The findings from one of our previous study showed that the indicators were too high compared to other literature. Thus we planned to carry out a study in small scale to validate this finding? Its is cross sectional study.
Compute the sample size by using the expected test power and effect size.
If you have previous effect sizes from other literature and set power to, say, 0.80 then you can calculate the needed sample size. (of course you will need to know the number of groups and the standard deviation of those which you will expect to be the same in your study).
G*Power can help you.
http://www.ats.ucla.edu/stat/gpower/indepsamps.htm
Hello Deepak,
I used G Power analysis previously. If you are not able to establish effect size from previous studies (metaanalysis sometimes provides this) you may set this as medium effect size to calculate your sample.
Best wishes
Deepak -
If you are looking at continuous data in a cross sectional survey, and wish to re-survey a subset of those surveyed previously, for the same information, to validate the previous results, you might try the following: For a given item on your survey (doing this for each key item), let x_i be the response in your first survey, and y_i be the corresponding response in the validation survey. (Going to the same source, however, you may just automatically be given the same response, and you won't learn anything. However, this might work for a case such as a US federal audit of a subset of State audits, for example.) If you plot those (xi, yi) data points on a scatterplot, you can see if a simple linear regression has a slope of one, with regression to the origin. Such a slope of unity would mean you are not getting changed results. However, if you are correct that the x's tend to be larger than the y's, the slope will be less than one. Your sample size would be determined by what it takes (assuming you can get good data) to make the standard error for your slope acceptably small. If you go to page 10, section 5, Option # 1 in
https://www.researchgate.net/publication/261474159_Some_Proposed_Optional_Estimators_for_Totals_and_their_Relative_Standard_Errors_for_a_set_of_Weekly_Quasi-Cutoff_Sample_Establishment_Surveys
you will see a set of six lines of equations - with more below that. Those equations involve a robust estimator for the slope b, and also the estimated total for a finite population. Just consider the second, fourth, and fifth lines, which look at b, its estimated variance, and the underlying sigma for the random factors of the estimated residuals. You can obtain these results, for example, from SAS PROC REG, if you let regression weight w be 1/x. You will need a preliminary new sample to estimate sigma (fifth line there) for any given item. Then you can estimate sample size needed to obtain whatever you deem an acceptable estimated standard error of b by using the variance estimate (fourth line). - Notice that on the second line I accidentally left off a subscript from an x. Oops!
If, however, you are doing a validation survey using a different sample, you could, for a given data item, find the estimated mean and its standard error in your survey to be validated, and do the same for the results of a new survey, and find a confidence interval around the difference in the two means. Again you would have to find results for a small preliminary set of new data (the previous one already being established), and then do the algebra to see what (complete) new sample size is needed to obtain a suitable confidence interval around the difference in those means for that data item. - Once again, for a give data item, you are getting an estimate of a constant sigma, but this time for the y data, not involving regression residuals, and then estimating the sample size needed to hopefully attain the accuracy you specify that you want.
I hope this might be relevant enough to give you a few ideas.
Cheers - Jim
PS - There may be some standard methods to handle this in your field, but these are the ideas that occurred to me.
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