Planning for Research: Selecting Participants Presentation 7 Leacock, Warrican & Rose (2009)

Identifying Sources of Data Population: The group of interest to the researcher. This is the group to which the researcher would like to generalize the research findings or about which the questions are being asked. e.g. 1) Primary school teachers 2) Children from middle class families 3) 10-year old girls who have diabetes Accessible (available) Population The group to which the researcher realistically has access. Leacock, Warrican & Rose (2009)

Leacock, Warrican & Rose (2009) The population may be small and may be accessible to the researcher or it may be very large and far-flung. It may be prudent to work with only a portion of the population, that is, the researcher may have to work with a sample of the population. Leacock, Warrican & Rose (2009)

Leacock, Warrican & Rose (2009) Sample A subset of the population to which the research findings are to be generalized or about which the questions are being asked. Sampling The process of selecting research participants in such a way that those chosen are representative of the larger group from which they are selected. The purpose of sampling is to learn something about the population. Leacock, Warrican & Rose (2009)

Leacock, Warrican & Rose (2009) Sampling Techniques Probability sampling A sample is selected in such a way that each member of the population has a nonzero chance of being selected and the probability of being selected is known. When probability sampling is used, statistical procedure can be used to make inferences about the population. Non-probability sampling The probability of a member of the population being selected is unknown. Statistical inferences about the population cannot be made. Leacock, Warrican & Rose (2009)

Probability Sampling Techniques Simple random sampling Systematic sampling Stratified sampling Cluster sampling Leacock, Warrican & Rose (2009)

Simple Random Sampling Each member of the sample is chosen at random. Each member of the population has an equal chance of being selected for the sample. At its simplest, all the names of those in the population can be put in a container and the sample selected by drawing names one at a time. Selected names must be returned to the container before the next name is drawn. OR Each member is assigned a number and a table of random numbers can be used to select. Me? Why me? I’ve got your number!! I never get picked! (Sigh!) Leacock, Warrican & Rose (2009)

Systematic Sampling Hmmm! Choose every second person. Population is arranged in some order, and every nth member selected. The stating point is randomly chosen. The starting point determines the sample. (n = population size / sample size) Start here! Leacock, Warrican & Rose (2009)

Stratified Sampling The population is divided into subgroups (STRATA) of members who share some characteristic(s) and then members of each stratum are randomly selected. Equal allocation – equal numbers from each strata are selected. Proportionate sampling or Proportional allocation – each group is represented in the sample in the same proportion as it exists in the population. Sampling fraction – ratio of sample size to population size. (n / N) I’ll take two of you! And two of you! And Hey! Get back here! Overlooked again! (Sigh!) Leacock, Warrican & Rose (2009)

Hmmm! How many of these clusters do I need! Cluster Sampling Groups or clusters of members of the population are randomly selected. The exact size of the sample is not known until after the sample is selected. How many clusters? Decide on the sample size (e.g. 100 employees) Ascertain the mean size of the clusters (approx. 25 in each department) Divide the sample size by the mean size of the clusters to decide how many clusters to select. (100/25 = 4 departments) Randomly select 4 clusters. Leacock, Warrican & Rose (2009)

Non-probability Sampling Techniques Purposive sampling Convenience sampling Snowball sampling Quota sampling Leacock, Warrican & Rose (2009)

Leacock, Warrican & Rose (2009) Purposive Sampling Now, all I need are the villagers who are in love! Members of the sample are selected based on their possession of specific characteristics that are critical to the research. They are information-rich. Leacock, Warrican & Rose (2009)

Leacock, Warrican & Rose (2009) The local YMCA may not be ideal, but it is the only place where I can find people at this time, so I’m going to grab them! Convenience Sampling Members of the sample are selected based on their availability. Leacock, Warrican & Rose (2009)

Leacock, Warrican & Rose (2009) Thanks for the interview Bugsy! Is there any other member of your group who might talk to me? Snowball Sampling The researcher identifies one member of the population with the desired characteristics. After this person is interviewed, he/she is used as an informant to identify other persons and so on. Often used when the persons of interest are not easily identified or prefer not to be identified. Leacock, Warrican & Rose (2009)

He walked right by me! (Sigh!) Quota Sampling Representatives from various groups in the population are sought. The researchers has guidelines to help them identify members of the various categories, and a quota for each category. The researchers then use various ways and means of finding such persons, and continue until they have satisfied their quota. Visit offices, homes, stop passers-by, intercept shoppers in car parks etc. Biases: Certain members of the population are excluded and not represented in the sample. E.g. homes with big dogs; offices with mean-looking security guards; unfriendly-looking shoppers. Sorry, Buddy! I’m busy! Excuse me! Can I ask you some questions? He walked right by me! (Sigh!) Leacock, Warrican & Rose (2009)

Leacock, Warrican & Rose (2009) Sample Size For quantitative studies, the size of the sample is influenced by the type of statistical procedure you want to run. For qualitative studies, sample sizes are relatively small. If using interviews, remember that it takes about 8 hours to transcribe a one-hour interview. Leacock, Warrican & Rose (2009)

Leacock, Warrican & Rose (2009) Sample Size Different writers give different advice about sample size. General guidelines for some quantitative designs are: For a survey, a minimum of 100 is suggested. For experiment, about 15 participants in each group For correlational and causal-comparative studies, a minimum of 30 Decisions! Decisions! Leacock, Warrican & Rose (2009)

Leacock, Warrican & Rose (2009) Sampling Error There are two types of “sampling error”. 1. Non-random sampling error Also known as sampling bias Is a FLAW in the sampling design Caused by inappropriate sampling technique Cannot be measured once the data are collected Can be fatal to your research MUST be avoided at all cost Leacock, Warrican & Rose (2009)

Leacock, Warrican & Rose (2009) Sampling Error 2. Random Sampling Error Is NOT a mistake made by the researcher It is the result of the nature of selecting a sample by a random method It comes about because the researcher selects only ONE of many possible samples, where some are better representations of the population than others. Random sampling error can be estimated. When large, it indicates that the sample measurement diverts significantly from that of the population Random sampling error can be reduced by increasing the sample size. Also called Standard Error of the Mean or Standard Error Leacock, Warrican & Rose (2009)

Leacock, Warrican & Rose (2009) Your aim is to Eliminate non-random sampling error Keep standard error as low as possible Standard error can be minimised by paying attention to the size of the sample. The smaller the sample, the higher the standard error is likely to be. A larger sample is likely to be a better representation of the population and hence more likely to have a small standard error. Some writers suggest that increasing your sample beyond a certain size does very little to reduce the standard error. For example, Gay & Airasian (2003) suggest that for a survey, increasing the sample size beyond 400 (no matter how large the population) will have a negligible effect on the sampling error. Leacock, Warrican & Rose (2009)

Effects Of Sampling On Validity & Reliability Sampling bias affects ability to generalise. Non-random sampling affects the type of statistical procedures you can use. [Some procedures (parametric ones) work on the assumption that the sample was randomly selected] Leacock, Warrican & Rose (2009)

Leacock, Warrican & Rose (2009) Remember: Sampling is a very important part of your study. The manner in which your sample is selected can have an impact on the types of analysis procedures that you can apply. The size of your sample can also have an impact. Recommendations are made for the minimum sample size for different research designs. Consult these when deciding on how many participants to select for your study. Give serious thought to your sample and ensure that you select one that meets the requirements of your study. Leacock, Warrican & Rose (2009)

Leacock, Warrican & Rose (2009) The End  Leacock, Warrican & Rose (2009)