developing the sampling plan
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Developing the Sampling Plan. Chapter 9, Student Edition. Learning Objectives. Explain the difference between a parameter and a statistic Explain the difference between a probability sample and a nonprobability sample List the primary types of nonprobability samples - PowerPoint PPT PresentationTRANSCRIPT
MR/Brown & Suter
© 2012 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
Developing the Sampling Plan
Chapter 9, Student Edition
1
© 2012 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
MR/Brown & Suter2
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
© 2012 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
MR/Brown & Suter3
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
© 2012 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
MR/Brown & Suter4
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
© 2012 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
MR/Brown & Suter5
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
© 2012 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
MR/Brown & Suter6
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
© 2012 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
MR/Brown & Suter7
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
© 2012 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
MR/Brown & Suter8
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
© 2012 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
MR/Brown & Suter9
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
© 2012 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
MR/Brown & Suter10
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
© 2012 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
MR/Brown & Suter11
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
© 2012 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
MR/Brown & Suter12
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
© 2012 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
MR/Brown & Suter13
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
© 2012 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
MR/Brown & Suter14
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.
© 2012 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
MR/Brown & Suter15
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
© 2012 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
MR/Brown & Suter16
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
© 2012 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
MR/Brown & Suter17
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
© 2012 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
MR/Brown & Suter18
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)
© 2012 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
MR/Brown & Suter19
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
© 2012 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
MR/Brown & Suter20
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
© 2012 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
MR/Brown & Suter21
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
© 2012 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
MR/Brown & Suter22
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