Philip Gana Malgwi

Please see the attachment. I think it will work & be convenient.

In the Indian Subcontinent, we are working with this.

Thanks

Certainly! Here are a few recent authors who discuss various sampling techniques in research methodology:

These authors and their publications should provide you with a solid starting point to explore various sampling techniques in research methodology. It's always a good idea to consult multiple sources and select the ones that align with your research context and objectives.

Philip Gana Malgwi -

It depends on what kind of research.  There are two ways to do inference from a sample to a population: (1) the probability-of-selection-based approach, and (2) the model-based approach.  (The latter is often called "prediction," which refers to the use of a random variable in regression, which is NOT a reference to forecasting.)  You can also combine the approaches or simply use a model to "assist" a randomized approach.  Here are some textbooks that could help:

Thompson, S.K.(2012), Sampling, 3rd ed, John Wiley & Sons.

Cochran, W.G.(1953), Sampling Techniques, 1st ed, John Wiley & Sons

Cochran, W.G.(1977), Sampling Techniques, 3rd ed, John Wiley & Sons

Blair, E., and Blair, J.(2015), Applied Survey Sampling, Sage Publications. 

Lohr, S.L(2010), Sampling: Design and Analysis, 2nd ed., Brooks/Cole.

Särndal, C.-E., Swensson, B., and Wretman, J.(1992), Model Assisted Survey Sampling, Springer-Verlang

Snijkers, G., Haraldsen, G., Jones, J., and Willimack, D.K.(2013), Designing and Conducting Business Surveys,  John Wiley & Sons, Inc.

Brewer, K.R.W.(2002), Combined Survey Sampling Inference: Weighing Basu's Elephants, Arnold: London and Oxford University Press

Chambers, R, and Clark, R(2012), An Introduction to Model-Based Survey Sampling with Applications, Oxford Statistical Science Series  

Valliant, R, Dorfman, A.H., and Royall, R.M.(2000), Finite Population Sampling and Inference: A Prediction Approach, Wiley Series in Probability and Statistics

Also, perhaps  ... 

Survey Sampling: Theory and Methods, First Edition, 1992,

Chaudhuri, A., Stenger, H.,

Marcel Dekker, lnc., New York, Basel, Hong Kong.

Survey Sampling: Theory and Methods, Second Edition

Arijit Chaudhuri, Horst Stenger

March 29, 2005 by CRC Press

Hansen, M.H., Hurwitz W.N. and Madow, W.G. (1953). Sample Survey Methods and Theory (2 vols.) (Republished 1993) Wiley, New York.

Kish, Leslie, 1965, Survey Sampling, John Wiley & Sons, Wiley Classics Library Edition Published 1995

Raj, D., Sampling Theory, 1968, McGraw-Hill

Cassel, Särndal, and Wretman, Foundations of Inference in Survey Sampling, 1993, Krieger Publishing Company

Hedayat, A.S., and Sinha, B.K., 1991, Design and Inference in Finite Population Sampling, John Wiley & Sons, Inc.

Deming, W.E. (1950, 1966), Some Theory of Sampling, John Wiley & Sons, Inc., Republished by Dover Publications

You may also find a lot of help by searching on a term online, and including "Pennsylvania State University" in the search. They provide a great deal of good/helpful information.

For sample size considerations, you want to control for any kind of bias, and then see, for your methodology, what sample size is needed with the population standard deviation (or strata standard deviations) present, to arrive at the standard error(s) you would like, based on your application. 

Here is a paper which looks at sample size needs for a single population or subpopulation or stratum, under the model-based approach, for a model-based classical ratio estimator model, and it compares the result to the "formula" for a simple random sample for continuous data, found in Cochran(1977), Sampling Techniques, Wiley: 

https://www.researchgate.net/publication/261947825_Projected_Variance_for_the_Model-based_Classical_Ratio_Estimator_Estimating_Sample_Size_Requirements 

It also shows the difference in sample size needs between balanced sampling and a cutoff sample. (A balanced sampling methodology might obtain a sample like one you might hope for doing random sampling.)

Further, currently there are researchers working on inference from nonprobability samples which may not have such simple modeling as that available from the use of a previous census.  These cases are more likely in research, and more difficult to use to infer to the population.  Bias can be a huge problem. For such cases, a number of covariates may be needed, either for modeling which may be much more complex, and less accurate than the above, or for quasi-random sampling where one may form pseudo-weights, or one might combine approaches.  Here is an example:

Valliant, R.(2019), "Comparing Alternatives for Estimation from Nonprobability Samples," Journal of Survey Statistics and Methodology, Volume 8, Issue 2, April 2020, Pages 231–263, preprint at 

https://www.researchgate.net/publication/337950671_Comparing_Alternatives_for_Estimation_from_Nonprobability_Samples 

Best wishes  -  Jim

Thank you so much; This is really helpful!

Depends upon what you want to accomplish: generalize or specify and even here .... Joseph Bensman studied one airplane factory. He worked there for a period of time. I studied college sports at two different colleges with relatively the same, but somewhat different results. If I could I would go back to the first to see if the results changed and return to the second ass well for the purpose of grasping both as a whole.

I remember when I was at the US Energy Information Administration that there were something over 100 plants of a certain type, and someone called one to see how they were doing a certain accounting. He then called another and they were doing their accounting the same way. He then made the very, very rash assumption that that was the way it was done. It turned out that there were two ways to do this accounting, and the one he heard about was the less popular method. One should never rely on such a small "sample."