# Distinguish between probability and nonprobability sampling methods.

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Distinguish between probability and nonprobability sampling methods.
Learning Objectives Distinguish between probability and nonprobability sampling methods. Understand the advantages and disadvantages of probability sampling and nonprobability sampling designs. Illustrate the factors necessary for determining the appropriate sample design. Understand the steps in developing a sampling plan. McGraw-Hill/Irwin

Distinguish between probability and nonprobability sampling methods
Exhibit 10.1 Distinguish between probability and nonprobability sampling methods McGraw-Hill/Irwin

Distinguish between probability and nonprobability sampling methods
Exhibit 10.2 Distinguish between probability and nonprobability sampling methods McGraw-Hill/Irwin

Types of Probability Sampling Designs
Distinguish between probability and nonprobability sampling methods Simple Random Sampling Probability 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 McGraw-Hill/Irwin

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 McGraw-Hill/Irwin

Distinguish between probability and nonprobability sampling methods
Exhibit 10.4 Distinguish between probability and nonprobability sampling methods McGraw-Hill/Irwin

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 McGraw-Hill/Irwin

Distinguish between probability and nonprobability sampling methods
Exhibit 10.5 Distinguish between probability and nonprobability sampling methods McGraw-Hill/Irwin

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 McGraw-Hill/Irwin

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 McGraw-Hill/Irwin

Distinguish between probability and nonprobability sampling methods
Exhibit 10.6 Distinguish between probability and nonprobability sampling methods McGraw-Hill/Irwin

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 McGraw-Hill/Irwin

Types of Nonprobability Sampling Designs
Distinguish between probability and nonprobability sampling methods 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 McGraw-Hill/Irwin

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 McGraw-Hill/Irwin

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 McGraw-Hill/Irwin

Exhibit 10.7 Critical Factors in Selecting the Appropriate Sampling Design
Illustrate the factors necessary for determining the appropriate sample design McGraw-Hill/Irwin

Exhibit 10.8 Steps Involved in Developing a Sampling Plan
Understand the steps in developing a sampling plan McGraw-Hill/Irwin

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 McGraw-Hill/Irwin

Learning Objectives 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. McGraw-Hill/Irwin

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. McGraw-Hill/Irwin

Overview of the Measurement Process
Explain what constructs are, how they are developed, and why they are important to measurement ad scale designs Overview of the Measurement Process 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 McGraw-Hill/Irwin

Overview of the Measurement Process
Explain what constructs are, how they are developed, and why they are important to measurement ad scale designs Overview of the Measurement Process 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 McGraw-Hill/Irwin

Overview of the Measurement Process
Explain what constructs are, how they are developed, and why they are important to measurement ad scale designs Overview of the Measurement Process 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 times 3-4 times 5 or more times McGraw-Hill/Irwin

Overview of the Measurement Process
Explain what constructs are, how they are developed, and why they are important to measurement ad scale designs Overview of the Measurement Process 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 McGraw-Hill/Irwin

Overview of the Measurement Process
Explain what constructs are, how they are developed, and why they are important to measurement ad scale designs Overview of the Measurement Process 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 McGraw-Hill/Irwin

Overview of the Measurement Process
Explain what constructs are, how they are developed, and why they are important to measurement ad scale designs Overview of the Measurement Process Heart of Construct Development - Determine exactly what is to be measured Objects that are relevant are identified first The objective and subjective properties of each object are specified Concreteness of an object–limited to measuring the object’s objective properties 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 McGraw-Hill/Irwin

Overview of the Measurement Process
Explain what constructs are, how they are developed, and why they are important to measurement ad scale designs Overview of the Measurement Process 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 Domain of observables –portfolio of identifiable and measurable components associated with an abstract construct 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. McGraw-Hill/Irwin

Overview of the Measurement Process
Discuss the integrated validity and reliability concerns underlying construct development and scale measurement Overview of the Measurement Process Assessing the Validity of the Construct an after-the-fact activity–after creating the scale, collecting the data, then the researcher performs the statistical analyses Content validity or “face” validity How well the construct measurable components represent that construct Convergent validity How well the construct measurement positively correlates with different measurements of the same construct Discriminant validity Whether the construct being investigated differs significantly form other constructs that are different Nomological validity How well one a theoretical construct compares to a established one McGraw-Hill/Irwin

Overview of the Measurement Process
Discuss the integrated validity and reliability concerns underlying construct development and scale measurement Overview of the Measurement Process 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 McGraw-Hill/Irwin

Overview of the Measurement Process
Discuss the integrated validity and reliability concerns underlying construct development and scale measurement Overview of the Measurement Process 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 McGraw-Hill/Irwin

Discuss the integrated validity and reliability concerns underlying construct development and scale measurement Exhibit 11.2 McGraw-Hill/Irwin

Basic Concepts of Scale Measurement
Explain what scale measurement is, and describe how to correctly apply it in collecting raw data from respondents Basic Concepts of 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 McGraw-Hill/Irwin

Basic Concepts of Scale Measurement
Explain what scale measurement is, and describe how to correctly apply it in collecting raw data from respondents Basic Concepts of Scale Measurement 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 McGraw-Hill/Irwin

Explain what scale measurement is, and describe how to correctly apply it in collecting raw data from respondents Exhibit 11.3 McGraw-Hill/Irwin

Basic Concepts of Scale Measurement
Explain what scale measurement is, and describe how to correctly apply it in collecting raw data from respondents Basic Concepts of Scale Measurement 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, 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 McGraw-Hill/Irwin

Identify and explain the four basic levels of scales and the amount of information they can provide
Exhibit 11.4 McGraw-Hill/Irwin

Basic Types of Scales Nominal Scales Ordinal Scales
Identify and explain the four basic levels of scales and the amount of information they can provide Basic Types of Scales 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 McGraw-Hill/Irwin

Identify and explain the four basic levels of scales and the amount of information they can provide
Exhibit 11.5 McGraw-Hill/Irwin

Exhibit 11.6 McGraw-Hill/Irwin

Basic Types of Scales True Class Interval Scales
Identify and explain the four basic levels of scales and the amount of information they can provide Basic Types of Scales 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 McGraw-Hill/Irwin

Exhibit 11.7 McGraw-Hill/Irwin

Basic Types of Scales Ratio Scales–
Identify and explain the four basic levels of scales and the amount of information they can provide Basic Types of Scales 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? McGraw-Hill/Irwin

Identify and explain the four basic levels of scales and the amount of information they can provide
Exhibit 11.9 McGraw-Hill/Irwin

Development and Refinement of Scaling Measurements
Discuss three components of scale development and explain why they are critical to gathering primary data Development and Refinement of Scaling Measurements 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 McGraw-Hill/Irwin

Discuss three components of scale development and explain why they are critical to gathering primary data Exhibit 11.10 McGraw-Hill/Irwin

Exhibit 11.11 Key Criteria in Scale Development
Discuss three components of scale development and explain why they are critical to gathering primary data Exhibit Key Criteria in Scale Development McGraw-Hill/Irwin

Development and Refinement of Scaling Measurements
Discuss three components of scale development and explain why they are critical to gathering primary data Development and Refinement of Scaling Measurements 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 McGraw-Hill/Irwin

Development and Refinement of Scaling Measurements
Discuss three components of scale development and explain why they are critical to gathering primary data Development and Refinement of Scaling 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 McGraw-Hill/Irwin

Development and Refinement of Scaling Measurements
Discuss three components of scale development and explain why they are critical to gathering primary data Development and Refinement of Scaling 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 McGraw-Hill/Irwin

Discuss three components of scale development and explain why they are critical to gathering primary data McGraw-Hill/Irwin

Development and Refinement of Scaling Measurements
Discuss three components of scale development and explain why they are critical to gathering primary data Development and Refinement of Scaling 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 McGraw-Hill/Irwin

Discuss three components of scale development and explain why they are critical to gathering primary data Exhibit 11.13 McGraw-Hill/Irwin

Summary 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 McGraw-Hill/Irwin