Chapter Nine Copyright © 2006 McGraw-Hill/Irwin Sampling: Theory, Designs and Issues in Marketing...
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Transcript of Chapter Nine Copyright © 2006 McGraw-Hill/Irwin Sampling: Theory, Designs and Issues in Marketing...
![Page 1: Chapter Nine Copyright © 2006 McGraw-Hill/Irwin Sampling: Theory, Designs and Issues in Marketing Research.](https://reader033.fdocuments.in/reader033/viewer/2022051820/56649e6c5503460f94b6b02f/html5/thumbnails/1.jpg)
Chapter NineChapter Nine
Copyright © 2006McGraw-Hill/Irwin
Sampling: Theory, Designs and Issues in Marketing Research
![Page 2: Chapter Nine Copyright © 2006 McGraw-Hill/Irwin Sampling: Theory, Designs and Issues in Marketing Research.](https://reader033.fdocuments.in/reader033/viewer/2022051820/56649e6c5503460f94b6b02f/html5/thumbnails/2.jpg)
McGraw-Hill/Irwin 2
1. Discuss the concept of sampling and list reasons for sampling.
2. Identify and explain the different roles that sampling plays in the overall information research process.
3. Demonstrate the basic terminology used in sampling decisions.
4. Understand the concept of error in the context of sampling.
Learning Objectives
![Page 3: Chapter Nine Copyright © 2006 McGraw-Hill/Irwin Sampling: Theory, Designs and Issues in Marketing Research.](https://reader033.fdocuments.in/reader033/viewer/2022051820/56649e6c5503460f94b6b02f/html5/thumbnails/3.jpg)
McGraw-Hill/Irwin 3
5. Discuss and calculate sampling distributions, standard errors, and confidence intervals and how they are used in assessing the accuracy of a sample.
6. Discuss the factors that must be considered when determined sample size.
7. Discuss the methods of calculating appropriate sample sizes.
Learning Objectives
![Page 4: Chapter Nine Copyright © 2006 McGraw-Hill/Irwin Sampling: Theory, Designs and Issues in Marketing Research.](https://reader033.fdocuments.in/reader033/viewer/2022051820/56649e6c5503460f94b6b02f/html5/thumbnails/4.jpg)
McGraw-Hill/Irwin 4
• Sampling• Selection of a small number of elements from a
larger defined target group—information gathered will allow judgments to be made about the larger group
– Census• Includes data about every member of the
defined target population
– Sampling• Used when it is impossible to conduct a
census of the population
Value of Sampling in Information Research
Discuss the concept of sampling and list reasons for sampling
![Page 5: Chapter Nine Copyright © 2006 McGraw-Hill/Irwin Sampling: Theory, Designs and Issues in Marketing Research.](https://reader033.fdocuments.in/reader033/viewer/2022051820/56649e6c5503460f94b6b02f/html5/thumbnails/5.jpg)
McGraw-Hill/Irwin 5
• Role of Sampling– Identifying, developing, and
understanding new marketing constructs that need to be investigated
– Plays an indirect role in the design of the questionnaire
– Enables the researchers to make decisions using limited information
Value of Sampling in Information Research
Identify and explain the different roles that sampling plays in the overall
information research process
![Page 6: Chapter Nine Copyright © 2006 McGraw-Hill/Irwin Sampling: Theory, Designs and Issues in Marketing Research.](https://reader033.fdocuments.in/reader033/viewer/2022051820/56649e6c5503460f94b6b02f/html5/thumbnails/6.jpg)
McGraw-Hill/Irwin 6
• Concept of Sampling
– Making the right decision in the selection of items (i.e., people, products or services)
– Feeling confident that data from the sample can be transformed into accurate information about the target population
Value of Sampling in Information Research
Discuss the concept of sampling and list reasons for sampling
![Page 7: Chapter Nine Copyright © 2006 McGraw-Hill/Irwin Sampling: Theory, Designs and Issues in Marketing Research.](https://reader033.fdocuments.in/reader033/viewer/2022051820/56649e6c5503460f94b6b02f/html5/thumbnails/7.jpg)
McGraw-Hill/Irwin 7
• Basic Sampling Terminology– Population
• Defined target population
– Element – Must be unique– Must be countable– Target population– Identify correctly
– Sampling Units– Sampling Frames
Overview: The Basics of Sampling Theory
Discuss the concept of sampling and list reasons for sampling
![Page 8: Chapter Nine Copyright © 2006 McGraw-Hill/Irwin Sampling: Theory, Designs and Issues in Marketing Research.](https://reader033.fdocuments.in/reader033/viewer/2022051820/56649e6c5503460f94b6b02f/html5/thumbnails/8.jpg)
McGraw-Hill/Irwin 8
• Main Factors Underlying Sampling Theory
– Sampling Discussions– Logic Behind this Perspective– Important Assumption
• Probability distribution• Sampling distribution
Overview: The Basics of Sampling Theory
Discuss the concept of sampling and list reasons for sampling
![Page 9: Chapter Nine Copyright © 2006 McGraw-Hill/Irwin Sampling: Theory, Designs and Issues in Marketing Research.](https://reader033.fdocuments.in/reader033/viewer/2022051820/56649e6c5503460f94b6b02f/html5/thumbnails/9.jpg)
McGraw-Hill/Irwin 9
Exhibit 9.2Discuss the concept of sampling
and list reasons for sampling
![Page 10: Chapter Nine Copyright © 2006 McGraw-Hill/Irwin Sampling: Theory, Designs and Issues in Marketing Research.](https://reader033.fdocuments.in/reader033/viewer/2022051820/56649e6c5503460f94b6b02f/html5/thumbnails/10.jpg)
McGraw-Hill/Irwin 10
Exhibit 9.3Discuss the concept of sampling
and list reasons for sampling
![Page 11: Chapter Nine Copyright © 2006 McGraw-Hill/Irwin Sampling: Theory, Designs and Issues in Marketing Research.](https://reader033.fdocuments.in/reader033/viewer/2022051820/56649e6c5503460f94b6b02f/html5/thumbnails/11.jpg)
McGraw-Hill/Irwin 11
• Central Limit Theorem– for almost all target populations the
sampling distribution of the sample mean or the percentage value derived from a simple random sample will be approximately normally distributed, provided that the sample size is sufficiently large ( i.e., when n is ≥ 30)
Overview: The Basics of Sampling Theory
Discuss the concept of sampling and list reasons for sampling
![Page 12: Chapter Nine Copyright © 2006 McGraw-Hill/Irwin Sampling: Theory, Designs and Issues in Marketing Research.](https://reader033.fdocuments.in/reader033/viewer/2022051820/56649e6c5503460f94b6b02f/html5/thumbnails/12.jpg)
McGraw-Hill/Irwin 12
• With an understanding the basics of the central limit theorem, the researcher can:
– Draw representative samples from any target population
– Obtain sample statistics from a random sample that serve as accurate estimate of the target population’s parameters
– Draw one random sample instead of many, reducing the costs of data collection
– Test more accurately the reliability and validity of constructs and scale measurements
– Statistically analyze data and transform them into meaningful into meaningful information about the target population.
Overview: The Basics of Sampling Theory
Discuss the concept of sampling and list reasons for sampling
![Page 13: Chapter Nine Copyright © 2006 McGraw-Hill/Irwin Sampling: Theory, Designs and Issues in Marketing Research.](https://reader033.fdocuments.in/reader033/viewer/2022051820/56649e6c5503460f94b6b02f/html5/thumbnails/13.jpg)
McGraw-Hill/Irwin 13
• Types of Errors• Classified as being either sampling
or non-sampling– Random sampling errors
– Sampling Error• Any type of bias that is attributable to
mistakes in either drawing a sample or determining sample size– Central Limit Theorem—sampling error can be
reduced by increasing the size of the sample
Overview: The Basics of Sampling Theory
Understand the concept of error in the context of sampling
![Page 14: Chapter Nine Copyright © 2006 McGraw-Hill/Irwin Sampling: Theory, Designs and Issues in Marketing Research.](https://reader033.fdocuments.in/reader033/viewer/2022051820/56649e6c5503460f94b6b02f/html5/thumbnails/14.jpg)
McGraw-Hill/Irwin 14
Exhibit 9.4Discuss the concept of sampling
and list reasons for sampling
![Page 15: Chapter Nine Copyright © 2006 McGraw-Hill/Irwin Sampling: Theory, Designs and Issues in Marketing Research.](https://reader033.fdocuments.in/reader033/viewer/2022051820/56649e6c5503460f94b6b02f/html5/thumbnails/15.jpg)
McGraw-Hill/Irwin 15
• Nonsampling Error– A bias that occurs in a research study
regardless of whether a sample or census is used• Population frame error• Measurement error• Response error• Errors in gathering and recording data
Overview: The Basics of Sampling Theory
Understand the concept of error in the context of sampling
![Page 16: Chapter Nine Copyright © 2006 McGraw-Hill/Irwin Sampling: Theory, Designs and Issues in Marketing Research.](https://reader033.fdocuments.in/reader033/viewer/2022051820/56649e6c5503460f94b6b02f/html5/thumbnails/16.jpg)
McGraw-Hill/Irwin 16
• Statistical Precision– Critical Level of Error– General Precision– Precise Precision
• Estimated standard Error– Measure of the sampling error and an
indication of how far the sample result lies from the actual target population
Overview: The Basics of Sampling Theory
Discuss and calculate sampling distributions, standard errors, and
confidence intervals
![Page 17: Chapter Nine Copyright © 2006 McGraw-Hill/Irwin Sampling: Theory, Designs and Issues in Marketing Research.](https://reader033.fdocuments.in/reader033/viewer/2022051820/56649e6c5503460f94b6b02f/html5/thumbnails/17.jpg)
McGraw-Hill/Irwin 17
Estimating Standard Error-General Precision
/S x s n [( )( )]p qSp
n
• Standard error of the sample mean=Estimated standard deviation of the sample mean divided by the square root of the Sample size
• Standard error of the sample percentage value = square root of [(the % of the sample possessing the characteristic times the % of the sample NOT possessing the characteristic) divided by the Sample size]
![Page 18: Chapter Nine Copyright © 2006 McGraw-Hill/Irwin Sampling: Theory, Designs and Issues in Marketing Research.](https://reader033.fdocuments.in/reader033/viewer/2022051820/56649e6c5503460f94b6b02f/html5/thumbnails/18.jpg)
McGraw-Hill/Irwin 18
Estimating Standard Error-General Precision
/S x s n [( )( )]p qSp
n
• A) Calculate the Standard error for the sample mean if 900 people were interviewed with a estimated sample deviation of 12.5
• The sample mean was 36 • B) Calculate the Standard error of sample percentage if 65% of the sample of 489
people have VCR’s q=(100-p)• The sample proportion was • A) = +- .406 • B) = +- 2.16%• We can then use estimated standard error to construct a confidence interval
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McGraw-Hill/Irwin 19
• Confidence Interval – Statistical range of values within which the true
value of the defined target population parameter is expected to be
_ -
Confidence Intervals
range from almost zero to almost 100 percent, but the most commonly used confidence levels are the 90, 95, and 99 percent levels
Confidence Interval
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McGraw-Hill/Irwin 20
Confidence Interval for population mean and proportions
( )( , )x B CLCI x S Z
( )( , )p B CLCI p p S Z
• Critical Z Value for 90% is 1.65, 95% is 1.96, 99% is 2.58• Calculate the Confidence interval for the population mean if the sample
mean is 25 and the Standard error of the sample mean is 2 with a 90% confidence level
• Answer = 25 +- 3.3 • Calculate the Confidence interval for a population proportion if the sample
proportion is 75% and the Estimated standard error of the sample proportion is 5% with a confidence level of 95%
• 75% +- 9.8%
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McGraw-Hill/Irwin 21
• Determining Sample Size– 3 Factors in Determining Sample Sizes
• Variability of the population characteristic under investigation
• Standard deviation
• Level of confidence desired in the estimate
• Degree of precision desired in estimating the population characteristic
Probability Sampling and Sample Sizes
u por
![Page 22: Chapter Nine Copyright © 2006 McGraw-Hill/Irwin Sampling: Theory, Designs and Issues in Marketing Research.](https://reader033.fdocuments.in/reader033/viewer/2022051820/56649e6c5503460f94b6b02f/html5/thumbnails/22.jpg)
McGraw-Hill/Irwin 22
• When estimating a population meann = (Z2
B,CL)(σ2/e2)
• When estimates of a population proportion are of concern
n = (Z2B,CL)([P x Q]/e2)
Estimate the sample size having a 95% confidence level, a estimate population standard deviation of 5 and a 3% tolerance level of error
Probability Sampling and Sample Sizes
Discuss the methods of calculating appropriate sample size
![Page 23: Chapter Nine Copyright © 2006 McGraw-Hill/Irwin Sampling: Theory, Designs and Issues in Marketing Research.](https://reader033.fdocuments.in/reader033/viewer/2022051820/56649e6c5503460f94b6b02f/html5/thumbnails/23.jpg)
McGraw-Hill/Irwin 23
• There is a direct relationship between the desired CL (90% 95%, 99%) and the require sample size– CL are directly associated with corresponding
critical z-values– The higher the level of confidence required the
larger the sample size• Acceptable tolerance level of error—amount of
precision desired (2%, 5%, or 10%)
Probability Sampling and Sample Sizes
Discuss the methods of calculating appropriate sample size
![Page 24: Chapter Nine Copyright © 2006 McGraw-Hill/Irwin Sampling: Theory, Designs and Issues in Marketing Research.](https://reader033.fdocuments.in/reader033/viewer/2022051820/56649e6c5503460f94b6b02f/html5/thumbnails/24.jpg)
McGraw-Hill/Irwin 24
• Sample Size– Not a product of the population size, it is
not a direct factor in determining sample size
• Finite Correction Factor
• Should be used if the sample size is greater than 5% of the population _FCF = √N-n/N-1
Probability Sampling and Sample Sizes
Discuss the methods of calculating appropriate sample size
![Page 25: Chapter Nine Copyright © 2006 McGraw-Hill/Irwin Sampling: Theory, Designs and Issues in Marketing Research.](https://reader033.fdocuments.in/reader033/viewer/2022051820/56649e6c5503460f94b6b02f/html5/thumbnails/25.jpg)
McGraw-Hill/Irwin 25
1. Determine if the sample size is more than 5% of the population by taking the calculated sample size and dividing it by the known defined target population size
2. If it is, then calculate the appropriate finite correction factor and multiply the originally calculated sample size by it to adjust the required sample size
3. If the target population size is ≤500 should consider doing a census
Probability Sampling and Sample Sizes
Discuss the methods of calculating appropriate sample size
![Page 26: Chapter Nine Copyright © 2006 McGraw-Hill/Irwin Sampling: Theory, Designs and Issues in Marketing Research.](https://reader033.fdocuments.in/reader033/viewer/2022051820/56649e6c5503460f94b6b02f/html5/thumbnails/26.jpg)
McGraw-Hill/Irwin 26
• Sample Size– Researchers can estimate the number of sampling
units that must be surveyed• Not all initial responses are usable
– Inactive mailing addresses
– Telephone number no longer in service
– Incomplete responses
– Factors to consider in drawing a sample• Reachable rate RR• Who is qualified to be included in the survey Overall
Incidence Rate OIR• Expected completion rate ECR
Sample Sizes Versus Usable Observations for
Data Analysis
Discuss the methods of calculating appropriate sample size
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McGraw-Hill/Irwin 27
Calculating the Number of Contacts
• Calculate the number of contacts you require if need a sample 1500 students but only 90% answer the phone and you have determine that 20% of students are taking marketing and do not qualify. Finally you estimate that only 90% will answer all the questions in the survey.
• Number of Contacts is 2315
( ) * ( ) * ( )
nNumber of Contacts
RR OIR ECR
![Page 28: Chapter Nine Copyright © 2006 McGraw-Hill/Irwin Sampling: Theory, Designs and Issues in Marketing Research.](https://reader033.fdocuments.in/reader033/viewer/2022051820/56649e6c5503460f94b6b02f/html5/thumbnails/28.jpg)
McGraw-Hill/Irwin 28
• Value of Sampling in Marketing Research
• Overview: The Basics of Sampling Theory
• Probability Sampling and Sample Sizes
• Nonprobability Sampling and Sample Size
• Sample Sizes versus Usable Observations
Summary