Gender#

From Sell, Goldberg, Conron: Gaps in data collection systems, as well as challenges associated with gathering data from rare and dispersed populations, render current health surveillance systems inadequate to identify and monitor efforts to reduce health disparities. Using sexual and gender minorities we investigated the utility of using a large nonprobability online panel to conduct rapid population assessments of such populations using brief surveys.

This data consists of responses from users of the Google Android Panel. These are users of the Google Opinion Rewards application who have Smart phones operated by Google’s Android operating system and receive small payments of up to one dollar per 10-item survey. Users of Google Opinion Rewards tend to represent earlier-adopters and heavier technology users than on average. For each survey an individual panel member is sent, Google informs the panel member of how data will be used and asks for their consent. Gender identity information was collected with the question “Gender Identity—What is your current gender identity? (Select all that apply)”.

Initialization#

library(fosdata)
data <- fosdata::gender

Accessing fields#

data <- fosdata::gender
gender_male <- data$gender_male # Just a random field in the dataset

Interactive R Sample#

You can use the R editor below to interactively explore the dataset and generate plots. This contains a fully self-contained R environment with fosdata, ggplot2, and dplyr loaded.

webR + fosdata Test

Console
Plot

    


    
    

      

        No plot generated yet.
      

      scatterplot
    

  



LLM instructions#


If using an LLM, you can copy-paste the following instructions to accompany your prompt to inform the model of the fields and their types in the dataset.


LLM Instructions


The fosdata::gender dataset containing the following fields:

fields[10]{name,type,values}:
  gender_male,logical,[FALSE,TRUE]
  gender_female,logical,[TRUE,FALSE]
  gender_trans,logical,[FALSE,TRUE]
  gender_queer,logical,[FALSE,TRUE]
  gender_not_sure,logical,[FALSE,TRUE]
  gender_unclear,logical,[FALSE,TRUE]
  gender_na,logical,[FALSE,TRUE]
  sex_at_birth,factor,[Female,Male]
  hispanic,factor,[No,Yes,Don't know]
  race,character,n/a


Fields#



  
      

          Name
          Description
          Type
          Min
          Max
          Values
      

  
  
      

          gender_male
          Male.
          logical
          -
          -
          FALSE, TRUE
      

      

          gender_female
          Female.
          logical
          -
          -
          TRUE, FALSE
      

      

          gender_trans
          Transgender.
          logical
          -
          -
          FALSE, TRUE
      

      

          gender_queer
          Genderqueer/Gender non-conforming.
          logical
          -
          -
          FALSE, TRUE
      

      

          gender_not_sure
          I am not sure of my gender identity.
          logical
          -
          -
          FALSE, TRUE
      

      

          gender_unclear
          I do not know what this question is asking.
          logical
          -
          -
          FALSE, TRUE
      

      

          gender_na
          None of the above.
          logical
          -
          -
          FALSE, TRUE
      

      

          sex_at_birth
          “Sex—What sex were you assigned at birth, on your original birth certificate?” Male, Female.
          factor
          -
          -
          Female, Male
      

      

          hispanic
          “Do you consider yourself to be Hispanic or Latino?” Yes, No, Don’t know.
          factor
          -
          -
          No, Yes, Don't know
      

      

          race
          “What race or races do you consider yourself to be?”. Multiple categories may be selected and are comma separated.
          character
          -
          -
          -
      

  



Source#


Sell R, Goldberg S, Conron K (2015) The Utility of an Online Convenience Panel for Reaching Rare and Dispersed Populations. PLOS ONE 10(12): e0144011. https://doi.org/10.1371/journal.pone.0144011



 
      

      

        
  





















  
  
  
  

    
    
      
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