Chapter 15Sampling

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