Developing the Sampling Plan

MR/Brown & Suter

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Developing the Sampling Plan

Chapter 9, Student Edition

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Learning Objectives1. Explain the difference between a parameter and

a statistic2. Explain the difference between a probability

sample and a nonprobability sample3. List the primary types of nonprobability samples4. List the primary types of probability samples5. Discuss the concept of total sampling elements

(TSE)6. Cite three factors that influence the necessary

sample size7. Explain the relationship between population size

and sample size


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and sample size


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Learning Objective 1 Parameter

A characteristic or measure of a population

If it were possible to take measures from all members of a population without error, a true value of a parameter could be determined

Statistic A characteristic or

measure of a sample Statistics are

calculated from sample data and used to estimate population parameters


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and sample size


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Learning Objective 2 Nonprobability Sample

A sample that relies on personal judgment in the element selection process

Neither sampling error nor the margin of sampling error can be estimated or calculated

Techniques include Convenience Judgment

Snowball Quota

Probability Sample A sample in which

each target population element has a known, nonzero chance of being included in the sample

Techniques include Simple Random Systematic Stratified Cluster

Area


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Learning Objective 2 Nonprobability Sample

Neither sampling error nor the margin of sampling error can be estimated or calculated

Inferences cannot be made about the population

Inferences are limited to the sample

Thus, results are not generalizable from the sample to the population

Probability Sample One can statistically

assess level of sampling error

Inferences can be made about the population, and not just the sample

Inferences are not limited to the sample

Thus, results are generalizable from the sample to the population


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Learning Objective 3 Convenience Sample (Nonprobability

Technique) Population elements are sampled simply because

they are in the right place at the right time Also called “Accidental” Sample

Example – Television news “question of the day” polls


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Learning Objective 3 Judgment Sample (Nonprobability Technique)

Population elements are handpicked because they are expected to serve the research purpose Example – Hire panelists who are knowledgeable about

the issue being researched rather than selecting them at random

Snowball Sample (Nonprobability Technique) Initial sample chosen by a probability technique (e.g.,

systematic sampling) then the population elements are asked for referrals of others they know who might be interested in participation Example – A demand study for a new product where initial

respondents know people with a high interest level within the product category


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Learning Objective 3 Quota Sample (Nonprobability Technique)

Sample chosen so that the proportion of sample elements with certain characteristics is about the same as the proportion of the elements with the characteristics in the target population

Stated more simply, certain important characteristics of the population are represented proportionately in the sample Example – Research Problem: Investigate 100 undergraduate

student attitudes toward a controversial new technology fee Known Population Parameters: Class (30% Freshman, 20%

Sophomores, 30% Juniors, 20% Seniors) and Gender (50% Female, 50% Male)

Approach: 10 students will interview 10 friends each for a total of 100 responses


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and sample size


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Learning Objective 4 Simple Random Sample (Probability

Technique) Walking down the street and passing out surveys

to unknown people “at random” is “random” in the everyday sense, but not random in a scientific sample sense Example – Sample is drawn by a computer or from a

physical list using a random number table


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Learning Objective 4 Systematic Sample (Probability Technique)

Sample in which every kth element (k = sampling interval) in the population is selected for the sample pool after a random start Example – Research Problem: Investigate 250

undergraduate student attitudes toward controversial new technology fee

Known Population: 5000 students published in the campus directory

Approach: k = 5000/250 = 20 or 1 out of every 20 students on campus will be surveyed. Randomly select the first name then count down 20 names. Select that person to be surveyed and then count down 20 names again. Select that person and so on until you get 250 names.


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Learning Objective 4 Stratified Sample (Probability Technique)

Sample in which (1) the population is divided into mutually exclusive and exhaustive subsets and (2) a simple random sample of elements is chosen independently from each group/subset

Most appropriate when subsets (or strata) are homogeneous within but heterogeneous between with respect to key variables Example – Phoenix is one subset, Tucson is a second

subset, and all other residents within the state of Arizona constitute a third subset


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Learning Objective 4 Cluster Sample (Probability Technique)

Like stratified sampling, (1) the population is divided into mutually exclusive and exhaustive subsets

Unlike stratified sampling, (2) a simple random sample of subsets (i.e., clusters) is chosen

Most appropriate when subsets (or strata) are heterogeneous within but homogeneous between with respect to key variables

Area Sampling (Probability Technique) A form of cluster sampling that uses census tracks or

city blocks as sampling units


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Learning Objective 5 It is common that information cannot be

collected from or about all elements chosen for a sample Bad contact information Refusal to participate Inability to reach the potential respondent

To overcome this inevitable situation, it is usually necessary to draw a larger number of sample elements to ultimately achieve the desired sample size

This larger number of elements is known as total sampling elements (TSE)


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Learning Objective 6 Three basic factors affect the size of sample

needed when working with a probability sample Amount of Diversity or Variation

As diversity/variation increases, larger samples are required Degree of Precision

As need for precision increases, larger samples are required Degree of Confidence

Confidence increases as sample size increases At any given sample size, there is a trade-off between

confidence and precision. Higher precision means lower confidence unless we can

increase the sample size


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and sample size


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Learning Objective 7 Size of the population has no bearing on the

size of the sample Desired variation, precision, and confidence

drive the sample size Variation is outside the researcher’s control; it’s an

artifact of the population Precision and Confidence are inversely related

The more similar the population elements, the few people needed regardless of how large the population is

Developing the Sampling Plan

Documents

cengage learning

sample data

population size

necessary sample sizeexplain

accessible website

nonprobability samplelist

sampling planchapter

population parameters