Sampling A sample is a finite part of a statistical population whose properties are studied to gain information about the whole(Webster, 1985). When dealing with people, it can be defined as a set of respondents (people) selected from a larger population for the purpose of a survey. A population is a group of individuals persons, objects, or items from which samples are taken for measurement for example a population of presidents or professors, books or students. (Conti…)

Sampling:

Sampling A process used in statistical analysis in which a predetermined number of observations will be taken from a larger population. The methodology used to sample from a larger population will depend on the type of analysis being performed, but will include simple random sampling, systematic sampling and observational sampling. The sample should be a representation of the general population.

Why to have SAMPLING?:

Why to have SAMPLING? To draw conclusions about populations from samples and to determine a population`s characteristics by directly observing only a portion it is cheaper to observe a part rather than the whole Impossible to reach whole of the population For convenience Accuracy and sampling A sample may be more accurate than a census. A sloppily conducted census can provide less reliable information than a carefully obtained sample (Conti…)

Why to have SAMPLING?:

Why to have SAMPLING? Quick information needed in research Because of large population There are Some populations that are so difficult to get access to that only a sample can be used. Like people in prison, like crashed aeroplanes in the deep seas, presidents etc. Hard areas to reach every house To avoid tough climatic conditions The destructive nature of the observation

Sampling Errors:

Sampling Errors When a sample is termed as unrepresentative of its population Sampling error comprises the differences between the sample and the population the less obvious sampling error against which nature offers very little protection The Causes of Sampling Errors the error that occurs just because of bad luck Biased Sampling

Non-Sampling Errors :

Non-Sampling Errors Measurement Errors May be produced by participants in the statistical study be an innocent by product of the sampling plans and procedures Related to the observation not the sample inaccurate measurements due to malfunctioning instruments Biased observations

The interviewers Effect :

The interviewers Effect No two interviewers are alike and the same person may provide different answers to different interviewers Difference in questions formation Techniques Individuals tend to provide false answers to particular questions The Respondent Effect Respondents might also give incorrect answers to impress the interviewer certain psychological factors induce incorrect responses

Types of SAMPLING:

Types of SAMPLING Probability Sampling (Representative Sampling) Non-Probability Sampling ( Non-Representative Sampling)

Probability Sampling:

Probability Sampling True Representative of the Population Provides the most credible and accurate results It reflects the characteristics of the population For example; residents of a particular community, students at an elementary school etc.

Types of Probability Sampling:

Types of Probability Sampling Random Probability Sampling and Stratified Probability Sampling

Random Probability Sampling:

Random Probability Sampling Each individual in the population of interest has an “EQUAL CHANCE” of selection BEING Random still it is very strict in its meanings – it does not mean that we are free to take the sample from anywhere Any variation between the sample characteristics and the population characteristics is only a matter of chance.

Types of Random Sampling:

Types of Random Sampling A simple random sample A systematic random sample

Stratified and Cluster Sampling:

Stratified and Cluster Sampling A stratified sample A cluster sample

Non-Probability Sampling:

Non-Probability Sampling It does not involve random selection Non-probability samples cannot depend upon the rationale of probability theory May or may not represent the population well in applied social research there may be circumstances not feasible, practical or theoretically sensible to do probability sampling, alternatively non-probability approach is adopted

Quota Sampling the researcher deliberately sets the proportions of levels within the sample

Purposive Sampling:

Purposive Sampling This sampling is done in information rich cases It works on in-depth study Types of Purposive Sampling Extreme and deviant case sampling, Intensity sampling , Maximum variation, Homogeneous sampling , Typical case sampling , Stratified purposeful sampling , Critical case sampling, Snowball or chain sampling, Criterion sampling , Theory based or operational construct sampling, Confirming and disconfirming cases, Opportunistic Sampling, Random purposeful sampling , Sampling politically important cases, Combination or mixed purposeful sampling

Convenience Sample:

Convenience Sample Gathering data as per your convenience without following any hard and fast rules for sampling Unguided Sampling Not even considered “Random” Useful in getting general ideas about the phenomenon of interest

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