1. Sampling: Theory and Methods6

2. Learning Objectives role of sampling in the research process
probability and nonprobability sampling
factors that determine sample size
steps to develop a sampling plan

3. The Future of Mobile Search

4. Sampling
Sampling is the process of selecting a small number of elements from a larger defined target group of elements such that the information gathered from the small group will allow judgments to be made about the larger groups

5. Census

6. Basics of Sampling Theory
Population
Element
Defined target
population
Sampling unit
Sampling frame

7. Sources of Error
Sampling error is any type of bias that is attributable to mistakes in either drawing a sample or determining the sample size
Nonsampling error is bias that occurs in a research study regardless of whether a sample or census is used, such as bias caused by measurement error, response errors, or coding errors

8. Sampling Methods
Probability
sampling
Nonprobability
sampling

9. Types of Sampling Methods
Probability
Simple random sampling
Systematic random sampling
Stratified random sampling
Cluster sampling
Nonprobability
Convenience sampling
Judgment sampling
Quota sampling
Snowball sampling

10. Sample Design (More Details)

11. Figure 12.7 Non-probability Sampling Techniques
Convenience
Sampling
Judgmental
Sampling
Quota
Sampling
Snowball
Sampling

12. Convenience Sampling
Convenience sampling attempts to obtain a sample of convenient elements. Often, respondents are selected because they happen to be in the right place at 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. A Graphical Illustration of Non-Probability Sampling Techniques Convenience SamplingD E 1 6 11 16 21 2 7 12 17 22 3 8 13 18 23 4 9 14 19 24 5 10 15 20 25
Group D happens to assemble at a convenient time and place. So all the elements in this Group are selected. The resulting sample consists of elements 16, 17, 18, 19 and 20. Note, no elements are selected from group A, B, C and E.

14. Judgmental Sampling
Judgmental sampling is a form of convenience sampling in which the population elements are selected 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

15. A Graphical Illustration of Non-Probability Sampling Techniques Judgmental Sampling1 6 11 16 21 2 7 12 17 22 3 8 13 18 23 4 9 14 19 24 5 10 15 20 25
The researcher considers groups B, C and E to be typical and convenient. Within each of these groups one or two elements are selected based on typicality and convenience. The
resulting sample consists of elements 8, 10, 11, 13, 22 and 24. Note, no elements are selected
from groups A and D.

16. Quota Sampling
Quota sampling may be viewed as two-stage restricted 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.
Population Sample composition composition Control Characteristic Percentage Percentage Number Sex Male Female ____ ____ ____

17. A Graphical Illustration of Non-Probability Sampling Techniques
Quota Sampling A B C D E 1 6 11 16 21 2 7 12 17 22 3 8 13 18 23 4 9 14 19 24 5 10 15 20 25
A quota of one element from each group, A to E, is imposed. Within each group, one element is selected based on judgment or convenience. The resulting sample consists of elements 3, 6, 13, 20 and 22. Note, one element is selected from each column or group.

18. Snowball Sampling
In snowball sampling, an initial group 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 referrals.

19. A Graphical Illustration of Non-Probability Sampling Techniques
Snowball Sampling
Random
Selection Referrals
Elements 2 and 9 are selected randomly from groups A and B. Element 2 refers elements 12 and 13. Element 9 refers
element 18. The resulting sample consists of elements 2, 9, 12, 13, and 18. Note, no element from group E. A B C D E 1 6 11 16 21 2 7 12 17 22 3 8 13 18 23 4 9 14 19 24 5 10 15 20 25

20. Figure 12.8 Probability Sampling Techniques
Simple Random
Sampling
Systematic
Sampling
Stratified
Sampling
Cluster
Sampling

21. Forms of Probability Sampling_1
Simple random sampling is a method of probability sampling in which every unit has an equal nonzero chance of being selected
Systematic random sampling is a method of probability sampling in which the defined target population is ordered and the sample is selected according to position using a skip interval

22. 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.

23. A Graphical Illustration of Probability Sampling Techniques
Simple Random Sampling A B C D E 1 6 11 16 21 2 7 12 17 22 3 8 13 18 23 4 9 14 19 24 5 10 15 20 25
Select five random numbers from 1 to 25. The resulting sample consists of population elements 3, 7, 9, 16, and 24. Note, there is no element from Group C.

24. Systematic Sampling
The sample is chosen by selecting a random starting point and then picking every ith element in succession from the sampling frame.
The sampling interval, i, 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 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.

25. Steps in Drawing a Systematic Random Sample
1: Obtain a list of units that contains an acceptable frame of the target population
2: Determine the number of units in the list and the desired sample size
3: Compute the skip interval
4: Determine a random start point
5: Beginning at the start point, select the units by choosing each unit that corresponds to the skip interval

26. Select a random number between 1 to 5, say 2.
A Graphical Illustration of Probability Sampling Techniques
Systematic Sampling A B C D E 1 6 11 16 21 2 7 12 17 22 3 8 13 18 23 4 9 14 19 24 5 10 15 20 25
Select a random number between 1 to 5, say 2.
The resulting sample consists of population 2,
(2+5=) 7, (2+5x2=) 12, (2+5x3=)17, and (2+5x4=) 22. Note, all the elements are selected from a single row.

27. Forms of Probability Sampling_2
Stratified random sampling is a method of probability sampling in which the population is divided into different subgroups and samples are selected from each
Cluster sampling is a method of probability sampling where the sampling units are selected in groups rather than individually

28. Steps in Drawing a Stratified Random Sample
1: Divide the target population into homogeneous subgroups or strata
2: Draw random samples fro each stratum
3: Combine the samples from each stratum into a single sample of the target population

29. 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.

30. 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.

31. Randomly select a number from 1 to 5
A Graphical Illustration of Probability Sampling Techniques
Stratified Sampling A B C D E 1 6 11 16 21 2 7 12 17 22 3 8 13 18 23 4 9 14 19 24 5 10 15 20 25
Randomly select a number from 1 to 5
for each stratum, A to E. The resulting
sample consists of population elements
4, 7, 13, 19 and 21. Note, one element
is selected from each column.

32. 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.

33. Randomly select 3 clusters, B, D and E.
A Graphical Illustration of Probability Sampling Techniques
Cluster Sampling (2-Stage) A B C D E 1 6 11 16 21 2 7 12 17 22 3 8 13 18 23 4 9 14 19 24 5 10 15 20 25
Randomly select 3 clusters, B, D and E.
Within each cluster, randomly select one
or two elements. The resulting sample
consists of population elements 7, 18, 20, 21, and 23. Note, no elements are selected from clusters A and C.

34. Factors to Consider in Sample Design
Research objectives
Degree of accuracy
Resources
Time frame
Knowledge of
target population
Research scope
Statistical analysis needs

35. Nonprobability Sampling Methods
Convenience sampling relies
upon convenience and access
Judgment sampling relies upon belief
that participants fit characteristics
Quota sampling emphasizes representation
of specific characteristics
Snowball sampling relies upon respondent
referrals of others with like characteristics

36. Factors Affecting Sample Size for Probability Designs
Variability of the population characteristic under investigation
Level of confidence desired in the estimate
Degree of precision desired in estimating the population characteristic

37. Probability Sampling and Sample Sizes
When estimating a population mean
n = (Z2B,CL)(σ2/e2)
When estimates of a population proportion are of concern
n = (Z2B,CL)([P x Q]/e2)
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38. Steps in Developing a Sampling Plan
1. Define the Target Population
2. Select the Data Collection Method
3. Identify the Sampling Frame(s) Needed
4. Identify the Appropriate Sampling Method
Steps in Developing a Sampling Plan
5. Determine Sample Sizes and Contact Rates
6. Create Plan for Selecting Sampling Units
7. Execute the Operational Plan