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Sampling 20 october 2012
Sampling 20 october 2012
Sampling 20 october 2012
Sampling 20 october 2012
Sampling 20 october 2012
Sampling 20 october 2012
Sampling 20 october 2012
Sampling 20 october 2012
Sampling 20 october 2012
Sampling 20 october 2012
Sampling 20 october 2012
Sampling 20 october 2012
Sampling 20 october 2012
Sampling 20 october 2012
Sampling 20 october 2012
Sampling 20 october 2012
Sampling 20 october 2012
Sampling 20 october 2012
Sampling 20 october 2012
Sampling 20 october 2012
Sampling 20 october 2012
Sampling 20 october 2012
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Sampling 20 october 2012

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  • 1. EDU 702_RESEARCH METHODOLOGY PREPARED FOR: DR TEOH PREPERED BY : NURUL AIN BINTI IBRAHIM NUR HIDAYAH BINTI ABDULHAMID NOOR HAZILA BT HASHIM
  • 2. SAMPLING
  • 3. Design and Procedures1) Overview2) Sample or Census3) The Sampling Design Process i. Define the Target Population ii. Determine the Sampling Frame iii. Select a Sampling Technique iv. Determine the Sample Size v. Execute the Sampling Process
  • 4. 4) Classification of Sampling Techniques i. Nonprobability Sampling Techniques a. Convenience Sampling b. Judgmental Sampling c. Quota Sampling d. Snowball Sampling ii. Probability Sampling Techniques a. Simple Random Sampling b. Systematic Sampling c. Stratified Sampling d. Cluster Sampling e. Other Probability Sampling Techniques
  • 5. 5. Choosing Nonprobability versus Probability Sampling6. Uses of Nonprobability versus Probability Sampling7. International Marketing Research8. Ethics in Marketing Research9. Internet and Computer Applications10. Focus On Burke11. Summary12. Key Terms and Concepts
  • 6. Sample vs. Census Condit ions Favoring t he Use ofType of St udy Sam ple Census1. Budget Sm all Large2. Tim e available Short Long3. Populat ion size Large Sm all4. Variance in t he charact erist ic Sm all Large5. Cost of sam pling errors Low High6. Cost of nonsam pling errors High Low7. Nat ure of m easurem ent Dest ruct ive Nondest ruct ive8. At t ent ion t o individual cases Yes No
  • 7. The Sampling Design Process Define the Population Determine the Sampling Frame Select Sampling Technique(s) Determine the Sample Size Execute the Sampling Process
  • 8. Define the Target PopulationThe target population is the collection of elements orobjects that possess the information sought by theresearcher and about which inferences are to bemade. The target population should be defined interms of elements, sampling units, extent, and time. An element is the object about which or from which the information is desired, e.g., the respondent. A sampling unit is an element, or a unit containing the element, that is available for selection at some stage of the sampling process. Extent refers to the geographical boundaries. Time is the time period under consideration.
  • 9. Define the Target PopulationImportant qualitative factors in determining thesample size the importance of the decision the nature of the research the number of variables the nature of the analysis sample sizes used in similar studies incidence rates completion rates resource constraints
  • 10. Sample Sizes Used in Marketing Research StudiesType of St udy Minim um Size Typical RangePr oblem ident if icat ion r esear ch 500 1,000- 2,500( e.g. m arket pot ent ial)Pr oblem -solving r esear ch ( e.g. 200 300- 500pr icing)Pr oduct t est s 200 300- 500Test m arket ing st udies 200 300- 500TV, radio, or print advert ising ( per 150 200- 300com m er cial or ad t est ed)Test - m ar ket audit s 10 st or es 10- 20 st oresFocus gr oups 2 gr oups 4- 12 groups
  • 11. Classification of Sampling Techniques Sampling Techniques Nonprobability Probability Sampling Techniques Sampling TechniquesConvenience Judgmental Quota Snowball Sampling Sampling Sampling Sampling Simple Systematic Stratified Cluster Other Random Sampling Sampling Sampling Sampling Sampling Techniques
  • 12. Convenience SamplingConvenience sampling attempts to obtain a sampleof convenient elements. Often, respondents areselected because they happen to be in the right placeat the right time. use of students, and members of social organizations mall intercept interviews without qualifying the respondents department stores using charge account lists “people on the street” interviews
  • 13. Judgmental SamplingJudgmental sampling is a form of conveniencesampling in which the population elements areselected based on the judgment of the researcher. test markets purchase engineers selected in industrial marketing research bellwether precincts selected in voting behavior research expert witnesses used in court
  • 14. Quota SamplingQuota sampling may be viewed as two-stage restricted judgmentalsampling. 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. Population Sample composition composition Control Characteristic Percentage Percentage Number Sex Male 48 48 480 Female 52 52 520 ____ ____ ____ 100 100 1000
  • 15. Snowball SamplingIn snowball sampling, an initial group ofrespondents 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 referrals.
  • 16. Simple Random Sampling• 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.
  • 17. Systematic Sampling• The sample is chosen by selecting a random starting point and then picking every it the element in succession from the sampling frame.• The sampling interval, is determined by dividing the population size N by the sample size n and rounding to the nearest integer.• When the ordering of the elements is related to the characteristic of interest, systematic sampling increases the representativeness of the sample.• If the ordering of the elements produces a cyclical pattern, systematic sampling may decrease the representativeness of the sample. For example, there are 100,000 elements in the population and a sample of 1,000 is desired. In this case the sampling interval, i, is 100. A random number between 1 and 100 is selected. If, for example, this number is 23, the sample consists of elements 23, 123, 223, 323, 423, 523, and so on.
  • 18. Stratified Sampling• 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.• 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.
  • 19. Stratified Sampling• The elements within a stratum should be as homogeneous as possible, but the elements in different strata should be as heterogeneous as possible.• The stratification variables should also be closely related to the characteristic of interest.• Finally, the variables should decrease the cost of the stratification process by being easy to measure and apply.• In proportionate stratified sampling, the size of the sample drawn from each stratum is proportionate to the relative size of that stratum in the total population.• In disproportionate stratified sampling, the size of the sample from each stratum is proportionate to the relative size of that stratum and to the standard deviation of the distribution of the characteristic of interest among all the elements in that stratum.
  • 20. Cluster Sampling• 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).• Elements within a cluster should be as heterogeneous as possible, but clusters themselves should be as homogeneous as possible. 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.
  • 21. Types of Cluster Sampling Cluster SamplingOne-Stage Two-Stage MultistageSampling Sampling Sampling Simple Cluster Probability Sampling Proportionate to Size Sampling
  • 22. THANK YOU

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