
raw_names <- load_data(file_name = "names.rds")

# file note 
raw_names <- load_data(file_note = "Raw names")

Select names

The select_names() function helps to choose the validated names that are intended to signal a particular race using the race argument. The options available for this argument are: Asian, Black, Hispanic, White.

The output of this data is a dataframe with nine columns: first (first name), last = last name, w.asian = westernized Asian name (1 = yes, 0 = no), name (full name), identity (the name's intended race), mean_correct = the percentage of the name's intended race correctly perceived (0-1), avg_income = average perceived income level (1-5), avg_education = average perceived education level (1-5), avg_citizenship = the percentage of perceived citizenship status (0-1).

asian_names <- select_names(race = "Asian") # Asian signalling names 

asian_names 

#>   first last  w.asian name      identity       mean_correct avg_income avg_education avg_citizenship
#>   <chr> <chr>   <dbl> <chr>     <fct>            <dbl>   <dbl>   <dbl>   <dbl>
#> 1 Dan   Yang        1 Dan Yang  Asian or Paci…   0.848    2.09    2.55   0.773
#> 2 Hong  Pham        0 Hong Pham Asian or Paci…   0.826    1.94    2.28   0.465
#> 3 May   Chen        1 May Chen  Asian or Paci…   0.878    2.07    2.37   0.789
#> 4 Hong  Le          0 Hong Le   Asian or Paci…   0.816    1.97    2.42   0.539
#> 5 Wei   Le          0 Wei Le    Asian or Paci…   0.806    1.93    2.18   0.418

You can change the threshold level of the names to be correctly perceived using the pct_correct argument. The default value for this argument is 0.8.

# change the threshold level of the names to be correctly perceived from 0.8 (the default value) to 0.7

high_thres_names <- select_names(race = "Asian", pct_correct = 0.7)

You can also change the number of the names to be selected using the n_names argument. The default value for this argument is 5. These names will be selected randomly.

# change the number of the names to be selected from 5 (the default value) to 10 

greater_n_names <- select_names(race = "Asian", n_names = 10)

Rather than using random sampling, it is possible to select names by ordering them according to a chosen variable. This is done by specifying the order_by_var argument. The values available for it are: pct_correct = the percentage of the name's intended race correctly perceived, avg_income = average perceived income level, avg_education = average perceived education level, and avg_citizenship = the percentage of perceived citizenship status.

top_correct_names <- select_names(race = "Asian", order_by = "pct_correct")

Finally, if you would like to select the names from all racial groups, use select_nams_all() instead of select_names(). Note that select_names_all() does not have the race argument, but all the other arguments are identical.

all_race_names <- select_names_all()

How to cite

Kim, J and Crabtree, C. (2022). validatednamesr: R package for viewing, loading, and visualizing the Validated Names for Experimental Studies on Race and Ethnicity datasets.