SlideShare a Scribd company logo
1 of 54
adjcp@mahidol.ac.th
3.
INTERPETAT EMPIRICAL   CONCEPTUAL PHASE
IVE PHASE PHASE




                          H
                        DESIGN
                       RESEARC
                                 Research questions
1.

2.
3.

4.
1.
     (Representativeness)


2.                 (Adequate
     size)
(Population)




EXAMPLE (Sample)
•




•
1.
             (Non- probability
     sampling)
1.1 Accidental sampling


     Quota sampling

     Purposive sampling


     Convenience sampling
Snowball sampling
Random Sampling



(Error)

                   100%


(Sampling error)
(Probability
    sampling)
               Probability
    Sampling
–                  (bias)

–
                (Sampling error)
SRS  (Simple random
   sampling, SRS)

          ” (Sampling frame)

EXAMPLE
(Sampling frame)
No   Name   Address


1           ……..
2           ……..
3           ……..
4           ……..
5           ……..
6           ……..
7           ……..
8           ……..
9           ……..
10              ……..

                                A
A

1)
-
-


    )
(Systematic random
           sampling)


                  n
                       N
       (Sampling interval); k =
N/n
                      1   k
k = 50/10
 , 7, 12, 17, 22, 27, 32, 37, 42,
47
(Stratified sampling)




EX “Child development
 study in Thailand”
O Curative
                           units
A   Promotion/Prevention
                           X Rehabitative
units                      units
(Cluster sampling)
Two-stage cluster sampling
EX:

•



•

          4-
      5        ?
(Multi-stage sampling)
(Stratified three-stage sampling)




       .                     1      .

                             4

                             4

                             4
85     85     -
(   .)   112    48    64
         112    48    64
         112    48    64
         112    48    64
         533   277   256
-            -      -             -
   -               -


       ภค
        า              รวม     ใ เข เท บ ล
                               น ต ศา        นก ต ศา
                                              อ เข เท บ ล
กรุงเท มห ร
      พ านค            1,530      1,530            -
กล (ย กท
   าง กเว้น ม.)        2,016       864           1,152
เหนือ                  2,016       864           1,152
ต อ งเห
 ะวันอ กเฉีย นือ       2,016       864           1,152
ใต้                    2,016       864           1,152
       รวม             9,594      4,986          4,608
When choosing a sample size, we must
  consider the following issues:
• Objectives: What population parameters
  we want to estimate/test hypothesis
• Sampling/research design is selected
• Degree of accuracy required for the
  study
• Spread/variation (variability) of the
  population
• Response rate, practicality: how hard is
  it to collect data
• Time and money available
1)
Sample size for Simple Random Sampling
To estimate mean
                     2     2
                    Z N
     n =
                2   2          2
            Z         ( N 1) E

                    Z2 2
      n

                     E2
Sample size for Simple Random Sampling
To estimate proportion

                      2
                 Z NP (1 P )
     n =     2                  2
            Z P (1 P ) ( N 1) E
                  n




               2P 1 P
              Z (    )
     n

                E 2
1,628
  (Pilot survey)

                         z 2 NP (1 P)
              5%
                     z 2 P (1 P ) NE 2      %

      n   =                 )2
                      (1.645 (1,6280.2)( .8)
                                    )( 0
                  (1.645 (0.2)( .8) (1,6280.052
                       )2      0         )( )

      n   =

          =        156.53            157
                                5%
90%
?      z 2 P (1 P )
              95% E 2

                  n =

              2(P 1)
                1
         (1.96 )(
             )                  P
               2 2               P = ½=0
           (0.052
               )
n=

     =   384.16        385
1.2
Equal sample
            L
        L       N2S2
                 hh
n =      h1
            L
      N2E2    NhS2
           h1 h

nh     n
       L
Proportional Allocation
                 L
            N NhS2
                 h
             h1
 n    =              L
          N2E2           NhS2
                            h
                     h1
            Nh
 nh        L
                 Nh
          h1
2) Sample size determination for
hypothesis testing

2.1 Sample size determination for the
test of one proportion
Example       In a particular province the
proportion of pregnant women provided with
prenatal care in the first trimester of pregnancy
is estimated to be 40% by the provincial
department of health.         Health officials in
another province are interested in comparing
their success at providing prenatal care with
these figures. How many women should be
sampled to test the hypothesis that the coverage
rate in the second province is      % against the
alternative that it is not   %? The investigators
wish to detect a difference of % with the
power of the test equal at     % and at
P : coverage rate
Ho: P = .       Ha: P   . ( .   or

MINITAB can be used to assist in this
sample size determination by
selecting
Stat > Power and sample size >
proportion.
If alternative values of p is equal to
.45, a sample size of 1022 would be
needed.

If alternative values of p is equal to
. , a sample size of        would be
needed.
We choose the large sample size, thus a
sample size of 1022 is needed for the
study.
2.2 Sample size determination for the
test of two proportions
Two-sided test
           (Z    2pq Z p2q2 p1q1)2
   n =       2
                   (p2 p1)2
Example 5 It is believed that the proportion
of patients who develop complications after
undergoing one type of surgery is % while
the proportion of patients who develop
complications after a second type of surgery
is    %. How large should the sample size be
in each of the two groups of patients if an
investigator wishes to detect, with a power
of    %, whether the second procedure has a
complication rate significantly higher than
the first at the % level of significance?
Use MINITAB, click Stat > Power
and sample size > proportion.
You would complete the dialog box.
You want to test one-sided test, click
on the options button and choose less
than
Power and Sample Size
Test for Two Proportions
Testing proportion = proportion (versus <)
Calculating power for proportion
Alpha =
        Sample Target Actual
Proportion     Size Power Power


A sample size of   would be needed in each
group.
2.3 Sample size determination for the tes
of one mean
Two-sided test
                                    2   2
                 (Z           Z )
         n            2
                      (   0     1 )2
Example Consider the cholesterol
study. Suppose that the null mean is
      mg% /ml, the alternative mean is
      mg%/ml, the standard deviation is
   , and we wish to conduct a
significance test for one-sided test at
the % level with a power of       %.
How large should the sample size be?
MINITAB> click Stat > Power and
sample size > sample Z.

You want to test one-sided test, click
on the options button and choose
greater than
-Sample Z Test
Testing mean = null (versus > nul
Calculating power for mean = null
Alpha = .     Sigma =
       Sample Target Actual
Difference Size Power Power

   Thus, 96 people are needed.
   To achieve a power of 90%
   using a 5% significance level
2.4 Sample size determination for the
test of two means

Two-sided test
                 (Z   Z )2( 1
                            2     2)2
                                  2
                  2
    n =
                      ( 2   1)2
Example Consider the blood pressure study
for drug A users and non-drug A users as a
pilot study conducted to obtain parameter
estimates to plan for a larger study. We wish
to test the hypothesis : = versus : .
Determine the appropriate sample size for
the large study using a two–sided test with a
significance level of .     and a power of

In the pilot study, we obtained   =     .   ,
S    =    .       =     . ,S
In the pilot study, we obtained
=     . ,S      =    .      =
    . ,S


n=(                                     -



 We would require a sample size of 152 people
 in each group

More Related Content

What's hot

Statistics lecture 10(ch10)
Statistics lecture 10(ch10)Statistics lecture 10(ch10)
Statistics lecture 10(ch10)jillmitchell8778
 
Quality perception of coding artifacts and packet loss in networked video com...
Quality perception of coding artifacts and packet loss in networked video com...Quality perception of coding artifacts and packet loss in networked video com...
Quality perception of coding artifacts and packet loss in networked video com...soojin kim
 
Matching Weights to Simultaneously Compare Three Treatment Groups: a Simulati...
Matching Weights to Simultaneously Compare Three Treatment Groups: a Simulati...Matching Weights to Simultaneously Compare Three Treatment Groups: a Simulati...
Matching Weights to Simultaneously Compare Three Treatment Groups: a Simulati...Kazuki Yoshida
 
2010 smg training_cardiff_day1_session1 (1 of 3)_mckenzie
2010 smg training_cardiff_day1_session1 (1 of 3)_mckenzie2010 smg training_cardiff_day1_session1 (1 of 3)_mckenzie
2010 smg training_cardiff_day1_session1 (1 of 3)_mckenziergveroniki
 
hypothesis testing-tests of proportions and variances in six sigma
hypothesis testing-tests of proportions and variances in six sigmahypothesis testing-tests of proportions and variances in six sigma
hypothesis testing-tests of proportions and variances in six sigmavdheerajk
 
Big Data Analysis
Big Data AnalysisBig Data Analysis
Big Data AnalysisNBER
 
Quantitative Analysis for Emperical Research
Quantitative Analysis for Emperical ResearchQuantitative Analysis for Emperical Research
Quantitative Analysis for Emperical ResearchAmit Kamble
 
Lect w7 t_test_amp_chi_test
Lect w7 t_test_amp_chi_testLect w7 t_test_amp_chi_test
Lect w7 t_test_amp_chi_testRione Drevale
 
Galambos N Analysis Of Survey Results
Galambos N Analysis Of Survey ResultsGalambos N Analysis Of Survey Results
Galambos N Analysis Of Survey ResultsNora Galambos
 

What's hot (14)

Statistics lecture 10(ch10)
Statistics lecture 10(ch10)Statistics lecture 10(ch10)
Statistics lecture 10(ch10)
 
Quality perception of coding artifacts and packet loss in networked video com...
Quality perception of coding artifacts and packet loss in networked video com...Quality perception of coding artifacts and packet loss in networked video com...
Quality perception of coding artifacts and packet loss in networked video com...
 
Matching Weights to Simultaneously Compare Three Treatment Groups: a Simulati...
Matching Weights to Simultaneously Compare Three Treatment Groups: a Simulati...Matching Weights to Simultaneously Compare Three Treatment Groups: a Simulati...
Matching Weights to Simultaneously Compare Three Treatment Groups: a Simulati...
 
2010 smg training_cardiff_day1_session1 (1 of 3)_mckenzie
2010 smg training_cardiff_day1_session1 (1 of 3)_mckenzie2010 smg training_cardiff_day1_session1 (1 of 3)_mckenzie
2010 smg training_cardiff_day1_session1 (1 of 3)_mckenzie
 
Causal Inference Opening Workshop - New Statistical Learning Methods for Esti...
Causal Inference Opening Workshop - New Statistical Learning Methods for Esti...Causal Inference Opening Workshop - New Statistical Learning Methods for Esti...
Causal Inference Opening Workshop - New Statistical Learning Methods for Esti...
 
Biostatistics ii4june
Biostatistics ii4juneBiostatistics ii4june
Biostatistics ii4june
 
hypothesis testing-tests of proportions and variances in six sigma
hypothesis testing-tests of proportions and variances in six sigmahypothesis testing-tests of proportions and variances in six sigma
hypothesis testing-tests of proportions and variances in six sigma
 
Big Data Analysis
Big Data AnalysisBig Data Analysis
Big Data Analysis
 
Quantitative Analysis for Emperical Research
Quantitative Analysis for Emperical ResearchQuantitative Analysis for Emperical Research
Quantitative Analysis for Emperical Research
 
Causal Inference Opening Workshop - Some Applications of Reinforcement Learni...
Causal Inference Opening Workshop - Some Applications of Reinforcement Learni...Causal Inference Opening Workshop - Some Applications of Reinforcement Learni...
Causal Inference Opening Workshop - Some Applications of Reinforcement Learni...
 
Lect w7 t_test_amp_chi_test
Lect w7 t_test_amp_chi_testLect w7 t_test_amp_chi_test
Lect w7 t_test_amp_chi_test
 
Comparison of wood, gaines,
Comparison of wood, gaines,Comparison of wood, gaines,
Comparison of wood, gaines,
 
Galambos N Analysis Of Survey Results
Galambos N Analysis Of Survey ResultsGalambos N Analysis Of Survey Results
Galambos N Analysis Of Survey Results
 
Testing a Claim About a Mean
Testing a Claim About a MeanTesting a Claim About a Mean
Testing a Claim About a Mean
 

Viewers also liked

โรงเรียนวัดนิมมานรดี
โรงเรียนวัดนิมมานรดีโรงเรียนวัดนิมมานรดี
โรงเรียนวัดนิมมานรดีUltraman Taro
 
โรงเรียนวัดจันทร์ประดิษฐาราม
โรงเรียนวัดจันทร์ประดิษฐารามโรงเรียนวัดจันทร์ประดิษฐาราม
โรงเรียนวัดจันทร์ประดิษฐารามUltraman Taro
 
นิเทศ ศิรินทร์และเพื่อน
นิเทศ ศิรินทร์และเพื่อนนิเทศ ศิรินทร์และเพื่อน
นิเทศ ศิรินทร์และเพื่อนUltraman Taro
 
อัลบั้มชุมชน นิเทศ วัดจันทร์ เขต1 ม3
อัลบั้มชุมชน นิเทศ วัดจันทร์ เขต1 ม3อัลบั้มชุมชน นิเทศ วัดจันทร์ เขต1 ม3
อัลบั้มชุมชน นิเทศ วัดจันทร์ เขต1 ม3Ultraman Taro
 
นิเทศ ชุมชนหน้าวัดโคนอน
นิเทศ ชุมชนหน้าวัดโคนอนนิเทศ ชุมชนหน้าวัดโคนอน
นิเทศ ชุมชนหน้าวัดโคนอนUltraman Taro
 
อัลบั้มชุมชน โรงเรียนวัดชัยฉิมพลี
อัลบั้มชุมชน โรงเรียนวัดชัยฉิมพลีอัลบั้มชุมชน โรงเรียนวัดชัยฉิมพลี
อัลบั้มชุมชน โรงเรียนวัดชัยฉิมพลีUltraman Taro
 
บทที่ 2 เอกสารและงานวิจัยที่เกี่ยวข้อง เนื้อหา
บทที่ 2 เอกสารและงานวิจัยที่เกี่ยวข้อง เนื้อหาบทที่ 2 เอกสารและงานวิจัยที่เกี่ยวข้อง เนื้อหา
บทที่ 2 เอกสารและงานวิจัยที่เกี่ยวข้อง เนื้อหาVisiene Lssbh
 

Viewers also liked (9)

โรงเรียนวัดนิมมานรดี
โรงเรียนวัดนิมมานรดีโรงเรียนวัดนิมมานรดี
โรงเรียนวัดนิมมานรดี
 
โรงเรียนวัดจันทร์ประดิษฐาราม
โรงเรียนวัดจันทร์ประดิษฐารามโรงเรียนวัดจันทร์ประดิษฐาราม
โรงเรียนวัดจันทร์ประดิษฐาราม
 
A1
A1A1
A1
 
นิเทศ ศิรินทร์และเพื่อน
นิเทศ ศิรินทร์และเพื่อนนิเทศ ศิรินทร์และเพื่อน
นิเทศ ศิรินทร์และเพื่อน
 
อัลบั้มชุมชน นิเทศ วัดจันทร์ เขต1 ม3
อัลบั้มชุมชน นิเทศ วัดจันทร์ เขต1 ม3อัลบั้มชุมชน นิเทศ วัดจันทร์ เขต1 ม3
อัลบั้มชุมชน นิเทศ วัดจันทร์ เขต1 ม3
 
นิเทศ ชุมชนหน้าวัดโคนอน
นิเทศ ชุมชนหน้าวัดโคนอนนิเทศ ชุมชนหน้าวัดโคนอน
นิเทศ ชุมชนหน้าวัดโคนอน
 
อัลบั้มชุมชน โรงเรียนวัดชัยฉิมพลี
อัลบั้มชุมชน โรงเรียนวัดชัยฉิมพลีอัลบั้มชุมชน โรงเรียนวัดชัยฉิมพลี
อัลบั้มชุมชน โรงเรียนวัดชัยฉิมพลี
 
A11
A11A11
A11
 
บทที่ 2 เอกสารและงานวิจัยที่เกี่ยวข้อง เนื้อหา
บทที่ 2 เอกสารและงานวิจัยที่เกี่ยวข้อง เนื้อหาบทที่ 2 เอกสารและงานวิจัยที่เกี่ยวข้อง เนื้อหา
บทที่ 2 เอกสารและงานวิจัยที่เกี่ยวข้อง เนื้อหา
 

Similar to การสุ่มตัวอย่างในงานวิจัยสาธารณสุข

Determination of sample size in scientific research.pptx
Determination of sample size in scientific research.pptxDetermination of sample size in scientific research.pptx
Determination of sample size in scientific research.pptxSam Edeson
 
Sample size in general
Sample size in generalSample size in general
Sample size in generalMmedsc Hahm
 
Formulas statistics
Formulas statisticsFormulas statistics
Formulas statisticsPrashi_Jain
 
Admission in India
Admission in IndiaAdmission in India
Admission in IndiaEdhole.com
 
Chapter one on sampling distributions.ppt
Chapter one on sampling distributions.pptChapter one on sampling distributions.ppt
Chapter one on sampling distributions.pptFekaduAman
 
Lesson06_new
Lesson06_newLesson06_new
Lesson06_newshengvn
 
Power Analysis and Sample Size Determination
Power Analysis and Sample Size DeterminationPower Analysis and Sample Size Determination
Power Analysis and Sample Size DeterminationAjay Dhamija
 
Basic Concepts of Standard Experimental Designs ( Statistics )
Basic Concepts of Standard Experimental Designs ( Statistics )Basic Concepts of Standard Experimental Designs ( Statistics )
Basic Concepts of Standard Experimental Designs ( Statistics )Hasnat Israq
 
Dr.Dinesh-BIOSTAT-Tests-of-significance-1-min.pdf
Dr.Dinesh-BIOSTAT-Tests-of-significance-1-min.pdfDr.Dinesh-BIOSTAT-Tests-of-significance-1-min.pdf
Dr.Dinesh-BIOSTAT-Tests-of-significance-1-min.pdfHassanMohyUdDin2
 
Lecture_9_Sample_size_calculation_Summer_2016.pptx
Lecture_9_Sample_size_calculation_Summer_2016.pptxLecture_9_Sample_size_calculation_Summer_2016.pptx
Lecture_9_Sample_size_calculation_Summer_2016.pptxMostafizurrahman500195
 
Special Double Sampling Plan for truncated life tests based on the Marshall-O...
Special Double Sampling Plan for truncated life tests based on the Marshall-O...Special Double Sampling Plan for truncated life tests based on the Marshall-O...
Special Double Sampling Plan for truncated life tests based on the Marshall-O...ijceronline
 

Similar to การสุ่มตัวอย่างในงานวิจัยสาธารณสุข (20)

Determination of sample size in scientific research.pptx
Determination of sample size in scientific research.pptxDetermination of sample size in scientific research.pptx
Determination of sample size in scientific research.pptx
 
Student t t est
Student t t estStudent t t est
Student t t est
 
T test statistics
T test statisticsT test statistics
T test statistics
 
Sample size in general
Sample size in generalSample size in general
Sample size in general
 
Formulas statistics
Formulas statisticsFormulas statistics
Formulas statistics
 
Admission in India
Admission in IndiaAdmission in India
Admission in India
 
Chapter one on sampling distributions.ppt
Chapter one on sampling distributions.pptChapter one on sampling distributions.ppt
Chapter one on sampling distributions.ppt
 
Sample size estimation
Sample size estimationSample size estimation
Sample size estimation
 
The T-test
The T-testThe T-test
The T-test
 
Lesson06_new
Lesson06_newLesson06_new
Lesson06_new
 
Chapter07.pdf
Chapter07.pdfChapter07.pdf
Chapter07.pdf
 
Two dependent samples (matched pairs)
Two dependent samples (matched pairs) Two dependent samples (matched pairs)
Two dependent samples (matched pairs)
 
Power Analysis and Sample Size Determination
Power Analysis and Sample Size DeterminationPower Analysis and Sample Size Determination
Power Analysis and Sample Size Determination
 
Design of experiments(
Design of experiments(Design of experiments(
Design of experiments(
 
Basic Concepts of Standard Experimental Designs ( Statistics )
Basic Concepts of Standard Experimental Designs ( Statistics )Basic Concepts of Standard Experimental Designs ( Statistics )
Basic Concepts of Standard Experimental Designs ( Statistics )
 
Dr.Dinesh-BIOSTAT-Tests-of-significance-1-min.pdf
Dr.Dinesh-BIOSTAT-Tests-of-significance-1-min.pdfDr.Dinesh-BIOSTAT-Tests-of-significance-1-min.pdf
Dr.Dinesh-BIOSTAT-Tests-of-significance-1-min.pdf
 
Medical statistics2
Medical statistics2Medical statistics2
Medical statistics2
 
Lecture_9_Sample_size_calculation_Summer_2016.pptx
Lecture_9_Sample_size_calculation_Summer_2016.pptxLecture_9_Sample_size_calculation_Summer_2016.pptx
Lecture_9_Sample_size_calculation_Summer_2016.pptx
 
Two variances or standard deviations
Two variances or standard deviations  Two variances or standard deviations
Two variances or standard deviations
 
Special Double Sampling Plan for truncated life tests based on the Marshall-O...
Special Double Sampling Plan for truncated life tests based on the Marshall-O...Special Double Sampling Plan for truncated life tests based on the Marshall-O...
Special Double Sampling Plan for truncated life tests based on the Marshall-O...
 

More from Ultraman Taro

About thaihealthexperts2
About thaihealthexperts2About thaihealthexperts2
About thaihealthexperts2Ultraman Taro
 
Participatory action research2
Participatory action research2Participatory action research2
Participatory action research2Ultraman Taro
 
Quantitive Research 2
Quantitive Research 2Quantitive Research 2
Quantitive Research 2Ultraman Taro
 
Sources of Data in Public Health
Sources of Data in Public HealthSources of Data in Public Health
Sources of Data in Public HealthUltraman Taro
 
Introduction and Role of Epidemiology
Introduction and Role of EpidemiologyIntroduction and Role of Epidemiology
Introduction and Role of EpidemiologyUltraman Taro
 
Surveillance Systems
Surveillance SystemsSurveillance Systems
Surveillance SystemsUltraman Taro
 
Participatory action research
Participatory action researchParticipatory action research
Participatory action researchUltraman Taro
 
Overviews Research Methodology
Overviews Research MethodologyOverviews Research Methodology
Overviews Research MethodologyUltraman Taro
 
Outbreak Investigation
Outbreak InvestigationOutbreak Investigation
Outbreak InvestigationUltraman Taro
 
Health Status Indicators Thai Experience
Health Status Indicators Thai ExperienceHealth Status Indicators Thai Experience
Health Status Indicators Thai ExperienceUltraman Taro
 
Logic and science as a foundation of research
Logic and science as a foundation of researchLogic and science as a foundation of research
Logic and science as a foundation of researchUltraman Taro
 
Introduction to Thesis
Introduction to ThesisIntroduction to Thesis
Introduction to ThesisUltraman Taro
 

More from Ultraman Taro (20)

Al1
Al1Al1
Al1
 
Aaa
AaaAaa
Aaa
 
About thaihealthexperts2
About thaihealthexperts2About thaihealthexperts2
About thaihealthexperts2
 
Functional Research
Functional ResearchFunctional Research
Functional Research
 
Participatory action research2
Participatory action research2Participatory action research2
Participatory action research2
 
Quantitive Research 2
Quantitive Research 2Quantitive Research 2
Quantitive Research 2
 
Sources of Data in Public Health
Sources of Data in Public HealthSources of Data in Public Health
Sources of Data in Public Health
 
Introduction and Role of Epidemiology
Introduction and Role of EpidemiologyIntroduction and Role of Epidemiology
Introduction and Role of Epidemiology
 
Research Format
Research FormatResearch Format
Research Format
 
Research process
Research processResearch process
Research process
 
Surveillance Systems
Surveillance SystemsSurveillance Systems
Surveillance Systems
 
Epidemiology of NCD
Epidemiology of NCDEpidemiology of NCD
Epidemiology of NCD
 
Epidemiology
EpidemiologyEpidemiology
Epidemiology
 
Questionnaire
QuestionnaireQuestionnaire
Questionnaire
 
Participatory action research
Participatory action researchParticipatory action research
Participatory action research
 
Overviews Research Methodology
Overviews Research MethodologyOverviews Research Methodology
Overviews Research Methodology
 
Outbreak Investigation
Outbreak InvestigationOutbreak Investigation
Outbreak Investigation
 
Health Status Indicators Thai Experience
Health Status Indicators Thai ExperienceHealth Status Indicators Thai Experience
Health Status Indicators Thai Experience
 
Logic and science as a foundation of research
Logic and science as a foundation of researchLogic and science as a foundation of research
Logic and science as a foundation of research
 
Introduction to Thesis
Introduction to ThesisIntroduction to Thesis
Introduction to Thesis
 

Recently uploaded

Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...Patryk Bandurski
 
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 3652toLead Limited
 
WordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your BrandWordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your Brandgvaughan
 
Dev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebDev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebUiPathCommunity
 
Key Features Of Token Development (1).pptx
Key  Features Of Token  Development (1).pptxKey  Features Of Token  Development (1).pptx
Key Features Of Token Development (1).pptxLBM Solutions
 
Streamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project SetupStreamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project SetupFlorian Wilhelm
 
Connect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck PresentationConnect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck PresentationSlibray Presentation
 
Benefits Of Flutter Compared To Other Frameworks
Benefits Of Flutter Compared To Other FrameworksBenefits Of Flutter Compared To Other Frameworks
Benefits Of Flutter Compared To Other FrameworksSoftradix Technologies
 
Build your next Gen AI Breakthrough - April 2024
Build your next Gen AI Breakthrough - April 2024Build your next Gen AI Breakthrough - April 2024
Build your next Gen AI Breakthrough - April 2024Neo4j
 
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmaticsKotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmaticscarlostorres15106
 
Pigging Solutions in Pet Food Manufacturing
Pigging Solutions in Pet Food ManufacturingPigging Solutions in Pet Food Manufacturing
Pigging Solutions in Pet Food ManufacturingPigging Solutions
 
Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!Commit University
 
Science&tech:THE INFORMATION AGE STS.pdf
Science&tech:THE INFORMATION AGE STS.pdfScience&tech:THE INFORMATION AGE STS.pdf
Science&tech:THE INFORMATION AGE STS.pdfjimielynbastida
 
New from BookNet Canada for 2024: BNC BiblioShare - Tech Forum 2024
New from BookNet Canada for 2024: BNC BiblioShare - Tech Forum 2024New from BookNet Canada for 2024: BNC BiblioShare - Tech Forum 2024
New from BookNet Canada for 2024: BNC BiblioShare - Tech Forum 2024BookNet Canada
 
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...shyamraj55
 
Gen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdfGen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdfAddepto
 
Beyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
Beyond Boundaries: Leveraging No-Code Solutions for Industry InnovationBeyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
Beyond Boundaries: Leveraging No-Code Solutions for Industry InnovationSafe Software
 

Recently uploaded (20)

Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
 
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
 
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptxE-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
 
WordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your BrandWordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your Brand
 
Dev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebDev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio Web
 
Vulnerability_Management_GRC_by Sohang Sengupta.pptx
Vulnerability_Management_GRC_by Sohang Sengupta.pptxVulnerability_Management_GRC_by Sohang Sengupta.pptx
Vulnerability_Management_GRC_by Sohang Sengupta.pptx
 
Key Features Of Token Development (1).pptx
Key  Features Of Token  Development (1).pptxKey  Features Of Token  Development (1).pptx
Key Features Of Token Development (1).pptx
 
Streamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project SetupStreamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project Setup
 
Connect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck PresentationConnect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck Presentation
 
Benefits Of Flutter Compared To Other Frameworks
Benefits Of Flutter Compared To Other FrameworksBenefits Of Flutter Compared To Other Frameworks
Benefits Of Flutter Compared To Other Frameworks
 
Build your next Gen AI Breakthrough - April 2024
Build your next Gen AI Breakthrough - April 2024Build your next Gen AI Breakthrough - April 2024
Build your next Gen AI Breakthrough - April 2024
 
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmaticsKotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
 
Pigging Solutions in Pet Food Manufacturing
Pigging Solutions in Pet Food ManufacturingPigging Solutions in Pet Food Manufacturing
Pigging Solutions in Pet Food Manufacturing
 
Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!
 
Science&tech:THE INFORMATION AGE STS.pdf
Science&tech:THE INFORMATION AGE STS.pdfScience&tech:THE INFORMATION AGE STS.pdf
Science&tech:THE INFORMATION AGE STS.pdf
 
New from BookNet Canada for 2024: BNC BiblioShare - Tech Forum 2024
New from BookNet Canada for 2024: BNC BiblioShare - Tech Forum 2024New from BookNet Canada for 2024: BNC BiblioShare - Tech Forum 2024
New from BookNet Canada for 2024: BNC BiblioShare - Tech Forum 2024
 
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
 
Gen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdfGen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdf
 
Hot Sexy call girls in Panjabi Bagh 🔝 9953056974 🔝 Delhi escort Service
Hot Sexy call girls in Panjabi Bagh 🔝 9953056974 🔝 Delhi escort ServiceHot Sexy call girls in Panjabi Bagh 🔝 9953056974 🔝 Delhi escort Service
Hot Sexy call girls in Panjabi Bagh 🔝 9953056974 🔝 Delhi escort Service
 
Beyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
Beyond Boundaries: Leveraging No-Code Solutions for Industry InnovationBeyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
Beyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
 

การสุ่มตัวอย่างในงานวิจัยสาธารณสุข

  • 2.
  • 3. 3.
  • 4. INTERPETAT EMPIRICAL CONCEPTUAL PHASE IVE PHASE PHASE H DESIGN RESEARC Research questions
  • 5.
  • 6.
  • 8. 1. (Representativeness) 2. (Adequate size)
  • 11. 1. (Non- probability sampling) 1.1 Accidental sampling Quota sampling Purposive sampling Convenience sampling
  • 13. Random Sampling (Error) 100% (Sampling error)
  • 14. (Probability sampling) Probability Sampling – (bias) – (Sampling error)
  • 15. SRS (Simple random sampling, SRS) ” (Sampling frame) EXAMPLE
  • 16. (Sampling frame) No Name Address 1 …….. 2 …….. 3 …….. 4 …….. 5 …….. 6 …….. 7 …….. 8 …….. 9 …….. 10 …….. A
  • 17. A 1) - - )
  • 18. (Systematic random sampling) n N (Sampling interval); k = N/n 1 k
  • 19. k = 50/10 , 7, 12, 17, 22, 27, 32, 37, 42, 47
  • 20. (Stratified sampling) EX “Child development study in Thailand”
  • 21. O Curative units A Promotion/Prevention X Rehabitative units units
  • 24. EX: • • 4- 5 ?
  • 27. 85 85 - ( .) 112 48 64 112 48 64 112 48 64 112 48 64 533 277 256
  • 28. - - - - - - ภค า รวม ใ เข เท บ ล น ต ศา นก ต ศา อ เข เท บ ล กรุงเท มห ร พ านค 1,530 1,530 - กล (ย กท าง กเว้น ม.) 2,016 864 1,152 เหนือ 2,016 864 1,152 ต อ งเห ะวันอ กเฉีย นือ 2,016 864 1,152 ใต้ 2,016 864 1,152 รวม 9,594 4,986 4,608
  • 29. When choosing a sample size, we must consider the following issues: • Objectives: What population parameters we want to estimate/test hypothesis • Sampling/research design is selected • Degree of accuracy required for the study • Spread/variation (variability) of the population • Response rate, practicality: how hard is it to collect data • Time and money available
  • 30. 1) Sample size for Simple Random Sampling To estimate mean 2 2 Z N n = 2 2 2 Z ( N 1) E Z2 2 n E2
  • 31. Sample size for Simple Random Sampling To estimate proportion 2 Z NP (1 P ) n = 2 2 Z P (1 P ) ( N 1) E n 2P 1 P Z ( ) n E 2
  • 32. 1,628 (Pilot survey) z 2 NP (1 P) 5% z 2 P (1 P ) NE 2 % n = )2 (1.645 (1,6280.2)( .8) )( 0 (1.645 (0.2)( .8) (1,6280.052 )2 0 )( ) n = = 156.53 157 5% 90%
  • 33. ? z 2 P (1 P ) 95% E 2 n = 2(P 1) 1 (1.96 )( ) P 2 2 P = ½=0 (0.052 ) n= = 384.16 385
  • 34. 1.2
  • 35. Equal sample L L N2S2 hh n = h1 L N2E2 NhS2 h1 h nh n L
  • 36. Proportional Allocation L N NhS2 h h1 n = L N2E2 NhS2 h h1 Nh nh L Nh h1
  • 37. 2) Sample size determination for hypothesis testing 2.1 Sample size determination for the test of one proportion
  • 38. Example In a particular province the proportion of pregnant women provided with prenatal care in the first trimester of pregnancy is estimated to be 40% by the provincial department of health. Health officials in another province are interested in comparing their success at providing prenatal care with these figures. How many women should be sampled to test the hypothesis that the coverage rate in the second province is % against the alternative that it is not %? The investigators wish to detect a difference of % with the power of the test equal at % and at
  • 39. P : coverage rate Ho: P = . Ha: P . ( . or MINITAB can be used to assist in this sample size determination by selecting Stat > Power and sample size > proportion.
  • 40.
  • 41. If alternative values of p is equal to .45, a sample size of 1022 would be needed. If alternative values of p is equal to . , a sample size of would be needed. We choose the large sample size, thus a sample size of 1022 is needed for the study.
  • 42. 2.2 Sample size determination for the test of two proportions Two-sided test (Z 2pq Z p2q2 p1q1)2 n = 2 (p2 p1)2
  • 43. Example 5 It is believed that the proportion of patients who develop complications after undergoing one type of surgery is % while the proportion of patients who develop complications after a second type of surgery is %. How large should the sample size be in each of the two groups of patients if an investigator wishes to detect, with a power of %, whether the second procedure has a complication rate significantly higher than the first at the % level of significance?
  • 44. Use MINITAB, click Stat > Power and sample size > proportion. You would complete the dialog box. You want to test one-sided test, click on the options button and choose less than
  • 45.
  • 46. Power and Sample Size Test for Two Proportions Testing proportion = proportion (versus <) Calculating power for proportion Alpha = Sample Target Actual Proportion Size Power Power A sample size of would be needed in each group.
  • 47. 2.3 Sample size determination for the tes of one mean Two-sided test 2 2 (Z Z ) n 2 ( 0 1 )2
  • 48. Example Consider the cholesterol study. Suppose that the null mean is mg% /ml, the alternative mean is mg%/ml, the standard deviation is , and we wish to conduct a significance test for one-sided test at the % level with a power of %. How large should the sample size be?
  • 49. MINITAB> click Stat > Power and sample size > sample Z. You want to test one-sided test, click on the options button and choose greater than
  • 50.
  • 51. -Sample Z Test Testing mean = null (versus > nul Calculating power for mean = null Alpha = . Sigma = Sample Target Actual Difference Size Power Power Thus, 96 people are needed. To achieve a power of 90% using a 5% significance level
  • 52. 2.4 Sample size determination for the test of two means Two-sided test (Z Z )2( 1 2 2)2 2 2 n = ( 2 1)2
  • 53. Example Consider the blood pressure study for drug A users and non-drug A users as a pilot study conducted to obtain parameter estimates to plan for a larger study. We wish to test the hypothesis : = versus : . Determine the appropriate sample size for the large study using a two–sided test with a significance level of . and a power of In the pilot study, we obtained = . , S = . = . ,S
  • 54. In the pilot study, we obtained = . ,S = . = . ,S n=( - We would require a sample size of 152 people in each group