Population and Sample

Population and Sample

To simply distinguish population from sample, the former is composed of all individuals of interest to the researcher (e.g. students and eligible voters) whereas the latter is composed of participants from a population of interest or the subset of population. The researcher has to consider some issues in choosing the sample to wit: Can the population be enumerated? Will the population cooperate? What are the geographic restrictions? The researcher must be aware and prepared how to tackle the mentioned issues. Some researcher cannot get satisfactory result due to lack of cooperation from the chosen population or sometimes due to population of interest is dispersed over too broad a geographic range. To avoid these issues, the researcher must foresee before undertaking the study the probable problems. If it is seems difficult to collect the data from the chosen population, the researcher should have a second thought of whether to pursue the chosen topic.

Population could be finite or infinite. Homogeneity and heterogeneity are the characteristics of population.

After having decided on the population, the next that the researcher should decide on is how large the sample is. There are also several issues involved in choosing the sample as follows: What data is available? Can respondents be found? Who is the respondent? Can all members of population be sampled? Are response rates likely to be a problem?

Sampling Techniques

Sampling techniques are broadly divided into:

1)    Probability Sampling

In this sampling technique each member of the population has a specifiable probability of being chosen. It is very important when you want to make precise statements about a specific population on the basis of results of survey

            2) Non-probability Sampling

In this type of sampling it is not certain the probability of any particular member of the population being chosen. It is quite common and useful in many circumstances.

 

                  Probability sampling is further subdivided into:

  

1) Simple random sampling – It is one where every member of the population has an equal opportunity of being selected for the sample.

      2) Stratified random sampling – It is one where the population is divided into sub-groups (strata), and random sampling techniques are then used to select sample members from each stratum. This technique is applicable to use if the researcher has chosen a big population (e.g. people in Thailand) as the subject of study.  

      3) Cluster sampling – In this technique rather than randomly sampling from a list of individuals, the researcher can identify “clusters” of individuals and then sample from these clusters. After the clusters are chosen, all individuals in each cluster are included in the sample.

 Non-probability sampling is further subdivided into:

1)     Haphazard sampling- It is also called “convenience” sampling.

In this technique, researcher uses “take-them-where-you-find-them” method of obtaining participants.

2)    Quota sampling - A researcher who uses this technique chooses a sample that reflects the numerical composition of various subgroups in the population.

 

 

In evaluating samples, the researcher must consider sampling frame and response rate. Sampling frame refers to the actual population of individuals (clusters) from which a random sample will be drawn. Response rate is simply the percentage of people in the sample who actually completed the survey.