SlideShare uma empresa Scribd logo
1 de 46
Baixar para ler offline
Sampling
 Design
D.A. Asir John Samuel, BSc (Psy),
MPT (Neuro Paed), MAc, DYScEd,
           C/BLS, FAGE
Basic definitions
• Population
- Collection of all the units that are of interest
  to the investigator
• Sample
- Representative part of population
• Sampling
- Technique of selecting a representative group
  from a population
                 Dr. Asir John Samuel (PT), Lecturer, ACP   2
Why ?

• Only feasible method for collecting information

• Reduces demands on resources (time, finance,.)

• Results obtained more quickly

• Better accuracy of collected data

• Ethically acceptable

                  Dr. Asir John Samuel (PT), Lecturer, ACP   3
Steps in sampling design
                  Target
                population


                  Study
                population



                    Sample



                      Study
               participation
      Dr. Asir John Samuel (PT), Lecturer, ACP   4
Characteristic of good sample design
• True representation of population

• May result in small sampling error

• Each member in population should get an
  opportunity of being selected

• Systematic bias can be controlled in a better way

• Results should be capable of being extrapolated
                  Dr. Asir John Samuel (PT), Lecturer, ACP   5
Types of sample design

• Probability/Random sampling
- Selection of subjects are according to any
 predicted chance of probability
• Non-probability/non-random sampling
- Does not depend on any chance of predecided
 probability
               Dr. Asir John Samuel (PT), Lecturer, ACP   6
Types of sample design

                                                             Sample
                                                             design


                      Random                                                                Non-random
                      sampling                                                               sampling


Simple   Stratified   Systematic         Cluster          Multistage          convenience     Quota      Judgment




                                   Dr. Asir John Samuel (PT), Lecturer, ACP                                7
Simple random sampling
• Equal and independent chance or probability
  of drawing each unit

• Take sampling population

• Need listing of all sampling units (sampling
  frame)

• Number all units

• Randomly draw units
                Dr. Asir John Samuel (PT), Lecturer, ACP   8
How to ensure randomness?
• Lottery method

• Table of random numbers

- e.g. Tippett’s series

- Fisher and Yates series

- Kendall and Smith series

- Rand corporation series
                  Dr. Asir John Samuel (PT), Lecturer, ACP   9
SRS - Merits

• No personal bias

• Easy to assess the accuracy




                Dr. Asir John Samuel (PT), Lecturer, ACP   10
SRS - Demerits

• Need a complete catalogue of universe

• Large size sample

• Widely dispersed




                Dr. Asir John Samuel (PT), Lecturer, ACP   11
Stratified Random Sampling

• Used for heterogeneous population

• Population is divided into homogeneous
 groups (strata), according to a characteristic of
 interest (e.g. sex, religion, location)

• Then a simple random sample is selected from
 each stratum
                 Dr. Asir John Samuel (PT), Lecturer, ACP   12
SRs - Merits

• More representative

• Greater accuracy

• Can   acquire        information                           about   whole
 population and individual strata



                  Dr. Asir John Samuel (PT), Lecturer, ACP               13
SRs - Demerits

• Careful stratification

• Random selection in each stratum

• Time consuming




                  Dr. Asir John Samuel (PT), Lecturer, ACP   14
Systematic Sampling

• Sampling units are selected in a systematic
 way, that is, every Kth unit in the population is
 selected
• First divide the population size by the,
 required sample size (sampling fraction). Let
 the sampling fraction be K

                Dr. Asir John Samuel (PT), Lecturer, ACP   15
Systematic Sampling

• Select a unit at random from the first K units
  and thereafter every Kth unit is selected

• If, N=1200

• And n=60

• Then, SF=20

                 Dr. Asir John Samuel (PT), Lecturer, ACP   16
SS - Merits

• Simple and convenient

• Less time and work




               Dr. Asir John Samuel (PT), Lecturer, ACP   17
SS - Demerits

• Need complete list of units

• Periodicity

• Less representation




                 Dr. Asir John Samuel (PT), Lecturer, ACP   18
Cluster Sampling

• The sampling units are groups or clusters

• The population is divided into clusters, and a
  sample of clusters are selected randomly

• All the units in the selected clusters are then
  examined or studied


                 Dr. Asir John Samuel (PT), Lecturer, ACP   19
Cluster Sampling

• It is always assumed that the individual items
  within each cluster are representation of
  population

• E.g. District, wards, schools, industries




                  Dr. Asir John Samuel (PT), Lecturer, ACP   20
CS - Merits

• Saving of travelling time and consequent
 reduction in cost

• Cuts down on the cost of preparing the
 sampling frame




                Dr. Asir John Samuel (PT), Lecturer, ACP   21
CS - Demerits

• Units close to each other may be very similar
  and so, less likely to represent the whole
  population

• Larger sampling error than simple random
  sampling


                Dr. Asir John Samuel (PT), Lecturer, ACP   22
Multistage Sampling
• Selection is done in stages until final sampling
  units are arrived

• At first stage, Random sampling of large sized
  sampling units are selected, from the selected
  1st stage sampling units another sampling
  units of smaller sampling units are selected,
  randomly       Dr. Asir John Samuel (PT), Lecturer, ACP   23
Multistage Sampling

• Continue until the final sampling units are
  selected

• E.g. Few states – District – Taulk




                  Dr. Asir John Samuel (PT), Lecturer, ACP   24
MS - Merits

• Cut down the cost of preparing the sampling
 frame




               Dr. Asir John Samuel (PT), Lecturer, ACP   25
MS - Demerits

• Sampling error is increased compared to
 simple random sampling




              Dr. Asir John Samuel (PT), Lecturer, ACP   26
Quota Sampling

• Interviewers are requested to find cases with

  particular types of people to interview




                 Dr. Asir John Samuel (PT), Lecturer, ACP   27
Judgment (Purposive Sampling)

• Researcher attempts to obtain sample that
 appear to be representative of the population
 selected by the researcher subjectively




                Dr. Asir John Samuel (PT), Lecturer, ACP   28
Convenience Sampling

• Sampling comprises subject who are simply
  avail in a convenient way to the researcher

• No randomness and likelihood of bias is high




                 Dr. Asir John Samuel (PT), Lecturer, ACP   29
Snowball Sampling

• Investigators start with a few subjects and
 then recruit more via word of mouth from the
 original participants




                Dr. Asir John Samuel (PT), Lecturer, ACP   30
Merits

• Easy

• Low cost

• Limited time

• Total list population



                 Dr. Asir John Samuel (PT), Lecturer, ACP   31
Demerits

• Selection bias

• Sample is not representation of population

• doesn’t allow generalization




                   Dr. Asir John Samuel (PT), Lecturer, ACP   32
Sample Size
Determination
p-value

• Probability of getting a minimal difference of
  what has observed is due to chance

• Probability that the difference of at least as
  large as those found in the data would have
  occurred by chance


                Dr. Asir John Samuel (PT), Lecturer, ACP   34
Hypothesis
• Alternate hypothesis (HA)

- Statement predict that a difference or
  relationship b/w groups will be demonstrated

• Null hypothesis (H0)

- Researcher anticipate “no difference” or “no
  relationship”

                  Dr. Asir John Samuel (PT), Lecturer, ACP   35
Decision for 5% LOS

• If p-value <0.05, then data is against null
 hypothesis

• If p-value ≥0.05, then data favours null
 hypothesis



               Dr. Asir John Samuel (PT), Lecturer, ACP   36
Type I & II errors
           Possible states of Null Hypothesis
 Possible                  True        False
actions on  Accept       Correct      Type II
   Null                  Action        error
Hypothesis  Reject        Type I     Correct
                          error       Action
           Prob (Type I error) – α (LoS)
           Prob (Type II error) – β
           1-β – power of test
                Dr. Asir John Samuel (PT), Lecturer, ACP   37
Z values


Z 0.05 – 1.96 – 95%
Z 0.10 – 1.282 – 90%
Z 0.20 – 0.84 – 80%


           Dr. Asir John Samuel (PT), Lecturer, ACP   38
Comparison of 2 means

           n= 2 [(Zα+Zβ)s/d]²

Zα – LoS
Zβ – power of study
s – pooled SD of the two sample
d – clinically significant difference


                  Dr. Asir John Samuel (PT), Lecturer, ACP   39
Eg. for Comparison of 2 means
• A RCT to study the effect of BP reduction. One
  group received a control diet and other-test
  diet. What would be the sample size in order
  to provide the study with power of 90% to
  detect a difference in sys. BP of 2 mm Hg b/w
  two groups at 5% LoS? The SD of sys. BP is
  observed to be 6 mmHg.



                Dr. Asir John Samuel (PT), Lecturer, ACP   40
Estimating proportion

          n = Z α² P (1-P) / d²

P – proportion of event in population
d – acceptable margin of error in estimating the
true population proportion




                 Dr. Asir John Samuel (PT), Lecturer, ACP   41
Eg. Estimating proportion
• To determine the prevalence of navicular drop
  in ACL injured population by anticipating of
  15% with acceptable margin of error is 3%

= (1.96)²(0.15)(0.85) / (0.03)²

= 544.2


                 Dr. Asir John Samuel (PT), Lecturer, ACP   42
Estimating mean

              n = (Zα σ / d)²

σ – anticipated SD of population
d – acceptable margin of error in estimating true
population mean




                 Dr. Asir John Samuel (PT), Lecturer, ACP   43
Eg. Estimating mean
• To determine the mean no. of days to
  ambulate pt undergoing stroke rehabilation
  among stroke pts. Where anticipated SD of
  days are 60 and acceptable margin of error is
  20 days

n = (1.96 x 60/20)²
n = (5.88)² = 34.6

                 Dr. Asir John Samuel (PT), Lecturer, ACP   44
Comparison of 2 proportions

 n = (Zα √2PQ + Zβ√P1Q1+P2Q2)²/(P1-P2)²

P = P1+P2/2   Q = 1-P




               Dr. Asir John Samuel (PT), Lecturer, ACP   45
Eg. Comparison of 2 proportions
• To see whether there is any sig. difference in
  percentage of strength increase after 4 wks of
  intervention b/w a new technique and
  standard one

• Standard one – 70% (P1)
• New technique – 75% (P2)


                Dr. Asir John Samuel (PT), Lecturer, ACP   46

Mais conteúdo relacionado

Mais procurados

Sampling Design
Sampling DesignSampling Design
Sampling DesignJale Nonan
 
Sampling techniques
Sampling techniquesSampling techniques
Sampling techniquesBharat Paul
 
Sampling, Types of Techniques & Simple Radom sampling
Sampling, Types of Techniques & Simple Radom samplingSampling, Types of Techniques & Simple Radom sampling
Sampling, Types of Techniques & Simple Radom samplingCentral University of Haryana
 
Sampling methods and sample size
Sampling methods and sample size  Sampling methods and sample size
Sampling methods and sample size mdanaee
 
sampling techniques used in research
sampling techniques used in research sampling techniques used in research
sampling techniques used in research kiran paul
 
1.introduction to research methodology
1.introduction to research methodology1.introduction to research methodology
1.introduction to research methodologyAsir John Samuel
 
sampling error.pptx
sampling error.pptxsampling error.pptx
sampling error.pptxtesfkeb
 
Scales of Measurement
Scales of MeasurementScales of Measurement
Scales of Measurementloranel
 
processng and analysis of data
 processng and analysis of data processng and analysis of data
processng and analysis of dataAruna Poddar
 
Variables And Measurement Scales
Variables And Measurement ScalesVariables And Measurement Scales
Variables And Measurement Scalesguesta861fa
 
probability and non-probability samplings
probability and non-probability samplingsprobability and non-probability samplings
probability and non-probability samplingsn1a2g3a4j5a6i7
 

Mais procurados (20)

Sampling Design
Sampling DesignSampling Design
Sampling Design
 
3.research design
3.research design3.research design
3.research design
 
Sampling techniques
Sampling techniquesSampling techniques
Sampling techniques
 
Research Design
Research Design Research Design
Research Design
 
Sampling, Types of Techniques & Simple Radom sampling
Sampling, Types of Techniques & Simple Radom samplingSampling, Types of Techniques & Simple Radom sampling
Sampling, Types of Techniques & Simple Radom sampling
 
Sampling methods and sample size
Sampling methods and sample size  Sampling methods and sample size
Sampling methods and sample size
 
sampling techniques used in research
sampling techniques used in research sampling techniques used in research
sampling techniques used in research
 
1.introduction to research methodology
1.introduction to research methodology1.introduction to research methodology
1.introduction to research methodology
 
sampling error.pptx
sampling error.pptxsampling error.pptx
sampling error.pptx
 
Sampling design ppt
Sampling design pptSampling design ppt
Sampling design ppt
 
sampling ppt
sampling pptsampling ppt
sampling ppt
 
Scales of Measurement
Scales of MeasurementScales of Measurement
Scales of Measurement
 
processng and analysis of data
 processng and analysis of data processng and analysis of data
processng and analysis of data
 
Sampling and its types
Sampling and its typesSampling and its types
Sampling and its types
 
SAMPLING
SAMPLINGSAMPLING
SAMPLING
 
Population and Sample
Population and SamplePopulation and Sample
Population and Sample
 
Variables And Measurement Scales
Variables And Measurement ScalesVariables And Measurement Scales
Variables And Measurement Scales
 
Data collection
Data collectionData collection
Data collection
 
probability and non-probability samplings
probability and non-probability samplingsprobability and non-probability samplings
probability and non-probability samplings
 
Validity & reliability
Validity & reliabilityValidity & reliability
Validity & reliability
 

Destaque

SAMPLING DESIGN AND STEPS IN SAMPLE DESIGN
SAMPLING DESIGN AND STEPS IN SAMPLE DESIGNSAMPLING DESIGN AND STEPS IN SAMPLE DESIGN
SAMPLING DESIGN AND STEPS IN SAMPLE DESIGNpra098
 
Sampling Methods in Qualitative and Quantitative Research
Sampling Methods in Qualitative and Quantitative ResearchSampling Methods in Qualitative and Quantitative Research
Sampling Methods in Qualitative and Quantitative ResearchSam Ladner
 
Sampling technique for 2 nd yr pbbsc nsg
Sampling technique for 2 nd yr pbbsc nsgSampling technique for 2 nd yr pbbsc nsg
Sampling technique for 2 nd yr pbbsc nsgsindhujojo
 
Sampling design
Sampling designSampling design
Sampling designBalaji P
 
Sampling design
Sampling designSampling design
Sampling designNijaz N
 
Sampling techniques market research
Sampling techniques market researchSampling techniques market research
Sampling techniques market researchKrishna Ramakrishnan
 
Sampling and Sample Types
Sampling  and Sample TypesSampling  and Sample Types
Sampling and Sample TypesDr. Sunil Kumar
 
Research methodology
Research methodology Research methodology
Research methodology Balaji P
 
Chapter 11 Marketing Reserach , Malhotra
Chapter 11 Marketing Reserach , MalhotraChapter 11 Marketing Reserach , Malhotra
Chapter 11 Marketing Reserach , MalhotraAADITYA TANTIA
 
census, sampling survey, sampling design and types of sample design
census, sampling survey, sampling design and types of sample designcensus, sampling survey, sampling design and types of sample design
census, sampling survey, sampling design and types of sample designParvej Ahmed Porag
 
6.method of data collection
6.method of data collection6.method of data collection
6.method of data collectionAsir John Samuel
 

Destaque (20)

SAMPLING DESIGN AND STEPS IN SAMPLE DESIGN
SAMPLING DESIGN AND STEPS IN SAMPLE DESIGNSAMPLING DESIGN AND STEPS IN SAMPLE DESIGN
SAMPLING DESIGN AND STEPS IN SAMPLE DESIGN
 
Sampling designs
Sampling designsSampling designs
Sampling designs
 
Sampling Methods in Qualitative and Quantitative Research
Sampling Methods in Qualitative and Quantitative ResearchSampling Methods in Qualitative and Quantitative Research
Sampling Methods in Qualitative and Quantitative Research
 
Sampling technique for 2 nd yr pbbsc nsg
Sampling technique for 2 nd yr pbbsc nsgSampling technique for 2 nd yr pbbsc nsg
Sampling technique for 2 nd yr pbbsc nsg
 
Sampling design
Sampling designSampling design
Sampling design
 
Sampling design
Sampling designSampling design
Sampling design
 
Sampling design
Sampling designSampling design
Sampling design
 
Sampling techniques market research
Sampling techniques market researchSampling techniques market research
Sampling techniques market research
 
Sampling and Sample Types
Sampling  and Sample TypesSampling  and Sample Types
Sampling and Sample Types
 
Sampling in Market Research
Sampling in Market ResearchSampling in Market Research
Sampling in Market Research
 
( research mythology)
( research  mythology)( research  mythology)
( research mythology)
 
C4 location
C4 locationC4 location
C4 location
 
Research methology
Research methologyResearch methology
Research methology
 
Research methodology
Research methodology Research methodology
Research methodology
 
8.processing
8.processing8.processing
8.processing
 
Chapter 11 Marketing Reserach , Malhotra
Chapter 11 Marketing Reserach , MalhotraChapter 11 Marketing Reserach , Malhotra
Chapter 11 Marketing Reserach , Malhotra
 
census, sampling survey, sampling design and types of sample design
census, sampling survey, sampling design and types of sample designcensus, sampling survey, sampling design and types of sample design
census, sampling survey, sampling design and types of sample design
 
2.research problem
2.research problem2.research problem
2.research problem
 
9.testing of hypothesis
9.testing of hypothesis9.testing of hypothesis
9.testing of hypothesis
 
6.method of data collection
6.method of data collection6.method of data collection
6.method of data collection
 

Semelhante a 4.sampling design

Biostatistics in Clinical Research
Biostatistics in Clinical ResearchBiostatistics in Clinical Research
Biostatistics in Clinical ResearchAbhaya Indrayan
 
Sampling designs in operational health research
Sampling designs in operational health researchSampling designs in operational health research
Sampling designs in operational health researchirfan ali
 
Designs and sample size in medical resarch
Designs and sample size in medical resarchDesigns and sample size in medical resarch
Designs and sample size in medical resarchAbhaya Indrayan
 
Sampling Techniques.pptx
Sampling Techniques.pptxSampling Techniques.pptx
Sampling Techniques.pptxMostaque Ahmed
 
Sampling Techniques, Scaling Techniques and Questionnaire Frame
Sampling Techniques, Scaling Techniques and Questionnaire FrameSampling Techniques, Scaling Techniques and Questionnaire Frame
Sampling Techniques, Scaling Techniques and Questionnaire FrameSonnappan Sridhar
 
Sampling Techniques.pptx
Sampling Techniques.pptxSampling Techniques.pptx
Sampling Techniques.pptxAB Rajar
 
Research method ch06 sampling
Research method ch06 samplingResearch method ch06 sampling
Research method ch06 samplingnaranbatn
 
RESEARCH DESIGNS AND METHODS_TORASIF by Stephen Opoku.pptx
RESEARCH DESIGNS  AND METHODS_TORASIF by Stephen Opoku.pptxRESEARCH DESIGNS  AND METHODS_TORASIF by Stephen Opoku.pptx
RESEARCH DESIGNS AND METHODS_TORASIF by Stephen Opoku.pptxTORASIF
 
z _Sample selection.pptx
z _Sample selection.pptxz _Sample selection.pptx
z _Sample selection.pptxaamnaemir
 
Chapter_2_Sampling.pptx
Chapter_2_Sampling.pptxChapter_2_Sampling.pptx
Chapter_2_Sampling.pptxSubodhPaudel6
 

Semelhante a 4.sampling design (20)

Biostatistics in Clinical Research
Biostatistics in Clinical ResearchBiostatistics in Clinical Research
Biostatistics in Clinical Research
 
5.measurement
5.measurement5.measurement
5.measurement
 
Sampling designs in operational health research
Sampling designs in operational health researchSampling designs in operational health research
Sampling designs in operational health research
 
Designs and sample size in medical resarch
Designs and sample size in medical resarchDesigns and sample size in medical resarch
Designs and sample size in medical resarch
 
Sampling Techniques.pptx
Sampling Techniques.pptxSampling Techniques.pptx
Sampling Techniques.pptx
 
Sampling Techniques, Scaling Techniques and Questionnaire Frame
Sampling Techniques, Scaling Techniques and Questionnaire FrameSampling Techniques, Scaling Techniques and Questionnaire Frame
Sampling Techniques, Scaling Techniques and Questionnaire Frame
 
Sampling Design
Sampling DesignSampling Design
Sampling Design
 
Survey
SurveySurvey
Survey
 
Sampling Techniques.pptx
Sampling Techniques.pptxSampling Techniques.pptx
Sampling Techniques.pptx
 
Sampling technique.pptx
Sampling technique.pptxSampling technique.pptx
Sampling technique.pptx
 
Sampling: An Introduction
Sampling: An IntroductionSampling: An Introduction
Sampling: An Introduction
 
Research method ch06 sampling
Research method ch06 samplingResearch method ch06 sampling
Research method ch06 sampling
 
RESEARCH DESIGNS AND METHODS_TORASIF by Stephen Opoku.pptx
RESEARCH DESIGNS  AND METHODS_TORASIF by Stephen Opoku.pptxRESEARCH DESIGNS  AND METHODS_TORASIF by Stephen Opoku.pptx
RESEARCH DESIGNS AND METHODS_TORASIF by Stephen Opoku.pptx
 
43911.ppt
43911.ppt43911.ppt
43911.ppt
 
Methods.pdf
Methods.pdfMethods.pdf
Methods.pdf
 
z _Sample selection.pptx
z _Sample selection.pptxz _Sample selection.pptx
z _Sample selection.pptx
 
Chapter_2_Sampling.pptx
Chapter_2_Sampling.pptxChapter_2_Sampling.pptx
Chapter_2_Sampling.pptx
 
Research Method
Research MethodResearch Method
Research Method
 
SAMPLING TECHNIQUES.pptx
SAMPLING TECHNIQUES.pptxSAMPLING TECHNIQUES.pptx
SAMPLING TECHNIQUES.pptx
 
File
FileFile
File
 

Mais de Asir John Samuel

Theory & Concept – Physical Assistive Modalities - Indications - Contraindica...
Theory & Concept – Physical Assistive Modalities - Indications - Contraindica...Theory & Concept – Physical Assistive Modalities - Indications - Contraindica...
Theory & Concept – Physical Assistive Modalities - Indications - Contraindica...Asir John Samuel
 
Temporomandibular Joint disorders
Temporomandibular Joint disordersTemporomandibular Joint disorders
Temporomandibular Joint disordersAsir John Samuel
 
Geriatrics – Handling old patients and their problems
Geriatrics – Handling old patients and their problemsGeriatrics – Handling old patients and their problems
Geriatrics – Handling old patients and their problemsAsir John Samuel
 
Quantative Research Methods
Quantative Research MethodsQuantative Research Methods
Quantative Research MethodsAsir John Samuel
 
Qualitative Research Methods
Qualitative Research MethodsQualitative Research Methods
Qualitative Research MethodsAsir John Samuel
 
Physiological anatomy of respiratory system
Physiological anatomy of respiratory systemPhysiological anatomy of respiratory system
Physiological anatomy of respiratory systemAsir John Samuel
 
Obstetric brachial plexus injury (OBPI)
Obstetric brachial plexus injury (OBPI)Obstetric brachial plexus injury (OBPI)
Obstetric brachial plexus injury (OBPI)Asir John Samuel
 
Treadmill training in children, by Dr. Asir John Samuel (PT)
Treadmill training in children, by Dr. Asir John Samuel (PT)Treadmill training in children, by Dr. Asir John Samuel (PT)
Treadmill training in children, by Dr. Asir John Samuel (PT)Asir John Samuel
 
Functional Electrical Stimulation in Spinal Cord Injury rehabilitation
Functional Electrical Stimulation in Spinal Cord Injury rehabilitationFunctional Electrical Stimulation in Spinal Cord Injury rehabilitation
Functional Electrical Stimulation in Spinal Cord Injury rehabilitationAsir John Samuel
 
Health fitness and promotion, based on ACSM
Health fitness and promotion, based on ACSMHealth fitness and promotion, based on ACSM
Health fitness and promotion, based on ACSMAsir John Samuel
 
Health fitness and promotion
Health fitness and promotionHealth fitness and promotion
Health fitness and promotionAsir John Samuel
 
10.computer technology in Research
10.computer technology in Research10.computer technology in Research
10.computer technology in ResearchAsir John Samuel
 

Mais de Asir John Samuel (19)

Theory & Concept – Physical Assistive Modalities - Indications - Contraindica...
Theory & Concept – Physical Assistive Modalities - Indications - Contraindica...Theory & Concept – Physical Assistive Modalities - Indications - Contraindica...
Theory & Concept – Physical Assistive Modalities - Indications - Contraindica...
 
Temporomandibular Joint disorders
Temporomandibular Joint disordersTemporomandibular Joint disorders
Temporomandibular Joint disorders
 
Geriatrics – Handling old patients and their problems
Geriatrics – Handling old patients and their problemsGeriatrics – Handling old patients and their problems
Geriatrics – Handling old patients and their problems
 
Post Polio syndrome
Post Polio syndromePost Polio syndrome
Post Polio syndrome
 
Cerebral Palsy
Cerebral PalsyCerebral Palsy
Cerebral Palsy
 
Quantative Research Methods
Quantative Research MethodsQuantative Research Methods
Quantative Research Methods
 
Qualitative Research Methods
Qualitative Research MethodsQualitative Research Methods
Qualitative Research Methods
 
Muscular dystrophy
Muscular dystrophyMuscular dystrophy
Muscular dystrophy
 
Hypoxia
HypoxiaHypoxia
Hypoxia
 
Neural regulation
Neural regulationNeural regulation
Neural regulation
 
Diffusion
DiffusionDiffusion
Diffusion
 
Physiological anatomy of respiratory system
Physiological anatomy of respiratory systemPhysiological anatomy of respiratory system
Physiological anatomy of respiratory system
 
Obstetric brachial plexus injury (OBPI)
Obstetric brachial plexus injury (OBPI)Obstetric brachial plexus injury (OBPI)
Obstetric brachial plexus injury (OBPI)
 
Treadmill training in children, by Dr. Asir John Samuel (PT)
Treadmill training in children, by Dr. Asir John Samuel (PT)Treadmill training in children, by Dr. Asir John Samuel (PT)
Treadmill training in children, by Dr. Asir John Samuel (PT)
 
Functional Electrical Stimulation in Spinal Cord Injury rehabilitation
Functional Electrical Stimulation in Spinal Cord Injury rehabilitationFunctional Electrical Stimulation in Spinal Cord Injury rehabilitation
Functional Electrical Stimulation in Spinal Cord Injury rehabilitation
 
Health fitness and promotion, based on ACSM
Health fitness and promotion, based on ACSMHealth fitness and promotion, based on ACSM
Health fitness and promotion, based on ACSM
 
Health fitness and promotion
Health fitness and promotionHealth fitness and promotion
Health fitness and promotion
 
10.computer technology in Research
10.computer technology in Research10.computer technology in Research
10.computer technology in Research
 
Motor cortex
Motor cortexMotor cortex
Motor cortex
 

Último

Explore the UiPath Community and ways you can benefit on your journey to auto...
Explore the UiPath Community and ways you can benefit on your journey to auto...Explore the UiPath Community and ways you can benefit on your journey to auto...
Explore the UiPath Community and ways you can benefit on your journey to auto...DianaGray10
 
Patch notes explaining DISARM Version 1.4 update
Patch notes explaining DISARM Version 1.4 updatePatch notes explaining DISARM Version 1.4 update
Patch notes explaining DISARM Version 1.4 updateadam112203
 
Graphene Quantum Dots-Based Composites for Biomedical Applications
Graphene Quantum Dots-Based Composites for  Biomedical ApplicationsGraphene Quantum Dots-Based Composites for  Biomedical Applications
Graphene Quantum Dots-Based Composites for Biomedical Applicationsnooralam814309
 
EMEA What is ThousandEyes? Webinar
EMEA What is ThousandEyes? WebinarEMEA What is ThousandEyes? Webinar
EMEA What is ThousandEyes? WebinarThousandEyes
 
Q4 2023 Quarterly Investor Presentation - FINAL - v1.pdf
Q4 2023 Quarterly Investor Presentation - FINAL - v1.pdfQ4 2023 Quarterly Investor Presentation - FINAL - v1.pdf
Q4 2023 Quarterly Investor Presentation - FINAL - v1.pdfTejal81
 
How to release an Open Source Dataweave Library
How to release an Open Source Dataweave LibraryHow to release an Open Source Dataweave Library
How to release an Open Source Dataweave Libraryshyamraj55
 
AI Workshops at Computers In Libraries 2024
AI Workshops at Computers In Libraries 2024AI Workshops at Computers In Libraries 2024
AI Workshops at Computers In Libraries 2024Brian Pichman
 
Technical SEO for Improved Accessibility WTS FEST
Technical SEO for Improved Accessibility  WTS FESTTechnical SEO for Improved Accessibility  WTS FEST
Technical SEO for Improved Accessibility WTS FESTBillieHyde
 
Trailblazer Community - Flows Workshop (Session 2)
Trailblazer Community - Flows Workshop (Session 2)Trailblazer Community - Flows Workshop (Session 2)
Trailblazer Community - Flows Workshop (Session 2)Muhammad Tiham Siddiqui
 
Scenario Library et REX Discover industry- and role- based scenarios
Scenario Library et REX Discover industry- and role- based scenariosScenario Library et REX Discover industry- and role- based scenarios
Scenario Library et REX Discover industry- and role- based scenariosErol GIRAUDY
 
2024.03.12 Cost drivers of cultivated meat production.pdf
2024.03.12 Cost drivers of cultivated meat production.pdf2024.03.12 Cost drivers of cultivated meat production.pdf
2024.03.12 Cost drivers of cultivated meat production.pdfThe Good Food Institute
 
The Importance of Indoor Air Quality (English)
The Importance of Indoor Air Quality (English)The Importance of Indoor Air Quality (English)
The Importance of Indoor Air Quality (English)IES VE
 
The New Cloud World Order Is FinOps (Slideshow)
The New Cloud World Order Is FinOps (Slideshow)The New Cloud World Order Is FinOps (Slideshow)
The New Cloud World Order Is FinOps (Slideshow)codyslingerland1
 
SIM INFORMATION SYSTEM: REVOLUTIONIZING DATA MANAGEMENT
SIM INFORMATION SYSTEM: REVOLUTIONIZING DATA MANAGEMENTSIM INFORMATION SYSTEM: REVOLUTIONIZING DATA MANAGEMENT
SIM INFORMATION SYSTEM: REVOLUTIONIZING DATA MANAGEMENTxtailishbaloch
 
Where developers are challenged, what developers want and where DevEx is going
Where developers are challenged, what developers want and where DevEx is goingWhere developers are challenged, what developers want and where DevEx is going
Where developers are challenged, what developers want and where DevEx is goingFrancesco Corti
 
Emil Eifrem at GraphSummit Copenhagen 2024 - The Art of the Possible.pptx
Emil Eifrem at GraphSummit Copenhagen 2024 - The Art of the Possible.pptxEmil Eifrem at GraphSummit Copenhagen 2024 - The Art of the Possible.pptx
Emil Eifrem at GraphSummit Copenhagen 2024 - The Art of the Possible.pptxNeo4j
 
Stobox 4: Revolutionizing Investment in Real-World Assets Through Tokenization
Stobox 4: Revolutionizing Investment in Real-World Assets Through TokenizationStobox 4: Revolutionizing Investment in Real-World Assets Through Tokenization
Stobox 4: Revolutionizing Investment in Real-World Assets Through TokenizationStobox
 
Extra-120324-Visite-Entreprise-icare.pdf
Extra-120324-Visite-Entreprise-icare.pdfExtra-120324-Visite-Entreprise-icare.pdf
Extra-120324-Visite-Entreprise-icare.pdfInfopole1
 
Key Trends Shaping the Future of Infrastructure.pdf
Key Trends Shaping the Future of Infrastructure.pdfKey Trends Shaping the Future of Infrastructure.pdf
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
 
Oracle Database 23c Security New Features.pptx
Oracle Database 23c Security New Features.pptxOracle Database 23c Security New Features.pptx
Oracle Database 23c Security New Features.pptxSatishbabu Gunukula
 

Último (20)

Explore the UiPath Community and ways you can benefit on your journey to auto...
Explore the UiPath Community and ways you can benefit on your journey to auto...Explore the UiPath Community and ways you can benefit on your journey to auto...
Explore the UiPath Community and ways you can benefit on your journey to auto...
 
Patch notes explaining DISARM Version 1.4 update
Patch notes explaining DISARM Version 1.4 updatePatch notes explaining DISARM Version 1.4 update
Patch notes explaining DISARM Version 1.4 update
 
Graphene Quantum Dots-Based Composites for Biomedical Applications
Graphene Quantum Dots-Based Composites for  Biomedical ApplicationsGraphene Quantum Dots-Based Composites for  Biomedical Applications
Graphene Quantum Dots-Based Composites for Biomedical Applications
 
EMEA What is ThousandEyes? Webinar
EMEA What is ThousandEyes? WebinarEMEA What is ThousandEyes? Webinar
EMEA What is ThousandEyes? Webinar
 
Q4 2023 Quarterly Investor Presentation - FINAL - v1.pdf
Q4 2023 Quarterly Investor Presentation - FINAL - v1.pdfQ4 2023 Quarterly Investor Presentation - FINAL - v1.pdf
Q4 2023 Quarterly Investor Presentation - FINAL - v1.pdf
 
How to release an Open Source Dataweave Library
How to release an Open Source Dataweave LibraryHow to release an Open Source Dataweave Library
How to release an Open Source Dataweave Library
 
AI Workshops at Computers In Libraries 2024
AI Workshops at Computers In Libraries 2024AI Workshops at Computers In Libraries 2024
AI Workshops at Computers In Libraries 2024
 
Technical SEO for Improved Accessibility WTS FEST
Technical SEO for Improved Accessibility  WTS FESTTechnical SEO for Improved Accessibility  WTS FEST
Technical SEO for Improved Accessibility WTS FEST
 
Trailblazer Community - Flows Workshop (Session 2)
Trailblazer Community - Flows Workshop (Session 2)Trailblazer Community - Flows Workshop (Session 2)
Trailblazer Community - Flows Workshop (Session 2)
 
Scenario Library et REX Discover industry- and role- based scenarios
Scenario Library et REX Discover industry- and role- based scenariosScenario Library et REX Discover industry- and role- based scenarios
Scenario Library et REX Discover industry- and role- based scenarios
 
2024.03.12 Cost drivers of cultivated meat production.pdf
2024.03.12 Cost drivers of cultivated meat production.pdf2024.03.12 Cost drivers of cultivated meat production.pdf
2024.03.12 Cost drivers of cultivated meat production.pdf
 
The Importance of Indoor Air Quality (English)
The Importance of Indoor Air Quality (English)The Importance of Indoor Air Quality (English)
The Importance of Indoor Air Quality (English)
 
The New Cloud World Order Is FinOps (Slideshow)
The New Cloud World Order Is FinOps (Slideshow)The New Cloud World Order Is FinOps (Slideshow)
The New Cloud World Order Is FinOps (Slideshow)
 
SIM INFORMATION SYSTEM: REVOLUTIONIZING DATA MANAGEMENT
SIM INFORMATION SYSTEM: REVOLUTIONIZING DATA MANAGEMENTSIM INFORMATION SYSTEM: REVOLUTIONIZING DATA MANAGEMENT
SIM INFORMATION SYSTEM: REVOLUTIONIZING DATA MANAGEMENT
 
Where developers are challenged, what developers want and where DevEx is going
Where developers are challenged, what developers want and where DevEx is goingWhere developers are challenged, what developers want and where DevEx is going
Where developers are challenged, what developers want and where DevEx is going
 
Emil Eifrem at GraphSummit Copenhagen 2024 - The Art of the Possible.pptx
Emil Eifrem at GraphSummit Copenhagen 2024 - The Art of the Possible.pptxEmil Eifrem at GraphSummit Copenhagen 2024 - The Art of the Possible.pptx
Emil Eifrem at GraphSummit Copenhagen 2024 - The Art of the Possible.pptx
 
Stobox 4: Revolutionizing Investment in Real-World Assets Through Tokenization
Stobox 4: Revolutionizing Investment in Real-World Assets Through TokenizationStobox 4: Revolutionizing Investment in Real-World Assets Through Tokenization
Stobox 4: Revolutionizing Investment in Real-World Assets Through Tokenization
 
Extra-120324-Visite-Entreprise-icare.pdf
Extra-120324-Visite-Entreprise-icare.pdfExtra-120324-Visite-Entreprise-icare.pdf
Extra-120324-Visite-Entreprise-icare.pdf
 
Key Trends Shaping the Future of Infrastructure.pdf
Key Trends Shaping the Future of Infrastructure.pdfKey Trends Shaping the Future of Infrastructure.pdf
Key Trends Shaping the Future of Infrastructure.pdf
 
Oracle Database 23c Security New Features.pptx
Oracle Database 23c Security New Features.pptxOracle Database 23c Security New Features.pptx
Oracle Database 23c Security New Features.pptx
 

4.sampling design

  • 1. Sampling Design D.A. Asir John Samuel, BSc (Psy), MPT (Neuro Paed), MAc, DYScEd, C/BLS, FAGE
  • 2. Basic definitions • Population - Collection of all the units that are of interest to the investigator • Sample - Representative part of population • Sampling - Technique of selecting a representative group from a population Dr. Asir John Samuel (PT), Lecturer, ACP 2
  • 3. Why ? • Only feasible method for collecting information • Reduces demands on resources (time, finance,.) • Results obtained more quickly • Better accuracy of collected data • Ethically acceptable Dr. Asir John Samuel (PT), Lecturer, ACP 3
  • 4. Steps in sampling design Target population Study population Sample Study participation Dr. Asir John Samuel (PT), Lecturer, ACP 4
  • 5. Characteristic of good sample design • True representation of population • May result in small sampling error • Each member in population should get an opportunity of being selected • Systematic bias can be controlled in a better way • Results should be capable of being extrapolated Dr. Asir John Samuel (PT), Lecturer, ACP 5
  • 6. Types of sample design • Probability/Random sampling - Selection of subjects are according to any predicted chance of probability • Non-probability/non-random sampling - Does not depend on any chance of predecided probability Dr. Asir John Samuel (PT), Lecturer, ACP 6
  • 7. Types of sample design Sample design Random Non-random sampling sampling Simple Stratified Systematic Cluster Multistage convenience Quota Judgment Dr. Asir John Samuel (PT), Lecturer, ACP 7
  • 8. Simple random sampling • Equal and independent chance or probability of drawing each unit • Take sampling population • Need listing of all sampling units (sampling frame) • Number all units • Randomly draw units Dr. Asir John Samuel (PT), Lecturer, ACP 8
  • 9. How to ensure randomness? • Lottery method • Table of random numbers - e.g. Tippett’s series - Fisher and Yates series - Kendall and Smith series - Rand corporation series Dr. Asir John Samuel (PT), Lecturer, ACP 9
  • 10. SRS - Merits • No personal bias • Easy to assess the accuracy Dr. Asir John Samuel (PT), Lecturer, ACP 10
  • 11. SRS - Demerits • Need a complete catalogue of universe • Large size sample • Widely dispersed Dr. Asir John Samuel (PT), Lecturer, ACP 11
  • 12. Stratified Random Sampling • Used for heterogeneous population • Population is divided into homogeneous groups (strata), according to a characteristic of interest (e.g. sex, religion, location) • Then a simple random sample is selected from each stratum Dr. Asir John Samuel (PT), Lecturer, ACP 12
  • 13. SRs - Merits • More representative • Greater accuracy • Can acquire information about whole population and individual strata Dr. Asir John Samuel (PT), Lecturer, ACP 13
  • 14. SRs - Demerits • Careful stratification • Random selection in each stratum • Time consuming Dr. Asir John Samuel (PT), Lecturer, ACP 14
  • 15. Systematic Sampling • Sampling units are selected in a systematic way, that is, every Kth unit in the population is selected • First divide the population size by the, required sample size (sampling fraction). Let the sampling fraction be K Dr. Asir John Samuel (PT), Lecturer, ACP 15
  • 16. Systematic Sampling • Select a unit at random from the first K units and thereafter every Kth unit is selected • If, N=1200 • And n=60 • Then, SF=20 Dr. Asir John Samuel (PT), Lecturer, ACP 16
  • 17. SS - Merits • Simple and convenient • Less time and work Dr. Asir John Samuel (PT), Lecturer, ACP 17
  • 18. SS - Demerits • Need complete list of units • Periodicity • Less representation Dr. Asir John Samuel (PT), Lecturer, ACP 18
  • 19. Cluster Sampling • The sampling units are groups or clusters • The population is divided into clusters, and a sample of clusters are selected randomly • All the units in the selected clusters are then examined or studied Dr. Asir John Samuel (PT), Lecturer, ACP 19
  • 20. Cluster Sampling • It is always assumed that the individual items within each cluster are representation of population • E.g. District, wards, schools, industries Dr. Asir John Samuel (PT), Lecturer, ACP 20
  • 21. CS - Merits • Saving of travelling time and consequent reduction in cost • Cuts down on the cost of preparing the sampling frame Dr. Asir John Samuel (PT), Lecturer, ACP 21
  • 22. CS - Demerits • Units close to each other may be very similar and so, less likely to represent the whole population • Larger sampling error than simple random sampling Dr. Asir John Samuel (PT), Lecturer, ACP 22
  • 23. Multistage Sampling • Selection is done in stages until final sampling units are arrived • At first stage, Random sampling of large sized sampling units are selected, from the selected 1st stage sampling units another sampling units of smaller sampling units are selected, randomly Dr. Asir John Samuel (PT), Lecturer, ACP 23
  • 24. Multistage Sampling • Continue until the final sampling units are selected • E.g. Few states – District – Taulk Dr. Asir John Samuel (PT), Lecturer, ACP 24
  • 25. MS - Merits • Cut down the cost of preparing the sampling frame Dr. Asir John Samuel (PT), Lecturer, ACP 25
  • 26. MS - Demerits • Sampling error is increased compared to simple random sampling Dr. Asir John Samuel (PT), Lecturer, ACP 26
  • 27. Quota Sampling • Interviewers are requested to find cases with particular types of people to interview Dr. Asir John Samuel (PT), Lecturer, ACP 27
  • 28. Judgment (Purposive Sampling) • Researcher attempts to obtain sample that appear to be representative of the population selected by the researcher subjectively Dr. Asir John Samuel (PT), Lecturer, ACP 28
  • 29. Convenience Sampling • Sampling comprises subject who are simply avail in a convenient way to the researcher • No randomness and likelihood of bias is high Dr. Asir John Samuel (PT), Lecturer, ACP 29
  • 30. Snowball Sampling • Investigators start with a few subjects and then recruit more via word of mouth from the original participants Dr. Asir John Samuel (PT), Lecturer, ACP 30
  • 31. Merits • Easy • Low cost • Limited time • Total list population Dr. Asir John Samuel (PT), Lecturer, ACP 31
  • 32. Demerits • Selection bias • Sample is not representation of population • doesn’t allow generalization Dr. Asir John Samuel (PT), Lecturer, ACP 32
  • 34. p-value • Probability of getting a minimal difference of what has observed is due to chance • Probability that the difference of at least as large as those found in the data would have occurred by chance Dr. Asir John Samuel (PT), Lecturer, ACP 34
  • 35. Hypothesis • Alternate hypothesis (HA) - Statement predict that a difference or relationship b/w groups will be demonstrated • Null hypothesis (H0) - Researcher anticipate “no difference” or “no relationship” Dr. Asir John Samuel (PT), Lecturer, ACP 35
  • 36. Decision for 5% LOS • If p-value <0.05, then data is against null hypothesis • If p-value ≥0.05, then data favours null hypothesis Dr. Asir John Samuel (PT), Lecturer, ACP 36
  • 37. Type I & II errors Possible states of Null Hypothesis Possible True False actions on Accept Correct Type II Null Action error Hypothesis Reject Type I Correct error Action Prob (Type I error) – α (LoS) Prob (Type II error) – β 1-β – power of test Dr. Asir John Samuel (PT), Lecturer, ACP 37
  • 38. Z values Z 0.05 – 1.96 – 95% Z 0.10 – 1.282 – 90% Z 0.20 – 0.84 – 80% Dr. Asir John Samuel (PT), Lecturer, ACP 38
  • 39. Comparison of 2 means n= 2 [(Zα+Zβ)s/d]² Zα – LoS Zβ – power of study s – pooled SD of the two sample d – clinically significant difference Dr. Asir John Samuel (PT), Lecturer, ACP 39
  • 40. Eg. for Comparison of 2 means • A RCT to study the effect of BP reduction. One group received a control diet and other-test diet. What would be the sample size in order to provide the study with power of 90% to detect a difference in sys. BP of 2 mm Hg b/w two groups at 5% LoS? The SD of sys. BP is observed to be 6 mmHg. Dr. Asir John Samuel (PT), Lecturer, ACP 40
  • 41. Estimating proportion n = Z α² P (1-P) / d² P – proportion of event in population d – acceptable margin of error in estimating the true population proportion Dr. Asir John Samuel (PT), Lecturer, ACP 41
  • 42. Eg. Estimating proportion • To determine the prevalence of navicular drop in ACL injured population by anticipating of 15% with acceptable margin of error is 3% = (1.96)²(0.15)(0.85) / (0.03)² = 544.2 Dr. Asir John Samuel (PT), Lecturer, ACP 42
  • 43. Estimating mean n = (Zα σ / d)² σ – anticipated SD of population d – acceptable margin of error in estimating true population mean Dr. Asir John Samuel (PT), Lecturer, ACP 43
  • 44. Eg. Estimating mean • To determine the mean no. of days to ambulate pt undergoing stroke rehabilation among stroke pts. Where anticipated SD of days are 60 and acceptable margin of error is 20 days n = (1.96 x 60/20)² n = (5.88)² = 34.6 Dr. Asir John Samuel (PT), Lecturer, ACP 44
  • 45. Comparison of 2 proportions n = (Zα √2PQ + Zβ√P1Q1+P2Q2)²/(P1-P2)² P = P1+P2/2 Q = 1-P Dr. Asir John Samuel (PT), Lecturer, ACP 45
  • 46. Eg. Comparison of 2 proportions • To see whether there is any sig. difference in percentage of strength increase after 4 wks of intervention b/w a new technique and standard one • Standard one – 70% (P1) • New technique – 75% (P2) Dr. Asir John Samuel (PT), Lecturer, ACP 46