There are a great many sites on the internet that will provide you with simple calculators for your sample size. They basically use two approaches. One is to ask how accurate an estimate you need for a mean or percentage. The other asks you about the "power" for statistical tests that you want to perform (i.e., the size of the effect that you want to be able to detect). If you are going to be doing statistical tests (rather than purely descriptive work), I would definitely recommend the second approach.

Try this Ruta

https://www.researchgate.net/publication/262564897_Easy-to-use_Sampling_Formulae

Data Easy-to-use Sampling Formulae

To estimate the needed sample size in quantitative research, the researcher has to determine 3 elements: the power {standard to use .80}, alpha level {standard to use .05}, and the effect size {standard to range between small, moderate, & Large}. All of these criteria also depend on the type of inferential statistics you will use; as for example moderate effect size in t-test is different from the moderate ES in ANOVA. Also, the number of groups in ANOVA has effect, and the number of IV in regression has effect.

Ready for further clarification if needed. good luck

You can estimate the sample size according to your information about the population which the sample will be taken from it specifically standard deviation :

Sample size from finite population can be estimate by :

n= N / ( 1+ N * e^2 ) 

and from infinite population by :

n= ( Z^2 * S^2) / E^2

where : Z  ( 1.96 for 0.05 and 2.58 for 0.01 )

S = standard deviation from previous studies or pilot  study

E = significant level 

Also, you can use software to calculate sample size ( SPSS , Minitab , Gpower )

The key component to estimate sample size ( when we fixed the other ) is variance of the trait .

I hope that be useful for you 

Good Luck

I agree with prof Muayyad. As you have known population and you need sample size, in this case the most important point is the effect size and which comes from the studies carried out previously. so it depends upon the type of study and type of variables in it.

Good Luck

Rafique

Probability sampling for inferential analysis

Probability sampling is the essence of “statistics”, ticklishly defined as the plural of the word “statistic” - the collection, organization and analysis of sample data to describe the population. It is very significant in the sense that it is only through a probability sample where one can claim that the sample is statistically or scientifically representative of the population.

Probability sampling is the only one general approach that allows the researcher to use the principles of statistical inference to generalize from the sample to the population (Frankfort- Nachmias & Leon-Guerrero 2002), its characteristic is fundamental to the study of inferential statistics (Davis, Utts & Simon, 2002). It is the sampling technique that uses the probability theory to calculate the likelihood of selecting a particular sample and allows the drawing of conclusions about the population from the sample.(Pelosi, Sandifer, & Sekaram, 2001) and it has the advantage of projecting the sample survey results to the population (McDaniel & Gates, 2002). Inferential statistical analyses are based on the assumption that the samples being analyzed are probability samples (Burns & Grove 1997).

The use of sample, instead of population in a study is always a very practical and in most cases, indispensable recourse. In surveys, sampling is always resorted to for budgetary reasons. If your population size is 1million, spending just P100 per participant would require P100 million. A statistically acceptable random size of 2,000 would make expenses affordable.

If you are in the business of producing and selling “suman” (rice cake) and you need to find out if the 1,000 pieces of the suman you produced in a period are acceptably delicious, you only have to taste a random sample of 5 pieces, for it would be foolish to take a crunch into each of the entire population of 1,000 (no customer in his right mind would want to buy a suman bitten and salivated by another person).

An agriculturist would settle for some sample strands instead of chopping the entire ground root system of the coconut tree to analyze pest incidence.

There are four formulae generally employed to determine a sample size wherein an inferential analysis is to be made:

1. Formula to estimate population mean where population size is known.

2. Formula to estimate population mean where population size is not known.

3. Formula to estimate population proportion where population size is known.

4. Formula to estimate population proportion where population size is not known.

Sincere thanks to everyone for Your input. From what I found in Your answers I see that I was on the right track, so thank You all for the boost! If someone will have anything to add to the already given answers, please do. Advice or sharing experience on sampling is always relevant.

Dear RG colleague,

Determining the sample sizes involve resource and statistical issues. Usually, researchers regard 100 participants as the minimum sample size when the population is large. However, In most studies the sample size is determined effectively by two factors: (1) the nature of data analysis proposed and (2) estimated response rate.

For example, if you plan to use a linear regression a sample size of 50+ 8K is required, where K is the number of predictors. Some researchers believes it is desirable to have at least 10 respondents for each item being tested in a factor analysis, Further, up to 300 responses is not unusual for Likert scale development according to other researchers.

Another method of calculating the required sample size is using the Power and Sample size program (www.power-analysis.com).

Regards,