Chapter Ten Copyright © 2006 McGraw-Hill/Irwin Sampling: Methods and Planning.

Chapter TenChapter Ten

Copyright © 2006McGraw-Hill/Irwin

Sampling: Methods and Planning

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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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Exhibit 10.1 Distinguish between probability and nonprobability sampling methods

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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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• 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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• 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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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




• 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


Types of Nonprobability

Sampling Designs


• 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



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




Sampling Designs


• 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



Sampling Designs


• 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


Exhibit 10.7 Critical Factors in Selecting the Appropriate

Sampling Design

Illustrate the factors necessary for determining the appropriate sample

design


Exhibit 10.8 Steps Involved in Developing

a Sampling PlanUnderstand the steps in

developing a sampling plan


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

Chapter Eleven

Chapter Eleven

Copyright © 2006McGraw-Hill/Irwin

Overview of Measurement:

Construct Development and

Scale Measurement


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


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


• 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


• 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



scale designs


• 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



scale designs


• 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



scale designs


• 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



scale designs


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



scale designs


• 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.



scale designs


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


Discuss the integrated validity and reliability concerns underlying

construct development and scale measurement


• 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





• 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





Exhibit 11.2Discuss the integrated validity and

reliability concerns underlying construct development and scale

measurement


• 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


• 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





Exhibit 11.3Explain what scale measurement is, and describe how to correctly



• 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





Exhibit 11.4Identify and explain the four basic levels of scales and the amount of

information they can provide


• 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



Exhibit 11.6


• 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





Exhibit 11.7


• 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?





• 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


Exhibit 11.10




Exhibit 11.11 Key Criteria in Scale

Development




• 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


Measurements




– 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


Measurements




– 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


Measurements




• 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


Measurements




Exhibit 11.13




• 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

Chapter Ten Copyright © 2006 McGraw-Hill/Irwin Sampling: Methods and Planning.

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