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    Types of Sampling Presenation

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    MAED CADIZ
    This document discusses different types of probability sampling methods that can be used to select samples from populations, including simple random sampling, systematic sampling with a random start, stratified random sampling, cluster sampling, and multistage sampling. It provides examples of how each method would be applied in research studies and outlines the key considerations and formulas for determining appropriate sample sizes.

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    Types of Sampling Presenation

    Uploaded by

    MAED CADIZ
    31 views13 pages
    This document discusses different types of probability sampling methods that can be used to select samples from populations, including simple random sampling, systematic sampling with a random start, stratified random sampling, cluster sampling, and multistage sampling. It provides examples of how each method would be applied in research studies and outlines the key considerations and formulas for determining appropriate sample sizes.

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    This document discusses different types of probability sampling methods that can be used to select samples from populations, including simple random sampling, systematic sampling with a random start, stratified random sampling, cluster sampling, and multistage sampling. It provides examples of how each method would be applied in research studies and outlines the key considerations and formulas for determining appropriate sample sizes.

    Copyright:

    © All Rights Reserved
    Download as PPTX, PDF, TXT or read online from Scribd
    Download as pptx, pdf, or txt
    31 views13 pages

    Types of Sampling Presenation

    Uploaded by

    MAED CADIZ
    This document discusses different types of probability sampling methods that can be used to select samples from populations, including simple random sampling, systematic sampling with a random start, stratified random sampling, cluster sampling, and multistage sampling. It provides examples of how each method would be applied in research studies and outlines the key considerations and formulas for determining appropriate sample sizes.

    Copyright:

    © All Rights Reserved
    Download as PPTX, PDF, TXT or read online from Scribd
    Download as pptx, pdf, or txt
    of 13

    SAMPLING

    Types of Probability Sampling

    • Simple Random Sampling


    • Systematic Sampling with a Random Start
    • Stratified Random Sampling
    • Cluster Sampling
    • Multistage Sampling
    Simple Random Sampling

    • Gives equal chances to all sampling units to be included as a


    sample
    1. drawing of lots or use of table of random digits
    Systematic Sampling with a Random Start

    • Selecting sample from a population by taking the kth units from an


    ordered population, from the first unit which is selected at random.
    • K is called the sampling interval= total population divided by sample
    size
    • Ordered list of population elements is required
    Stratified Random Sampling

    • Selecting a random sample from subgroups or strata into which a population has been subdivided
    using either simple random or systematic sampling

    • Requires grouping of the population into less homogenous subgroups or strata before sampling is
    drawn

    • Sample per group is drawn using=population (sub group)/total


    population*desired sample size
    Cluster Sampling

    • Selecting a sample of groups or cluster of elements


    • Usually exclusive sub-populations, which together comprise a population
    • Usually heterogeneous sections, where each section is considered a cluster
    • Size of the population of clusters=the number of clusters in the population
    • Cluster size=number of elements in a cluster
    A study on Attitudes Towards Cheating of College Freshmen In a
    Cluster Private School . 100 students as respondents and 50 is the desired
    sample size. Students ,may be divided into 10 heterogeneous
    Sampling: groups

    Population: All the 10 classes/clusters


    Frame: List of 10 clusters/groups
    Sampling Unit: One group/cluster with 10 members
    Steps:
    1. Number the 10 groups from 1 to 10
    2. Using simple random or systematic sampling with random, draw 5
    numbers. Example, 2 6 7 9 1 are chosen
    3. Identify the groups represented by the numbers drawn
    4. Study all the students in the five sample classes/groups drawn
    Multistage Sampling

    • Selection of sample is accomplished in two or more stages


    • Used when population can be divided into hierarchies
    In a Study of Men’s Participation in Child Care where a sample of
    Multistage 135 married men with 0-6 years old children from a certain
    province. You select 3 of the 7 towns of the province and 3
    Sampling barangays in each sample town and 15 married men in each
    barangay

    Population: All the men with 0-6 years old children in the province
    Sampling Frame: List of men with 0-6 years old children
    Sampling Unit: A man with 0-6 years old children
    Steps:
    1. Draw 3 sample towns in the province using simple random or systematic sampling
    2. Draw a sample of 3 barangays in each of the 3 sample towns using simple random
    or systematic sampling
    3. Draw a sample of married men with 0-6 years old children in each of the 3 sample
    barangays in each of the 3 sample towns using simple random or systematic
    sampling
    Determination of Sample Size

    • CONSIDERATIONS:
    1. availability of resources
    2. the requirements of a proposed plan of analysis
    How Sample Size is Computed?

    • If Population is more than 10,000


    Z2pq
    n = ---------------
    d2
    Where:
    n = the desired sample size
    Z = the standard normal deviate, usually set at 1.96, corresponds to the 95% level
    of confidence
    p = proportion in the target population, if none then use 50 %
    q = 1.0-p
    d = degree of accuracy desired, usually set at either 0.05,0.2, or 0.01
    How Sample Size is Computed? (Parel, et.al., 1985)

    • The Total Population N is considered


    NZ2 (p (1-p))
    n = ------------------------------
    Nd2 + Z2 (p (1-p))
    Where:
    N = Population
    n = the desired sample size
    Z = the standard normal deviate, set at 1.96, corresponds to the 95% level of
    confidence
    p = proportion in the target population, if none then use 50 %
    d = degree of accuracy desired, usually set at either 0.05, 0.25, or 0.01
    How Sample Size is Computed?

    • Slovin’s Formula
    N
    n = ------------------
    ( 1 + N) (05)2
    Where:
    N = Population
    n = the desired sample size

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