For categorical variables such as gender (for most of the surveys) we can test few things only, unfortunately.

Let’s look at frequencies for gender identity, for example.

Frequencies

sncs_data$gender
Type: Character
Valid Total
gender Freq % % Cum. % % Cum.
Man 177 40.78 40.78 36.27 36.27
Woman 257 59.22 100.00 52.66 88.93
<NA> 54 11.07 100.00
Total 488 100.00 100.00 100.00 100.00

Chi-square test is used to check whether there is a relationship between two categorical variables. For example, let’s see if there is a relationship between having personal life that provides helpful support and gender (assuming ‘male’ and ’ female’).

gender
personal_support Man Woman <NA> Total
I do not require support at this time 54 ( 30.5% ) 53 ( 20.6% ) 3 ( 5.6% ) 110 ( 22.5% )
Yes 117 ( 66.1% ) 200 ( 77.8% ) 15 ( 27.8% ) 332 ( 68.0% )
<NA> 6 ( 3.4% ) 4 ( 1.6% ) 36 ( 66.7% ) 46 ( 9.4% )
Total 177 ( 100.0% ) 257 ( 100.0% ) 54 ( 100.0% ) 488 ( 100.0% )
 Χ2 = 5.5609   df = 1   p = .0184

In the output p-value shows weak-to-moderate relationship between having personal life that provides helpful support and gender (assuming ‘male’ and ’ female’)

gender
feel_comfortable_office Man Woman <NA> Total
No 59 ( 33.3% ) 106 ( 41.2% ) 6 ( 11.1% ) 171 ( 35.0% )
Yes 78 ( 44.1% ) 91 ( 35.4% ) 7 ( 13.0% ) 176 ( 36.1% )
<NA> 40 ( 22.6% ) 60 ( 23.3% ) 41 ( 75.9% ) 141 ( 28.9% )
Total 177 ( 100.0% ) 257 ( 100.0% ) 54 ( 100.0% ) 488 ( 100.0% )
 Χ2 = 3.3125   df = 1   p = .0688

P-value here indicates that there is, perhaps, a difference between genders when it comes to feeling comfortable returning to the office.

gender
i_have_money Man Woman <NA> Total
Agree 135 ( 76.3% ) 214 ( 83.3% ) 12 ( 22.2% ) 361 ( 74.0% )
Disagree 11 ( 6.2% ) 15 ( 5.8% ) 0 ( 0.0% ) 26 ( 5.3% )
<NA> 31 ( 17.5% ) 28 ( 10.9% ) 42 ( 77.8% ) 101 ( 20.7% )
Total 177 ( 100.0% ) 257 ( 100.0% ) 54 ( 100.0% ) 488 ( 100.0% )
 Χ2 = .0247   df = 1   p = .8750
Last example here is the question about financial security and gender. P-value in the output provides that there is no difference between genders in relation to having money.