1   1
SAMPLINGDesign and Procedures               PRAVIN DADMAL 2                             2               SHASHANK JAIN
What is sampling?       Sampling is the act, process, or technique of selecting asuitable sample, or a representative part...
Purpose of Sampling:To draw conclusions about populations from samples, we mustuse inferential statistics which enables us...
Population:-It is the aggregate of all the elements that share some common set ofcharacteristics and that comprise the uni...
Sample Vs. Census                                    Conditions Favoring the UseType of Study                       Sample...
Design sampling procedure:-1.Should a sample be taken?2. If so what process should be followed?3. What kind of sample shou...
The Sampling Design Process       Define the target Population      Determine the Sampling Frame       Select Sampling Tec...
9
STEP-1           TARGET POPULATIONThe target population is the collection of elementsor objects that possess the informati...
STEP-2                SAMPLING FRAME• Sampling frame: A representation of the element of the  target population. It consis...
STEP-3    SELECT A SAMPLING TECHQUINIQUE• Bayesian approach:  A selection method in which the element are selected  sequen...
STEP-4     DETERMINE THE SAMPLE SIZENumber of elements to be included in a study.                                   13
SAMPLE SIZEImportant qualitative factors in determining the sample size  Importance of the decision  Nature of the resea...
SAMPLE SIZES USED IN MARKETING            RESEARCH STUDIESType of Study                          Minimum Size Typical Rang...
STEP-5   EXECUTE THE SAMPLING PROCEDURE• It requires detailed specifications of how the sampling  design decisions with re...
Classification of Sampling Techniques                           Sampling Techniques                Nonprobability         ...
SAMPLING TECHNIQUES• Non probability sampling: Sampling techniques that  do not use chance selection procedures. Rather th...
• The difference between Non probability sampling  technique and probability sampling technique is that  Non probability s...
USE OF SAMPLING TECHNIQUESNon probability sampling technique: it is used inconcept test, packaged test, name test and copy...
CONVENIENCE SAMPLING           ( NON PROBABILITY SAMPLING TECHNIQUE)Convenience sampling attempts to obtain a sampleof con...
JUDGMENTAL SAMPLING            ( NON PROBABILITY SAMPLING TECHNIQUE)Judgmental sampling: It is a form ofconvenience sampli...
QUOTA SAMPLING            ( NON PROBABILITY SAMPLING TECHNIQUE)Quota sampling may be viewed as two-stagerestricted judgmen...
SNOWBALL SAMPLING              ( NON PROBABILITY SAMPLING TECHNIQUE)In snowball sampling, an initialgroup of respondents i...
SIMPLE RANDOM SAMPLING                  (PROBABILITY SAMPLING TECHNIQUE)• Each element in the population  has a known and ...
SYSTEMATIC SAMPLING                (PROBABILITY SAMPLING TECHNIQUE)• The sample is chosen by selecting a random  starting ...
Systematic sampling
STRATIFIED SAMPLING                  (PROBABILITY SAMPLING TECHNIQUE)• A two-step process in which  the population is part...
• Next, elements are selected from each stratum by a  random procedure, usually SRS.• A major objective of stratified samp...
CLUSTER SAMPLING                 (PROBABILITY SAMPLING TECHNIQUE)• The target population is first  divided into mutually e...
• Ideally, each cluster should be a small-scale  representation of the population.• In probability proportionate to size s...
TYPES OF CLUSTER SAMPLING                       Cluster SamplingOne-Stage                    Two-Stage                 Mul...
OTHER PROBABILITY SAMPLING                 TECHNIQUES• SEQUENTIAL SAMPLING: A probability sampling  technique in which the...
Non Probability Sampling Technique    Strengths                          WeaknessesConvenience   Least expensive, least   ...
Probability SamplingTechnique       Strengths                   WeaknessesSimple random   Easily understood,         Diffi...
THANK YOU            36
Upcoming SlideShare
Loading in …5
×

Sampling final

2,601 views

Published on

shashank

Published in: Education
0 Comments
15 Likes
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total views
2,601
On SlideShare
0
From Embeds
0
Number of Embeds
2
Actions
Shares
0
Downloads
0
Comments
0
Likes
15
Embeds 0
No embeds

No notes for slide
  • 2
  • Sampling final

    1. 1. 1 1
    2. 2. SAMPLINGDesign and Procedures PRAVIN DADMAL 2 2 SHASHANK JAIN
    3. 3. What is sampling? Sampling is the act, process, or technique of selecting asuitable sample, or a representative part of a population for thepurpose of determining parameters or characteristics of thewhole population. 3 3
    4. 4. Purpose of Sampling:To draw conclusions about populations from samples, we mustuse inferential statistics which enables us to determine apopulation`s characteristics by directly observing only aportion (or sample) of the population. 4 4
    5. 5. Population:-It is the aggregate of all the elements that share some common set ofcharacteristics and that comprise the universe for the purpose of themarketing research problem .Census:-A census involves a complete enumeration of the elements of apopulation.Sample:-A sample is a subgroup of the population selected for participation in thestudy. 5
    6. 6. Sample Vs. Census Conditions Favoring the UseType of Study Sample Census1. Budget Small Large2. Time available Short Long3. Population size Large Small4. Variance in the characteristic Small Large5. Cost of sampling errors Low High6. Cost of nonsampling errors High Low7. Nature of measurement Destructive Nondestructive8. Attention to individual cases Yes No 6
    7. 7. Design sampling procedure:-1.Should a sample be taken?2. If so what process should be followed?3. What kind of sample should be taken?4. How large should it be?5. What can be done to control and adjust for nonresponsiveerror? 7
    8. 8. The Sampling Design Process Define the target Population Determine the Sampling Frame Select Sampling Techniques Determine the Sample Size Execute the Sampling Process 8 8
    9. 9. 9
    10. 10. STEP-1 TARGET POPULATIONThe target population is the collection of elementsor objects that possess the information required bythe researcher and about which inferences are tobe made. The target population should be definedin terms of elements, sampling units, and time. 10 10
    11. 11. STEP-2 SAMPLING FRAME• Sampling frame: A representation of the element of the target population. It consist of a list or set of direction for identifying target population. 11 11
    12. 12. STEP-3 SELECT A SAMPLING TECHQUINIQUE• Bayesian approach: A selection method in which the element are selected sequentially. It is explicitly (clearly) incorporate prior information about population parameter as well as cost and probabilities associated with making a wrong decisions.• Sample with replacement: A sampling technique in which an element can be included in the sample more than once.• Sampling without replacement: A sampling technique in which an element can not be included In the sample more than once. 12 12
    13. 13. STEP-4 DETERMINE THE SAMPLE SIZENumber of elements to be included in a study. 13
    14. 14. SAMPLE SIZEImportant qualitative factors in determining the sample size  Importance of the decision  Nature of the research  Number of variables  Nature of the analysis  Sample sizes used in similar studies  Incidence rates  Completion rates  Resource constraints 14 14
    15. 15. SAMPLE SIZES USED IN MARKETING RESEARCH STUDIESType of Study Minimum Size Typical RangeProblem identification research 500 1,000-2,500(e.g. market potential)Problem-solving research (e.g. 200 300-500pricing)Product tests 200 300-500Test marketing studies 200 300-500TV, radio, or print advertising (per 150 200-300commercial or ad tested)Test-market audits 10 stores 10-20 storesFocus groups 2 groups 4-12 groups
    16. 16. STEP-5 EXECUTE THE SAMPLING PROCEDURE• It requires detailed specifications of how the sampling design decisions with respect to the population, sampling frame, sampling unit, sampling technique and sample size are to be implemented. 16
    17. 17. Classification of Sampling Techniques Sampling Techniques Nonprobability Probability Sampling Techniques Sampling TechniquesConvenience Judgmental Quota Snowball Expert Purposive Sampling Sampling Sampling Sampling sampling sampling Simple Random Systematic Stratified Cluster Other Sampling Sampling Sampling Sampling Sampling Techniques 17 17
    18. 18. SAMPLING TECHNIQUES• Non probability sampling: Sampling techniques that do not use chance selection procedures. Rather they rely on the personal judgment of the research.• Probability sampling: A sampling procedure in which each element of the population has a fixed probabilistic chance of being selected for the sample.
    19. 19. • The difference between Non probability sampling technique and probability sampling technique is that Non probability sampling does not involves random sampling and probability sampling does. 19
    20. 20. USE OF SAMPLING TECHNIQUESNon probability sampling technique: it is used inconcept test, packaged test, name test and copy testwhere projections to be populations are usually notneeded.Probability sampling technique: It is used when thereis a need for highly accurate estimates to market share orsales volume for entire market. It generally employtelephone interview , stratified and systematic samplingare combined with some form of random digit dialing toselect the response. 20
    21. 21. CONVENIENCE SAMPLING ( NON PROBABILITY SAMPLING TECHNIQUE)Convenience sampling attempts to obtain a sampleof convenient elements. Often, respondents areselected because they happen to be in the right placeat the right time. Mall intercept interviews without qualifying the respondents “People on the street” interviews 21
    22. 22. JUDGMENTAL SAMPLING ( NON PROBABILITY SAMPLING TECHNIQUE)Judgmental sampling: It is a form ofconvenience sampling in which thepopulation elements are selected based on thejudgment of the researcher. 22
    23. 23. QUOTA SAMPLING ( NON PROBABILITY SAMPLING TECHNIQUE)Quota sampling may be viewed as two-stagerestricted judgmental sampling.  The first stage consists of developing control categories, or quotas, of population elements.  In the second stage, sample elements are selected based on convenience or judgment. 23
    24. 24. SNOWBALL SAMPLING ( NON PROBABILITY SAMPLING TECHNIQUE)In snowball sampling, an initialgroup of respondents is selected,usually at random. After being interviewed, these respondents are asked to identify others who belong to the target population of interest Subsequent respondents are selected based on the referral. 24
    25. 25. SIMPLE RANDOM SAMPLING (PROBABILITY SAMPLING TECHNIQUE)• Each element in the population has a known and equal probability of selection.• Each possible sample of a given size (n) has a known and equal probability of being the sample actually selected.• This implies that every element is selected independently of every other element. 25
    26. 26. SYSTEMATIC SAMPLING (PROBABILITY SAMPLING TECHNIQUE)• The sample is chosen by selecting a random starting point and then picking every it element in succession from the sampling frame. 26
    27. 27. Systematic sampling
    28. 28. STRATIFIED SAMPLING (PROBABILITY SAMPLING TECHNIQUE)• A two-step process in which the population is partitioned into subpopulations, or strata.• The strata should be mutually exclusive and collectively exhaustive in that every population element should be assigned to one and only one stratum and no population elements should be omitted. 28
    29. 29. • Next, elements are selected from each stratum by a random procedure, usually SRS.• A major objective of stratified sampling is to increase precision without increasing cost. 29
    30. 30. CLUSTER SAMPLING (PROBABILITY SAMPLING TECHNIQUE)• The target population is first divided into mutually exclusive and collectively exhaustive subpopulations, or clusters.• Then a random sample of clusters is selected, based on a probability sampling technique such as SRS.•• For each selected cluster, either all the elements are included in the sample (one-stage) or a sample of elements is drawn probabilistically (two-stage). 30
    31. 31. • Ideally, each cluster should be a small-scale representation of the population.• In probability proportionate to size sampling, the clusters are sampled with probability proportional to size. In the second stage, the probability of selecting a sampling unit in a selected cluster varies inversely with the size of the cluster. 31
    32. 32. TYPES OF CLUSTER SAMPLING Cluster SamplingOne-Stage Two-Stage MultistageSampling Sampling Sampling Simple Cluster Probability Sampling Proportionate to Size Sampling 32
    33. 33. OTHER PROBABILITY SAMPLING TECHNIQUES• SEQUENTIAL SAMPLING: A probability sampling technique in which the population elements are sample sequentially, data collection and analysis are done at each stage and decision is made as to whether additional population elements should be sampled.• Double sampling: A sampling technique in which certain population element are sampled twice.
    34. 34. Non Probability Sampling Technique Strengths WeaknessesConvenience Least expensive, least Selection bias, sample not time-consuming, most representative, not recommended forsampling convenient descriptive or causal researchJudgmental Low cost, convenient, Does not allow generalization,sampling not time-consuming subjective Quota Sample can be controlled Selection bias, no assurance of sampling for certain characteristics representativenessSnowball Can estimate rare Time-consumingsampling characteristics 34
    35. 35. Probability SamplingTechnique Strengths WeaknessesSimple random Easily understood, Difficult to construct samplingsampling frame, expensive, lower precision, results projectable no assurance of representativeness.Systematic Can increase Can decrease representativenesssampling representativeness, easier to implement than sampling frame not necessaryStratified Include all important Difficult to select relevantsampling subpopulations, stratification variables, not feasible to precision stratify on many variables, expensiveCluster Easy to implement, cost Imprecise, difficult to compute andsampling effective interpret results 35
    36. 36. THANK YOU 36

    ×