# © 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.

## Presentation on theme: "© 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."— Presentation transcript:

© 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 MR/Brown & Suter 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. Learning Objectives MR/Brown & Suter2 1. Explain the difference between a parameter and a statistic 2. Explain the difference between a probability sample and a nonprobability sample 3. List the primary types of nonprobability samples 4. List the primary types of probability samples 5. Discuss the concept of total sampling elements (TSE) 6. Cite three factors that influence the necessary sample size 7. 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. Learning Objectives MR/Brown & Suter3 1. Explain the difference between a parameter and a statistic 2. Explain the difference between a probability sample and a nonprobability sample 3. List the primary types of nonprobability samples 4. List the primary types of probability samples 5. Discuss the concept of total sampling elements (TSE) 6. Cite three factors that influence the necessary sample size 7. 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. Learning Objective 1 MR/Brown & Suter4  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. Learning Objectives MR/Brown & Suter5 1. Explain the difference between a parameter and a statistic 2. Explain the difference between a probability sample and a nonprobability sample 3. List the primary types of nonprobability samples 4. List the primary types of probability samples 5. Discuss the concept of total sampling elements (TSE) 6. Cite three factors that influence the necessary sample size 7. 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. Learning Objective 2 MR/Brown & Suter6  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. Learning Objective 2 MR/Brown & Suter7  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. Learning Objectives MR/Brown & Suter8 1. Explain the difference between a parameter and a statistic 2. Explain the difference between a probability sample and a nonprobability sample 3. List the primary types of nonprobability samples 4. List the primary types of probability samples 5. Discuss the concept of total sampling elements (TSE) 6. Cite three factors that influence the necessary sample size 7. 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. Learning Objective 3 MR/Brown & Suter9  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. Learning Objective 3 MR/Brown & Suter10  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. Learning Objective 3 MR/Brown & Suter11  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. Learning Objectives MR/Brown & Suter12 1. Explain the difference between a parameter and a statistic 2. Explain the difference between a probability sample and a nonprobability sample 3. List the primary types of nonprobability samples 4. List the primary types of probability samples 5. Discuss the concept of total sampling elements (TSE) 6. Cite three factors that influence the necessary sample size 7. 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. Learning Objective 4 MR/Brown & Suter13  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. Learning Objective 4 MR/Brown & Suter14  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. Learning Objective 4 MR/Brown & Suter15  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. Learning Objective 4 MR/Brown & Suter16  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. Learning Objectives MR/Brown & Suter17 1. Explain the difference between a parameter and a statistic 2. Explain the difference between a probability sample and a nonprobability sample 3. List the primary types of nonprobability samples 4. List the primary types of probability samples 5. Discuss the concept of total sampling elements (TSE) 6. Cite three factors that influence the necessary sample size 7. 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. Learning Objective 5 MR/Brown & Suter18  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. Learning Objectives MR/Brown & Suter19 1. Explain the difference between a parameter and a statistic 2. Explain the difference between a probability sample and a nonprobability sample 3. List the primary types of nonprobability samples 4. List the primary types of probability samples 5. Discuss the concept of total sampling elements (TSE) 6. Cite three factors that influence the necessary sample size 7. 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. Learning Objective 6 MR/Brown & Suter20  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. Learning Objectives MR/Brown & Suter21 1. Explain the difference between a parameter and a statistic 2. Explain the difference between a probability sample and a nonprobability sample 3. List the primary types of nonprobability samples 4. List the primary types of probability samples 5. Discuss the concept of total sampling elements (TSE) 6. Cite three factors that influence the necessary sample size 7. 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. Learning Objective 7 MR/Brown & Suter22  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