The number of questions in a survey or the research issue (organizational sustainability) are not relevant to sample size. The numerosity of the questions in the survey may be relevant to reliability test, i.e. internal consistency for purpose of instrument calibration. The subject matter may be relevant to construct / criterion validity issues. Both are not relevant to sample size.

The function of sample size is to assure fair representation of the population, i.e. the descriptive statistics of the sample may be used as the basis for the inferential statistic so that one can infer the characteristics of the population by observing the sample. How much sample to collect? What should be the minimum sample size?

One approach for sample size calculation is provided bu NIST at link below. The method is straight forward. I hope this is helpful.

https://www.itl.nist.gov/div898/handbook/prc/section2/prc222.htm

Hello Sharon

In order to answer this question/problem, several remarks have to be studied.

1. General remarks:

Research studies are usually carried out on sample of subjects rather than whole populations. The most challenging aspect of fieldwork is drawing a random sample from the target population to which the results of the study would be generalized. The key to a good sample is that it has to be typical of the population from which it is drawn. When the information from a sample is not typical of that in the population in a systematic way, we say that error has occurred. In actual practice, the task is so difficult that several types of errors, i.e. sampling error, non-sampling error, Response error, Processing error,…

2. The most important error is the Sampling error, which is statistically defined as the error caused by observing a sample instead of the whole population. The underlying principle that must be followed if we are to have any hope of making inferences from a sample to a population is that the sample be representative of that population.

3. A key way of achieving this is through the use of “randomization”. There several types of random samples, Some of which are: Simple Random Sampling, Stratified Random Sampling, Double-stage Random Sampling... Moreover, the most important sample is the simple random sample which is a sample selected in such a way that every possible sample of the same size is equally likely to be chosen. In order to reduce the sampling error, the simple random sample technique and a large sample size have to be developed.

4. Specific remarks:

The following factors are highly affected the sample size and need to be identified:

• Population Size,
• Margin of Error,
• Confidence Level (level of significance) and
• Standard of Deviation.

5. The Cochran formula allows you to calculate an ideal sample size given a desired level of precision, desired confidence level, and the estimated proportion of the attribute present in the population.

Cochran’s formula is considered especially appropriate in situations with large populations. A sample of any given size provides more information about a smaller population than a larger one, so there’s a ‘correction’ through which the number given by Cochran’s formula can be reduced if the whole population is relatively small.

The Cochran formula is:

Necessary Sample Size = (z-score)2 * StdDev*(1-StdDev) / (margin of error)2 .

6.

For more details, please refer to

Cochran,W.G. (1977). Sampling techniques, Wiley.

Snedecor, W.G. and Cochran,W.G. (1989). Statistical Methods, Blackwell.

Regards,

Zuhair

The first issue is it all depends on the research questions, which coincide with the hypotheses, purpose and problem statements.

Also, you need to take a sample that us representative of the total population to reduce standard error. Then you need to conduct a pilot study to test the validity and reliability of the survey instrument. Then test the G Power and Chronback Alpha. You want to ensure randomisation to reduce bias.

I hope this helps

Dr Réagan

Determination of sample size depends on two aspects:

1) sample variability

2) number of items

Higher the variability greater should be sample size.

Number of respondents in a sample should be 5-10 more than number of items e.g. if your questionnaire has 10 items then minimum sample size should be 50 or more

For sample size calculation, the criteria of (Chou, Bentler, & Satorra, 1991) was adopted. the total questions of questionnaire was taken and multiplied by 5, 7, or 15 to obtain an adequate sample size for fulfilling the criteria of SEM analysis.

I came across this also during my search.

Dear Deborah

Apology for late response, due to my travel.Hope by time you might be concluded your sample size. In general the sample size should not less than 10% of the population, otherwise high sampling error. All the best.

Thank you all for the response :)

Hello Deborah

Have you completed you data collection?