Chapter Ten Copyright © 2006 McGraw-Hill/Irwin Sampling: Methods and Planning.
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Transcript of Chapter Ten Copyright © 2006 McGraw-Hill/Irwin Sampling: Methods and Planning.
![Page 1: Chapter Ten Copyright © 2006 McGraw-Hill/Irwin Sampling: Methods and Planning.](https://reader035.fdocuments.net/reader035/viewer/2022062715/56649d6d5503460f94a4e145/html5/thumbnails/1.jpg)
Chapter TenChapter Ten
Copyright © 2006McGraw-Hill/Irwin
Sampling: Methods and Planning
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McGraw-Hill/Irwin 2
1. Distinguish between probability and nonprobability sampling methods.
2. Understand the advantages and disadvantages of probability sampling and nonprobability sampling designs.
3. Illustrate the factors necessary for determining the appropriate sample design.
4. Understand the steps in developing a sampling plan.
Learning Objectives
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McGraw-Hill/Irwin 3
Exhibit 10.1 Distinguish between probability and nonprobability sampling methods
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McGraw-Hill/Irwin 4
Exhibit 10.2 Distinguish between probability and nonprobability sampling methods
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McGraw-Hill/Irwin 5
Types of Probability Sampling Designs
Distinguish between probability and nonprobability sampling methods
• Simple Random SamplingProbability of Selection = Size of sample/Size of Population
PoS=n/N• Example: Sample of 1500 from a population of 10,000 =15%
chance of being selected
– Advantages• Easy
• Unbiased estimates of population’s characteristics – Disadvantages
• Difficult to obtain complete listing of the target population elements
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Types of Probability Sampling Designs
Distinguish between probability and nonprobability sampling methods
• Systematic Random Sampling (SYMRS)– Skip interval = defined target population list size/ desired
sample size– Skip interval = Defined N/Desired n– A sample of 1000 from a target population of 15,000 the skip
interval would be 15• Advantages
• Easy and economical• Unbiased estimates of population’s characteristics
• Disadvantages• Difficult to obtain complete listing of the target population elements • Must know exactly how many sampling units make up the defined target
population Target population must be ordered in some random way• Sample may have hidden patterns not found by the researcher• IE Stores sales sampled every 7 days falling on a Saturday
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Exhibit 10.4 Distinguish between probability and nonprobability sampling methods
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McGraw-Hill/Irwin 8
Types of Probability Sampling Designs
Distinguish between probability and nonprobability sampling methods
• Stratified Random Sampling (STRS)– Goal– Three basic steps for drawing a STRS– Two common methods used
• Proportionate stratified method– Sample size from each stratum dependant on stratum’s relative
size to the defined target population
• Disproportionate stratified method– Sample size from each stratum is independent on stratum’s
relative size to the defined target population
– Optimal allocation• Consideration is given to relative size of stratum
as well as variability within the particular stratum
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Exhibit 10.5 Distinguish between probability and nonprobability sampling methods
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Types of Probability Sampling Designs
Understand the advantages and disadvantages of probability
sampling designs
• Advantages– Assurance of Representativeness– Opportunity to study each stratum– Ability to make estimates for target population
• Disadvantages– Difficult in determining the basis for stratifying
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Types of Probability Sampling Designs
Distinguish between probability and nonprobability sampling methods
• Cluster Sampling– Each cluster is representative of the heterogeneity of the
target population– Area sampling
• One-step approach• Two-step approach
Advantages– Cost effectiveness– Ease of implementation– Often only reliable method available
Disadvantages• Tendency for clusters to be homogeneous in nature• Appropriateness of the factors used to sample within clusters
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Exhibit 10.6 Distinguish between probability and nonprobability sampling methods
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Types of Nonprobability
Sampling Designs
Distinguish between probability and nonprobability sampling methods
• Convenience Samples– Drawn based at the convenience of the researcher
• Assumption samples represent population– Not likely
• Advantages– Large number of respondents– Used in early stage of research
• Disadvantages– Problems of reliability– Do respondents represent the target population– Results are not generalizable
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Types of Nonprobability
Sampling Designs
• Judgment sampling– Selected based on an experienced
individual’s belief
• Advantages– Based on the experienced person’s
judgment
• Disadvantages– Cannot measure the respresentativeness
of the sample
Distinguish between probability and nonprobability sampling methods
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Types of Nonprobability
Sampling Designs
Distinguish between probability and nonprobability sampling methods
• Quota sampling– Based on prespecified quotas regarding
demographics, attitudes, behaviors, etc
• Advantages– Contains specific subgroups in the proportions
desired– May reduce bias
• Disadvantages– Dependent on subjective decisions– Not possible to generalize
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Types of Nonprobability
Sampling Designs
Distinguish between probability and nonprobability sampling methods
• Snowball Sampling– Respondents identify additional people to
included in the study• The defined target market is small and unique• Compiling a list of sampling units is very difficult
• Advantages– Identifying small, hard-to reach uniquely defined
target population– Useful in qualitative research
• Disadvantages– Bias can be present– Limited generalizability
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Exhibit 10.7 Critical Factors in Selecting the Appropriate
Sampling Design
Illustrate the factors necessary for determining the appropriate sample
design
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Exhibit 10.8 Steps Involved in Developing
a Sampling PlanUnderstand the steps in
developing a sampling plan
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Summary
• Value of Sampling Methods in Marketing Research
• Types of Probability Sampling Designs
• Types of Nonprobability Sampling Designs
• Determining the Appropriate Sampling Design
• Steps in Developing a Sampling Plan
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Chapter Eleven
Chapter Eleven
Copyright © 2006McGraw-Hill/Irwin
Overview of Measurement:
Construct Development and
Scale Measurement
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1. Explain what constructs are, how they are developed, and why they are important to measurement and scale designs.
2. Discuss the integrated validity and reliability concerns underlying construct development and scale measurement.
3. Explain what scale measurement is, and describe how to correctly apply it in collecting raw data from respondents.
Learning Objectives
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4. Identify and explain the four basic levels of scales and discuss the amount of information they can provide a researcher or decision maker.
5. Discuss the hybrid ordinally-interval scale design and the types of information it can provide researchers.
6. Discuss three components of scale development and explain why they are critical to gathering primary data.
Learning Objectives
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• Measurement– Process of determining the amount of
information about persons, events, ideas, and/or objects of interest and their relationship to business problems or opportunities
Overview of the Measurement Process
Explain what constructs are, how they are developed, and why they are important to measurement ad
scale designs
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• Researchers assign numbers or label to– People’s thoughts, feelings, behaviors and
characteristics– Features or attributes of objects– Aspect of ideas– Any types of phenomenon or event using
specific rules to represent quantities and/or qualities of a factor
Overview of the Measurement Process
Explain what constructs are, how they are developed, and why they are important to measurement ad
scale designs
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• Construct Development– Goal is to precisely identifying and
defining what’s to be measured– Buyers attitudes on price or quality
• Scale Measurement– Goal is to determine how precisely
measure each construct– Scale: sometimes, always, never vs. 1-2 times 3-4
times 5 or more times
Overview of the Measurement Process
Explain what constructs are, how they are developed, and why they are important to measurement ad
scale designs
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• Object– Any tangible item in a person’s environment that can be
clearly and easily identified through his or her senses– Car, cheesecake, student
• Elements – Researches want to measure elements that make up the
objects:– Objective properties:
» a car’s colour, horsepower» a student’s income, weight, sex
– Subjective properties are abstract and intangible like: » a car’s style, a student’s opinion of an instructor, purchase
intentions
• Construct– Hypothetical variable made up of a set of component
responses or behaviors that are thought to be related
Overview of the Measurement Process
Explain what constructs are, how they are developed, and why they are important to measurement ad
scale designs
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• Construct development– an integrative process in which researchers determine what
specific data should be collected for solving the defined research problem
– Need to determine what exactly needs to be measured to solve the problem.
• Accurate identification of what should be investigated requires
– Knowledge and understanding of constructs
– Knowledge of dimensionality
– Knowledge of validity
– Knowledge of operationalization
Overview of the Measurement Process
Explain what constructs are, how they are developed, and why they are important to measurement ad
scale designs
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Heart of Construct Development - Determine exactly what is to be measured
1. Objects that are relevant are identified first2. The objective and subjective properties of each object
are specified3. Concreteness of an object–limited to measuring the
object’s objective properties4. Object’s subjective properties–must identify
measurable subcomponents that can be used to clarify the abstractness associated with the object’s subjective properties
Rule of thumb is that if an object’s features can be directly measured using physical instruments, then that feature is not a construct
Overview of the Measurement Process
Explain what constructs are, how they are developed, and why they are important to measurement ad
scale designs
![Page 29: Chapter Ten Copyright © 2006 McGraw-Hill/Irwin Sampling: Methods and Planning.](https://reader035.fdocuments.net/reader035/viewer/2022062715/56649d6d5503460f94a4e145/html5/thumbnails/29.jpg)
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• Determining What Is to Be Measured– the need to acquire relevant, high-quality data to
support management’s decisions– Example Dealer Service Quality=Abstract construct
1. Domain of observables –portfolio of identifiable and measurable components associated with an abstract construct
2. This preliminary information gathered from secondary research and exploratory research such as focus groups is then used as a guideline for collecting data from a more representative sample about attributes
Domain of observables- What do customers think is important when evaluating dealer service quality? Waiting room, employee attitudes, repairs done on time, clean car when returned.
Overview of the Measurement Process
Explain what constructs are, how they are developed, and why they are important to measurement ad
scale designs
![Page 30: Chapter Ten Copyright © 2006 McGraw-Hill/Irwin Sampling: Methods and Planning.](https://reader035.fdocuments.net/reader035/viewer/2022062715/56649d6d5503460f94a4e145/html5/thumbnails/30.jpg)
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Assessing the Validity of the Construct– an after-the-fact activity–after creating the scale, collecting the
data, then the researcher performs the statistical analyses1. Content validity or “face” validity
How well the construct measurable components represent that construct
2. Convergent validityHow well the construct measurement positively correlates with
different measurements of the same construct
3. Discriminant validityWhether the construct being investigated differs significantly form other constructs that are different
4. Nomological validityHow well one a theoretical construct compares to a established one
Overview of the Measurement Process
Discuss the integrated validity and reliability concerns underlying
construct development and scale measurement
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• Approaches Used to Collect Data for Assessing Construct Validity– Pilot study
• Using 50 people who represent the defined target population• Using a panel of experts—independently judge the
dimensionality of the construct
• Inappropriate Scale Measurement Formats– Ways to overcome After-the-fact data
• Direct cognitive structural analysis– Ask respondents to tell you what attribute is important and
how important that attribute is to the construct being examined
• Scale reliability
Overview of the Measurement Process
Discuss the integrated validity and reliability concerns underlying
construct development and scale measurement
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• Inappropriate set of respondents– Using college students to represent the population as a whole– Using college students in construct development–drawn using a
convenience sampling approach
• Construct Operationalization
– Operationalization
• explaining a construct’s meaning in measurement terms by specifying the activities or operations necessary to measure it.
– The process focuses on the design and use of questions and scale measurements to gather the needed data
Overview of the Measurement Process
Discuss the integrated validity and reliability concerns underlying
construct development and scale measurement
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McGraw-Hill/Irwin 33
Exhibit 11.2Discuss the integrated validity and
reliability concerns underlying construct development and scale
measurement
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• Types of Data Collected in Research Practices– State-of-Being Data
• Factual characteristics that can be collected or verified in other ways. Sex, age, income
– State-of-Mind Data• Mental attributes or emotional feelings that are not directly
observable or available from other sources– Feelings, beliefs
– State-of-Behavior Data• Current observable actions or past actions
– Can collect data by asking the individual, observing them or researching sales data Air Miles does this
– State-of-Intention Data• Asking targets what their intentions are
– Difficult to verify intentions with actual behaviour
Basic Concepts of Scale Measurement
Explain what scale measurement is, and describe how to correctly
apply it in collecting raw data from respondents
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• Scale Measurement– process of assigning descriptors to represent the range of
possible responses to a question about a particular object or construct
• The quantity and quality of the responses associated with any question or observation technique depend directly on the scale measurements used by the researcher
• The focus is on measuring the existence of various characteristics of person’s response
• Scale measurement directly determines the amount of raw data that can be ascertained from a given questioning or observation method
• Scale Points– designated degrees of intensity assigned to the responses in a
given questioning or observation method
Basic Concepts of Scale Measurement
Explain what scale measurement is, and describe how to correctly
apply it in collecting raw data from respondents
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Exhibit 11.3Explain what scale measurement is, and describe how to correctly
apply it in collecting raw data from respondents
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• Properties of Scale Measurements– Assignment Property
• Description or category » Favorite colour? Do you own a car
– Order Property• 3 relationships between responses A and B
– A > B or A < B, A = B– Identifies relative differences not absolute
» Not satisfied with service, satisfied with, very satisfied
– Distance Property• Expresses the absolute difference between each of the descriptors or scale
points• Restricted to responses with some type of natural numerical answer
» How much do you spend at Loblaws per month» $0-10,11-20, 21-30, 31-40 etc.
– Origin Property• Refers to the use of a unique starting point in a set of scale points that is
designated as being a “true natural zero”• Each scaling property builds on the pervious one
Basic Concepts of Scale Measurement
Explain what scale measurement is, and describe how to correctly
apply it in collecting raw data from respondents
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Exhibit 11.4Identify and explain the four basic levels of scales and the amount of
information they can provide
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• Nominal Scales– type of scale in which the questions require
respondents to provide only some type of descriptor as the raw response
– Does not contain level of intensity• Yes or no, male or female, shop or not shop at
• Ordinal Scales– allows a respondent to express relative
magnitude between the answers to a question• Never, Few times, often, always
Basic Types of Scales
Identify and explain the four basic levels of scales and the amount of
information they can provide
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McGraw-Hill/Irwin 40
Exhibit 11.5Identify and explain the four basic levels of scales and the amount of
information they can provide
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Exhibit 11.6
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• True Class Interval Scales– Not only an assignment and order but also a distance
property – demonstrate the absolute differences between each
scale point • Shopped 2 times versus 5 times at Safeway per month• Spend $20 dollars vs. $100 per visit.
• Hybrid Ordinally-Interval Scale– an ordinal scale that is artificially transformed into an
interval scale by the researcher• Primary scale point descriptors• Secondary scale descriptors
Basic Types of Scales
Identify and explain the four basic levels of scales and the amount of
information they can provide
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Exhibit 11.7
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• Ratio Scales–– the researcher can identify the absolute differences,
not only between each scale point but also to make comparisons between the raw responses
• Enable a “true natural zero” or “true state of nothing” response to be a valid raw response to the question
• Ratio scales requests that respondents provide a specific numerical value as their response, regardless of whether or not a set of scale points is used
» How many cars do you own?
» How much do you spend on maintenance?
Basic Types of Scales
Identify and explain the four basic levels of scales and the amount of
information they can provide
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McGraw-Hill/Irwin 45
Exhibit 11.9Identify and explain the four basic levels of scales and the amount of
information they can provide
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• Key to Designing High-quality Scales – Understanding the defined problem– Establishing detailed data requirements– Identifying and developing the constructs– A complete measurement scale consists of
three critical components• The question• The attribute• The scale point description
• Development of the constructs
Development and Refinement of Scaling
Measurements
Discuss three components of scale development and explain why they
are critical to gathering primary data
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Exhibit 11.10
Discuss three components of scale development and explain why they
are critical to gathering primary data
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McGraw-Hill/Irwin 48
Exhibit 11.11 Key Criteria in Scale
Development
Discuss three components of scale development and explain why they
are critical to gathering primary data
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• Criteria for Scale Development– Intelligibility of the Questions
• Degree respondents understand question• Instructions can be used to improve
– Appropriateness of Primary Scale Descriptors• The extent to which the scale point elements match the
data being sought
– Discriminatory Power of the Scale Descriptors• The scales ability to significantly differentiate between the
categorical scale responses• Generally scales have 3-7 points
Development and Refinement of Scaling
Measurements
Discuss three components of scale development and explain why they
are critical to gathering primary data
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– Reliability of the Scale• Scale reliability
– Test-retest» Repeating the scale measurement with either the same
sample at different times or two different samples from the same target population.
» If there are few differences the scale is viewed as stable and reliable
– Equivalent Form» Measure and correlating the measures of two equivalent
scaling instruments – Internal consistency
» Split-half tests» Coefficient alpha
Development and Refinement of Scaling
Measurements
Discuss three components of scale development and explain why they
are critical to gathering primary data
![Page 51: Chapter Ten Copyright © 2006 McGraw-Hill/Irwin Sampling: Methods and Planning.](https://reader035.fdocuments.net/reader035/viewer/2022062715/56649d6d5503460f94a4e145/html5/thumbnails/51.jpg)
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– Balancing positive/negative scale descriptors
• Having equal relative magnitudes of positive and negative scale measures
– Inclusion of a neutral response choice• Forced-choice scale
– No neutral descriptive forces respondent to make choice
• Free-choice scale– Includes a neutral descriptive
Development and Refinement of Scaling
Measurements
Discuss three components of scale development and explain why they
are critical to gathering primary data
![Page 52: Chapter Ten Copyright © 2006 McGraw-Hill/Irwin Sampling: Methods and Planning.](https://reader035.fdocuments.net/reader035/viewer/2022062715/56649d6d5503460f94a4e145/html5/thumbnails/52.jpg)
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Discuss three components of scale development and explain why they
are critical to gathering primary data
![Page 53: Chapter Ten Copyright © 2006 McGraw-Hill/Irwin Sampling: Methods and Planning.](https://reader035.fdocuments.net/reader035/viewer/2022062715/56649d6d5503460f94a4e145/html5/thumbnails/53.jpg)
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• Measures of Central Tendency– Mean
• The average of all data– Median
• Split of data half below half above– Mode
• Most frequently given response• Measures of Dispersions
– Frequency Distribution• Summary of how many times each possible response was recorded
– Ranges• Represents the grouping of responses in mutually exclusive subgroups,
each with an upper and lower boundary– Sample standard deviations
• Statistical value that specifies the degree of variation in the data responses
Development and Refinement of Scaling
Measurements
Discuss three components of scale development and explain why they
are critical to gathering primary data
![Page 54: Chapter Ten Copyright © 2006 McGraw-Hill/Irwin Sampling: Methods and Planning.](https://reader035.fdocuments.net/reader035/viewer/2022062715/56649d6d5503460f94a4e145/html5/thumbnails/54.jpg)
McGraw-Hill/Irwin 54
Exhibit 11.13
Discuss three components of scale development and explain why they
are critical to gathering primary data
![Page 55: Chapter Ten Copyright © 2006 McGraw-Hill/Irwin Sampling: Methods and Planning.](https://reader035.fdocuments.net/reader035/viewer/2022062715/56649d6d5503460f94a4e145/html5/thumbnails/55.jpg)
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• Value of Measurement within Information Research
• Overview of the Measurement Process• Basic Concepts of Scale Measurement• Basic Levels of Scales• Development and Refinement of Scaling
Measurements
Summary