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Chapter 7 Selecting Samples

Population, sample and individual cases
Selecting samples Population, sample and individual cases Source: Saunders et al. (2009) Figure 7.1 Population, sample and individual cases

Sampling- a valid alternative to a census when
The need to sample Sampling- a valid alternative to a census when A survey of the entire population is impracticable Budget constraints restrict data collection Time constraints restrict data collection Results from data collection are needed quickly

Overview of sampling techniques
Source: Saunders et al. (2009) Figure 7.2 Sampling techniques

Probability sampling The four stage process
Identify sampling frame from research objectives Decide on a suitable sample size Select the appropriate technique and the sample Check that the sample is representative

Identifying a suitable sampling frame
Key points to consider Problems of using existing databases Extent of possible generalisation from the sample Validity and reliability Avoidance of bias

Choice of sample size is influenced by
Confidence needed in the data Margin of error that can be tolerated Margin of error (also called The confidence interval ) is the plus-or-minus figure usually reported in newspaper or television opinion poll results. For example, if you use a margin of error of 4 and 47% percent of your sample picks an answer you can be "sure" that if you had asked the question of the entire relevant population between 43% (47-4) and 51% (47+4) would have picked that answer. Types of analyses to be undertaken Size of the sample population and distribution

The importance of response rate
Key considerations Non- respondents and analysis of refusals Obtaining a representative sample Calculating the active response rate Estimating response rate and sample size

Selecting a sampling technique
Five main techniques used for a probability sample Simple random Systematic Stratified random Cluster Multi-stage

Simple random sampling
Number each of the cases in your sampling frame with a unique number. Select cases using random numbers until, actual sample size is reached. Computer aided telephone interviewing (CATI) software

Systematic Random Sampling
Number each of the cases in your sampling frame with a unique number. Select the first case using a random number Calculate the sampling fraction Select subsequent cases systematically using the sampling fraction to determine the frequency of selection. Sampling fraction = actual sample size/ total population

Stratified random sampling
Choose the stratification variable or variables Divide the sampling frame into the discrete strata. Number each of the cases within each stratum with a unique number Select your sample using either simple random or systematic random sampling

Cluster sampling Choose the cluster grouping for your sampling frame.
Number each of the clusters with a unique number. Select sample of clusters using random sampling

Multi-stage sampling

Non- probability sampling (1)
Key considerations Deciding on a suitable sample size Data saturation Selecting the appropriate technique

Non- probability sampling (2)
Sampling techniques Quota sampling (larger populations) Purposive sampling Snowball sampling Self-selection sampling Convenience sampling

Quota Sampling Divide the population into specific groups.
Calculate quota for each group based on relevant and available data Collect data from each quota

Purposive sampling Extreme case/deviant sampling: unusual or special case enable to learn the most about the RQ. Heterogeneous or maximum variation sampling: representing different subgroups Homogeneous sampling: One subgroup. Critical case sampling: If it happen there, it will happen everywhere.

Snowball sampling Make contact with one or two cases in the population. Ask these cases to identify further cases. Ask these new case to identify further new cases. Stop when either no new cases are given or the sample is large enough.

Self select sampling Publicize your need for cases
Collect data from those who respond

Haphazard sampling Also called purposive or availability sampling.
Select case based on ease or convenience.

Summary: Chapter 7 Choice of sampling techniques depends upon the research question(s) and their objectives Factors affecting sample size include: - confidence needed in the findings - accuracy required - likely categories for analysis

Summary: Chapter 7 Probability sampling requires a sampling frame and can be more time consuming When a sampling frame is not possible, non- probability sampling is used Many research projects use a combination of sampling techniques

All choices depend on the ability to gain access to organisations
Summary: Chapter 7 All choices depend on the ability to gain access to organisations

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