Sampling
Module 3
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INTRODUCTION
Sampling is the process of selecting observations (a sample) to provide an adequate description and inferences of the population.
- Sample
- It is a unit that is selected from population
- Represents the whole population
- Purpose to draw the inference
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- Why Sample?
- Sampling Frame
Listing of population from which a sample
SAMPLING
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What you want to talk about
Population
Sampling Frame
Sampling Process
Inference
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IF THE POPULATION IS HOMOGENEOUS
IF THE POPULATION IS HETEROGENEOUS
SAMPLING DESIGN PROCESS
Define Population
Determine Sampling Frame
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Determine Sampling Procedure
Probability Sampling
- Simple Random Sampling
- Stratified Sampling
- Cluster Sampling
- Systematic Sampling
- Multistage Sampling
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Non-Probability Sampling
- Convenience
- Judgment
- Quota
- Snowball
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Determine Appropriate Sample Size
Execute Sampling Design
(i) Type of universe/Population
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- The first step in developing any sample design is to define the set of objects, technically called the Universe, to be studied.
- The universe can be finite or infinite.
- In finite universe the number of items is certain but in an infinite universe the number of items is infinite,
The population of a city, the number of workers in a factory are examples of finite universes, whereas the number of stars in the sky, listeners of a specific programme, throwing of a dice etc. are examples
2. Sampling frame
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- 'Sampling Frame' is from which sample is to be selected.
- It contains the names of all items of a universe (in case of finite universe only).
- If source list is not available, researcher has to prepare it and it should be comprehensive, correct, reliable and appropriate. It is extremely important for the source list to be as representative of the population as possible.
- if you want to learn about scooter owners in a town, you can obtain the frame..
(iii) Sampling unit:
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- A decision has to be taken concerning the sampling unit before selecting sample.
- Sampling unit may be a geographical one such as state, district, village, etc., or
- A construction unit such as house, flat, etc., or it may be a social unit such as family, club, school, etc., or it may be an individual.
- The researcher will have to decide one or more of such units that he has to select for his study.
(iv) Size of sample:
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- This refers to the number of items to be selected from the universe to constitute a sample. This a major problem before a researcher.
- The size of sample should neither be excessively large, nor too small.
(vii) Sampling procedure:
- Finally, the researcher must decide the type of sample he will use.
- i.e., he must decide about the technique to be used in selecting the items for the sample.
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- Probability
- Non-Probability
Types of samples
Probability samples
- Simple Random
- Systematic Random
- Stratified
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Non-probability samples
- Convenience
- Snowball
- Quota
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PROBABILITY SAMPLING
SIMPLE RANDOM SAMPLING
- All subsets of the frame are given an equal probability.
- Random number generators
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SIMPLE RANDOM SAMPLING
Advantages:
- Minimal knowledge of population needed
- Easy to analyze data
Disadvantages:
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- Low frequency of use
- Does not use researchers' expertise
- Larger risk of random error
STRATIFIED RANDOM SAMPLING
- Population is divided into two or more groups called strata
- Subsamples are randomly selected from each of the strata
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List of Clients
Strata
Caucasians
African-American
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Random Subsample
STRATIFIED RANDOM SAMPLING
Advantages:
- Assures representation of all groups in sample population
- Characteristics of each stratum can be estimated and comparisons made
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Disadvantages:
- Requires accurate information on proportions of each stratum
- Stratified lists costly to prepare
CLUSTER SAMPLING
- The population is divided into subgroups (clusters) such as families.
- A simple random sample is taken from each cluster
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Section 1
Section 2
Section 4
CLUSTER SAMPLING
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Advantages:
- Can estimate characteristics of both the cluster and population
Disadvantages:
- The cost to reach an element to sample is often very high
- Each stage in cluster sampling introduces sampling error—the more stages there are, the more error there tends to be
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SYSTEMATIC RANDOM SAMPLING
- Order all units in the sampling frame
- Then every nth number on the list is selected
- N= Sampling Interval
SYSTEMATIC RANDOM SAMPLING
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Advantages:
- Moderate cost; moderate usage
- Simple to draw sample
- Easy to verify
Disadvantages:
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- Periodic ordering required
NONPROBABILITY SAMPLES
NONPROBABILITY SAMPLES
- The probability of each case being selected from the total population is not known.
- Units of the sample are chosen on the basis of personal judgment or convenience.
- There are NO statistical techniques for measuring random sampling error in a non-probability sample.
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NONPROBABILITY SAMPLES
- A. Convenience Sampling
- B. Quota Sampling
- C. Judgmental Sampling (Purposive)
- D. Snowball sampling
- E. Self-selection sampling
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A. CONVENIENCE SAMPLING
- Convenience sampling involves choosing individuals at the convenience of the researcher.
Advantages
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- Very low cost
- Extensively used/understood
Disadvantages
- Variability and bias cannot be measured
- Projecting data beyond sample not justified
- Restriction of Generalization.
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B. QUOTA SAMPLING
- The population is first segmented into mutually exclusive sub-groups, just as in stratified sampling.
Advantages
- Used when research budget is limited
- Very extensively used/understood
- No need for list of population elements
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Disadvantages
- Variability and bias cannot be measured
- Time Consuming
- Projecting data beyond sample not justified
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Quota Sampling - Example
Population
1500 elderly living in a residence
1000 females
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500 males
Quota Sample
100 females
50 males
Quota Vs Stratified Sampling
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QUOTA SAMPLING
STRATIFIED
In Quota Sampling, interviewer selects first available subject who meets criteria: is a convenience sample.
In Stratified, selection is random.
C. JUDGEMENTAL SAMPLING
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- Researcher employs his or her own 'judgment about.
Advantages
- There is a assurance of Quality response
- Meet the specific objective.
Disadvantages
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- Bias selection of sample may occur
- Time consuming process.
D. SNOWBALL SAMPLING
A snowball sample is one in which the researcher collects data on the few members of the target population he or she can locate, then asks those individuals to provide information needed to locate other members of that population whom they know.
Advantages
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- Low cost
- Useful in specific circumstances & for specific populations
Disadvantages
- Not independent
- Projecting data beyond sample not justified
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SAMPLING ERROR
Components of total error
Point estimate from survey 40%
Total error
Prevalence 0%
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Nonsampling bias
Sampling bias
Types of Sampling
*Sample Errors
*Non Sample Errors
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Sample Errors
*Error caused by the act of taking a sample
*They cause sample results to be different from the results of census
*Differences between the sample and the population that exist only because of the observations that happened to be selected for the sample
*Statistical Errors are sample errors
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*We have no control over
Non Sample Errors
Not Control by Sample Size
*Non Response Errors
*Response Error
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Non Response
A non-response error occurs when units selected as part of the sampling procedure do not respond in whole or in part
Response Error
A response or data error is any systematic bias that occurs during data collection, analysis, or interpretation
- Respondent error (e.g., lying, forgetting)
- Interviewer bias
- Recording errors
- Poorly designed questionnaires
- Measurement error
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Respondent
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- respondent gives an incorrect answer due to social or competence implications, or due to perceived undesirability of question
- respondent misunderstands the requirements of the question
- lack of motivation to give an accurate answer
- "lazy" respondent gives an "average" answer
- question requires memory/recall
- proxy respondents are used, i.e. someone other than the respondent
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Interviewer
- Different interviewers administer questions in different ways
- Differences occur in reactions to different interviewers, e.g. to interviewer's own sex or own ethnic group
- Inadequate training of interviewers
- Inadequate attention to the selection of interviewers
- There is too high a workload for interviewers
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Measurement Error
- The question is unclear, ambiguous or hard to answer
- The list of possible answers suggests ideas the instrument is incomplete
- Requested information assumes knowledge unfamiliar to the respondent
- The definitions used by the survey are not those used by the respondent (e.g. how many employees do you have? See next slide)
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METHODS OF REDUCING SAMPLING ERROR
- Specific problem selection.
- Systematic documentation of related data.
- Effective enumeration.
- Effective pre testing.
- Controlling methodological bias.
- Selection of appropriate sampling technique.
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NON-SAMPLING ERRORS
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- Non-sampling errors refers to biases and mistakes in selection of sample.
CAUSES FOR NON-SAMPLING ERRORS
- Sampling operations
- Inadequate of response
- Misunderstanding the concept
- Lack of knowledge
- Concealment of the truth.
- Loaded questions
- Processing errors
- Sample size
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95% confidence limits (or interval)
95% of survey results
Survey result
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