# Chapter 5 Copyright © Allyn & Bacon 2008 This multimedia product and its contents are protected under copyright law. The following are prohibited by law:

## Presentation on theme: "Chapter 5 Copyright © Allyn & Bacon 2008 This multimedia product and its contents are protected under copyright law. The following are prohibited by law:"— Presentation transcript:

Chapter 5 Copyright © Allyn & Bacon 2008 This multimedia product and its contents are protected under copyright law. The following are prohibited by law: Any public performance or display, including transmission of any image over a network; Preparation of any derivative work, including the extraction, in whole or in part, of any images; Any rental, lease, or lending of the program.

Discussion Topics Participants, subjects, and samples Probability sampling Non-probability sampling Issues related to sampling Criteria for evaluating sampling procedures Copyright © Allyn & Bacon 2008

Subjects, Participants, and Samples Participant or Subject Person from whom data are collected The term “subject” is gradually being phased out It is being replaced by “participant” and “source of data” Sample – the collective group of subjects or participants from whom data are collected Copyright © Allyn & Bacon 2008

Sampling Procedures Two types of procedures Probability Statistically driven sampling techniques where the probability of being selected is known Purpose is to select a group of participants representative of the larger group of subjects from which they are selected Non-probability Pragmatically driven sampling techniques where the probability of being selected is not known Purpose is to select participants who can be particularly informative about the research issues Copyright © Allyn & Bacon 2008

Probability Sampling Method of sampling in which participants are selected randomly from a population in such a way that the researcher knows the probability of selecting each participant. In a sample of 10 from a population of 100, each subject has a 10% chance of being included in the sample In a sample of 50 from a population of 100, each participant has a 50% chance of being in included in the sample Copyright © Allyn & Bacon 2008

Probability Sampling Population: a large group of individuals to whom the results of a study can be generalized Target population: the group to whom the results are intended to be generalized Sampling frame (i.e., survey population or accessible population) The group to whom the researcher has access and from which the actual sample will be drawn Often the sampling frame and the target population are different Copyright © Allyn & Bacon 2008

Probability Sampling The goal of probability sampling is to select a sample that is representative of the population from which it is selected Sampling error - the difference between the “true” result and the “observed” result that can be attributed to using samples rather than populations Sampling error Sampling bias - the difference between the “observed” and “true” results that is attributed to the sampling mistakes of the researcher. Sampling bias Copyright © Allyn & Bacon 2008

Probability Sampling Types of probability techniques Simple random - a number is assigned to each subject in the population and a table of random numbers or a computer is used to select subjects randomly from the population Systematic - a number is assigned to each subject in the population, and every n th member of the population is selected Copyright © Allyn & Bacon 2008

Probability Sampling Types of probability techniques Stratified sampling - similar to random sampling with the exception that subjects are selected randomly from strata, or subgroups, of the population Strata: homogeneous subgroups within a population Males and females Certified and non-certified teachers Proportional stratified sample Disproportional stratified sampling Copyright © Allyn & Bacon 2008

Probability Sampling Types of probability techniques Cluster sampling: similar to random sampling except that naturally occurring groups are randomly selected first, then subjects are randomly selected from these sampled groups Useful when it is impossible to identify all of the individuals in a population Typical educational clusters are districts, schools, or classrooms Copyright © Allyn & Bacon 2008

Probability Sampling Five steps in selecting probability samples Define the target population Identify the sampling frame Determine the sample size Select the sampling strategy (i.e., procedure) Select the sample Copyright © Allyn & Bacon 2008

Non-Probability Sampling Method of sampling in which the probability of selecting a participant is unknown It is often not possible to use probability sampling techniques due to access, time, resource or financial constraints It is often desirable to select subjects who can be particularly informative about the research issues Copyright © Allyn & Bacon 2008

Non-Probability Sampling Three categories of non-probability sampling procedures Convenience sampling Purposive Quota Copyright © Allyn & Bacon 2008

Non-Probability Sampling Convenience sampling: selecting a participant or group of participants based on their availability to the researcher Typical of much educational research given the constraints under which it is conducted The major concern is the limited generalizability of the results from the sample to any population Examples Students enrolled in the researcher’s classes Fourth-grade students in two local, parochial schools to which the researcher has access Copyright © Allyn & Bacon 2008

Non-Probability Sampling Purposive sampling: selection of particularly informative or useful participants Typically selects a few information-rich participants who are studied in-depth Also known as purposeful sampling Examples It is reasonable to select “expert” teachers if one is trying to understand how teachers use effective instructional strategies It is reasonable to select physically fit individuals if one is trying to identify effective exercise behaviors Copyright © Allyn & Bacon 2008

Non-Probability Sampling Quota sampling: non-random sampling representative of a larger population Used when the researcher cannot use probability sampling procedures but does want a sample that is somewhat representative of the population Similar to stratified sampling with the exception that the subjects are selected non-randomly Copyright © Allyn & Bacon 2008

Non-Probability Sampling Types of non-probability techniques Typical case: selecting a representative participant Extreme case: selecting a unique or atypical participant Maximum variation: selecting participants to represent extreme cases Snowball (i.e., network): selecting participants from recommendations of other participants Critical case: selecting the most important participants to understand the phenomena being studied Copyright © Allyn & Bacon 2008

Using Sampling Procedures Quantitative studies The desired use of probability sampling due to the ability to generalize the results to the larger population Frequent use of non-probability techniques - particularly convenience sampling - due to access, time, resource, or financial constraints Qualitative studies Almost exclusive reliance on non-probability techniques - particularly purposeful sampling Copyright © Allyn & Bacon 2008

Sampling and Results How might the sampling procedures affect the results? Need to identify the sampling procedure used Need to evaluate the sampling procedure in light of the research problems and conclusions Need to consider the strengths and weaknesses of specific sampling procedures (see Table 5.2, p. 123) Copyright © Allyn & Bacon 2008

Sampling and Results Sampling error: the difference between the “true” result and the “observed” result that can be attributed to using samples rather than populations In a sample of 99 from a population of 100 The observed result is likely to be very, very close to the true result Sampling error is minimal In a sample of 2 from a population of 100 The observed result is likely to be somewhat different from the true result Sampling error is high Copyright © Allyn & Bacon 2008

Sampling and Results Sampling bias: the difference between the “observed” and “true” results that is attributed to the sampling mistakes of the researcher Deliberately sampling participants with certain attributes Positive attitudes High self-esteem High level of achievement Using participants from different populations and assigning them to different treatment groups Males to an experimental treatment group and females to a traditional treatment group Copyright © Allyn & Bacon 2008

Sampling and Results How might the characteristics of the participants affect the results? Volunteer samples Different characteristics between volunteers and non-volunteers can lead to different responses Educational level Socio-economic status Need for social approval Conformity Commonly used due to their availability Copyright © Allyn & Bacon 2008

Sampling and Results Subject motivation Specific characteristics of the sample can predispose them to respond in certain ways Only selecting teachers using holistic language strategies would likely predispose them to respond favorably to an attitudinal scale focusing on holistic language instruction Only selecting students who participate in extra-curricular activities might predispose them to certain types of responses Copyright © Allyn & Bacon 2008

Sampling and Results Sample size - general rules of thumb Quantitative studies 30 participants for correlational research 15 participants in each group for experimental research Approximately 250 responses for survey research Qualitative studies - a sufficient number of participants are needed to ensure that no new information is forthcoming from additional cases Copyright © Allyn & Bacon 2008

Sampling and Results Sample Size Need to interpret results very carefully - results form studies using very large or very small samples can be misleading Results indicating “no difference” or “no relationship” in studies with small samples an be problematic Results of “differences” or “relationships” in studies can be problematic Copyright © Allyn & Bacon 2008

Criteria for Evaluating Sampling Procedures Participants should be described clearly with specific and detailed information related to demographic and other personal characteristics The population should be clearly defined. The sampling procedure should be clearly described. Copyright © Allyn & Bacon 2008

Criteria for Evaluating Sampling Procedures The return rate should be reported and analyzed. Less than a 60% return rate requires further analysis Comparison of the responses of respondents to non- respondents The selection of participants should be free of bias. Copyright © Allyn & Bacon 2008

Criteria for Evaluating Sampling Procedures Selection procedures should be appropriate for the problem being investigated. Adequate sample sizes should be used. Qualitative studies should have informative and knowledgeable subjects. Copyright © Allyn & Bacon 2008

Download ppt "Chapter 5 Copyright © Allyn & Bacon 2008 This multimedia product and its contents are protected under copyright law. The following are prohibited by law:"

Similar presentations