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    Probability Sampling

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    Leslie Joy Yata
    This document discusses different types of probability sampling designs used in research including simple random sampling, stratified sampling, systematic sampling, cluster sampling, and multistage sampling. It provides definitions and examples to illustrate each type. Simple random sampling involves randomly selecting cases from a sampling frame. Stratified sampling divides the population into groups and then randomly samples from each group. Systematic sampling assumes a randomly ordered population and samples every kth unit. Cluster sampling divides a population into clusters and randomly samples clusters to measure all units within.

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    Probability Sampling

    Uploaded by

    Leslie Joy Yata
    180 views15 pages
    This document discusses different types of probability sampling designs used in research including simple random sampling, stratified sampling, systematic sampling, cluster sampling, and multistage sampling. It provides definitions and examples to illustrate each type. Simple random sampling involves randomly selecting cases from a sampling frame. Stratified sampling divides the population into groups and then randomly samples from each group. Systematic sampling assumes a randomly ordered population and samples every kth unit. Cluster sampling divides a population into clusters and randomly samples clusters to measure all units within.

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    This document discusses different types of probability sampling designs used in research including simple random sampling, stratified sampling, systematic sampling, cluster sampling, and multistage sampling. It provides definitions and examples to illustrate each type. Simple random sampling involves randomly selecting cases from a sampling frame. Stratified sampling divides the population into groups and then randomly samples from each group. Systematic sampling assumes a randomly ordered population and samples every kth unit. Cluster sampling divides a population into clusters and randomly samples clusters to measure all units within.

    Copyright:

    © All Rights Reserved
    Download as PPTX, PDF, TXT or read online from Scribd
    Download as pptx, pdf, or txt
    180 views15 pages

    Probability Sampling

    Uploaded by

    Leslie Joy Yata
    This document discusses different types of probability sampling designs used in research including simple random sampling, stratified sampling, systematic sampling, cluster sampling, and multistage sampling. It provides definitions and examples to illustrate each type. Simple random sampling involves randomly selecting cases from a sampling frame. Stratified sampling divides the population into groups and then randomly samples from each group. Systematic sampling assumes a randomly ordered population and samples every kth unit. Cluster sampling divides a population into clusters and randomly samples clusters to measure all units within.

    Copyright:

    © All Rights Reserved
    Download as PPTX, PDF, TXT or read online from Scribd
    Download as pptx, pdf, or txt
    of 15

    Probability Sampling

    Types of Probability Sampling


    Designs

    Simple random sampling

    Stratified sampling

    Systematic sampling

    Cluster (area) sampling

    Multistage sampling
    Some Definitions


    N = the number of cases in the
    sampling frame

    n = the number of cases in the sample
    Simple Random Sampling

    Have a list of all members of the


    population; write each name on a card
    and choose cards through a pure-chance
    selection.
    Simple Random Sampling
    Example:

    Small service agency.

    Client assessment of quality of service.

    Get list of clients over past year.

    Draw a simple random sample of n/N.
    Simple Random Sampling

    List of clients
    Stratified Random Sampling

    Sometimes called "proportional" or


    "quota" random sampling.
    Objective: Population of N units
    divided into nonoverlapping strata
    N1, N2, N3, ... Ni such that N1 + N2
    + ... + Ni = N; then do simple random
    sample of n/N in each strata.
    Stratified Sampling - Purposes:
    To insure representation of each
    strata, oversample smaller population
    groups.
    Administrative convenience -- field
    offices.
    Sampling problems may differ in each
    strata.
    Increase precision (lower variance) if
    strata are homogeneous within (like
    blocking).
    Stratified Random Sampling

    List of clients
    Stratified Random Sampling

    List of clients

    African-American Hispanic-American Others

    Strata
    Stratified Random Sampling

    List of clients

    African-American Hispanic-American Others

    Strata

    Random subsamples of n/N


    Systematic Random Sampling


    Assumes that the population is
    randomly ordered.

    Advantages: Easy; may be more
    precise than simple random sample.
    F:\LESLIE-PC\Downloads\Video\Systema
    tic Sampling -
    YouTube.MKV
    Cluster Sampling

    Procedure:

    Divide population into clusters.

    Randomly sample clusters.

    Measure all units within sampled
    clusters.
    Cluster Sampling


    Advantages: Administratively useful,
    especially when you have a wide
    geographic area to cover.


    Examples: Randomly sample from city
    blocks and measure all homes in
    selected blocks.
    Cluster Sampling

    Example: if you want to have a sample of 120
    out of 1,000 students, you can randomly
    select three sections with 40 students to
    constitute the sample.

    40 40 40

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