If you know population size, variability, and the effect size then there are more exact approaches to estimating sample size. With no other information ….
The survey will have two types of questions. Questions for context and questions for information. Context questions will be something like race, sex, education, and income. Generally these are fixed effects, and if there are 5 categories for race, and 2 for sex then there are 5*2=10 categories. I would take sufficient surveys such that the least frequently encountered category has as many surveys as 20 times the number of information questions.
This sample size can be reduced through purposive sampling or a careful consideration of the goals of the survey.
It also depends on your purpose. In general the more complex the analysis, the larger the sample you need to get adequate statistical power and stable results. For example, a mean is simple, and generally does not require large samples to compare groups. Moderated regression, on the other hand, is complex and requires far larger samples for adequate power.
If the population size is 30, then do a census (all 30). Then you are no longer estimating a mean, you calculate the population values and are done.
Be very careful about interpretation. If the population size is 30, then you are describing the population. There is no prediction, because prediction assumes that things will change and that only happens .if the population changes.
On Timothy A Ebert 's be very careful statement, researcher often assume that your population is much larger than your target population (and then make arguments why inference from TP to P is still okay). As he says, if your population is this single group of people fixed in time there is not much inference to be done.
That said, there would be some. Your measurements are not without error so this would be considered.
Spector (1992) argued that a minimum of 100 participants / respondents is adequate to achieve the reliability and validity of a study's outcome. Sample size depends on the data analysis. For regression based techniques, Hair et al (2014) requires a minimum of 10-15 participants per predictor variable as a basis for sample size determination. But larger sample sizea usually greater than 450i ncreases or makes the statistical power very optimistic.
Sekaran, U., 2003. Research methods for business: A skill building approach. John Wiley & Sons.
Sekaran (20013) wrote:
"Roscoe (1975) proposes the following rules of thumb for determining sample size:
1. Sample sizes larger than 30 and less than 500 are appropriate for most research.
2. Where samples are to be broken into sub-samples; (male/females, juniors/seniors, etc.), a minimum sample size of 30 for each category is necessary.
3. In multivariate research (including multiple regression analyses), the sample size should be several times (preferably 10 times or more) as large as the number of variables in the study.
4. For simple experimental research with tight experimental controls (matched pairs, etc.), successful research is possible with samples as small as 10 to 20 in size."
Reference
Sekaran, U., 2003. Research methods for business: A skill building approach. John Wiley & Sons.