Learning Reinforcement Activity No. 3-1: SAMPLING TECHNIQUES

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    1. Non-probability Sampling
    2. Non-probability Sampling
    3. Probability Sampling
    4. Probability Sampling

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    1. Cluster Sampling. Sampling is to be conducted by considering a random sample of stores (the stores will be regarded as clusters). All customers of randomly chosen stores will be interviewed.

    2. Cluster Sampling. This sampling technique would be applied as the main procedure in a more general sampling methodology known as multi-stage sampling. The regions of the Philippines serves as primary clusters, and these can be further broken down into secondary clusters (provinces), which in turn can be broken further into tertiary clusters (municipalities).

    3. Stratified Random Sampling. The population of Otto Han University Students will be stratified according to sex. From each strata, simple random samples of males and females are then taken.

    4. Stratified Random Sampling. The population of practicing electronics engineers are stratified into two categories: those working in the communications industry and those in the electronics industry. From each subgroup of the population, simple random samples will be taken for the study.

    5. Systematic Random Sampling. It would be more appropriate to perform systematic random sampling where for each of the two leading tire brands, systematic random samples of tires will be taken from the production line.

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  1. The method described is stratified sampling. The sample obtained will not be a simple random sample of 10 students for the reason that different samples of students have different chances (or likelihood) of being selected. Specifically, in simple random sampling it is very unlikely to select a sample with the same size from each gender.