Tuesday, January 21, 2025

CLUSTER SAMPLING & INCIDENTAL SAMPLING

 


Cluster Sampling & Incidental Sampling


Learning Outcomes 


  • Understand Cluster Sampling:  Learn the concept and when to use it.
  • Identify Benefits and Limitations:  Recognize cost efficiency and potential biases.
  • Design and Implementation:  Know how to form clusters and select samples.
  • Evaluate Bias and Errors:  Analyze sampling error and reduce bias.
  • Apply Statistical Analysis:  Use methods for cluster-based data interpretation.
  • Compare Sampling Methods:  Differentiate cluster sampling from other techniques.
  • Practical Application:  Implement cluster sampling in real-world research.
  •         

    Cluster sampling

    In cluster sampling the sample unit contains groups of elements ( clusters ) instead of individual members or items in the population. A cluster sample usually produces a larger sampling error than a simple random sample of the same size , for each cluster – such as a block in given neighborhood – may be composed of units that are like one another , which reduces the representativeness of the sample. ( van delan , deobold b, 1978)


    The area or cluster sample is a variation of the simple random sample that is a particularly appropriate when the population of interest is infinite, when a list of the members of the population does not exist, or when the geographical distribution of the individuals is widely scattered. Only when a simple random sample would be impracticable is this method recommended. ( david dooley 2001)

    Figure 1

     

    Cluster Sampling

     




     Definition Of Cluster Sampling

     

    A Cluster sample as a random sample in which each sample unit is a collection, or elements’.

                                                                          -     Mendenhall, Ott, and Scheaffer (1971, p. 121)

    Cluster sampling, sometimes called area sampling , is generally used when it is impossible or impractical to construct a sampling frame in which the sampling units are the sampling elements themselves.  ( C R Kothari , Gaurav Garg 2019)

     

    Area Sampling

     

    In sampling of this kind small areas are designed as sampling units and the households interviewed include all or a specified fraction of those found in a canvas of these designated small areas.  The basic sampling units or segments chosen may be relatively large or relatively small depending on such factors as the type of area being studied, population distribution, the availability of suitable maps and other information and the nature and desired accuracy of the data being collected. ( Nandan K. Mandal 2012)

     Multi- Stage Sampling

     

    Sampling may be done as one process or in stages , known as multi-stage sampling. Multi- stage designs are common when populations are widely dispersed. ( Nandan K. Mandal 2012)

    In multi-stage cluster sampling the researcher chooses a sample in two or more stages because either the researchers cannot easily identify the population or the population is extremely large. If this case, it can be difficulty to obtain a complete list of the members of the population. However, getting a complete list of groups or clusters in the population might be possible  (Vogt, 2005)

    Multi-stage area sampling involves two or more steps that combine some of the probability techniques already described. Typically , progressively smaller ( lower- population) geographic areas will be randomly selected in a series of steps . ( C R Kothari . Gaurav Garg 2019)

    Multi-stage sampling is used in large scale surveys for more comprehensive investigation. The researcher may have to use two, three or four stage. This method also helps the researcher to check on the consistency of the information obtained from the first sample. This sampling is comparatively convenient, less time-consuming and less expensive method of sampling. (Lokesh Kaul , 1984)

    Figure 2

     

    Muti Stage sampling

     




     How To Do Cluster Sampling

     

    1.    1.  Define the population and clusters

    2.    2.  Randomly select clusters

    3.    3.  Determine cluster size

    4.  4.    Sample elements within clusters

    5.    5.  Collect data

      




    Figure 3

     

    Cluster Sampling




    Advantages Of Cluster Sampling

     

                In cluster sampling, when larger geographical areas are to be covered , it is easier to use area sampling than any other method of probability sampling .it is easier in the sense that the investigator need not have the list of individuals inhabiting a given area. In area sampling respondents can readily be substituted for other respondents within the same random section. Area sampling save both time and money. And area or cluster sampling possesses the trait of flexibility.

     

    Disadvantages Of Cluster Sampling

     

                In cluster sampling , there are increased risk of sampling errors. Less accurate compared to simple random sampling .can lead to biased results if clusters are not homogeneous. Analysis can be more complex. Requires a larger sample size to achieve the same level of precision . May require multiple stages of sampling .



    Figure 4

       



     

    Incidental Sampling  Or  Convenience Sampling


    Learning outcomes

     

  • Understand Incidental Sampling:  Learn the concept and its use as a non-probability sampling method.
  • Identify Advantages:  Recognize its ease, low cost, and convenience in data collection.
  • Acknowledge Limitations:  Understand the risks of bias and lack of generalizability.
  • Design Simple Studies:  Use incidental sampling effectively for exploratory research.
  • Interpret Results Cautiously:  Analyze data while acknowledging the sampling method's limitations.
  • Compare Methods:  Differentiate incidental sampling from systematic and probability-based methods.
  • Practical Use:  Apply in contexts like pilot studies or resource-constrained research.
  •  

    Incidental Sampling

     The incidental sampling also known as convenient sampling and accidental sampling. One such method convenience sampling , depends on the availability of respondents. In this procedure . Subjects select themselves. (John W. Best And James V. Kahn 2003).

    Let’s Look At An Example Of Sampling.

    A researcher conducting a study involving native american students finds that a large percentage of students in one school are native americans.  The researcher decides to study this group at this one school because they are available and because the researcher has the permission of the principal and can gain consent from the native american students to participate in the study. This is a convenience sample because the participants are convenient to the researcher and are available for the study. ( John W. Creswell 2012) .

     

    Figure 4

     

    Convenience Sampling


     

        Figure 5       

      

    Convenience Sampling                                                                                                              



    Advantages Of Incidental Sampling

     

    Incidental sampling is the most convenient method of sampling. This method of sampling possesses the trait of economy. This method saves time, money , labour of the investigator.

     

     

    Disadvantages Of Incidental Sampling

     

    In incidental samples remains no longer representative of the population. It can be biased. In incidental sampling the probability of sampling errors is high.



    Figure 6

     

    Convenience Sampling

     


    Table  1

     

    Difference Between Cluster And Incidental

     

    Cluster Sampling

    Incidental Sampling

    Cost reduction

    Easy to use

    Selected clusters

    Quick

    Division naturally formed

    Cost- effective

    More errors

    Useful for small populations

    Homogeneity extremely

    Accurate results



    References

     

    1.    Agrawal,L .P,2005,,Dominant publishers &Distributions,ISBN 81 7888 336 8 ,p 165

    2.    Best,J.W &Kahn,J.V, 2003, Research in Education, Asoke.K.Ghosh , Prentice hall of India pvt limited , ISBN 978 81 203 2833 4, P 83

    3.    Creswel, W. John, 2012, Educational Research Planning, Conducting , and Evaluating Qantitative Qualitative Research , Pearson Education , ISBN 978 93 325 4947 0 p 145-146

    4.    Dalen,B. Van Deobold,1979, Understanding Educational Research An Introduction, Library of congress cataloguing in publication data , ISBN 0 07 66883 3, p 135

    5.    D , Dooley, 2001,Social Research Methods , Prentice Hall,ISBN 81203 3119 2 , p 17

    6.    Kothari, C.R, 2019, Research Methodology Methods & Techniques, New sage International Publishers, ISBN 978 93 86649 22 5 , p 71-73

    7.    Koul, Lokesh, 1984, Methodology of Educational, Vikas publishing house pvt ltd, p 121-122

    8.    Mandal , K, Nandan, 2012, Social work Research &Statistics , Centrum press, ISBN 978 93 81293 66 9 , p 202-203

    9.    Singh, K. Arun, Test, Measurement and research methods in Behavioural Sciences, Bharati Bhawan, p 296-300

    10. Sapsford, R , 1999, Survey Research, Sage Publication, ISBN 07 61955275 p 86



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    CLUSTER SAMPLING & INCIDENTAL SAMPLING

      Cluster Sampling & Incidental Sampling Learning Outcomes  Understand Cluster Sampling:  Learn the concept and when to use it. Identi...