sampling techniques
TRANSCRIPT
Tahir Mahmood
LecturerDepartment of Statistics
Sampling Theory and Methods
Outlines:
Explain the role of sampling in the research processDistinguish between probability and non probability
sampling Understand the factors to consider when determining
sample sizeUnderstand the steps in developing a sampling plan
What is Sampling?Sampling is the procedure a
researcher uses to gather people, places, or things to study.
Samples are always subsets or small parts of the total number that could be studied.
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
What is your population of interest?To whom do you want to generalize your results?
All doctorsSchool childrenIndiansWomen aged 15-45 yearsOtherCan you sample the entire population?
Why sampling?
Get information about large populations
Less costs
Less field time
More accuracy i.e. Can Do A Better Job of
Data Collection
When it’s impossible to study the whole
population
Important Factors in selecting a Sample Design
Research objectives Degree of accuracy
Time frame
Research scope
Statistical analysis needs
Resources
Knowledge oftarget population
Common Methods:– Budget/time available– Executive decision– Statistical methods– Historical data/guidelines
Common Methods for Determining Sample Size
How many completed questionnaires do we need to have a representative sample?
Generally the larger the better, but that takes more time and money.
Answer depends on:– How different or dispersed the population is.– Desired level of confidence.– Desired degree of accuracy.
Determining Sample Size
IMPORTANT STATISTICAL TERMS
Population:
a set which includes all measurements of interest to the researcher(The collection of all responses, measurements, or counts that are of interest)Sample:A subset of the population
Sampling FrameA list of population elements (people, companies,
houses, cities, etc.) from which units to be sampled can be selected.
Difficult to get an accurate list.
Sample frame error occurs when certain elements of the population are accidentally omitted or not included on the list.
See Survey Sampling like HIES PDHS, PSLM, MICS
Sampling Methods
probability sampling
Nonprobability sampling
Probability Sampling
A probability sampling scheme is one in which every unit in the population has a chance (greater than zero) of being selected in the sample, and this probability can be accurately determined.
Non-Probability SamplingNon probability sampling is any sampling
method where some elements of the population have no chance of selection (these are sometimes referred to as 'out of coverage‘ / 'under covered'), or where the probability of selection can't be accurately determined.
It involves the selection of elements based on assumptions regarding the population of interest, which forms the criteria for selection.
Types of Sampling Methods
Probability Simple random sampling Systematic random sampling Stratified random sampling Cluster sampling
Non probability Convenience sampling Judgment sampling Quota sampling Snowball sampling
Simple Random Sampling
Simple random sampling is a method of probability sampling in which every unit has an equal non zero chance of being selected
Simple random sampling
Systematic Random Sampling
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
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
Systematic sampling
Stratified Random Sampling
Stratified random sampling is a method of probability sampling in which the population is divided into different subgroups and samples are selected from each.
Steps in Drawing a Stratified Random Sample
1: Divide the target population into homogeneous subgroups or strata
2: Draw random samples from each stratum3: Combine the samples from each stratum into a single
sample of the target population
Example:
Cluster samplingCluster sampling is an example of 'two-stage sampling' . First stage a sample of areas is chosen; Second stage a sample of respondents within those
areas is selected. Population divided into clusters of homogeneous units,
usually based on geographical contiguity.Sampling units are groups rather than individuals.A sample of such clusters is then selected.All units from the selected clusters are studied.
Cluster sampling
Section 4
Section 5
Section 3
Section 2Section 1
Accidental, Haphazard or convenience sampling
members of the population are chosen based on their relative ease of access. To sample friends, co-workers, or shoppers at a single mall, any one on the street
Snowball method
The first respondent refers to next and then a chain starts Example: Addicts, HIV etc.
Judgmental sampling or Purposive sampling
The researcher chooses the sample based on who they think would be appropriate for the study. This is used primarily when there is a limited number of people that have expertise in the area being researched.
Quota sampling: There are two types of quota sampling: proportional. In proportional
quota sampling you want to represent the major characteristics of the population by sampling a proportional amount of each.
Non proportional:
Non proportional quota sampling is a bit less restrictive. the minimum number of sampled units is specified in each category. not concerned with having numbers that match the proportions in the population
Ad hoc quotas: A quota is established (say 65% women) and researchers are free to
choose any respondent they wish as long as the quota is met. Expert Sampling Expert sampling :involves the assembling of a sample of persons with
known or demonstrable experience and expertise in some area. Often, we convene such a sample under the auspices of a "panel of
experts." There are actually two reasons you might do expert sampling. First, because it would be the best way to elicit the views of persons who have specific expertise.
Errors in sample
Systematic error (or bias) Inaccurate response
(information bias)– Selection bias
Sampling error (random error)Sampling error is any type of bias that is attributable to mistakes in either drawing a sample ordetermining the sample size
Type-I Error
The probability of finding a difference with our sample compared to population, and there really isn’t one….
Known as the α (or “type 1 error”)Usually set at 5% (or 0.05)
Type-II Error
The probability of not finding a difference that actually exists between our sample compared to the population…
Known as the β (or “type 2 error”)Power is (1- β) and is usually 80%
Factors Affecting Sample Size for Probability Designs
Variability of the population characteristic under investigation
Level of confidence desired in the estimateDegree of precision desired in estimating the
population characteristic
Comparison b/w Probability and Nonprobability Sampling
The difference between nonprobability and probability sampling is that nonprobability sampling does not involve random selection and probability sampling does.
Nonprobability sampling techniques cannot be used to infer from the sample to the general population.
Any generalizations obtained from a nonprobability sample must be filtered through one's knowledge of the topic being studied.
Performing nonprobability sampling is considerably less expensive than doing probability sampling, but the results are of limited value.
When estimating a population mean
n = (Z2B,CL)(σ2/e2)
n estimates of a population proportion are of concern
n = (Z2B,CL)([P x Q]/e2)
Probability Sampling and Sample Sizes
Probability Sampling Advantages
Less prone to biasAllows estimation of magnitude of
sampling error, from which you can
determine the statistical significance
of changes/differences in indicators
Probability Sampling Disadvantages
Requires that you have a list of all
sample elements More time-consumingMore costly No advantage when small
numbers of elements are to be chosen
Non Probability Sampling Advantages
More flexibleLess costly Less time-consuming Judgmentally representative samples may be preferred
when small numbers of elements
are to be chosen.
Non Probability Sampling Disadvantages
Greater risk of biasMay not be possible to
generalize to program target
population Subjectivity can make it
difficult to measure changes in
indicators overtime No way to assess precision
or reliability of data
Thank you