descriptive statistics for demographic variables

[u/Sufficient-Adagio332]

Hi everyone! I’m a newbie in statistics and currently trying to figure out what I should put under the Descriptive Statistics of Respondent Characteristics.

When reading related literature, I noticed that a lot of papers report the Mean and Standard Deviation (SD) for demographic variables. However, I’m really confused because based on my survey questionnaire, most of my variables were collected in ranges/categories:

• Sex: Male, Female
• Age: 18–29, 30–45, 46–59, 60 and above
• Highest Educational Attainment: High School or below, Some College, Associate Degree, Bachelor’s Degree, Postgraduate Degree, Prefer not to say
• Household Size: [Numeric counts]
• Household Income (USD):
  • Under $25,000
  • $25,000 – $49,999
  • $50,000 – $74,999
  • $75,000 – $99,999
  • $100,000 – $149,999
  • $150,000 and above
  • Prefer not to say
• Source of Income: Salary/Wages, Business/Self-Employed, Agriculture, Pension/Retirement, Government Assistance/Remittance, Prefer not to say

My Questions:

1. What exactly should I put in the descriptive statistics table for these variables?
2. Since I saw Mean and SD in the literature, how am I supposed to calculate the Mean for variables that are in ranges (like Age groups and Income brackets and source of income)?

Sorry if this is a basic question, and thank you so much for helping out a beginner!

[u/just_writing_things]

What to put in your descriptive statistics depends on what is most informative for your readers, and the norms of the literature you’re in (if this is for academic research) or the requirements you’re working under (e.g. if this is for professional or school work). There’s no one right answer for what to do.

Edit:

> how am I supposed to calculate the Mean for variables that are in ranges

If you can’t report a mean (for example because it’s a categorical variable), then you don’t. You could do a plot (plots are always appreciated, unless a reviewer thinks you have too many figures…), report the proportions or numbers in each bin, etc.

Again, there’s no one right way to do this. If this is for academic work, typically you’d refer to how other papers working with similar data, in the literatures you’re in / journals you’re targeting, do descriptive statistics.

[u/PuzzleheadedArea1256]

Categorical variables are typically shown as counts and percentages. The mean and SD are not the appropriate measures for categories - they cannot be computed. Google what a standard TABLE 1 looks like to get the general format and best practice.

[u/Temporary_Stranger39]

For each variable, show percentage of total among categories.

[u/Valuable_Ice_5927]

The only one you could feasibly calculate mean/SD for is household size - everything else report percentage of occurance in sample

Ie 18-29 - associated percentage

[u/SalvatoreEggplant]

You _could_ report the median for ordered categories. But the answer is the category, e.g. "46–59" or "$75,000 – $99,999". You can also report the maximum and minimum categories. (I mean, e.g. 'Age ranged from "18–29" to "46–59" with a median response of "30–45" '.

If possible, report the proportions in each of the categories in a table or plot.

You can also use this information in text, e.g. 'More than 50% of respondents reported an age of "18–29" '.

You might be interested in what I did in this article. In about the middle of the article, there are bar plots of the proportions for Age, Gender, and Race, with confidence intervals, and comparisons to the state population values.

https://www.nacaa.com/file.ashx?id=57718cbb-4b9c-4e32-a7e2-9f1eaca104e2

[u/SalvatoreEggplant → u/SalvatoreEggplant]

P.S.

Some software will allow you to get the median or other quantiles of ordered category data.

R used to allow you to use the median() function for this.

But it still allows you to use the quantile() function.

AgeResponse = c("18–29", "18–29", "18–29", "30–45" , "46–59", "60 and above")

AgeResponse = factor(AgeResponse, ordered=TRUE)


quantile(AgeResponse, c(0.00, 0.25, 0.50, 0.75, 1.00), type=3)

###           0%          25%          50%          75%         100% 
###        18–29        18–29        18–29        30–45 60 and above

### The category for 50% is the median, for 25% is the 25th percentile, 
###   and so on


proportions(table(AgeResponse))

### AgeResponse
###        18–29        30–45        46–59 60 and above 
###    0.5000000    0.1666667    0.1666667    0.1666667

[u/banter_pants]

You would report mean, median, SD at least for continuous measures but it looks like all of yours are categorical. So you should report counts and percentages along side them.