Business Research Methods [session 3- Sampling Techniques] 544s28

Sampling Overview •

University of Technology, Mauritius

Target population

To whom you wish to generalise

BUSINESS RESEARCH METHODS

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Session 3

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By Reza Beebeejaun

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Sampling frame

Those who have a chance to be selected Sample

Those who were selected and responded Representativeness

The extent to which the sample is a good indicator of the target population 2

Population & Sample

What is sampling? “Sampling is the process of selecting units (e.g., people, organizations) from a population of interest so that by studying the sample we may fairly generalize our results back to the population from which they were chosen.” - Trochim (2006)

A sample is a set of data drawn from the population.

A population in statistics refers the entire set of individuals under study.

Population

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Sample Subset

Parameter Populations have Parameters A descriptive measure of a population

Statistic Samples have Statistics A descriptive measure of a sample

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Representativeness of a sample depends on:

Why sample? • •

Reduces cost, time, sample size etc. If the sample is representative, the sample data allows inferences to be drawn about the total population.

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Adequacy of sampling frame Sampling method Adequacy of sample size Response rate – both the % & representativeness of people in sample who actually complete survey It is better to have a small, representative sample than a large, unrepresentative sample.

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Sampling process

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Types of samples

１．Identify target population ２．Select sampling method ３．Calculate sample size

for desired power.

４．Maximise return rate

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Random/probability sampling

Non-random / non-probability •

Each unit has an equal chance of selection Selection occurs entirely by random chance Also called representative sampling

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Also called purposive or judgemental sampling Useful for exploratory research and case study research Able to get large sample size quickly Limitations include potential bias and nonrepresentativeness

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Simple random sampling •

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Systematic Samples

Everyone in the target population has an equal chance of selection Useful if clear study area or population is identified Similar to a lottery:

A systematic sample is a sample in which each member of the population is assigned a number. A starting number is randomly selected and sample are selected at regular intervals.

•List

of names are assigned #s and randomly select #s of respondents • Randomly select # through table of random #s or by computer

Every fourth member is chosen.

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Stratified Samples

Cluster Samples A cluster sample has all from randomly selected segments of a population. This is used when the population falls into naturally occurring subgroups.

A stratified sample has from each segment of a population. This ensures that each segment from the population is represented. Sub-divide population into strata (e.g., by gender, age, or location) Then random selection from within each stratum

All in each selected group are used. Freshmen

Sophomores

Juniors

Seniors

The city of Port louis divided into city blocks. 13

Multistage sampling

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Convenience Samples A convenience sample consists only of available of the population.

• With multistage sampling, we select a sample by using combinations of different sampling methods.

Example: You are doing a study to determine the number of years of education each teacher at your college has. Identify the sampling technique used if you select the samples listed.

For example, in Stage 1, we might use cluster sampling to choose clusters from a population. Then, in Stage 2, we might use simple random sampling to select a subset of elements from each chosen cluster for the final sample.

1.) You randomly select two different departments and survey each teacher in those departments. 2.) You select only the teachers you currently have this semester. 3.) You divide the teachers up according to their department and then choose and survey some teachers in each department. Continued. 15

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Snowball sampling •

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The Quota Sample

Useful for difficult to access populations e.g., illegal immigratnts, drug s Respondents recommend other respondents e.g., in studying ecstasy s, gain trust of a few potential respondents and ask them to recommend the researcher to other potential respondents

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Determine what the population looks like in of specific qualities. Create “quotas” based on those qualities. Select people for each quota.

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When should we use which types of sampling ?

Purposive sampling •

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Respondents selected for a particular purpose e.g., because they may be “typical” respondents e.g., select sample of tourists aged 40-60 as this is the typical age group of visitors to Canberra e.g., Frequent flyers to regarding service quality in an airline setting

•It depends on our

purpose, population, type of universe and the situation. •Suppose, you are able to get clusters, which have elements representing the entire population, you may go for cluster sampling. •If you want to really use a good method, have random sampling in that method.

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What is sampling error?

Sample size? To determine the sample size, you need to calculate the necessary sample size for a different combination of levels of precision, confidence level (95%), and variability.

•The difference

in the value identified by sample and the population parameter is called sampling error. •For example, population mean is 20 but sample mean is 18, so sampling error = 2

Where n is the sample size, N is the population size e is the level of precision

The level of precision, sometimes called sampling error, is the range in which the true value of the population is estimated to be. This range is often expressed in percentage points, (e.g., ±5 percent) A proportion of .5 indicates the maximum variability in a population 21

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