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You are to explain in detail errors in marketing research. In your response, give examples to back up your explanation.

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2Focusing You
Research Design
Your company has decided to create a smartphone app and the vice president has asked you to be the
team leader. Your team’s assignment is to nurture the concept. When you meet with your VP next week,
you    will    specify    the    kind    of    apps    your    company    might    develop,    determine    what    the    different    apps    might    
do, and focus on your target audience for each possible app.
As your project matures, you understand how important it is to have a research design. Such a plan will
guide your team and your company’s decision makers. It will lay out the methods and procedures you
need to employ as you collect information.
To develop a research design, you will rely on three types of studies: exploratory studies, descriptive
studies, and causal studies.    Each    depends    on    different    information    that    will    help    you.    No    matter    how    
large or small your project, conducting surveys and establishing a research design is vital to your success.
If you don’t know where your project is going, you won’t know if it’s succeeding.
EXPLORATORY STUDIES
First, you need to do an exploratory study. This is the problem finding phase. An exploratory study forces
you to focus the scope of your project. It helps you anticipate the problems and variables that might arise
in your project.
Perhaps the most common problem is size. Your project must be kept focused. If the scope of a project is
too    big,    it    will    not    get    off    the    ground.    Too    much    information    is    overwhelming.    An    important    objective    
of an exploratory study is keeping your project manageable. The larger your project’s scope, the more
difficult it is to control. This process will help you weed out problems.
In the case of developing an app, for example, an exploratory study would help your research team take
an abstract idea and develop it into a focused plan. The specific app would be market-driven. This process
takes    legwork,    but    the    results    are    worth    the    effort.
16 | RESEARCH DESIgN
Exploratory studies generally encompass three distinct methods:
1. Literature search
2. Expert interviews
3. Case studies
LITERATURE SEARCH
A literary search means you go to secondary sources of information: the internet, the public li
ary,
company or government records. These sources are usually easy and inexpensive to access.
For example, your development team would search online. They would look at other kinds of apps on the
market, the prefe
ed phone to develop an app, the pricing of similar products, and any other information
necessary to set parameters on their project.
EXPERT INTERVIEWS
After a literature search, your team would have a useful background for the project. They know what
questions to ask and how to set up their project. After the literary search, the next step is to interview
experts. These experts might include company executives or consumers. They would also talk to people
who used similar products. Your team would seek out professionals who have careers relating to the
esearch project.
Your    team    knows    that    one    effective    way    to    gain    information    from    experts    is    through    focus    groups.    A    
focus group includes 6-8 individuals who share a common background (software development, market
analysis, administration, dog
eeding, fly fishing) who participate in a joint interview. The secret to a
successful    focus    group    is    ignoring    the    traditional    question/answer    format.    Instead,    you    encourage    the    free    
flow of ideas and discussion.
CASE STUDIES
Every research project will have pitfalls. Thus, case studies become a vital tool because they allow you to
examine another business’s managerial problems and solutions. If another study deals with similar issues,
you can avoid these pitfalls by learning from its mistakes. Case studies include histories of other projects
and    simulations    of    possible    alternatives.    A    good    “What    if?”    can    save    a    lot    of    time    and    resources.
DESCRIPTIVE STUDIES
Who    are    you    selling    to?    An    exploratory    study    helped    you    establish    what    you    are    selling,    but    the    
descriptive study will help you find your market and understand your customer. Since you will not be able
to sell to everyone, a descriptive study is necessary to focus your project and resources.
RESEARCH DESIgN | 17
There    are    different    kinds    of    studies    you    can    implement    to    better    understand    your    market.    Consider    the    
following descriptive studies:
•     Market    potential:    description    of    the    number    of    potential    customers    of    a    product.
•     Market-share:    identification    of    the    share    of    the    market    received    by    your    product,    company    and        
your competitors.
•     Sales    analysis:    description    of    sales    by    te
itory,    type    of    account,    size    or    model    of    product.
•     Product    research:    identification    and    comparison    of    functional    features    and    specifications    of    com-    
petitive products.
•     Promotion    research:    description    of    the    demographic    characteristics    of    the    audience    being        
reached by the cu
ent advertising program.
•     Distribution    research:    determining    the    number    and    location    of    retailers    handling    the    company’s        
products. These are supplied by wholesalers and distributed by the company.
•     Pricing    research:    identifying    competitors’    prices    by    geographic    area.
These studies will help you formulate solutions. At the same time, they indicate how potential customers
might react.
CAUSAL STUDIES
Even though descriptive studies describe and sometimes predict relationships, results, or events, you may
want to know why. If you can discover the reasons behind your solutions, then you can assemble your own
predictive models. Such models can be used in the future. As a marketing researcher, knowing why will
make your job easier. Causal studies try to find out the relationship between a specific cause and a specific
effect.
FIGURING OUT CAUSAL RELATIONSHIPS
Cause    and    effect    have    to    be    related.    Before    a    cause    and    effect    can    be    established,    a    logical    implication    (or    
theoretical justification) has to be found.
There are three types of evidence that can be used to establish causal relationships:
1. Associative variation
2. Sequence of events
3. Absence of other possible causal factors
18 | RESEARCH DESIgN
Associative Variation
Associative variation involves taking two variables and seeing how often they are associated. The more
they show up in studies, the more likely they are related. Associative variation can be
oken down into
two distinctions: association by presence and association by change.
Association by presence measures how closely presence of one variable is associated with presence of
another. However, association by change measures the extent to which a change in the level of one
variable is associated with a change in the level of the other.
For example, if you wanted to find a causal relationship between a salesperson’s success in sales and
training, you would have to establish a relationship between the two variables. Do sales only increase
after    training?    Can    sales    increase    before    training?        If    you    find    that    one    variable    is    affected    by    another,    you    
know which variable to adjust.
Sequence of Events
In    order    to    establish    a    cause/effect    relationship,    you    must    first    establish    that    the    causal    factor    occu
ed    
first. For example, in order for salesperson training to result in increased sales, the training must have
taken    place    prior    to    the    sales    increase.    If    the    cause    does    not    precede    the    effect,    then    there    is    no    causal    
elationship.
Absence of Other Possible Causal Factors
You    must    also    demonstrate    that    other    factors    did    not    cause    the    effect.    Once    you    have    proved    this,    you    can    
logically conclude that the remaining factor is the cause. For example, if we can control all other factors
affecting    the    sales    item,    then    we    have    to    conclude    that    the    increase    in    sales    comes    from    training.
SOURCES OF MARKETING INFORMATION
The app development team knows it must do careful market research before it can begin development.
But    where    do    they    start?
There are four major sources for finding marketing information. We’ll
iefly describe each in this
section. However, we will discuss these in-depth in later chapters. These four sources include:
1. Secondary sources
2. Respondents
3. Natural and controlled experiments
4. Simulation
RESEARCH DESIgN | 19
Secondary Sources
Secondary information is information that someone else researched for a solution to a problem other
than yours. Even though this information wasn’t intended for your project, it could provide valuable
insights. For example, PetMD conducted a study and found that sixty-nine percent of dog owners need
help understanding their dog’s nutritional needs. Meanwhile, Hill’s®ScienceDiet is developing a new
chicken and whole grain dog food that meets the full spectrum of nutritional needs. While the purpose
of    PetMD’s    study    had    a    different    purpose,    Hill’s    can    use    PetMD’s    research    to    better    its    own    market    
products.
Respondents
Information from respondents plays a huge role in research. Customers’ ve
al and behavioral responses
provide useful information. Later in this book, we’ll look at how both asking questions and observing
ehavior come together to form a complete response.
Natural and Controlled Experiments
Natural experiments are just what they would seem. The investigator only measures results, having
no control over the elements of the experiment. For example, if Nielson wanted to research a TV
audience’s response to a specific television commercial, a natural experiment would involve the researcher
monitoring viewership and interviewing the audience. The results would then be compared to a control
group who had not watched the commercial. Natural experiments are useful when you want to gauge
general results.
Controlled experiments measure specific variables and require the researcher to be more involved.
Experimental results are then compared to a control group in order to measure the chosen variable.
Two kinds of intervention are required in controlled experiments:
1. Manipulating at least one causal variable.
2. Random assignment of subjects to experimental and control groups.
Controlled experiments work best when the researcher controls all but one causal variable. The researcher
assigns subjects to an experimental group where the causal variable is manipulated or to a control group
where no causal variables are manipulated. The researcher measures the dependent variable in both
situations    and    then    tests    for    differences    between    the    groups.    This    strict    control    allows    differences    between    
the groups, if present, to be attributed to the manipulated variable.
Qualtrics.com online will be invaluable when you do field experiments. This tool is quick, easy to use,
and    cost    effective.    Qualtrics    provides    a    rich    assortment    of    tools    for    conducting    experiments.    They    include:    
advanced
anching logic, randomization, question block presentation, question response timing, and
JavaScripting capabilities.
20 | RESEARCH DESIgN
Simulation
Experimentation    can    be    expensive    and    time-consuming.    It    might    be    more    cost    effective    to    create    a    
simulation model instead of doing real-world experiments.
Simulations    are    effective    when    the    project    has    a    scope    larger    than    regular    experiments    can    cover.    
Environmentally rich models (containing complex interactions and nonlinear relationships) are usually
too difficult to solve by standard analytical methods such as calculus or other mathematical programming
techniques. Rather, the analyst views a simulation model as a limited imitation of the process or system
under study and attempts to run the system on a computer to see what would happen if a particular set of
conditions    were    put    into    effect.
Simulations are often developed for marketing systems, and include marketing-mix elements (new-
product, price, advertising, and sales-force variables).
TYPES OF ERRORS AFFECTING RESEARCH RESULTS
Information gained from research projects should be as accurate as possible. Any research project is
subject    to    e
ors,    so    a    research    designer    must    do    everything    he/she    can    to    minimize    them.    As    shown    in    
Table 2.1, two general e
ors have important implications in research designs:
1. E
ors related to improper selection of respondents
2. E
ors related to accuracy of responses
RESPONDENT SELECTION ERRORS ACCURACY OF RESPONSE ERRORS

1. Population Specification E
o
2. Sampling E
ors
3. Selection E
ors
4. Frame E
ors
5. Survey Non-Response E
ors

1. Non-Response E
ors
2. Su
ogate Information E
ors
3. Measurement E
ors from Interviewers
4. Measurement e
ors from questions
5. Measurement e
ors from respondents
TABLE 2.1
COMMON ERROR TYPES
RESEARCH DESIgN | 21
The next discussion considers some strategies that can be employed to make your
esearch project relatively e
or-free.
Understanding the possible e
ors that can taint the accuracy of information in your
study is key to avoiding and co
ecting sampling e
ors. Below is a
ief explanation of
possible e
ors.
RESPONDENT SELECTION ERRORS
POPULATION SPECIFICATION ERROR
This type of e
or occurs when the researcher selects an inappropriate population or
universe from which to obtain data.
Example: Packaged goods manufacturers, for example, frequently survey
housewives because they are an easy contact. Also, it is assumed housewives
decide what is to be purchased and do the actual purchasing for a household.
However, in this situation, there often is population specification e
or. In-
creasingly, husbands may purchase a significant share of the packaged goods
and have significant influence over what is bought.
SAMPLING ERROR
Sampling e
or occurs when a sample does not accurately represent the population.
Example: Suppose that we used tweets (Twitter) to recruit a random sample
of 500 people from the general adult population. After an analysis, though,
we find our study was composed only of people aged 18 to 35. Because the
sampling pool shares so many age group specific traits, the data isn’t accurate
in representing the general population.
The more homogeneous the population (meaning people who are similar), the smaller
the sampling e
or; and as sample size increases, sampling e
or decreases. If a census
were conducted (i.e., all elements of the population were included) there would be no
sampling e
or.
22 | RESEARCH DESIgN
SELECTION ERROR
Selection e
or is the sampling e
or that occurs when a sample is selected by a
nonprobability method.
Example: Interviewers conducting a mall intercept study have a natural
tendency to select those respondents who are the most accessible and
agreeable. Such samples often comprise friends and associates who are rarely
representative of the desired population.
Selection e
or often reflects people who are easily reached, are better dressed, have
etter kept homes, or are more pleasant. These types of samples rarely represent
the desired population. Having clear, written procedures that specify how to select
espondents can help to reduce selection e
or.
FRAME ERROR
A sampling frame supposedly represents all the members of the population. It is
usually a listing of the respondents you want to sample.
Example: The sample frame for a study at a shopping mall includes all
shoppers in the mall during the time of data collection. In years past, a
commonly used frame for consumer research was the telephone directory.
Over time, this frame has increasingly introduced e
or because many
elements of the population (households, singles, students) are no longer
included in the directory. There are also unlisted phone numbers, move-ins,
and cell phones to consider. Some elements are listed more than once, and
non-population elements are also included (businesses and people who have
left the area).
A perfect frame identifies each member of the targeted population once, but only once,
and does not include members outside of that specific population.
SURVEY NON-RESPONSE ERROR
Non-response    e
or    occurs    when    respondents    and    non-respondents    are    too    different.    
Your respondents should accurately represent the population you want to sample. If
non-respondents are not equally distributed across the population, you will not have an
accurate sample.
There
Answered 1 days After May 25, 2023

Solution

Ayan answered on May 26 2023
25 Votes
WRITTEN ASSIGNMENT        4
WRITTEN ASSIGNMENT
Table of contents
Introduction    3
Sampling E
ors    3
Measurement E
ors    3
Sampling Frame E
ors    4
Analysis and Interpretation E
ors    5
Conclusion    7
References    8
Introduction
    In order for organizations to make wise judgments, comprehend customer behavior, and create successful strategies, marketing research is essential. However, just like any other research project, marketing research is prone to a number of mistakes that might risk the validity and dependability of the results. In this article, we will examine the most frequent mistakes made in marketing research, provide instances of each, and talk about prevention methods.
Sampling E
ors
    Sampling mistakes happen when the target population is not co
ectly represented in the sample chosen for the study. This mistake may provide skewed findings and false conclusions (Hassan et al., 2023). Non-response bias, which occurs when particular population segments do not participate and distort the findings, is a frequent sampling mistake. A low response rate, mostly from unhappy customers, in a survey designed to assess consumer preferences for a new product, for instance, might lead to an overestimation of positive comments. Researchers can use strategies like random sampling, stratified sampling, or quota sampling to reduce sampling mistakes. Additionally, through efficient survey design and incentives, efforts should be made to promote participation and reduce non-response bias.
Measurement E
ors
    When the study instrument is unable to precisely measure the specified constructs or variables, measurement e
ors arise. Measurement inaccuracies can in a variety of forms, including –
· Questionnaire Design E
ors: Inaccurate statistics might result from poorly written or confusing survey questions that skew respondents' responses. For instance, asking customers to assess the quality of a product on a scale of 1 to 10 without specifying the standards for each rating point may result in replies that are inconsistent and untrustworthy. Researchers should carefully create and pretest their surveys to guarantee the relevance, clarity, and simplicity of the questions in order to minimize questionnaire design mistakes. Before undertaking the full-scale research, pilot testing with a small sample can assist detect and address any potential problems.
· Response E
ors: Due to memory gaps, social desirability bias, or misinterpretation of the question, respondents may give e
oneous or misleading answers (Rasmussen & Simkovic, 2020). For instance, participants in a research evaluating the success of an advertising campaign may exaggerate their recollection of certain commercials in order to conform to social norms or may just forget the specifics. By using methods like cognitive testing, giving clear...
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