Sample size depends on the questions you wish to answer and the statistics you wish to run. For specific complex statistics (e.g., factor analysis, multiple regression, MANOVA), the literature can provide guidance as to minimum sample sizes, but keep in mind there are differences of opinion and different criteria out there. Textbooks that cover these statistics would be a good place to start.

it depends on the target population. But for now, I will suggest Purposive and convenience sampling

Sample size depends on the questions you wish to answer and the statistics you wish to run. For specific complex statistics (e.g., factor analysis, multiple regression, MANOVA), the literature can provide guidance as to minimum sample sizes, but keep in mind there are differences of opinion and different criteria out there. Textbooks that cover these statistics would be a good place to start.

I have no idea. Thank you for sharing your question with me.

There are four main criteria that will need to be defined and clear (combined quantities or some) to determine and choose the appropriate sample size: level of accuracy or adjustment, level of confidence or risk, degree of variability and variance in qualities being standardized, size of the original community

Please visit this website for more information

https://www.surveysystem.com/sscalc.htm

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Selecting Sample Sizes

Consider these things when selecting a sample sizeWhen choosing a sample size, we must consider the following issues:

• What population parameters we want to estimate
• Cost of sampling (importance of information)
• How much is already known
• Spread (variability) of the population
• Practicality: how hard is it to collect data
• How precise we want the final estimates to be

Cost of taking samplesThe cost of sampling issue helps us determine how precise our estimates should be. As we will see below, when choosing sample sizes we need to select risk values.  If the decisions we will make from the sampling activity are very valuable, then we will want low risk values and hence larger sample sizes.Prior informationIf our process has been studied before, we can use that prior information to reduce sample sizes. This can be done by using prior mean and variance estimates and by stratifying the population to reduce variation within groups.Inherent variabilityWe take samples to form estimates of some characteristic of the population of interest. The variance of that estimate is proportional to the inherent variability of the population divided by the sample size:

Var(p^)≈σ2n

with p^ denoting the parameter we are trying to estimate. This means that if the variability of the population is large, then we must take many samples. Conversely, a small population variance means we don't have to take as many samples.

PracticalityOf course the sample size you select must make sense. This is where the trade-offs usually occur. We want to take enough observations to obtain reasonably precise estimates of the parameters of interest but we also want to do this within a practical resource budget. The important thing is to quantify the risks associated with the chosen sample size.Sample size determinationIn summary, the steps involved in estimating a sample size are:

Sampling proportionsWhen we are sampling proportions we start with a probability statement about the desired precision. This is given by:

Pr(|p^−P|≥δ)=αwhere

• p^ is the estimated proportion
• P is the unknown population parameter
• δ is the specified precision of the estimate
• α is the probability value (usually low)

This equation simply shows that we want the probability that the precision of our estimate being less than we want isα. Of course we like to set α low, usually .1 or less. Using some assumptions about the proportion being approximately normally distributed we can obtain an estimate of the required sample size as:

n=z2α(pqδ2)

where z is the ordinate on the Normal curve corresponding to α.

ExampleLet's say we have a new process we want to try. We plan to run the new process and sample the output for yield (good/bad). Our current process has been yielding 65% (p=.65, q=.35). We decide that we want the estimate of the new process yield to be accurate to within δ = .10 at 95% confidence (α = .05, zα = -2). Using the formula above we get a sample size estimate of n=91. Thus, if we draw 91 random parts from the output of the new process and estimate the yield, then we are 95% sure the yield estimate is within .10 of the true process yield.Estimating location: relative errorIf we are sampling continuous normally distributed variables, quite often we are concerned about the relative error of our estimates rather than the absolute error. The probability statement connecting the desired precision to the sample size is given by:

Pr(∥∥y^−μμ∥∥≥δ))=α

where μ is the (unknown) population mean and y¯ is the sample mean.

Again, using the normality assumptions we obtain the estimated sample size to be:

n≈z2ασ2δ2μ2

with σ2 denoting the population variance.

Estimating location: absolute errorIf instead of relative error, we wish to use absolute error, the equation for sample size looks alot like the one for the case of proportions:

n≈z2α(σ2δ2)

where σ is the population standard deviation (but in practice is usually replaced by an engineering guesstimate).

ExampleSuppose we want to sample a stable process that deposits a 500 Angstrom film on a semiconductor wafer in order to determine the process mean so that we can set up a control chart on the process. We want to estimate the mean within 10 Angstroms (δ = 10) of the true mean with 95% confidence (α = .05, zα = -2). Our initial guess regarding the variation in the process is that one standard deviation is about 20 Angstroms. This gives a sample size estimate of n= 16. Thus, if we take at least 16 samples from this process and estimate the mean film thickness, we can be 95% sure that the estimate is within 10 angstroms of the true mean value.

It's difficult to say without additional information, but many power analyses for correlational studies (such as most surveys) indicate that 150-300 completed surveys would be good.

There are certain guidelines in participants selections.

https://stat.uiowa.edu/sites/stat.uiowa.edu/files/techrep/tr303.pdf

500 would be fairly good

I think its depend on the subject and variance of the subject. If the scope of subject clear, I propose probability sample and the amount of sample around 5- 10 % population if the population more than 1000. but if less than 1000 I prefer between 400 - 500 person.

I convey my regards to all those who have rendered effective answers It has indeed a great help.

If you can estimate your population size, there are online tools to indicate sample sizes, which are usually quite large:

Pretty thorough: http://www.raosoft.com/samplesize.html

and:

https://surveysystem.com/sscalc.htm

http://powerandsamplesize.com/

I agree with prof. Romie

Thank you for your answers Dr Romie and Dr Majd

depends upon the size of the population, accordingly you can define the sample size