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PERSAMPELAN - DEFINISI DAN TUJUAN PERSAMPELAN ADALAH PROSES PEMILIHAN UNSUR ATAU ELEMEN DARI POPULASI BAGI SESUATU KAJIAN TERTENTU DI MANA ELEMEN INI DAPAT MEWAKILI POPULASI YANG DIKAJI. TUJUAN PERSAMPELAN IALAH UNTUK MENGGUNAKAN SAMPEL BAGI

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  • PERSAMPELAN - DEFINISI DAN TUJUAN

  • PERSAMPELAN ADALAH PROSES

  • PEMILIHAN UNSUR ATAU ELEMEN DARI

  • POPULASI BAGI SESUATU KAJIAN

  • TERTENTU DI MANA ELEMEN INI DAPAT

  • MEWAKILI POPULASI YANG DIKAJI.

  • TUJUAN PERSAMPELAN IALAH UNTUK

  • MENGGUNAKAN SAMPEL BAGI

  • MEDAPATKAN MAKLUMAT (MEMBUAT

  • KESIMPULAN) MENGENAI POPULASI

  • YANG DIKAJI.


Istilah utama persampelan
Istilah Utama Persampelan

  • Sampel

  • Populasi atau universe

  • Unsur Populasi

  • Bancian


Proses

Dalam

Pemilihan

Sampel

Tentukan Sasaran populasi

Pilih Rangka Persampelan

Tentukan Kaedah Persampelan

- Kebarangkaian/ Bukan Kebarangkalian

Pelan Prosedur Pemilihan Unit Persampelan

Tentukan Saiz Sampel

Memilih Unit Persampelan Sebenar

Perlaksanaan


Populasi sasaran
Populasi Sasaran

  • Populasi relevan

  • Ditakrifkan secara operasi


Rangka persampelan
Rangka Persampelan

  • Senarai elemen dimana pemilihan sampel dibuat

  • Populasi untuk di laksanakan

  • Senarai untuk dimelkan --- data base untuk pemasar

  • Ralat rangka persampelan


Unit persampelan
Unit Persampelan

  • Kumpulan yang dipilih untuk disampel

  • Unit Persampelan utama (PSU)

  • Unit persampelan sekunder

  • Unit persampelan peringkat ketiga


Dua jenis atau kategori persampelan
Dua Jenis atau Kategori Persampelan

  • Persampelan kebarangkalian (Probability sampling)

    • Kebanrangkalian atau peluang tiap unsur dipilih adalah diketahui

  • Persampelan Bukan Kebarangkalian (Nonprobability sampling)

    • Kebarangkalian tiap unsur dipilih tidak diketahui


  • Apakah sampel yang baik
    APAKAH SAMPEL YANG BAIK

    • BAGI MEWAKILI CIRI-CIRI POPULASI SAMPEL

    • PERLULAH SAHIH

    • TEPAT - TIDAK WUJUD “BIAS”. TIDAK WUJUD

    • VARIANS PERBEZAAN DENGAN POPULASI

    • SECARA SISTEMATIK (CONDONG KEPADA

    • SATU ARAH )

    • “PRECISION” - RALAT SECARA RAMBANG -

    • RALAT PERSAMPELAN RAMBANG.


    Ralat berkaitan dengan persampelan
    Ralat Berkaitan Dengan Persampelan

    • Ralat Rangka Persampelan

    • Ralat Persampelan Rambang

    • Ralat Tidak-Respons


    Ralat sistematik
    Ralat Sistematik

    • Ralat Bukan Persampelan

    • Sampel tidak mewakili populasi

    • Bukan disebabkan kebarangkalian

    • Disebabkan oleh rekabentuk kajian atau ketdaktepatan perlaksanaan.


    Faktor mempengaruhi keputusan pemilihan rekabentuk persampelan
    FAKTOR MEMPENGARUHI KEPUTUSAN PEMILIHAN REKABENTUK PERSAMPELAN

    • OBJEKTIF KAJIAN -

    • KETEPATAN HASIL KAJIAN

      • DIPERLUKAN (KAJIAN PENEROKAAN

      • KEPADA KAJIAN BERSEBAB).

    • PENYELURUHAN HASIL KAJAIN

    • KECEKAPAN (EFFICIENCY)

    • PENGGUNAAN SUMBER DAN MASA



    Pengertian konsep bagi membantu memaham persampelan kebarangkalian
    PENGERTIAN KONSEP BAGI MEMBANTU MEMAHAM PERSAMPELAN KEBARANGKALIAN

    • RALAT PIAWAIAN MEAN

    • SELANG KEYAKINAN

    • TEOREM HAD MEMUSAT


    Rekabentuk persampelan kebarangkalian
    REKABENTUK PERSAMPELAN KEBARANGKALIAN KEBARANGKALIAN

    • RAMBANG MUDAH

    • SISTEMATIK

    • BERSTRATA

      • BERKADARAN

      • TIDAK BERKADARAN

    • BERGUGUSAN


    Persampelan bukan kebarangkalian
    Persampelan Bukan Kebarangkalian KEBARANGKALIAN

    • Mudah (Convenience)

    • Pertimbangan (Judgment)

    • Kuato (Quota)

    • Bertumbuh (Snowball)


    Persampelan bukan kebarangkalian1
    Persampelan Bukan Kebarangkalian KEBARANGKALIAN

    • Mudah (Convenience)

    • Pertimbangan (Judgment)

    • Kuota (Quota)

    • Bertumbuh (Snowball)


    Rekabentuk persampelan kebarangkalian1
    REKABENTUK PERSAMPELAN KEBARANGKALIAN KEBARANGKALIAN

    • RAMBANG MUDAH

    • SISTEMATIK

    • BERSTRATA

      • BERKADARAN

      • TIDAK BERKADARAN

    • BERGUGUSAN


    Convenience sampling
    Convenience Sampling KEBARANGKALIAN

    • Also called haphazard or accidental sampling

    • The sampling procedure of obtaining the people or units that are most conveniently available


    Judgment sampling
    Judgment Sampling KEBARANGKALIAN

    • Also called purposive sampling

    • An experienced individual selects the sample based on his or her judgment about some appropriate characteristics required of the sample member


    Quota sampling
    Quota Sampling KEBARANGKALIAN

    • Ensures that the various subgroups in a population are represented on pertinent sample characteristics

    • To the exact extent that the investigators desire

    • It should not be confused with stratified sampling.


    Snowball sampling
    Snowball Sampling KEBARANGKALIAN

    • A variety of procedures

    • Initial respondents are selected by probability methods

    • Additional respondents are obtained from information provided by the initial respondents


    Simple random sampling
    Simple Random Sampling KEBARANGKALIAN

    • A sampling procedure that ensures that each element in the population will have an equal chance of being included in the sample


    Systematic sampling
    Systematic Sampling KEBARANGKALIAN

    • A simple process

    • Every nth name from the list will be drawn


    Stratified sampling
    Stratified Sampling KEBARANGKALIAN

    • Probability sample

    • Subsamples are drawn within different strata

    • Each stratum is more or less equal on some characteristic

    • Do not confuse with quota sample


    Cluster sampling
    Cluster Sampling KEBARANGKALIAN

    • The purpose of cluster sampling is to sample economically while retaining the characteristics of a probability sample.

    • The primary sampling unit is no longer the individual element in the population

    • The primary sampling unit is a larger cluster of elements located in proximity to one another


    Kriteria pemilihan rekabentuk persampelan
    Kriteria Pemilihan Rekabentuk Persampelan KEBARANGKALIAN

    • Darjah ketepatan

    • Sumber

    • Masa

    • Maklumat mengenai populasi

    • Skop dan kawasan kajian

    • Keperluan analisis statistik


    Persampelan bukan kebarangkalian2
    PERSAMPELAN BUKAN KEBARANGKALIAN KEBARANGKALIAN

    • Mengapa menggunakan persampelan Bukan Kebarangkalian.

      • Kaedah bukan kebarangkalian dapat memenuhi objektif persampelan dengan memuaskan

      • kos rendah

      • masa terhad

      • Ralat pemilihan dijangkakan lebih rendah

        berbanding dengan sampel rawak

      • Senarai populasi tidak terdapat


    FAKTOR MEMPENGARUHI SAIZ SAMPEL KEBARANGKALIAN

    1. KESERAGAMAN UNIT PERSAMPELAN - CONTOH CITARASA PELAJAR LEBIH SERAGAM JIKA DIBANDINGKAN DENGAN PENGGUNA LAIN DALAM KUMPULAN UMUR YANG SAMA.

    2. KEYAKINAN TERHADAP PARAMETER POPULASI. KEYAKINAN MERUJUK KEPADA TAHAP KEPASTIAN OLEH PENYELIDIK BAHAWA MEREKA BENAR-BENAR MENGANGGAR PARAMETER POPULASI SEBENAR- SEPERTI MEAN POPULASI. CONTOH PENYELIDIK ADALAH 95% YAKIN BAHAWA BELIAU BENAR-BENAR MENGUKUR TAHAP KEPUASAN PELAJAR DAN BUKAN KUMPULAN YANG LAIN.


    3. KEJITUAN (PRECISION KEBARANGKALIAN) - MERUJUK KEPADA KETEPATAN DALAM MENGANGGAR MEAN POPULASI SEBENAR.

    4. KAEDAH ANALITIKAL - TERDAPAT BEBERAPA TEKNIK ANALITIKAL YANG MENENTUKAN MINIMUM SAIZ SAMPEL YANG DIPERLUKAN.

    5. KEPERLUAN SUMBER - KOS, MASA DAN TENAGA KERJA.


    What does statistics mean
    What does Statistics Mean? KEBARANGKALIAN

    • Descriptive statistics

      • Number of people

      • Trends in employment

      • Data

    • Inferential statistics

      • Make an inference about a population from a sample


    Population parameter
    Population Parameter KEBARANGKALIAN

    • Variables in a population

    • Measured characteristics of a population

    • Greek lower-case letters as notation


    Sample statistics
    Sample Statistics KEBARANGKALIAN

    • Variables in a sample

    • Measures computed from data

    • English letters for notation


    Making data usable
    Making Data Usable KEBARANGKALIAN

    • Frequency distributions

    • Proportions

    • Central tendency

      • Mean

      • Median

      • Mode

    • Measures of dispersion


    Population mean
    Population Mean KEBARANGKALIAN


    Sample mean
    Sample Mean KEBARANGKALIAN


    Measures of dispersion or spread
    Measures of Dispersion KEBARANGKALIANor Spread

    • Range

    • Mean absolute deviation

    • Variance

    • Standard deviation


    Low dispersion verses high dispersion
    Low Dispersion Verses High Dispersion KEBARANGKALIAN

    5

    4

    3

    2

    1

    Low Dispersion

    Frequency

    150 160 170 180 190 200 210

    Value on Variable


    Low dispersion verses high dispersion1
    Low Dispersion Verses High Dispersion KEBARANGKALIAN

    5

    4

    3

    2

    1

    High dispersion

    Frequency

    150 160 170 180 190 200 210

    Value on Variable


    The variance
    The Variance KEBARANGKALIAN


    Variance
    Variance KEBARANGKALIAN




    The normal distribution
    The Normal Distribution KEBARANGKALIAN

    • Normal curve

    • Bell shaped

    • Almost all of its values are within plus or minus 3 standard deviations

    • I.Q. is an example


    Normal distribution
    Normal Distribution KEBARANGKALIAN

    MEAN


    Normal distribution1
    Normal Distribution KEBARANGKALIAN

    13.59%

    13.59%

    34.13%

    34.13%

    2.14%

    2.14%


    Normal curve iq example
    Normal Curve: IQ Example KEBARANGKALIAN

    70

    145

    85

    115

    100


    Standardized normal distribution
    Standardized Normal Distribution KEBARANGKALIAN

    • Symetrical about its mean

    • Mean identifies highest point

    • Infinite number of cases - a continuous distribution

    • Area under curve has a probability density = 1.0

    • Mean of zero, standard deviation of 1


    Standard normal curve
    Standard Normal Curve KEBARANGKALIAN

    • The curve is bell-shaped or symmetrical

    • About 68% of the observations will fall within 1 standard deviation of the mean

    • About 95% of the observations will fall within approximately 2 (1.96) standard deviations of the mean

    • Almost all of the observations will fall within 3 standard deviations of the mean


    A standardized normal curve
    A Standardized Normal Curve KEBARANGKALIAN

    z

    1

    2

    -2

    -1

    0



    Standardized scores
    Standardized Scores KEBARANGKALIAN


    Standardized values
    Standardized Values KEBARANGKALIAN

    • Used to compare an individual value to the population mean in units of the standard deviation


    Linear transformation of any normal variable into a standardized normal variable
    Linear Transformation of Any Normal Variable Into a Standardized Normal Variable

    s

    s

    m

    X

    m

    Sometimes the

    scale is stretched

    Sometimes the

    scale is shrunk

    -2 -1 0 1 2



    Population distribution
    Population Distribution Standardized Normal Variable

    m

    -s

    s

    x


    Sample distribution
    Sample Distribution Standardized Normal Variable

    _

    C

    X

    S


    Sampling distribution
    Sampling Distribution Standardized Normal Variable


    Standard error of the mean
    Standard Error of the Mean Standardized Normal Variable

    • Standard deviation of the sampling distribution


    Central limit theorem
    Central Limit Theorem Standardized Normal Variable


    Standard error of the mean1
    Standard Error of the Mean Standardized Normal Variable


    Confidence interval
    Confidence Interval Standardized Normal Variable


    Estimating the standard error of the mean
    Estimating the Standard Error of the Mean Standardized Normal Variable



    Sample size
    Sample Size Standardized Normal Variable

    • Variance (standard deviation)

    • Magnitude of error

    • Confidence level


    Sample size formula
    Sample Size Formula Standardized Normal Variable


    Sample size formula example
    Sample Size Formula - Standardized Normal VariableExample

    Suppose a survey researcher, studying expenditures on lipstick, wishes to have a 95 percent confident level (Z) and a range of error (E) of less than $2.00. The estimate of the standard deviation is $29.00.


    Sample size formula example1
    Sample Size Formula - Example Standardized Normal Variable


    Sample size formula example2
    Sample Size Formula - Example Standardized Normal Variable

    Suppose, in the same example as the one before, the range of error (E) is acceptable at $4.00, sample size is reduced.


    Sample size formula example3
    Sample Size Formula - Example Standardized Normal Variable


    2 Standardized Normal Variable

    2

    é

    ù

    é

    ù

    (

    2

    .

    57

    )(

    29

    )

    (

    2

    .

    57

    )(

    29

    )

    =

    =

    n

    n

    ê

    ú

    ê

    ú

    4

    2

    ë

    û

    ë

    û

    2

    2

    é

    ù

    é

    ù

    74

    .

    53

    74

    .

    53

    =

    =

    ê

    ú

    ê

    ú

    4

    2

    ë

    û

    ë

    û

    ]

    [

    [

    ]

    2

    2

    =

    =

    6325

    18

    .

    37

    .

    265

    =

    =

    347

    1389

    Calculating Sample Size

    99% Confidence


    Standard error of the proportion
    Standard Error of the Proportion Standardized Normal Variable


    Confidence interval for a proportion
    Confidence Interval for a Proportion Standardized Normal Variable


    Sample size for a proportion
    Sample Size for a Proportion Standardized Normal Variable


    2 Standardized Normal Variable

    z

    pq

    =

    n

    2

    E

    Where:

    n = Number of items in samples

    Z2 = The square of the confidence interval

    in standard error units.

    p = Estimated proportion of success

    q = (1-p) or estimated the proportion of failures

    E2 = The square of the maximum allowance for error

    between the true proportion and sample proportion

    or zsp squared.


    = Standardized Normal Variable

    p

    .

    6

    2

    (

    96

    )(.

    1.

    )

    (.

    6

    4

    )

    =

    n

    =

    ( .

    035

    )

    2

    q

    .

    4

    (

    3

    .

    8416

    )(.

    24

    )

    =

    001225

    .

    922

    =

    .

    001225

    =

    753

    Calculating Sample Size

    at the 95% Confidence Level


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