Chapter 15 Sampling

Overview  Introduction  Nonprobability Sampling  Selecting Informants in Qualitative Research  Probability Sampling  Sampling and Bias  Probability Sampling Designs  Multistage Cluster Sampling

Introduction  Sampling is the process of selecting observations – Probability Sampling (random) – Nonprobability Sampling  Sample: a subset of a population that is observed for purposes of making inferences about the nature of the total population

Nonprobability Sampling  Used when probability or random sampling is not possible or appropriate (e.g., homeless individuals)  Generally less reliable, but often easier and cheaper  3 types: – Reliance on available subjects (convenient sample) – Purposive or judgmental sampling (based on your knowledge) – Snowball sampling

Types of Nonprobability Sampling  Reliance on Available Subjects – Sampling from subjects who are available (e.g., how much an agency’s services help a particular client or group of clients)  Purposive or Judgmental Sampling – When a researcher uses his or her own judgment in selecting sample members (e.g., handpick community leaders or experts known for their expertise on target population)

Four Types of Nonprobability Sampling  Snowball Sampling – Process of accumulation as each located subject suggests other participants

Selecting Informants in Qualitative Research  Informants are members of the group or other people knowledgeable about it who are willing to talk about the group  When informants are used, they should be selected in such a fashion as to provide a broad, diverse view of the group under study

Probability Sampling: The Logic  Chief criterion of the quality of a sample: – Degree to which a sample is representative – that is, the extent to which the characteristics of the sample resemble those of the population for which it was selected  Probability sampling methods are one approach to selecting samples that will be quite representative

Probability Sampling: The Logic  Basic principle is that all members of population will have an equal chance of being selected in the sample, known as equal probability of selection method  Even the most carefully selected sample will almost never perfectly represent the population from which it was selected  There will always be some degree of sampling error, which can be estimated

Probability Sampling: The Ultimate Purpose  To select a set of elements from a population in such a way that descriptions of those elements accurately portray the total population from which elements are selected  The key to this process is random selection, where each element has an equal chance of selection independent of any other event in the selection process

Probability Sampling: Sampling Frames and Populations  A sampling frame is a list or quasi-list of members of a population (e.g., student roster, list of census blocks, telephone directory)  Examples of populations that can be sampled from a sampling frame include elementary school children, high school students, church members, factory workers, and members of professional associations

Probability Sampling: Biases to Avoid  Overgeneralization occurs when sampling frames are not consonant to which we seek to generalize  Nonresponse bias occurs when a substantial number of people in a randomly selected sample choose not to participate

Probability Sampling: Biases to Avoid  Cultural bias is the unwarranted generalization of research findings to the population as a whole when one culture or ethnic group is not adequately represented in the sample  Gender bias is the unwarranted generalization of research findings to the population as a whole when one gender is not adequately represented in the sample

Probability Sampling in Review  May be extremely simple or extremely difficult, time-consuming, and expensive  However, it remains the most effective method for selecting study elements:  Avoids conscious or unconscious biases in selecting elements  Permits estimates of sampling error