1. SAMPLING TECHNIQUES
Shamindra Nath Sanyal
11/28/2018
SNS

2. Sampling Techniques
In practice, most of the information obtained by organizations about any population will come from examining a small, representative subset of the population. This is called a sample. For example:
A company might examine one in every twenty of their invoices for a month to determine the average amount of a customer order.
A newspaper might commission a research company to ask 1000 potential voters their opinions on a forthcoming election.
11/28/2018
SNS

3. Assumptions in Quantitative Sampling
We want to
generalize
to the population
Random events
are predictable
We can compare
random events
to our results
Probability sampling
is the best
approach
11/28/2018
SNS

4. Assumptions in Qualitative Sampling
Social actors
are not
predictable like
objects
Randomized
events are
irrelevant to
social life
Probability
sampling
is expensive
and inefficient
Non-Probability
sampling
is the best
approach
11/28/2018
SNS

5. Types of Sampling Probability: Simple Random Sampling
Systematic Sampling
Stratified Sampling
Cluster Sampling
11/28/2018
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6. Types of Sampling Non-Probability Convenience Sampling
Judgment Sampling
Quota Sampling
Snowball Sampling
11/28/2018
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7. Probability Sampling
Simple Random: Ensures that each member of the population has an equal chance of being chosen for the sample.
Examples of where this method might be used:
By a large company, to sample 10% of their orders to determine their average value.
By an auditor, to sample 5% of a firm’s invoices for completeness and compatibility with total yearly turnover.
11/28/2018
SNS

8. Probability Sampling
Systematic: The sample is chosen by selecting a random starting point and then picking every i-th 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.
It is particularly useful for populations that are of same kind or are uniform.
11/28/2018
SNS

9. Probability Sampling Stratified:
Stage 1: The population is partitioned into sub-populations, or strata. The strata should be mutually exclusive and collectively exhaustive.
Stage 2: The elements are selected from each stratum by a random procedure.
11/28/2018
SNS

10. Probability Sampling Cluster:
Stage 1: The target population is first divided into mutually exclusive and collectively exhaustive subpopulations, or clusters.
Stage 2: A random sample of clusters is selected, based on a probability sampling technique.
For each selected cluster, either all the elements are included in the sample or a sample of elements is drawn probabilistically.
11/28/2018
SNS

11. Non-Probability Sampling
Convenience:
Attempts to obtain a sample of convenient elements.
The selection of sample units is left primarily to the interviewer.
11/28/2018
SNS

12. Non-Probability Sampling
Judgment:
It is a form of Convenience Sampling in which the population elements are selected based on the judgment of the researcher.
Example:
Test markets selected to determine the potential of a new product.
Departmental stores selected to test a new merchandising display system.
11/28/2018
SNS

13. Non-Probability Sampling
Quota:
May be viewed as a two-stage restricted judgmental sampling.
Stage 1: Consists of developing control categories, or quotas, of population elements.
Stage 2: Sample elements are selected from the quota based on convenience or judgment.
11/28/2018
SNS

14. Non-Probability Sampling
Snowball:
It involves asking identified respondents for other persons who they know would fit the target respondents.
They could also ask people who are not target respondents but are likely to know them.
11/28/2018
SNS

15. Cluster vs. Stratified Sampling
A sample of subpopulations (cluster) is chosen.
All the subpopulations (strata) are selected.
Objective is to increase sampling efficiency by decreasing costs.
Objective is to increase precision.
Elements within a cluster should be heterogeneous but clusters themselves should be homogeneous.
Elements within a stratum should be homogeneous, but the elements in different strata should be heterogeneous.
11/28/2018
SNS

16. PRIMARY SCALES OF MEASUREMENT
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17. Nominal Scale Numbers identify and classify objects
Basic
Characteristics
Common Examples
Marketing Examples
Descriptive Statistics
Inferential Statistics
Numbers identify and classify objects
Social security numbers, numbering of cricket players
Brand numbers, store types
Percentages, Mode
Chi-square test, binomial test
11/28/2018
SNS

18. Ordinal Scale
Basic
Characteristics
Common Examples
Marketing Examples
Descriptive Statistics
Inferential Statistics
Numbers indicate the relative positions of the objects but not the magnitude of differences between them.
Quality rankings, rankings of teams in a tournament
Preference rankings, market positions, social class
Percentile, median
Chi-square test, binomial test
11/28/2018
SNS

19. Interval Scale
Basic
Characteristics
Common Examples
Marketing Examples
Descriptive Statistics
Inferential Statistics
Differences between objects can be compared; zero point is arbitrary
Temperature
(Celsius,
Fahrenheit)
Attitudes
Opinions
Index numbers
Range, mean, standard deviation
T-tests, ANOVA, regression, factor analysis
11/28/2018
SNS

20. Thank You
11/28/2018
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