Answer:
The standard deviation of the sampling distribution is 0.0122 = 1.22%
Step-by-step explanation:
Central Limit Theorem
The Central Limit Theorem estabilishes that, for a normally distributed random variable X, with mean
and standard deviation
, the sampling distribution of the sample means with size n can be approximated to a normal distribution with mean
and standard deviation
.
For a skewed variable, the Central Limit Theorem can also be applied, as long as n is at least 30.
For a proportion p in a sample of size n, the sampling distribution of the sample proportion will be approximately normal with mean
and standard deviation 
A survey asks a random sample of 1500 adults in Ohio
This means that 
34% of all adults in Ohio support the increase.
This means that 
The standard deviation of the sampling distribution is

The standard deviation of the sampling distribution is 0.0122 = 1.22%