Sampling TechniquesBy: Renascence

Group

Learning Objectives Define Population, Sample, and Sampling. Identify the Purpose of Sampling.Compare and Contrast a Population and a Sample.Define Probability and Non-probability Sampling.Identify the Types of Probability and Non-

Probability Sampling. Compare the Advantages and Disadvantages of

specific Probability and Non-probability Sampling.

Population

What is Sampling?Population: the entire group under study as

defined by research objectives. Sometimes called the “universe.”

Sampling is a process of selecting a portion or subset of the designated population to represent the entire population.

Sample is a set of elements that make up the population.

Why Sampling?To Ger Information About Large Populationswith Less Costs Less field Time More Accuracy i.e. Can Do A Better Job

of Data Collection& Analysis When it’s Impossible to study the whole

population

Types of Sampling

Probability Samples

Every unit of the population has the Equal Probability of being included in the sample.

Chance Mechanism is used in the selection process. Eliminates Bias in the selection process

Advantages Of Probability Sampling1. Information from a representative cross-section

2. Sampling error can be computed

3. Results are Projectable to the Total Population. Disadvantages Of Probability Sampling

1. More expansive than non-probability samples

2. Take more time to Design and Execute.

Non-probability Samples

•Every unit of the population Does not have Equal Probability of being included in the sample.

• Open the selection Bias• Advantages of Non-probability Samples

1.Less Cost than probability

2.Can be conducted more quickly 3. Produces samples that are reasonably representative

Disadvantages of Non-Probability Samples

1.Sampling error cannot be computed

2.Representativeness of the sample is not known

3.Results cannot be Projected to the population.

4.Not appropriate data collection methods for most statistical methods

Types Probability Sampling MethodsSimple Random Sampling

A probability sample is a sample in which every element of the population has a known and equal probability of being selected into the sample.

Probability of Selection=Sample Size (n) Population Size (N)

• Blind Draw Method (e.g. names “placed in a hat” and then drawn randomly)

• Random Numbers Method (all items in the sampling frame given numbers, numbers then drawn using table or computer program)

• Advantages: • Known and equal chance of selection• Easy method when there is an electronic database

Systematic Sampling An initial starting point is selected by a random process,

and then every kth number on the list is selectedProbability of Selection (K) =Sample Size (n)

Population Size (N) The number of population elements between the units

selected for the sampleAdvantages of systematic sampling

Typically simpler to implement than SRSCan provide a more uniform coverage

Potential disadvantage of systematic samplingCan produce a bias if there is a systematic pattern in the

sequence of items from which the sample is selected

1 16 31 46

2 17 32 473 18 33 484 19 34 495 20 35 506 21 36 517 22 37 528 23 38 539 24 39 5410 25 40 5511 26 41 5612 27 42 5713 28 43 5814 29 44 5915 30 45 60

ExampleTotal Population(N)= 60Want Sample of 10Interval Size(k) =60/10 = 6Select Random Start b/n 1 & 6Eg. 5Select Every Sixth Unit First Randomly selected sample

Stratified Sampling: The population is separated into homogeneous

groups/segments/strata, according to some criterion, such as geographic location, grade level, age, or income, and sub-samples are randomly selected from each strata.

The results are then combined to get the picture of the total population.

It allows the researcher to allocate a larger sample size to strata with more variance and smaller sample size to strata with less variance. Thus, for the same sample size, more precision is achieved.

Advantagesguarantees coverage across stratacan over-sample some strata in order to obtain precise within-stratum estimates

Disadvantageswith unequal sampling probabilities, sampling weights must be included in analysismore complicated requires special software

Clusters SamplingThe population is divided into subgroups (clusters)

like families. A simple random sample is taken of the subgroups and then all members of the cluster selected are surveyed.

Example :In cluster sampling the sample units contain groups

of elements (clusters) instead of individual members or items in the population.eg:- Rather than listing all elementary school children in a given city and random selecting 15 per cent these students for the sample, a researcher lists all of the elementary schools in the city, selects at random 15 percent of these clusters of units, and uses all of the children in the selected schools as the sample.

Advantages• More convenient for geographically dispersed

populations• Reduced travel costs to contact sample elements• Simplified administration of the survey• Unavailability of sampling frame prohibits using

other random sampling methods Disadvantages

• Statistically less efficient when the cluster elements are similar

• Costs and problems of statistical analysis are greater than for simple random sampling

Non-Probability Sampling

Convenience Sample1.Convenience/Accidental sampling: It means selecting sample units in a ‘1 hit and miss fashion’. Example: interviewing people whom we happen to meet. Involves on selecting whatever sampling units are conveniently available.

Example A teacher may select students in his class.Also known as Accidental sampling because the respondents whom the researcher meets accidentally are included in the sample.

Advantages Moderate cost, Commonly used/understoodSample will meet a specific objective

DisadvantagesBias, Projecting data beyond sample not justified.

•Purposive or Judgment Sampling• Iis deliberate selection of sample units that conform

to some pre-determined criteria. This is also known as judgment sampling. This involves selection of cases which we judge as the most appropriate ones for the given study. It is based on the judgment of the researcher or some expert. It does not aim at searching a cross section of a population.

Quota SamplingThis is a form of convenient sampling involving selection

of quota groups of accessible sampling units by traits such as Sex, Social class etc. In specific proportions, each investigator may be given an assignment of quota groups specified by the pre-determined traits in specific proportions. He can then select accessible persons belonging to those groups in the area assigned to him.Quota sampling is therefore, a method of stratified sampling in which the selection within strata is non-random. Quota sampling is used in studies like marketing survey, opinion poll, and readership survey which do not aim at precision but to get quickly some crude results.

Snow Ball Sampling: Is a technique of building up a list or a sample of a

special population by using an initial set of its members as informants. For example a researcher wants to study the problem faced by African Migrants in another country, Say, he may identify an initial group of Migrants through some source like Embassy, Then he can ask each one of them to supply names of others known to them and continue this procedure until he gets an exhaustive list from which he can draw a sample or make a census survey.

Technique Strengths Weaknesses Non-Probability Sampling

Convenience sampling

Least expensive, least time-consuming, most convenient

Selection bias, sample not representative, not recommended for descriptive or causal research

Judgmental sampling Low cost, convenient, not time-consuming

Does not allow generalization, subjective

Quota sampling Sample can be controlled for certain characteristics

Selection bias, no assurance of representativeness

Snowball sampling Can estimate rare characteristics

Time-consuming

Probability Sampling Simple random sampling (SRS)

Easily understood, results projectable

Difficult to construct sampling frame, expensive, lower precision, no assurance of representativeness.

Systematic sampling Can increase representativeness, Easier to implement than SRS, sampling frame not necessary

Can decrease representativeness

Stratified sampling Include all important subpopulations, precision

Difficult to select relevant stratification variables, not feasible to stratify on many variables, expensive

Cluster sampling Easy to implement, cost effective

Imprecise, difficult to compute and interpret results

Sampling Errors Data from nonrandom samples are not appropriate

for analysis by inferential statistical methods. Sampling Error occurs when the sample is not

representative of the population Non-Sampling Errors

• Missing Data, Recording, Data Entry, and Analysis Errors

• Poorly conceived concepts , unclear definitions, and defective questionnaires

• Response errors occur when people so not know, will not say, or overstate in their answers

How many is enough? For Probability Sample, The Size of the Sample needs to be such that it gives reasonable confidence of generalizability to the wider population. In this sense, the answer to this question is a statistical one. It depends on the size of the wider population, and what sample size is needed to achieve particular degrees of confidence about its representativenessIn the case of Non-probability Sampling, No ‘correct’ number of participants. Instead it is more

useful to think in terms of the richness of the data in terms of answering (or refining) the researcher’s core questions.

ConclusionSampling can be a powerful tool for accurately measuring opinions and characteristics of a population. However, there is a genuine potential for misuse of this tool by researchers who do not understand the limitations of various sampling procedures.

Non- probability sampling techniques can provide valuable information but the results cannot be generalized to a larger population nor can statistics indicating the reliability of the results be calculated

Well conducted probability samples provide the researcher with the ability to gather information from a relatively small number of members of a large population and accurately generalize the results to the entire population.

Enable the re-searcher to calculate statistics that indicate the precision of the data.

The researcher goals inform which sampling method is best for the research to be conducted.

The main choice in regards to sample method choice is whether or not the researcher wants to generalize the findings from the sample to the whole of the population being studied.

Being aware of possible errors due to the sample method chosen is also very important because giving possible errors within the results section allows the study to be regarded as valid.

Many sample method choices are available; the researcher must choose the method that is right for the study.

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