Research Methods in Crime and Justice

1. Research Methods in Crime and Justice Chapter 8 Sampling

2. Sampling • Sampling is a scientific technique that allows a researcher to learn something about a population by studying a few members, or a sample of that population. • There are numerous types of sampling. • Some sampling techniques allow researchers to predict something about a population. • Other sampling techniques merely provide researchers insight into a population.

3. Sampling • Sampling allows researchers to learn a great deal about a population without having to solicit information from every member of that population. • Without sampling, researchers would not have the time or money to study and learn about large populations.

4. Sampling Basics • Scientifically, sampling is based on the central limit theorem. • Proposes that numerous samples from a population will produce about the same result. • The results from these samples (when plotted on a graph, will be normally distributed (i.e. cluster around the middle).

5. Sampling Basics • A population is the entire set of individuals or groups that is relevant to a research project. • A census collects information from an entire population. • A sample collects information from a group within that population.

6. Sampling Basics • The terms members, cases and elements are used interchangeably to describe the individual components of a population or sample. • A list of the individual components of a population is referred to as a sampling frame. • The exact process used to select the sample is called a sampling plan.

7. Sample Bias and Precision • Bias is a condition that causes a sample to be unrepresentative of the population from which it came. There are two primary causes. • Random sampling error. • Selection bias

8. Sample Bias and Precision • Random sampling error is one form of bias. • It represents the difference between the results the researcher gets from the sample and what the results might have been had the entire population been polled. • Samples from highly diverse populations tend to have more sampling error.

9. Sample Bias and Precision • Bias may also be caused by selection. • Selection bias is any process that systematically increases or decreases the chances that certain members of a population will be selected into the sample. • Ideally, all members of a population should have an equal chance of being selected into the sample.

10. Sample Bias and Precision • A sample’s level of precision is a measure of a sample’s representativeness to the population from which it came.

11. Sample Bias and Precision • A sample’s level of precision is determined by; • The size of the sample in relation to the size of the population. • Larger populations tend to require larger samples. • The diversity within the population. • Highly diverse populations require larger samples. • The frequency at which the social phenomenon of interest occurs within the population. • Rare or infrequent phenomenon require larger samples.

12. Types of Sampling • There are two major types of sampling • Probability sampling techniques • Rely on the random selection of cases • Allow researchers to predict what is happening in the population based on what they learn from the sample. • Non-Probability sampling techniques • Do not rely on the random selection of cases. • Do not allow researchers to predict what is happening in the population based on what they learn from the sample.

13. Probability Sampling • In simple random sampling, a researcher randomly selects cases into a sample directly from a population. • Each member must have an equal and non-zero chance of being selected into the sample.

14. Probability Sampling • In systematic random sampling, a researcher uses a structured process to randomly select cases into a sample. • The researcher might select every tenth case from the population.

15. Probability Sampling • In cluster sampling (a form of multi-stage sampling), researchers identify natural groupings (i.e., clusters) within the population. • Some of these natural groupings are randomly selected in the initial stage of the sampling process. • Cases are then randomly selected from the chosen clusters until an appropriate sized sample has been collected.

16. Probability Sampling • In stratified random sampling (a form of multi-stage sampling) , researchers create groupings within the population. • From each of these groupings (i.e. strata) the researcher will randomly select cases until an appropriate sized sample is collected.

17. Non-Probability Sampling • Although prediction from the sample to the population is not possible, non-probability sampling techniques offer clear advantages. • Allows researchers to take advantage of a long term association with of research subjects. • A definitive list of the population (sampling frame) may not be available. • Allows researchers to study distinctive or generally inaccessible research subjects.

18. Non-Probability Sampling • A convenience sample is created when a researcher selects a sample from a group of people who are at hand or easily available. • A convenience sample As the name implies, these members are convenient to or known by the researcher. • Also known as an availability sample.

19. Non-Probability Sample • A snowball sampling relies on the sample members themselves to increase the sample size. • After a member of the population is identified the researcher asks the member to identify other members of the population. • The researcher contacts these prospective members and repeats the process until the sample produces meaningful results.

20. Non-Probability Sample • Sometimes a sample can be a single case. • Case studies are highly detailed inquiries into or descriptions of a population or phenomenon. There are two types of case samples. • Typical case sample • Extreme case sample

21. Non-Probability Sample • A typical case sample involves a single case that exemplifies a common or typical pattern within the population. • An extreme case sample involves a single case that is atypical, uncharacteristic or uncommon within the population.

22. Getting to the Point • Sampling is a scientific technique that allows a researcher to learn something about a population by studying only a few members of the population. • Sampling is based on a concept called the central limit theorem. The central limit theorem gives us confidence that if we collect a large enough sample, the sample will be representative of the larger population.

23. Getting to the Point • A population is the entire set of individuals or groups that is relevant to a research project. • A census collects information from an entire population.

24. Getting to the Point • The terms members, cases and elements are used interchangeably to describe the individual components of a population or sample. • A list of the individual components of a population is referred to as a sampling frame. • The exact process used to select the sample is called a sampling plan.

25. Getting to the Point • Random sampling error is one form of bias. • It represents the difference between the results the researcher gets from the sample and what the results might have been had the entire population been polled. • Samples from highly diverse populations tend to have more sampling error.

26. Getting to the Point • Selection bias is another form of bias. • It is caused by any process that systematically increases or decreases the chances that a member of a population will be selected into the sample.

27. Getting to the Point • A sample’s representativeness of a population is referred to as its level of precision. • The level of precision is influenced by the size of the population; • the amount of variability within the population, and • the frequency with which relevant social phenomena occurs.

28. Getting to the Point • Probability sampling is a general type of sampling that relies on random selection. • Random selection means that each member of a population has an equal and non-zero chance of being selected into the sample.

29. Getting to the Point • In simple random sampling, a researcher randomly selects cases into a sample directly from a population, similar to drawing names out of a hat. • In systematic random sampling, a researcher uses a structured process to randomly select cases into a sample. For example, the researcher might select every tenth case from the population.

30. Getting to the Point • In cluster sampling, researchers identify natural groupings (i.e., clusters) that exist within the population. • Some of these natural groupings are randomly selected in the initial stage of the sampling process. • Cases are then randomly selected from the chosen clusters until an appropriate sized sample has been reached.

31. Getting to the Point • Stratified random sampling is a multi-stage probability sampling technique that involves randomly selecting cases from groups created within the population. • These groups, called strata, are defined by the researcher. • This form of probability sampling helps researchers insure the sample will be representative of the overall population.

32. Getting to the Point • Non-probability sampling techniques do not rely on random selection and therefore do not allow a researcher to use the sample to predict what might be happening in the larger population. • Even so, non-probability samples can provide in-depth information on a population that might not otherwise be accessible and/or information that can be used to develop theories about various phenomena.

33. Getting to the Point • Convenience samples, also known as availability samples, are created when a researcher selects a sample from a group of people who are at hand or easily available. • Normally, researchers rely upon their own experience and judgment when creating a convenience sample.

34. Getting to the Point • Snowball sampling is a non-probability sampling technique that relies on the sample members themselves to increase the sample size. • Members recruited into the sample identify other members of the population and refer the researcher to these contacts until the sample ‘snowballs’ in size.

35. Getting to the Point • Typical and extreme case samples consist of a single member of a population . • In a typical case sample, a researcher uses a case study to illustrate a common or typical pattern. • In an extreme case sample, a researcher uses a case study to illustrate an uncommon or atypical pattern.

36. Research Methods in Crime and Justice Chapter 8 Sampling