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Principles of Marketing

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					    MKGT 368
Review for Exam 1
    Spring 2011
           Role of
     Marketing Research
in Managerial Decision-Making

         Chapter 1
                       What is Marketing?
• American Marketing Association Definition:
   • Marketing is an organizational function and a set of processes for
     creating, communicating, and delivering value to customers and for
     managing customer relationships in ways that benefit the organization
     and its stakeholders.

• In sum, marketing is about…
   • meeting needs
   • delivering value to all people affected by a transaction
   • getting the right product to the right folks at the right time/place for the
      right price using an appropriate combination of promotional techniques
      (the four Ps)
               What is Marketing Research?
• American Marketing Association (p. 4 in your book):
   • …the function that links an organization to its market through the
      gathering of information. This information allows for the identification
      and definition of market-driven opportunities and problems and allows
      for the generation, refinement and evaluation of marketing actions. It
      allows for the monitoring of marketing performance and improved
      understanding of marketing as a business process.
• Malhotra & Peterson (2006, p. 5):
   • …the systematic and objective identification, collection, analysis,
      dissemination, and use of information that is undertaken to improve
      decision making related to identifying and solving problems (also known
      as opportunities) in marketing.
• Feinberg et al. (2008, p. 4):
   • … the systematic process of using formal research and consistent data
      gathering to improve the marketing function within an organization.
      This information is used to identify opportunities and problems,
      monitor performance, and link marketing inputs with outputs of
      interest, such as awareness, satisfaction, sales, share and profitability.
                    The “Marketing Concept”
• Need for marketing research based on “marketing concept”

• Idea introduced in 1952, GE’s Annual Report:
    • The (marketing) concept introduces the marketer at the beginning
       rather than at the end of the production cycle and integrates
       marketing into each phase of the business. Thus, marketing,
       through its studies and research, will establish for the engineer, the
       designer, and manufacturer, what the customer wants in a given
       product, what price he (or she) is willing to pay, and where and
       when it will be wanted. Marketing will have authority in product
       planning, production scheduling, and inventory control, as well as
       in sales, distribution, and servicing of the product.


• Gave rise to the “Marketing System”
   • Conceptual model linking Independent Variables (causes) to
     Dependent Variables (outcomes)
   • Understanding the link between IVs and DVs (and reducing
     uncertainty) is a key function of marketing research 
                       Marketing System
Independent Variables                           Dependent Variables

                       Understanding relationship    Behavior
   Marketing Mix
   (controllable)        between IVs and DVs
                        is a key function of MR      Awareness
                                                     Knowledge
   Pricing                                           Liking
   Promotion                                         Preference
   Product                                           Intent to buy
   Distribution                                      Purchase



 Situational Factors
 (uncontrollable)                                   Performance
                                                    Measures
 Demand
 Competition                                        Sales
 Legal/political                                    Market share
 Economic climate                                   Profit
 Technology                                         ROI
 Gov regulation                                     Image

                                                                From Feinberg et al. (2008)
              The Decision-Making Process

1. Recognize a unique marketing problem or opportunity


2. Clarify the decision (what do we need to know?)


3. Identify alternative courses of action


4. Evaluate the alternatives


5. Select a course of action


6. Implement selected course of action and monitor results



                                                             From Feinberg et al. (2008)
                       Common Questions Addressed
                         by Marketing Researchers
•   Where are new market opportunities (based on macroenvironmental trends)?

•   How should we segment the market (based on customer characteristics)?

•   How are we doing (compared to the competition)? Are consumers satisfied with our
    product or service? If not, what should we improve?

•   How should we position our product (relative to the competition)?

•   How will people respond to a new product concept? Test marketing…

•   If our product is priced at $100, what will be the expected demand?

•   How effective is our advertising? Promotions? Sales force?

•   What’s in store for the future, and how should we adapt?
   Marketing Research Process:
Transforming Data into Information

            Chapter 2
                              Overview
• Types of Marketing Research Firms

• When is Marketing Research Needed?

• Decision-Makers vs. Researchers

• Iceberg Principle: Symptoms vs. Underlying Problems

• Steps in Marketing Research

• Elements in a Marketing Research Proposal

• Unethical Activities in Marketing Research
                  Marketing Research Industry
              Research Supplier

 Internal                           External




              Full Service                                  Limited Service



Syndicated    Customized      Internet          Field         Data Coding         Data
                                               Services        and Entry         Analysis
 AC Nielsen    Synovate       Greenfield
                               On-Line         Field Work        Davis              SDR
                                                 Chicago         Coding            Atlanta
                                                                 Group




                                                                          Malhotra & Peterson (2006)
                       Exhibit 2.3    When is Marketing Research Needed?
                               Type of     Can decision problem be resolved                    YES
                            information
Decision



                                             with subjective information?
 Maker



                                                              NO
                                                                                 NO     Don’t undertake the
                             Nature of
                                           Is problem of strategic importance?
                             decision                                                  Info research process
                                                               YES
                            Availability   Is secondary data inadequate for           NO
                             of data           addressing the problem?
                                                               YES
Marketing Researcher




                               Time                                                   NO
                                             Is there enough time to collect
                            constraints      data for managerial decision?
      Bring in




                                                                 YES
                                                                                      NO
                            Resources          Are there enough resources
                             required      ($, people) to carry out the study?
                                                                YES
                           Cost/Benefit        Does value of research                 NO
                              Ratio          outweigh costs of research?
                                                                                   Do undertake the
                                                                YES
                                                                                 Info research process
             When NOT to conduct research…

1. Sufficient information for a decision already exists

2. Insufficient time for research – must make an immediate decision

3. Insufficient resources for research

4. When costs of research are greater than its benefits
               Components of the Research Proposal

1. Purpose of proposed research plan (problem, objectives)

2. Type of study (e.g., exploratory, causal, primary, secondary etc.)

3. Define target population and sample size

4. Describe sampling technique and actual data collection methods to be used

5. Research instruments to be used

6. Possible managerial benefits

7. Proposed cost of whole project

8. Describe primary researchers and research firm

9. Proposed tables (how data might be presented)
             Researchers vs. Decision-Makers
Researchers                           Decision-Makers
• Like to explore new questions       • Want info to confirm decision

• Can tolerate long investigations    • Want quick information

• Not concerned about cost            • Less willing to pay for more info

• Enjoy surprises                     • Dislike & reject surprises

• Tentative; speak in probabilities   • Decision- and results-oriented

• Interested in past behavior         • Interested in future performance
Iceberg Principle: Symptoms vs. Problems
Four Broad Phases in Information Research




             Ten steps 
Ten Steps in Information Research
                        Unethical Activities…
•   by Client (End User)
     • Solicit proposals, but choose none. Use proposals as a guideline for how to
        conduct one’s own study.
     • Promise a long-term relationship to get a low introductory rate, but then
        never follow through with more projects

•   by Researcher
     • Unethical pricing: promise low price, then jack it up
     • Fail to provide (promised) incentives to research subjects
     • Abuse respondents; promise short survey that turns into an hour; pass along
        information without permission; collect information without permission
     • Selling useless research services
     • Interviewers make up data (“curbstoning” or “rocking chair” interviewing)
     • Interviewers create “phantom” data (duplicate actual data to boost sample)
     • Change or fail to report results in an effort to reach a certain conclusion

•   by Respondent
     • Give misleading responses (can include “socially desirable” responding)
    Qualitative Methods:
Interviews and Focus Groups

       Chapters 6-7
        Qualitative vs. Quantitative Methods
• Qualitative
   • Used in exploratory designs to gain prelminary insights into
     decision problems and opportunities

• Quantitative
   • Using formalized standard questions and predetermined
     response options (yes, no) in questionnaires or surveys
     administered to large numbers of respondents

• Differences Between Qualitative and Quantitative Approaches 
                           Focus Groups
• Focus Groups

• Formalized small group of people have an interactive,
  spontaneous discussion on one topic or concept

• Can…
   • help identify root problem underlying symptoms
   • help identify questions to ask in a survey
   • provide insights into quantitative results
   • uncover hidden needs, wants, attitudes, feelings,
     perceptions and motives regarding products/services
   • lead to new ideas for products/services
   • help develop new measures for quantitative survey
   • provide insights into how people “experience”
     products/services (what they mean to them)
                  Composing a Focus Group
• Selecting Participants

• Select a good group of participants (relatively homogenous groups make
  people feel comfortable, but should have some variability in views)

• Potential group members should have enough knowledge to contribute

• Try to incorporate some randomization in selection (within a target group)

• Size should be between 8-12 people with a moderator

• Use a friendly invite and provide incentives (typically between $75-100)

• Pick a comfortable location
        Some Additional Interview Techniques
• Case Study
   • Analyze in depth one or more situations similar to the problem
     you are trying to solve

• Experience Interviews
   • Interview people believed to be knowledgeable about the
     problem you are trying to solve

• Protocol Interviews
   • Ask people to verbalize the thought processes and activities
     they would go through in a given situation (e.g., buying a car)

• Articulative Interviews
   • Listening to people in order to identify value conflicts they may
      have (e.g., want to buy a nice bike but also be frugal)
                     Projective Techniques
• Definition
   • Techniques that allow a person to “project” their thoughts, feelings
      or motives onto others, a situation, or an object

• Types
   • Word association: When I say “GO” you say_____
   • Sentence completion: Students at WSU are _____
   • Picture tests: Write or tell a story in response to a picture
   • Thematic apperception test: series of pictures; you tell the story
   • Cartoon (balloon) test: fill in the dialogue of a cartoon
   • Role playing in a given situation
                   Analyzing Qualitative Data
•   Inductive Approach

•   Goal is understanding why people do what they do and what
    products/service mean to them

•   Insights and theory-development are “bottom up”

•   They emerge as researchers read and interpret responses

•   Insights are “contextualized” within a culture/subculture (thick description)
          Nisbett & Wilson (1977, Psych Review)
             Telling More Than We Can Know:
           Verbal Reports on Mental Processes


1. Not aware of a response (snake phobics)
2. Not aware of a stimulus (the cord puzzle)
3. Not aware of a connection between stimulus and
   response (nylons)
4. So what? So this: we may need more creative
   interview techniques to get into people’s minds.
5. Enter Dr. Clotaire Rapaille 
        Dr. Clotaire Rapaille


Archetype Discoveries Worldwide
         http://www.rapailleinstitute.com/


 I don’t care what you’re going to tell me intellectually.

        I don’t care. Give me the reptilian. Why?

           Because the reptilian always wins.
                     Dr. Clotaire Rapaille

• Internationally known expert in Archetype Discoveries and Creativity

• Archetype: In psychology, according to the theory of psychologist Carl Jung, an idea
  or way of thinking that has been inherited from the experience of the race and
  remains in the consciousness of the individual, influencing his perception of the
  world. (Webster’s)

• Dr. Rapaille's technique for market research based on his work in the areas of
  psychiatry, psychology, and cultural anthropology.

• Dr. Rapaille searches for the “code” behind certain words and ideas (e.g., luxury),
  and uses these insights to help marketers promote their products.
                     Dr. Clotaire Rapaille

• On the Limitations of Traditional Marketing Researchers:

• “They are too cortex, which means that they think too much, and then they ask people
  to think and to tell them what they think. Now, my experience is that most of the time,
  people have no idea why they’re doing what they’re doing. They have no idea, so they’re
  going to try to make up something that makes sense. Why do you need a Hummer to go
  shopping? “Well, you see, because in case there is a snowstorm.” No. Why [do] you buy
  four wheel drive? “Well, you know, in case I need to go off-road.” Well, you live in
  Manhattan; why do you need four wheel drive in Manhattan? “Well, you know,
  sometime[s] I go out, and I go—” You don’t need to be a rocket scientist to understand
  that this is disconnected. This is nothing to do with what the real reason is for people to
  do what they do. So there are many limits in traditional market research.”

• Dr. Rapaille in action: Finding the code for “luxury”  (42:20)
                The Reptilian Brain

                                                         Reptilian
                                                         Oldest part of brain
                                                         from an evolutionary
                                                         perspective




Paul D. MacLean (1913 - 2007)   Triune Brain Theory
American physician              • Reptilian brain (instincts)
Neuroscientist                  • Limbic system (emotion)
Yale, NIMH                      • Neocortex (higher order thought)
  Descriptive Designs:
Surveys & Observations

      Chapter 8


                  Is X related to Y?
  When Are Descriptive Designs Appropriate?

1. Want to describe current characteristics of a market (e.g.,
   attitudes toward an existing product or certain aspects of
   the marketing mix)

2. Want to understand your target market’s characteristics
   (e.g., demographics, psychographics)

3. Want to understand relationships between variables (e.g.,
   price and purchase) or differences between groups (e.g.,
   attitudes toward water filters between hikers and
   backpackers)
     Sampling vs. Nonsampling Errors
• Sampling Error
   • Statistically speaking, the difference between the sample
     results and the population parameter
   • Assuming perfect survey, sampling frame, execution, and
     respondents, we will still have error due to sampling
   • Sampling error becomes smaller with larger sample

• Nonsampling (or Systematic) Error
   • A variety of errors that are not related to sampling error
     and/or sample size
    Four Characteristics of Systematic Error
• Nonsampling (Systematic) Error …
   1. Leads to “systematic variation” in responses (e.g., skewed
      toward more socially desirable responses)
   2. Is controllable (e.g., via good survey design and
      procedures)
   3. Can not be estimated (whereas sampling error can be
      estimated; margin of error in a poll)
   4. Are interdependent (i.e., one type of systematic error can
      lead to another)

•   Conceptual breakdown (Exhibit 7.2) – Handout in class
                   Non-Response Errors
• Non-response error occurs when…
   • The final sample differs from the planned sample

• Often happens when you can’t contact those in the planned sample or
  they refuse to participate

• Those who choose not to respond often of lower income, education, and
  more likely to be male

• Non-response can limit generalizability of findings to broader population

• Strategies for reducing non-response error
   – Create good rapport, respect respondent’s time, enhance credibility of
      research sponsor, use shorter questionnaires
                   Response Error (Bias)
• Response error occurs when…
   • The responses people give are not accurate

• May occur due to
   • Deliberate falsification (e.g., social desirability, hostility)
   • Unconscious misrepresentation (e.g., faulty memory, desire to please
     researcher)

• Might be able to detect with reaction times
   • Very fast or very slow RTs may tell you something
                        Sampling Errors
• Population specification (frame) error
   • Your population is all Republicans, but you define your population as
     Republicans in WA

• Sample selection error
   • When an inappropriate sample is selected from the desired population
   • May be due to either poor sampling procedures or intentionally
     excluding certain people from the sample

• Sample frame error
   • Sample frame = list of potential people in your target population
   • Sample frame error = when the sample frame is not representative of
     your population (e.g., only those with email addresses)
 Four Broad Categories of Survey Methods
• Person Administered
   • In-home, executive, mall-intercept, purchase-intercept

• Telephone Administered
   • Either by a person or completely automated

• Self Administered
   • Panels, drop off, mailed survey

• Computer Assisted
   • Fax, email, internet
                    Person Administered Surveys

Advantages                                  Disadvantages
•   Interviewer can adapt to respondent     •   Can be slow



•   Interviewer can create good rapport     •   Interviewers may incorrectly interpret
    with respondents                            response (selective listening)



•   Interviewer can clarify questions and   •   Interviewers may give off “clues” to the
    get insight via non-verbal responses        “correct” response



•   Interviewers can ensure they are        •   Can be expensive
    sampling the correct people
                                Telephone Surveys

Advantages                                     Disadvantages
•   Can monitor interviewers for quality       •   Can’t use visual stimuli (though might
    control
                                                   be possible with cell phones)

•   Less expensive than person
    administered                               •   Can be hard to keep a large amount of
                                                   info in memory during interview
•   Following up if respondent not available
    first time is inexpensive
                                               •   People bail on long phone interviews

•   People who don’t agree to person
    administered (e.g., due to time            •   Public is distrusting; can limit sample
    constraints) may be more willing to do a
    telephone interview
                       Self Administered Surveys

Advantages                                Disadvantages
•   Low cost (no need for interviewer)    •   Can’t obtain any information beyond
                                              what is presented on survey (no follow
                                              up questions or probing possible)
•   Respondents not rushed, can take
    time if they want to                  •   Low response rates


                                          •   If respondent doesn’t understand, can’t
•   Interviewer can’t bias response
                                              ask an interviewer; may lead to response
                                              errors
•   Anonymity can lead to more truthful
    responses                             •   Data comes in slowly; may require several
                                              re-contacts
Paco Underhill

  The “King”
of Observation



www.envirosell.com
               When to Use Observation
• When the respondent may not be able to accurately recall the frequency
  of a behavior, and/or may be inclined to give misleading answers

• When the response in question is a behavior (rather than a feeling)

• When the behavior in question is relatively frequent and occurs within a
  limited time frame

• When the behavior in question can be observed (e.g., in public)
                                 Observation

Advantages                                 Disadvantages
•   Gain data on actual behavior (rather   •   Generalizing from a limited number of
    than self-reported behavior which          observations can be difficult
    may be biased)

                                           •   May be difficult to understand why the
                                               behavior occurred



                                           •   If doing observation in person (not
                                               recorded), possible to miss important
                                               behaviors (or other people)
    Causal Designs

        Chapter 9

Understanding when (and why)
           XY
               Theories and Hypotheses
•   Theory

    •   A body of interconnected propositions about how a phenomenon
        works (recall animosity model)

•   Hypothesis Testing

    •    Null (Dull) Hypothesis (Ho):
        • Nothing interesting is going on
        • Any differences we are observing are completely due to chance

    •    Alternative Hypothesis (H1)
        • Something interesting is going on
        • Differences in DV are due to IV
       Experiments: Some Key Definitions
•    Independent Variable (X, the cause, the predictor)
    •    The variable you “manipulate” (good vs. bad aroma in store)

•    Dependent Variable (Y, the outcome, the criterion)
    •   What happens after you manipulate the IV (sales of a product)

•    Control variables
    •   Variables that you don’t allow to vary along with the IV
    •   If any variable covaries with the IV, then there is a confound (e.g., if
        music systematically varies along with aroma, then you can’t tell if it’s
        the aroma or the music that influences sales)

•    Extraneous variables (or noise)
    •    “Stuff happens” during an experiment, but it evens out across the levels
         of the independent variable (e.g., different music at different times, but
         it doesn’t systematically vary by the level of the IV)
                         Factorial Designs
•    Factorial Design
    •    When the researcher is examining the impact of two independent (or
         predictor) variables on a DV
    •    Can have two main effects (overall impact of each IV) and an interaction
         (combined effect of two IVs)

                        No Service    Service
                         Failure      Failure        Row Means
     Low Hostility          2            3              2.5              Main Effect of
     High Hostility         2            5              3.5              Hostility


     Column Means            2           4



                           Main Effect of
                           Service Failure
                                                                         Interaction 
                                                                         (Moderation)
                                               Moderation
•    Moderation (under what conditions is a relationship stronger/weaker)
    •   When the effect of one IV (service failure) on the DV (negative word of
        mouth) depends on the level of another IV (trait hostility)

                                           Service Failure  NWOM

                                                            Trait
                                                           Hostility
                                     6                                    Trait Hostility
            Negative Word of Mouth




                                     5                                         (IV 2)
                                     4                                        Low
                                                                              Hostility
                     (DV)




                                     3
                                     2                                        High
                                                                              Hostility
                                     1
                                     0
                                         No Failure     Service Failure
                                            Service Failure (IV 1)
                              Mediation
•   Mediation (like a combination shot in pool)

    •   When the effect of one IV on the DV occurs through an “intermediary”
        variable (think cue ball hits one ball hits eight ball)

    •   For example, assume a person experiences a service failure

    •   They infer a negative motive, feel angry, and spread NWOM

    •   Here, anger is the mediator between Inference of Negative Motive and
        NWOM


         Inference of              Anger             Negative Word
        Negative Motive                                Of Mouth
             Validity: Some Key Definitions
•    Validity (in general)
    •     The extent to which conclusions drawn from a study are true

•    Internal Validity
    •    When a researcher can clearly identify cause and effect relationships (i.e.,
         there are no confounds)

•    External Validity
    •    The extent to which what you find in your study can be generalized to your
         target population

•    Construct Validity
    •   Extent to which your constructs of interest (e.g., sensation seeking) are
        accurately and completely identified (measured)
    •   In other words, the extent to which you are actually measuring what you say
        you are measuring (your sensation seeking scale really does measure the
        true construct of sensation seeking)
                        Threats to Internal Validity
•    History Effect
    •     When something (an historical event) happens during the course of a study that
          affects the dependent variable
•    Maturation Effect
    •     Similar to a history effect; something happens over time (changes in the individual)
          that affects the DV
•    Testing Effect
    •     In a pretest-posttest design, you affect the time 2 DV by pretesting at time 1; the
          simple act of measuring the DV at time 1 changes the DV at time 2
•    Instrumentation Effect
    •     The mere fact that you are measuring something (e.g., observing behavior) changes
          the behavior
•    Statistical Regression
    •     When you select groups based on extreme scores, they regress toward the mean,
          changing your groups
•    Selection Bias
    •     When groups (control, experimental) differ before experimental manipulation; creates
          unequal groups (a confound)
•    Mortality
    •     Some drop out or die (attrition), and these drop-outs change scores in the condition;
          those who stick around may be different than those who drop out
           Experimental Research Designs
•    Terminology
    •    X = subjects are exposed to a treatment (independent variable)
    •    O = the outcome (dependent variable)
    •    [R] = random assignment of subjects to conditions
    •    EG = experimental group
    •    CG = control group
    •     = time

•    Pre-experimental (“Crude” experimental) designs
    •    Either have no control group or non-random assignment to groups
    •    Suffer from low internal validity because it is not possible to compare
         groups without the possibility of confounding factors
    •    Types: One-shot, One-group, and Static Group comparisons
    Pre-Experimental Research Designs - 1
•   One Shot Study

    •   X  O1

    •   How do customers respond (O) to a single product like gatorade (X)?

    •   Problem? No control group. Response could be driven by many factors
        that covary along with the product (e.g., lighting, context). That is, there
        are many opportunities for extraneous variables to “confound” the
        manipulation of the IV.

    •   Internal validity low
    Pre-Experimental Research Designs - 2
•   One Group Pre-test Post-Test

    •   O1  X  O2

    •   How do sales of sweaters at time 1 (O1) change at time 2 (to O2) after the
        introduction of a new product display (X)?

    •   Problem? No control group. History and Testing Effects. Response could
        again be driven by many factors that covary along with the manipulation
        of the product display, that change over time with the introduction of
        the new product display (e.g., changes in store music or changes in the
        economy), or are related to testing at time 1. In other words, there are
        many opportunities for extraneous variables to “confound” the
        manipulation of the IV.

    •   Internal validity low
    Pre-Experimental Research Designs - 3
•   Static Group Comparison (Non-random assignment to groups)

    •   Experimental Group (EG): X  O1
    •   Control Group (CG):         O2

    •   Compare two stores. In Store 1 (EG), use a promo display for nose strips.
        In Store 2 (CG), don’t use a promo display. Compare sales.

    •   Problem? Non-random assignment to groups. This again allows factors
        other than the promo display to affect sales. For example, in Store 1, it
        could be that the pharmacists are more friendly and more likely to
        recommend nose strips to their weary-eyed customers.

    •   Internal validity low
    True Experimental Research Designs - 1
•   Pre-test, Post-test Control Group (subjects randomly assigned to groups)
•   Also called a mixed design (one within-subject variable, time; and one
    between-subject variable, the experimental manipulation)

    •   Experimental Group (EG): [R] O1  X  O2
    •   Control Group (CG):        [R] O3 …….. O4
    •   Treatment effect = (O2-O1) – (O4-O3): The difference between differences

    •   Test all subjects (O1, O3), then randomly assign to experimental or
        control group, then test again (O2, O4)

    •   Eliminates testing effects, maturation, and (with good control over
        experimental conditions) confounding factors.

    •   Internal validity higher than earlier designs, but if not careful (low
        control over conditions), internal validity could be threatened
    True Experimental Research Designs - 2
•   Post-test Only (but with subjects randomly assigned to groups)

    •   Experimental Group (EG): [R] X  O1
    •   Control Group (CG):      [R]     O2

    •   Randomly assign to experimental group or control group, then compare
        levels of dependent variable (O)

    •   No testing effects. With good control over experimental conditions,
        eliminates confounding factors.

    •   Internal validity higher than static group comparison, but if not careful
        (low control over conditions), internal validity could be threatened.
    True Experimental Research Designs - 3
•   Solomon Four Group Design


•   Design 1 (Pre-test, post-test control group design)
    •   Experimental Group (EG):    [R] O1  X  O2
    •   Control Group (CG):         [R] O3 …….. O4


•   Design 2 (Post-test only design)
    •   Experimental Group (EG):    [R]      X O5
    •   Control Group (CG):         [R]         O6


    •   Let’s say that O = anger with waiting in line, and X = pleasant fragrances.

    •   If [O2 < O1], [O2 < O4], [O5 < O6], [O5 < O3], strong internal validity!
    Quasi-Experimental Research Designs - 1
•    Non-equivalent control group
•    Like pre-test, post-test control group, but it is groups of subjects (not
     individual subjects) who are randomly assigned to conditions. Hence there is
     no [R] shown below. For example, you could randomly assign stores to
     conditions (experimental vs. control), but you can’t randomly assign people
     to conditions, and you can’t control everything about the stores that may be
     confounded with the experimental manipulation.

     •   Experimental Group (EG): O1  X  O2
     •   Control Group (CG):        O3 …….. O4
     •   Treatment effect = (O2-O1) – (O4-O3): The difference between differences
     •   Equivalence of groups prior to treatment: (O3-O1)

•    Eliminates testing effects, maturation, and (with good control over
     experimental conditions) confounding factors. Scores at pre-test (O1 and O3)
     can be used as a control variable in data analysis. If in field, external validity
     is heightened over straight lab studies.
    Quasi-Experimental Research Designs - 2
•    Separate sample pretest-posttest
•    Some folks (Sample 1) are tested before an advertising campaign (O1)
•    Then an advertising campaign occurs (X)
•    Then another group is tested after the campaign (O2)
•    Can’t be sure people were exposed to treatment (X), which is why it’s in
     parentheses

     •   Sample 1: O1  (X)
     •   Sample 2:      (X)  O2

•    Some problems with internal validity (history, maturation), but external
     validity is high due to its naturalistic setting
              Sampling
           It is often said that without water,
                      life would be impossible.
      Similarly, without sampling, marketing
research as we know it would be impossible.
   Feinberg, Kinnear, & Taylor (2008, p. 290)
         Probability vs. Nonprobability Sampling

• Probability Sampling
   • Each sampling unit has a known probability of being
     included in the sample

• Nonprobability sampling
   • When the probability of selecting each sampling unit is
     unknown
               Probability Sampling Procedures

• Simple Random Sampling
   • A sampling approach in which each sampling unit in a target
     population has a known and equal probability of being included
   • Advantage: Good generalizability and unbiased estimates
   • Disadvantage: must be able to identify all sampling units within
     a given population; often, this is not feasible

• Systematic Random Sampling
   • Similar to random sampling, but work with a list of sampling
      units that is ordered in some way (e.g., alphabetically).
   • Select a starting point at random, then survey each nth person
      where the “skip interval” = (population size/desired sample size)
   • Advantage: quicker and easier than SRS
   • Disadvantage: may be hidden “patterns” in the data
               Probability Sampling Procedures

• Stratified Random Sampling
• Break up population into meaningful groups (e.g., men, women),
  then sample within each “strata”, then combine
• Proportionate stratified sampling: here you sample based on the
  size of the populations (i.e., sample more from the bigger strata:
  e.g., Caucasians)
• Disproportionate stratified sampling: sample the same number of
  units from each strata, regardless of the strata’s size in the pop.
   • A variant is optimal allocation: here you use smaller sample
      sizes for strata within which there is low variability (as the lower
      variability will give you more precision with lower N).
• Advantages: more representative; can compare strata
• Disadvantages: Can be hard to figure out what to base strata on
  (Gender? Ethnicity? Political party?)
              Probability Sampling Procedures

• Cluster Sampling

• Similar to stratified random sampling, but with stratified random
  sampling, the strata are thought to possibly differ between strata
  (men vs. women), but be homogeneous within strata.

• In cluster sampling, you divide overall population into
  subpopulations (like SRS), but each of those subpopulations (called
  “clusters”) are assumed to be mini-representations of the
  population (e.g., survey customers at 10 Red Robins in WA).

• Area sampling: clusters based on geographic region
               Probability Sampling Procedures

• Cluster Sampling
• One-step clustering: just select one cluster (e.g., one store);
  problem = may not be representative of population

• Two-step cluster sampling: break into meaningful subgroups (Red
  Robins in big cities vs. Red Robins in suburbs), then randomly
  sample within each of those clusters

• Advantages: easy to generate sampling frame; cost efficient;
  representative; can compare clusters

• Disadvantages: must be careful in selecting the basis for clusters;
  also, within clusters, often little variability (they’re homogeneous),
  and this lack of variability leads to less precise estimates
             Nonprobability Sampling Procedures

•   Convenience Sample
•   Survey people based on convenience (e.g., college students)
•   Advantage: is fast and easy
•   Disadvantage: may not be representative

•   Judgment Sampling
•   Use your judgment about who is best to survey
•   Advantage: Can be better than convenience if judgment is right
•   Disadvantage: but if judgment wrong, may not be
    representative/generalizable
            Nonprobability Sampling Procedures

• Quota Sampling
• Sample fixed number of people from each of X categories, possibly
  based on their relative prevalence in the population
• Advantage: Can ensure that certain groups are included
• Disadvantage: but b/c you aren’t using random sampling,
  generalizability may be questionable

• Snowball Sampling
• You contact one person, they contact a friend (e.g., one cancer
  survivor is in contact with other survivors, and so recruits them)
• Advantages: can make it easier to contact people in hard to reach
  groups
• Disadvantage: there may be bias in the way people recruit others
     Factors Affecting Choice of Sampling Procedure

• Use some type of random sampling if:

• You are collecting quantitative data that you want to use to arrive at
  accurate generalizations about population

• You have sufficient resources and time

• You have a good sense for the population

• You are sampling over a broader range (e.g., of states, nations)
   Computing the Sample Size Based on Usable Rates

• Several factors can reduce your sample size

• Thus, you may want to plan for more than your final sample size
  (i.e., use a higher “number of contacts” to achieve your final sample
  size). You adjust using the following three factors:
   • RR = reachable rate (e.g., how many people on a telephone list
       will you actually be able to reach?)
   • OIR = overall incidence rate (i.e., % of target population that will
       qualify for inclusion; e.g., can’t use people over 40)
   • ECR = expected completion rate (i.e., some folks won’t complete
       your survey)

• For example, -
   Computing the Sample Size Based on Usable Rates

• You want a sample size of n = 500

• You figure you can reach 95% of the folks on your list (RR = .95)

• You think 60% will be 40 or younger (OIR = .60)

• You predict that 70% will complete your survey (ECR = .70)

• Based on these numbers, you should contact 1,253 people

                                  n                    500
 Number of contacts                                                 1,253
                        (RR) x (OIR) x (ECR)   (. 95 )(. 60 )(. 70 )
                         Some Key Terms
•   Sampling
     • Selection of a small number of elements from a larger defined target group of
        elements and expecting that the information gathered from the small group
        will allow judgments to be made about the larger group
•   Population
     • The identifiable set of elements of interest to the researcher and pertinent to
        the information problem
•   Defined Target Population
     • The complete set of elements identified for investigation
•   Element
     • A person or object (e.g., a firm) from the defined target population from
        which information is sought
•   Sampling Units
     • The target population elements available for selection during the sampling
        process
•   Sampling Frame
     • The list of all eligible sampling units
                      Some Key Terms
• Total Error = Sampling Error + Nonsampling Error

• Sampling Error
   • Any type of bias that is attributable to mistakes in either drawing a
     sample or determining the sample size

• Nonsampling Error (controllable)
   • A bias that occurs in a reesearch study regardless of whether a sample
     or a census is used (recall all the different types of errors we
     discussed)
       • Respondent Errors (non response, response errors)
       • Researcher’s measurement/design errors (survey, data analysis)
       • Problem definition errors
       • Administrative errors (data input errors, interview errors, poor
         sample design)
             Central Limit Theorem
• A theory that states that, regardless of the shape of the
  population from which we sample (e.g., positively skewed), as
  long as our sample size is > 30, the sampling distribution of
  the mean (x-bar) will be normally distributed with the
  following characteristics:

   x  
                    The mean of the sampling distribution of the mean
                    will equal the mean in the population.


             s           The standard error of the sampling distribution of
   sx                   the mean will equal sample standard deviation (s)
              n          divided by sample size (n). This is a sample
                         estimate of the true standard error in population.
                         The larger the sample size, the more precise we can
                         get about our estimate of the true mean in the
                         population (e.g., in our confidence interval).
                variance




Note: Dr. Joireman does not put
a “bar” above s or s2.
Computing Standard Deviation

        Assume your data are continuous
         (i.e., are not just yes/no data).
For example, let’s say we want to know how much
people would be willing to pay for a tennis racquet.
 We sample 7 folks and wish to generalize to the
                    population….

                    Results 
          Formulas for
Variance and Standard Deviation
                                             Sum of Squared
              POPULATION
                                               Deviations

                           SS
  PopulationVariance          2
                           N
                                        SS
  Population Standard Deviation   
                                        N


                 SAMPLE

                                    SS
 Sample Variance  s 2 
                                   N 1

                                      SS
 Sample Standard Deviation  s 
                                     N 1
            The Sum of Squared Deviations (SS)

          Conc eptualFormula
               SS  ( X i  X ) 2

                Highlights Concept
                   Tells a Story



            Raw Score Formula
                                       ( X ) 2
             SS  X           2
                                     
                                          N

               “Crank it Out”
           Faster, Less Meaningful

• Both Formulas Give Identical Answers!
• SS = NUMERATOR of the Variance
• Examples on board…
 Example of Computing Standard Deviation (for a Sample)

   Xi      Mean    Xi-Mean        (Xi-Mean)2         X2


  60        75        -15            225            3600

  65        75        -10            100            4225

  70        75         -5             25            4900

  75        75         0              0             5625

  80        75         5              25            6400

  85        75        10             100            7225

  90        75        -15            225            8100
ΣX = 525          Σ(X-M) = 0 !   Σ(X-M)2 = 700   ΣX2 = 40075
                ConceptualFormula for SS
                   SS  ( X i  X ) 2  700
                      Raw ScoreFormula for SS
             (X ) 2             (525) 2 
  SS  X  
           2
                        40075             40074  39375  700
             N                   7 

                                        SS   700
            Sample Variance  s 2               116.67
                                       N 1 7 1

                                             SS        700
     Sample Standard Deviation  s                         10 .82
                                            N 1       7 1

                                             s    10 .82
         Standard Error of Mean  s X                   4.09
                                              n       7
This is the “standard deviation” of the sampling distribution of means.
This (4.09) will naturally be smaller than our sample standard deviation (10.82)
based on our single sample of scores, and it will become smaller as n increases.
     Confidence Intervals

A confidence interval is the statistical range of
   values within which the true value of the
target population parameter is expected to lie.
                     Common Z-Critical Values
• To be 90% confident, you use a z-critical value of 1.65
• To be 95% confident, you use a z-critical value of 1.96
• To be 99% confident, you use a z-critical value of 2.58


                                 An example…
                     Z-critical values for 95% confidence
                          (put ½ of .05 on each side)




           .025                                             .025



                  -1.96                           +1.96
               Computing Confidence Intervals
 • 95% Confidence Interval:
 • We are 95% confident that the mean of the population from which we
   took our sample has a mean between these lower and upper limits.
 • To compute, we need:
                                                    Critical Z-value for our
                          Standard error
                                                  desired level of confidence
   Mean of our sample        of mean
                                              (see next page for Z-critical values)




Confidence Interval.95  CI  x  (sx )(Zb, cl )  75  (4.09)(1.96)  75  8.02


           Restated, Confidence Interval.95  66.98 X  83.02

  Based on these results, we are 95% confident that the mean in the population
  from which we sampled is between 66.98 and 83.02. Cool beans!

				
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