Question

# ____   Simple Random Sample ____   Cluster sample ____   Systematic sample ____   Stratified sample 1.   While this...

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Simple Random Sample

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Cluster sample

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Systematic sample

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Stratified sample

1.
While this type of sampling is very eacy to carry out, and hence saves time and money, it can very easily yield samples which are non-representative of the population, unintentionally. Often, data comes "in cycles", where the beginning and the end of each cycle is "atypical". If the "kth" individual is picked at the beginning or at the end of a cycle more often than a random individual, the sample picked is non-representative.

2.
This type of sampling saves time and money, but can very easily yield samples which are non-representative of the population, unintentionally. One atypical "cluster" may yield a large proportion of atypical individuals, so the sample becomes atypical because only individuals from randomly selected clusters are picked to become part of the sample.

3.
This type of sampling gives every individual an equal chance to be picked and gives every sample of a given size an equal chance to be picked - which is good- but at a great money and time investment. In addition, all individuals in the population have to be known before sampling can proceed. This is a problem when studying large populations or populations of unknown size, for example, earthworms.

4.
This type of sampling allows comparison of unequal size groups, or insuring proportional representation of groups with low response. However, determining a unique category for individuals may be problematic, as in should a 1/2 African American/1/2 Asian individual be counted as an African American? counted as both African American and Asian? thrown out of the study?Throwing out individuals equals non-response...

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Please match the following "probability" samples types to their description.

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Simple Random Sample (SRS)

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Stratified Sample

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Systematic Sample

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Cluster Sample

1.
A sample in which all individuals in the population are numbered, and random individuals from the population are picked using a Random Number Generator or a Random Digit Table. It is like randomly picking slips out of a hat, but that is impractical for large populations.

2.
A sample which is picked by subdividing the populations into (preferably diverse or heterogeneous) clusters, then picking a random sample of the clusters, then subdividing the clusters picked into smaller (preferably diverse) groups, picking a random sample of groups, etc.

3.
A sample which is picked by numbering all individuals in the population and then picking every "kth" individual, like giving the questionnaire to every 17th customer who walks through the door of a doctor's office.

4.
A sample which is picked by subdividing the population into unambiguous uniform or homogeneous groups, then picking random samples of these groups. The total random sample may give all groups proportional representation or equal representation.

1. Simple random sample (SRS)

(Since,each individual in population has equal chance of being included)

2. Cluster sample

(Since, in cluster sampling divides population into heterogeneous groups and a simple random sample of groups selected)

3.Systematic sample

(Since,in systematic sampling one of the first k selected randomly then every kth individual selected in sample)

4.Stratified sample

(Since, in stratified sampling divides data into homogeneous groups called as strata and random sample is taken from each statum)