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Chapter 7 Selecting Samples

Chapter 7 Selecting Samples. Selecting samples. Population, sample and individual cases Source: Saunders et al . (2009). Figure 7.1 Population, sample and individual cases. The need to sample. Sampling- a valid alternative to a census when

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Chapter 7 Selecting Samples

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  1. Chapter 7Selecting Samples

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

  3. 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

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

  5. The sampling frame • The sampling frame for any probability sample is a complete list of all the cases in the population from which your sample will be drown.

  6. 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

  7. 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

  8. Sample size Choice of sample size is influenced by • Confidence needed in the data • Margin of error that can be tolerated • Types of analyses to be undertaken • Size of the sample population and distribution

  9. 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

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

  11. Simple random(Random sampling) • Involves you selecting at random frame using either random number tables, a computer or an online random number generator such as Research Randomizer

  12. Systematic sampling • Systematic sampling involves you selecting the sample at regular intervals from the sampling frame. • Number each of the cases in your sampling frame with a unique number . The first is numbered 0, the second 1 and so on. • Select the first case using a random number. • Calculate the sample fraction. • Select subsequent cases systematically using the sample fraction to determine the frequency of selection

  13. Stratified random sampling • Stratified random sampling is a modification of random sampling in which you divide the population into two or more relevant and significant strata based in a one or a number of attributes. In effect, your sampling frame is divided into a number of subsets. A random sample (simple or systematic) is then drown from each of the strata. Consequently stratified sampling shares many of the advantages and disadvantages of simple random or systematic sampling

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