Management Question

Question 1: Between 480 – 550 words.
A good sample must represent all the characteristics of the population.
Take a real-life example and explain why it is necessary? If it is not true,
what can happen? (Refer Chapter-5)
Embed course material concepts, principles, and theories (which require
supporting citations), along with two scholarly peer-reviewed references in
support of your answer. Keep in mind that these scholarly references can
be found in the Saudi Digital Library by conducting an advanced search
specific to scholarly references.
Be sure to support your statements with logic and argument, citing all
sources referenced. Post your initial response early and check back often
to continue the discussion. Be sure to respond to your peers’ posts as well.
Answer all questions posted by your fellow students as well as your
professor. These post replies need to be substantial and constructive in
nature. They should add to the content of the post and evaluate/analyze that
post’s answer.
Question 2: Reply to two discussion (between 70 – 150
words) for each student.
Discussion – Student 1
*Research Requirements for a Representative Sample*
For study results to be accurate and reliable, a sample needs to reflect the
population as a whole. Researchers must ensure that their samples are
representative of the population at large in order to minimize sampling bias
and draw more accurate conclusions. Improper conclusions, affecting
decision-making and policy-making, might result from not ensuring
representativeness.
### Practical Illustration: Medical Studies on the Efficiency of Drugs
Take a study testing a new medication for hypertension as an example of a
real-world application of medical research. Findings will be skewed if the
study’s sample doesn’t reflect the full population that might use the medicine.
This could happen if specific age groups, genders, or ethnicities are excluded.
Because hypertension manifests itself in various ways in different people, this
is an issue (Creswell & Creswell, 2018). An example of this would be a trial
that mostly involves younger, healthier patients; this could lead to an
exaggeration of the drug’s effectiveness when tested on a more diverse
population, such as older people with various health issues.
Reasons Why It’s Important to Use a Representative Sample
1. *Generalizability*: One of the main aims of most research projects is to be
able to apply the findings to a larger population. It is possible to draw more
trustworthy conclusions from a sample if it is representative of the population
as a whole (Bhattacherjee, 2012). Our example shows that doctors and
lawmakers can benefit from generalizable results if the sample is reflective of
all patients who could take the medicine.
Second, a representative sample lessens the possibility of sampling bias by
making sure that no one group is grossly under- or over-represented.
Mistakes regarding the safety or effectiveness of a medicine could result from
sample bias in medical research. For instance, the sample’s inability to
capture the effects of various genetic origins and lifestyle choices on
medication efficacy could be due to a lack of diversity.
3. Enhancing Reliability and Validity: A high-quality sample is crucial for
ensuring the validity and reliability of research results. That can be
jeopardized if the sample is not representative. According to Saunders et al.
(2019), validity is the degree to which the study’s conclusions are correct,
while reliability is the degree to which the results are consistent. Inferences
drawn from a sample may not be valid if it does not accurately represent the
population.
### Implications of Samples That Do Not Represent the Whole
Results could be *misleading* if the sample does not reflect the population as
a whole:
– *Improper Extrapolation*: Results could not be applicable to the full
population, which reduces their practicality. For instance, due to an
unrepresentative sample, a medicine may show promise in a trial but not in
the real world.
Policies that fail to appropriately meet the needs of all population groups
might be the consequence of judgments based on non-representative
samples in public health, educational, or social policy contexts (Creswell &
Creswell, 2018).
– *Economic Costs*: Investing in interventions that don’t work or marketing a
drug with unknown side effects for some groups are examples of how biased
research can contribute to economic inefficiencies.
#### In summary
For research to be credible, trustworthy, and applicable to the larger
community, it is essential to choose a sample that is representative of that
group. Poor decision-making, skewed findings, and inaccurate generalizations
might emerge from not collecting a representative sample. In order to avoid
these problems and provide useful insights to their disciplines, researchers
should meticulously plan their investigations to capture all important
demographic features.
References ###
Bhattacherjee, A. (2012). Social science research: Principles, methods, and practices
(2nd ed.). University of South Florida Scholar Commons.

Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative,
.quantitative, and mixed methods approaches (5th ed.). SAGE Publications
Saunders, M., Lewis, P., & Thornhill, A. (2019). Research methods for
.business students (8th ed.). Pearson Education
Answer: (between 70 – 150 words)
Discussion – Student 2
For numerous reasons, it is critical in research to represent all characteristics of a
population:
•
Scientific rigor requires that study be as thorough and comprehensive as
feasible. Omitting traits might be interpreted as a flaw in the research
technique, thereby lowering the study’s credibility and the confidence in its
conclusions.
•
Inclusion: Promoting inclusion in research ensures that all persons and groups
are acknowledged and appreciated. This may result in increased engagement
in research investigations and a more favorable impact on society.
•
Policy and Decision Making: Research frequently informs policy decisions. If
research does not accurately represent the population, actions based on that
study may be ineffective or may unintentionally hurt specific populations.
Comprehensive representation can result in more informed and equitable
policy.
•
Ethical Considerations: Excluding certain qualities can be problematic,
particularly when it comes to vulnerable people. Researchers have an ethical
obligation to guarantee that their research does not discriminate against or hurt
any specific population. Ensuring representation helps to defend against
ethical infractions.
•
Generalizability: Researchers seek to derive conclusions that can be applied to
a larger population or situation. If any traits are left out or underrepresented,
the results may not be applicable or generalizable to the full population. This
can limit the study’s external validity.
Example:
To provide equitable access and efficient service, public transportation networks in
cities must represent all demographic characteristics. Here’s why this matters:
•
Work and School Commuters: It is critical to understand the commuting
patterns of diverse groups, including students, workers, and visitors. Different
groups may have different peak travel hours and destinations, which should be
considered when planning service schedules and routes.
•
Environmental Sustainability: Many communities want to lessen the
environmental impact of transportation. Representing the population entails
taking into account the interests of people who value environmentally friendly
modes of transportation, such as bike lanes, electric buses, and carpooling
efforts.
•
Cities frequently have culturally and linguistically diverse populations. Public
transportation information, signage, and services should be offered in several
languages so that all residents and visitors may simply traverse the system.
•
Age Diversity: Cities are home to individuals of different ages, from
youngsters to retirees. Children and the elderly should have access to safe and
reliable public transit. This includes factors such as stroller and wheelchair
accessibility, seating options, and safety precautions.
•
Socioeconomic Diversity: People from varied socioeconomic backgrounds use
public transit. Some people may not own a car and rely on buses or railroads
for daily transportation. Ensuring affordability and easy access for low-income
households is critical to their mobility and economic possibilities.
•
Accessibility for People with Disabilities: A diverse community comprises
people with various mobility needs, such as wheelchair users, vision
impairments, and others. Public transportation networks must be built to
accommodate these people, including ramps, elevators, and other accessibility
facilities.
Cities may design transportation systems that are more inclusive, efficient, and
responsive to their inhabitants’ different demands by incorporating all demographic
variables into public transportation planning. This not only improves the quality of
life for individuals, but also helps to reduce traffic congestion, improve air quality,
and create a more sustainable urban environment.
When researchers fail to reflect all aspects of a community in their research, various
undesirable outcomes may occur:
•
Loss of Credibility: If the research is perceived to be biased or incomplete, its
credibility may suffer. Researchers may endure criticism and suspicion from
their peers and the larger community.
•
Stereotype Reinforcement: Failure to express various features can reinforce
stereotypes and perpetuate discriminatory behavior. This can have a long-term
harmful impact on persons and societies.
•
Policy and Decision-Making Errors: Research frequently informs policy
choices. If research is not representative, the policies that result from it may be
ineffectual or even harmful to some populations.
•
Ethical Concerns: Research that discriminates or marginalizes specific
qualities or groups may generate ethical concerns. It could contravene ethical
ideals such as fairness, respect for others, and justice.
References
Brooks, J. M. (2013, January 1). Improving Characterization of Study Populations: The
Identification Problem. Www.ncbi.nlm.nih.gov; Agency for Healthcare Research
and Quality (US).
Villegas, F. (2022, August 16). Study Population: Characteristics & Sampling
Techniques. QuestionPro.
Young, J. (2023). Representative Sample is often used to extrapolate broader
sentiment. Investopedia.
Answer: (between 70 – 150 words)
>chapter 5
Stage 2:
Sampling Design
“When you sample
something, you’re using
the crutch of borrowing chords
and melodies from a song that’s
already great, that’s already stood
the test of time, that’s already special.”
Gerald Earl Gillum (G-Eazy),
American rapper and record producer
>learningobjectives
After reading this chapter, you should understand . . .
LO5-1
The six tasks that comprise sampling design.
LO5-2
The premises on which sampling theory is based.
LO5-3
The characteristics of accuracy and precision for measuring sample validity.
LO5-4
The two categories of sampling methods and the variety of sampling techniques within each category.
LO5-5
The various sampling techniques and when each is used.
LO5-6
The ethical issues related to sampling design.
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>part II The Design of Business Research
>Sampling Design
This subprocess of research design answers the question: From whom or what (target population)
does the data need to be collected and how and from how many (cases)? The steps in this subprocess
(Exhibit 5-1) include:
1. Define the target population and a case (describe those entities—collectively and individually—
that possess the desired information about the chosen variables and its parameters).
2. Define the population parameters of interest (summary descriptors—proportion, mean, variance—
of study variables) in the population.
3. Identify and evaluate the sample frame (list of cases within the target population) or create one.
4. Define the number of cases needed (choose between a census or sample; choose the size of any
sample).
5. Define the appropriate sampling method (the type of sample to be used).
6. Define the sampling selection and recruitment protocols (choose standardized procedures or
custom-design ones).
>Exhibit 5-1 Sampling Design in the Research Process
Investigative Questions
Sampling Design
Define Target Population
& Case
Define Population Parameters
Define Number of Cases
Choose Sample
Choose Census
Define & Evaluate Sample Frames
Identify Existing
Sample Frames
Evaluate Existing
Sample Frames
Accept
Define Sampling Method
Nonprobability
Select Sample
Frames
Probability
Reject
Modify or Construct
Sample Frames
Define Selection & Recruiting
Protocols
Draw
Cases
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>chapter 5 Stage 2: Sampling Design
If a study’s objective is to understand data security breaches, then the target population might be “any
event where data were accessed by an unauthorized source.” Using operational definitions for “accessed”
and “unauthorized source” would be critical. IT experts indicate exploratory data breaches by hackers are
often so well hidden that they may not be discovered for months, if at all, until a major breach occurs. Would
these exploratory breaches also be part of the target population? For each data breach (case), the population
parameters of interest might be whether data were accessed but not modified, what modifications occurred,
whether data were permanently lost, the method of access, etc. The sample frame would be the list of every
such data breach within a specified period of time (e.g., five years). The number of cases we need depends
on the size, variability, and accessibility of the target population. Depending on the number of breaches a
company experienced, using a census (evaluating all instances of unauthorized access) might be feasible. If
the number of breaches is very large, and exploration showed that one method of access and type of breach
was common (limited variability), then using a sample (examining only a portion of unauthorized access
records) might be chosen. In this instance, a researcher would need special forensic computing skills, thus
using a sample might also be more desirable due to time constraints on a limited pool of researchers.
>Define the Target Population and Case
In business, a target population can be any of the following (Exhibit 5-2), with a case being a single element drawn from that target population:
• People (individuals or groups: e.g., employees, customers, suppliers).
• Organizations or institutions (companies, trade associations, professional online communities, unions).
• Events and happenings (e.g., trade association meetings, presentations to financial analysts,
industry conventions, employee picnics).
• Objects or artifacts (e.g., products, machines, production waste or byproducts, tools, maps,
process models, ads).
• Settings and environments (e.g., warehouses, stores, factories, distribution facilities).
• Texts (e.g., annual reports, productivity records, social media posts, emails, memos, reports).
>Exhibit 5-2 Common Types of Target Populations in Business Research
People
Texts &
Records
Organizations
Target
Population
Settings
Events
Objects
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>part II The Design of Business Research
>snapshot
Ford Reenergizes by Changing Its Sampling Design
In the midst of the financial crisis in the automobile industry,
Ford’s James Farley decided his research was excluding a very
important target population: dealers. With dealers controlling 75
percent of advertising expenditures for the auto giant, Farley
thought excluding them as research participants was suicidal.
So he recruited 30 of the most influential dealers to fly to Detroit
to provide information and critique the creative proposals of the
Ford ad agency, Team Detroit.
Farmington Hills (MI) full-service research firm Morpace put
the dealers through an intensive group interview experience.
The dealers were soon challenged with questions. “Which
incentives work and which don’t?” “What does the Ford brand
mean to you?” “What is wrong with Ford’s advertising?” In
subsequent sessions, the dealers were asked to critique ad
slogans and branding strategies and recommend those that
best captured the Ford experience. The dealers left the 72-hour
marathon session enthusiastic about the direction Ford was taking and with significant buy-in for the next ad campaign. Farley’s
©Joe Raedle/Staff/Getty Images
actions gave voice to its dealers with its altered research sampling design.
www.ford.com; www.morpace.com; www.teamdetroit.com
The definition of the target population may be apparent from the management problem or the research question(s), as it was in our data breach study, but often it is not so obvious. Sometimes there is
more than one option for the target population. The researcher will choose one or more options that will
provide him or her with the most answers to the investigative questions.
In the discussion that follows, we will use a dining study on a college campus: The researchers at
Metro University (Metro U) are exploring the feasibility of creating a dining club whose facilities
would be available on a membership basis. To launch this venture, they will need to make a substantial investment. Research will allow them to reduce many risks. Thus, the research question is: Would
a membership dining club be a viable enterprise? Some investigative questions that flow from the
research question include:
1. Who would patronize the club, and on what basis?
2. How many would join the club under various membership and fee arrangements?
3. How much would the average member spend per month?
4. What days would be most popular?
5. What menu and service formats would be most desirable?
6. What lunch times would be most popular?
7. Given the proposed price levels, how often per month would each member have lunch or dinner?
8. What percent of the people in the population say they would join the club, based on the projected rates and services?
Is the target population for the dining club study at Metro University defined as “full-time day
students on the main campus of Metro U”? Or should the population include “all persons employed
at Metro U”? Or should townspeople who live in the neighborhood be included? Without knowing
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>chapter 5 Stage 2: Sampling Design
>Define the Population
Parameters
Population parameters are summary descriptors (e.g.,
incidence proportion, mean, variance, etc.) of variables of interest in the population. Sample statistics are
descriptors of those same relevant variables computed
from sample data. Sample statistics are used as estimators of population parameters. The sample statistics are
the basis of our inferences about the population. Exhibit 5-3 indicates population parameters for the Metro
U dining study.
Depending on how measurement questions are phrased, each will collect a different level of data.
Each different level of data also generates different sample statistics. Thus, choosing the parameters of
interest will actually dictate your sample type and its size. Data have different properties depending on
how they were collected. Exhibit 5-4 reviews the data types and these properties.
When the variables of interest in the study are measured on interval or ratio scales, we use the
sample mean to estimate the population mean and the sample standard deviation to estimate the
population standard deviation. When the variables of interest are measured on nominal or ordinal
scales, we use the sample proportion of incidence (p) to estimate the population proportion and the
pq to estimate the population variance where q = (1 − p). The population proportion of incidence “is
equal to the number of cases in the population belonging to the category of interest, divided by the
total number of cases in the population.”1 Proportion measures are necessary for nominal data and
are widely used for other measures as well. The most frequent proportion measure is the percentage.
In the Metro U study, examples of nominal data are the proportion of a population that expresses
interest in joining the club (e.g., 30 percent; therefore p is equal to 0.3 and q, those not interested,
©Wavebreak Media/Getty Images
the likely patron for the new venture, it is not obvious which of these is the appropriate target population. Assume the Metro University Dining Club is to
be solely for the students and employees on the main
campus. The researchers might define the population
as “currently-enrolled students and employees (fulland part-time) of Metro U, main campus, and their
families.” Thus, any single student or employee would
be a likely case.
>Exhibit 5-3 Example Population Parameters in the Metro U Dining Study
Population Parameter of Interest
Data Level and Measurement Scale
Frequency of eating on or near campus at a
restaurant within the last 30 days
!
Ratio data (actual number of eating experiences)
!
Ordinal data (less than 5 times per month, greater than 5
but fewer than 10 times per month, greater than 10 times per
month)
Proportion of student/employees expressing
interest in the dining club
!
Nominal data (interested, not interested)
Proportion of students/employees spending
money per person per visit
!
Interval data ($5–9.99, $10–14.99, $15–19.99, $20–24.99,
$25–29.99)
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>part II The Design of Business Research
>Exhibit 5-4 Data Types and Characteristics
Data Type
Data Characteristics
Example
Nominal
Classification
Respondent type (faculty, staff, student)
Ordinal
Classification and Order
Preferred doneness of steak (well done,
medium well, medium rare, rare)
Interval
Classification, order, &
distance
How rated last restaurant experience
(scale of 1-10; l=very poor, 10=exceptional)
Ratio
Classification, order,
distance & natural origin
Average $ amount spent per person for
last dinner in restaurant.
equals 0.7) or the proportion of married students who report they now eat in restaurants at least
five times a month.
There may also be important subgroups in the population about whom we would like to make estimates. For example, we might want to draw conclusions about the extent of dining club use that could be
expected from married faculty versus single students, residential students versus commuter students, and
so forth. Such questions have a strong impact on the nature of the sampling frame we accept (we would
want the list organized by these subgroups, or within the list each characteristic of each case would need
to be noted), the design of the sample, and its size.
>Define the Sampling Frame
The sampling frame is the list of cases in the target population from which the sample is actually drawn.
Ideally, it is a complete and correct list of population members only. As a practical matter, however, the
sampling frame often differs from the desired population. For the dining club study, the Metro U directory would be the logical first choice as a sampling frame. Published directories are usually accurate
when published in the fall, but suppose the study is being done in the spring. The directory may contain
errors and omissions because some people will have withdrawn or left since the directory was published,
while others will have enrolled or been hired. Usually university directories don’t mention the families
of students or employees. Just how much inaccuracy one can tolerate in choosing a sampling frame is a
©Ollyy/Shutterstock
With the growing number of
people in cell-phone-only
households, the printed phone
directory has become obsolete
as a sample frame for household research. Specialized
business directories are still
viable options for business-tobusiness research.
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>chapter 5 Stage 2: Sampling Design
matter of judgment. You might use the directory anyway, ignoring the fact that it is not a fully accurate
list. However, if the directory is a year old, the amount of error might be unacceptable. One way to make
the sampling frame for the Metro U study more representative of the population would be to secure a
supplemental list of the new students and employees as well as a list of the withdrawals and terminations
from Metro U’s registrar and human resources databases. You could then craft your own sample frame
by adding and deleting information from the original directory. Or, if their privacy policies permit, you
might just request a current listing from each of these offices and combine these lists to create your
sampling frame.
A greater distortion would be introduced if a branch campus population were included in the
Metro U directory. This would be an example of a too-inclusive frame—that is, a frame that includes
many cases other than the ones in which we are interested. A university directory that includes faculty
and staff retirees is another example of a too-inclusive sampling frame.
Often you have to accept a sampling frame that includes people or cases beyond those in
whom you are interested. You may have to use a telephone directory to draw a sample of business
telephone numbers. Fortunately, this is easily resolved. You draw a sample from the larger population
and then use a screening procedure to eliminate those who are not members of the group you wish
to study.
The Metro U dining club survey is an example of a sampling frame problem that is readily solved.
Often one finds this task much more of a challenge. Suppose you need to sample the members of an
ethnic group, say, Asians residing in Little Rock, Arkansas. There is probably no list of this population.
Although you may use the general city directory, sampling from this too-inclusive frame would be costly
and inefficient because Asians represent only a small fraction of Little Rock’s population. The screening task would be monumental. Because ethnic groups frequently cluster in certain neighborhoods, you
might identify these areas of concentration and then use a city directory, which is organized by street
address, to draw the sample.
It is not until we begin talking about sampling frames and sampling methods that international research starts to deviate. International researchers often face far more difficulty in locating or building
sample frames. Countries differ in how each defines its population; this affects census and relevant
population counts.2 Some countries purposefully over sample to facilitate the analysis of issues of particular national interest; this means we need to be cautious in interpreting published aggregate national
figures.3 These distinctions and difficulties may lead the researcher to choose nonprobability techniques
or different probability techniques than they would choose if doing such research in the United States or
other developed countries. In a study that is fielded in numerous countries at the same time, researchers
may use different sampling methodologies, resulting in hybrid studies that will need care to be combined.
It is common practice to weight sample data in cross-national studies to develop sample data that are
representative.4 Choice of sampling methods is often dictated by culture as much as by communication
and technology infrastructure. Just as all advertising campaigns would not be appropriate in all parts of
the world, all sampling techniques would not be appropriate in all subcultures. The discussion in this
text focuses more on domestic than international research. It is easier to learn the principles of research
in an environment that you know versus one in which many students can only speculate. Ethnic and
cultural sensitivity should influence every decision of researchers, whether they do research domestically
or internationally.
>Define the Number of Cases
The ultimate test of a sampling design is how well any cases we measure represent the characteristics of
the target population the design purports to represent.
Sample versus Census
Most people intuitively understand the idea of why drawing a sample works. One taste from a drink tells
us whether it is sweet or sour; we don’t need to drink the whole glass. If we select a few ads from a magazine, we assume our selection reflects the characteristics of the full set. If some members of our staff favor
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>part II The Design of Business Research
>closeup
Who’s Really Taking Your Surveys?
Early in panel development, panelists were offered $100 to
$150 to join and participate in qualitative studies. Historically,
these participants participated in longer engagements (online qualitative, ethnography studies or face to face in-depth
interviews). As random digit dialing became less productive for recruiting respondents and the Internet became
more widely used, researchers started using panels—also
called communities—to recruit participants for quantitative
research. Quantitative respondents who are engaged for
much shorter periods are more likely to be paid $1 to $10 per
survey completed.
The professional respondent—one who takes repeated
surveys—was once considered a deterrent to quality research.
“As researchers, ” explained Jessica Broome, PhD, principal of
Jessica Broome Research, “we wanted to keep the ‘cheaters’ and
‘repeaters’ out of our studies, believing they biased results.”
As decision cycles shorten, the demand for better and more
timely information means attracting and retaining qualified participants. During the last three decades, increasingly this means
researchers turn to panels, and by design, these participants
are asked to participate in numerous studies.
“We wanted to know ‘Who are these people willing to take
repeated surveys?’” explained Broome. “And given that some surveys are overly long and others poorly designed, ‘Why do they
do this?’” Broome teamed with Kerry Hecht, Director of Research
Services, Recollective, a division of Ramius Corporation, to find
out what motivates panel respondents and if their motivations are
likely to reduce the quality of the information they provide.
The Qualitative Study
Broome and Hecht designed a multistage study that drew participants from multiple panel providers, including Critical Mix,
Schlesinger Associates, and Swagbucks. “We started with a
5-day online qualitative community study with 20 people to
explore what got them started as a survey panelist and what
kept them going,” explained Hecht. “While money is a motivator
in keeping panelists engaged, they also shared the influences
of intrinsic motivators like fun, feeling useful, contributing to
important decisions, and participation being more interesting
than time spent on social media.”
For any particular study, panelists are often screened exclusively on demographics. “Because participants derive intrinsic
benefits, panelists will sometimes fudge on screening information in an attempt to be included in a study, ” shared Broome.
When a panelist doesn’t meet the desired demographic parameters, they are told “You don’t qualify” but are rarely told why.
“But once they are included, ” explained Broome, “participants
claim honesty drives their responses; they think lying on survey
questions would undermine the study.”
“Often panelists expressed feeling abused, misled, and disrespected. For example, when they are told about survey length,
they often felt deceived when a promised 15-minute survey took
45 minutes, or when the survey was not only long, but boring,”
explained Broome. Increasingly, panelists in quantitative studies are tech-savvy. “They understand what current technology
should permit a survey company to do—like eliminate the need
to ask demographic questions repeatedly in the same survey
process or use earlier answers to filter later questions, ” claimed
Hecht. “They basically think researchers can make the experience so much better.” “The research industry needs their insights, but can treat panelists with disdain,” claimed Broome.
The Quantitative Study
Broome and Hecht followed their qualitative exploration study
with Phase 2, a mobile-optimized quantitative study of 1,499
participants, also drawn from various panel providers and fielded
by Propeller Insights. Each panelist took a topical survey that
included a creativity assessment. Additionally, half the group
(750) took a VARK assessment. VARK assesses visual, aural/
audio, read/write, and kinesthetic learning preferences through
a series of learning scenario questions. Creativity was assessed
through a battery of 38 statements requiring agreement or disagreement, as well as a checklist of 54 descriptors. “We discovered that participants didn’t favor any one of the learning
approaches, nor were they outliers on the creativity assessment,”
shared Broome.
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>chapter 5 Stage 2: Sampling Design
>closeupcont’d
Broome and Hecht’s Phase 2 research revealed factors essential for reducing participant discontinuation: Don’t ask screen-
powerful motivator.” Many also cited meeting interesting people
and hearing different viewpoints as motivation. “They also love
ing questions within a survey once a participant has answered
a detailed screener to qualify; merge the data. Participants don’t
care that different companies do different aspects of the research;
they want researchers to avoid duplication. Don’t ask essentially
learning the results of studies they participate in and understanding why we as researchers do what we do or ask what we
ask,” shared Broome.
Recently, a SurveyMonkey study also found panelists gave
the same question in different ways; once is enough. Use previous question responses to determine later questions asked;
panelists are willing to share their thoughts, behaviors, and lives
but don’t want their time wasted. Participants have different pref-
thoughtful, consistent answers over time. It released results of a
1,000-person international panel assessment study, using three
surveys with the same respondents, one in each of three sequential months, which checked for quality-reducing behaviors
erences and styles; let them respond as they wish—with text or
video, exclusively on a mobile device or a computer. “They shared
lots of ideas for making surveys more engaging and interesting,”
claimed Hecht, “including, adding music or video, getting rid of
like straight lining (repeatedly choosing the same answer choice
in matrix questions), poor open response validity (responding
with nonhelpful, gibberish answers), and whether they were unfocused or not paying attention. Of the panelists, 97 percent,
grid questions, and reducing survey time to 15 minutes or less.”
Using information extracted from an open question and analyzed with OdinText, Phase 2 research also revealed that “most
participants were a member of one or two panels but struggled
to remain engaged with their panels,” shared Hecht. “Participants
appreciate feeling like a valued part of a full process, not just
an interchangeable cog in a wheel. They like to participate
in studies that are interesting to them and about products or
services that are relevant to their daily lives—knowledge is a
97 percent, and 94 percent or more passed each of these tests,
respectively, with no difference between men and women.
And in terms of response reliability, over the three waves of
surveys, in 23 indicators SurveyMonkey tracked, only three
items showed significantly significant change among U.S. participants: time-to-complete, choice of the “other” response, and
attitude about “moral acceptability of alcohol use.”
jessicabroomeresearch.com; recollective.com;
surveymonkey.com
a particular software training strategy, we infer that others will also. The basic idea of taking a sample is
that by selecting some cases in a population, we may draw conclusions about the entire target population.
There are several compelling reasons for using a sample (a subset of the target population) rather than
a census (all cases within a population), including (1) lower cost, (2) greater speed of data collection,
(3) availability of population cases, and (4) greater accuracy of results. The advantages of taking a
sample over a census are less compelling when two conditions exist: (1) a census is feasible due to a
small target population and (2) a census is necessary when the cases are quite different from each other.5
When the population is small and variable, any sample we draw may not be representative of the population from which it is drawn. The resulting values we calculate from the sample are incorrect as estimates
of the population values.
Lower Cost
The economic advantages of taking a sample rather than a census are massive. Consider the cost of
taking a census. The 2020 U.S. Census of the Population is expected to cost as much as $30 billion,
barring any natural disasters and using 2010 costs as a barometer; the Census Bureau’s own estimate is
$22 billion.6 By any reasonable standard, continuing to take a census of the population every 10 years is
unsustainable. Is it any wonder that researchers in all types of organizations ask, “Why should we spend
thousands of dollars interviewing thousands of employees in our company if we can find out what we
need to know by asking only a few hundred?”
Greater Speed of Data Collection
Due to the smaller number of cases in a sample, using a sample drawn from a target population will
always take less time than conducting a census.
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>Exhibit 5-5 Sources of Error
What
Sampling
Error
Why
Sample
estimate is
not the
population
parameter
Non-sampling Error
Inappropriate
Sampling
Method
Errors in
Measurement
Instrument
Behavioral
Effects
Availability of Population Cases
Some situations require using a sample. Safety is a compelling appeal for most vehicles. Yet we must have
evidence to make safety claims. Therefore, we crash-test cars to evaluate bumper strength or efficiency of
airbags to prevent injury. In testing for such evidence, we destroy the cars we test. A census would require
complete destruction of all cars manufactured. Drawing a sample is also the only process possible if the
population is infinite.
Better Quality Results
W. Edwards Deming, who developed the sampling techniques used by the U.S. Census Bureau and the
Bureau of Labor Statistics, argues that the quality of a study is often better with a sample than with a
census. He suggests samples possess “the possibility of better interviewing (testing), more thorough investigation of missing, wrong, or suspicious information, better supervision, and better processing than
is possible with complete coverage.”7 Error related to research comes from two sources: errors related to
the sample itself (sampling error, or estimates of a variable drawn from a sample differ from true value
of a population parameter) and error not related to the sample but to all other decisions made in the
research design (nonsampling error). As Exhibit 5-5 depicts, and research supports, nonsampling error
often exceeds sampling error.8 For a sample to be valid, it must offer both accuracy and precision. The
U.S. Bureau of the Census, while mandated to take a census of the population every 10 years, shows its
confidence in sampling by taking sample surveys to check the accuracy of its census. The U.S. Bureau of
the Census knows that in a census, segments of the population are seriously undercounted.
Accuracy
Accuracy is the degree to which bias is absent from the sample. When the sample is drawn properly,
the measure of behavior, attitudes, or knowledge (the measurement variables) of some cases will be less
than (thus, underestimate) the measure of those same variables drawn from the population. Also, the
measure of the behavior, attitudes, or knowledge of other cases will be more than the population values
(thus, overestimate them). Variations in these sample values offset each other, resulting in a sample value
that is close to the population value. Thus an accurate (unbiased) sample is one in which the underestimators offset the overestimators. For these offsetting effects to occur, however, there must be enough
cases in the sample, and they must be drawn in a way that favors neither overestimation nor underestimation. Only when the population is small, accessible, and highly variable is accuracy likely to be greater
with a census than a sample.
For example, assume in the Metro U study you measured likelihood to join the dining club. Hypothetically, you could measure via sample or census. In a hypothetical census, 32 percent of the population (currently enrolled students and current employees) said they would join. However, 52 percent of
a sample made that choice. With both results for comparison, you would know that your sample was
not representative because it significantly overestimated the population value of 32 percent. Without the
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census information, you might not know that you have significant error. Unfortunately, in most studies
taking a census is not feasible, so we need an estimate of the amount of error.9
Systematic variance has been defined as “the variation in measures due to some known or unknown
influences that ‘cause’ the scores to lean in one direction more than another.”10 Homes on the corner of
the block, for example, are often larger and more valuable than those within the block. Thus, a sample
that selects only corner homes will cause us to overestimate home values in the area.
Increasing the sample size can reduce systematic variance as a cause of error. However, even the
larger size won’t reduce error if the sample frame from which you draw your cases is biased. The classic example of a sample with systematic variance was the Literary Digest presidential election poll in
1936, in which more than 2 million people participated. The poll predicted Alfred Landon would defeat
Franklin Roosevelt for the presidency of the United States. Your memory is correct; we’ve never had
a president named Alfred Landon. The poll drew its sample from telephone owners, who were in the
middle and upper classes—at the time, the bastion of the Republican Party—while Roosevelt appealed to
the much larger working class, whose members could not afford to own phones and typically voted for
the Democratic Party candidate.
Precision
A second criterion of a valid sample is precision of estimate. Researchers accept that no sample will
fully represent its population in all respects. However, to interpret the findings of research, we need a
measure of how closely the sample estimate represents the population parameter on any variable of interest. The numerical descriptors that describe samples may be expected to differ from those that describe
populations because of random fluctuations inherent in the sampling process. Sampling error reflects
the influence of chance in drawing the sample cases. Sampling error is what is left after all known
sources of systematic variance have been accounted for. In theory, sampling error consists of random
fluctuations only, although some unknown systematic variance may be included when too many or too
few cases possess a particular characteristic.
Precision is measured by the standard error of estimate, a type of standard deviation measurement;
the smaller the standard error of estimate, the higher is the precision of the sample. The ideal sample
produces a small standard error of estimate. However, not all types of sample design provide estimates
of precision, and samples of the same size can produce different amounts of error.
Sample Size
So assume you have chosen a sample rather than a census. Much folklore surrounds the question: How
many cases should comprise your sample. The most pervasive myths are (1) a sample must be large or
it is not representative and (2) a sample should bear some proportional relationship to the size of the
population from which it is drawn. With nonprobability samples, researchers use subgroups, rules of
thumb, and budget considerations to settle on a sample size. In probability sampling, how large a sample
should be is a function of the variation in the population parameters under study and the estimating
precision needed by the researcher. Some principles that influence sample size include:
• The greater the dispersion or variance within the population, the larger the sample must be to provide estimation precision.
• The greater the desired precision of the estimate, the larger the sample must be.
• The narrower or smaller the error range, the larger the sample must be.
• The higher the desired confidence level in the estimate, the larger the sample must be.
• The greater the number of subgroups of interest within a sample, the greater the sample size must
be, as each subgroup must meet minimum sample size requirements.
Cost considerations influence decisions about the size and type of sample and the data collection
methods. Probability sample surveys incur list costs for sample frames, callback costs, and a variety
of other costs that are unnecessary when nonprobability samples are used. As research has budgetary
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>part II The Design of Business Research
constraints, this may encourage a researcher to use a nonprobability sample. When the data collection
method is changed, the amount and type of data that can be obtained also change. Note the effect of a
$2,000 budget on hypothetical sampling considerations:11
• Simple random sampling: $25 per interview; 80 completed interviews.
• Telephone interviews: $10 per participant; 200 completed interviews.
• Self-administered questionnaire: $12 per participant; 167 completed instruments.
• Geographic cluster sampling: $20 per interview; 100 completed interviews.
The investment required to open the dining club at Metro U justifies the more careful probability approach which means the researcher needs a quality sample frame. See the chapter appendix, Calculate
the Sample Size.
If a census is chosen, then the researcher need not determine sample method or size. For most business research, however, we use samples. The remainder of the chapter will focus on sampling designs
using samples.
>Define the Sampling Method
The researcher faces a basic choice in sampling method: a probability sample or nonprobability sample.
Any discussion of the relative merits of probability versus nonprobability sampling clearly shows the
technical superiority of the former. Yet businesses often use nonprobability methods.
Key to the difference between nonprobability and probability samples is the term random. In the
dictionary, random is defined as “without pattern”or as “haphazard.” In sampling, random means something else entirely. Probability sampling is based on the concept of random selection—a controlled procedure that assures that each case is given a known nonzero chance of selection. This procedure is never
haphazard. Only probability samples provide estimates of precision. When a researcher is making a decision that will influence the expenditure of thousands, if not millions, of dollars, an estimate of precision
is critical. Under such conditions, we can have substantial confidence that the sample is representative
of the population from which it is drawn. In addition, with probability sample designs, we can estimate
an error range within which the population parameter is expected to fall. Thus, we can reduce not only
the chance for sampling error, but also estimate the range of probable sampling error present. Also, only
probability samples offer the opportunity to generalize the findings to the population of interest from
the sample population. Although exploratory research does not necessarily demand this, explanatory,
descriptive, and causal studies do.
Alternatively, nonprobability sampling is arbitrary and subjective; when we choose subjectively,
we usually do so with a pattern or scheme in mind (e.g., only talking with department heads or only
talking with women). Each member of the target population does not have a known nonzero chance
of being included. Allowing data collectors to use their judgment in drawing records or choosing participants is arbitrary. Early Internet samples had all the drawbacks of nonprobability samples. Those
individuals who frequented the Internet were not representative of most target populations, because
far more young, technically-savvy men frequented the Internet than did any other demographic group.
As Internet access has reached saturation, with some estimates near 88 percent in the United States,
this concern is diminished.12 Such samples now closely approximate non-Internet samples. Of increasing concern, however, is what the Bureau of the Census labels the “great digital divide”—low-income
and ethnic subgroups’ underrepresentation in their use of technology compared to the general population.13 Additionally, many Internet samples were, and still are, drawn substantially from panels or
communities. These are composed of individuals who have self-selected to become part of a pool of
individuals interested in participating in online research. There is much discussion among professional researchers about whether Internet samples should be treated as probability or nonprobability
samples. Some admit that any sample drawn from a panel is more appropriately treated as a nonprobability sample; others vehemently disagree, citing the success of such well-known panels as Nielsen
Media’s People Meter panels for radio audience assessment.14 As you study the differences here, you
should draw your own conclusion.
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>chapter 5 Stage 2: Sampling Design
>Exhibit 5-6 Types of Sampling Designs
Representation Basis
Case Selection
Probability
Nonprobability
Unrestricted
Simple random
Convenience
Restricted
Complex random
Purposive
!
Systematic
!
Judgment
!
Cluster
!
Quota
!
Stratified
!
Snowball
!
Double
With a probability sample, a researcher can make probability-based confidence estimates of various
parameters that cannot be made with nonprobability samples. Choosing a probability sampling technique has several consequences. A researcher must follow appropriate procedures so that:
• Interviewers or others cannot modify the selections made.
• Only the selected cases from the original sampling frame are included.
• Substitutions are excluded except as clearly specified and controlled according to predetermined
protocols.
Despite all due care, the actual sample drawn will not match perfectly the probability sample that is originally
planned. Some people will refuse to participate, and others will be difficult, if not impossible, to find. Thus, no
matter how careful we are in replacing those who refuse or are never located, sampling error is likely to rise.
With personnel records available at Metro U and a population that is geographically concentrated,
a probability sampling method is possible in the dining club study. University directories are generally
available, and the costs of using a simple random sample would not be great here. Then, too, because the
researchers are thinking of a major investment in the dining club, they would like to be highly confident
they have a representative sample.
The researcher makes several decisions when choosing a design that uses a sample. These are represented in Exhibit 5-6. The sampling decisions flow from the management question, the research question, and the specific investigative questions that evolve from the research question. These decisions
are influenced by requirements of the project and its objectives, level of risk the researcher can tolerate,
budget, time, available resources, and culture.
>Probability Sampling
Simple Random Sampling
The unrestricted, simple random sample is the purest form of probability sampling. Because all probability samples must provide a known nonzero probability of selection for each population element, the
simple random sample is considered a special case in which each population element has a known and
equal chance of selection.
Sample size
Probability of selection = ___________
Population size
The Metro U dining club study has a population of 20,000. If the sample size is 300, the probability
of selection is 1.5 percent (300/20,000 = 0.015). In this section, we use the simple random sample to
build a foundation for understanding sampling procedures and choosing probability samples. The simple
random sample is easy to implement with automatic dialing (random dialing) and with computerized
voice response systems. However, it requires a list of population elements, can be time-consuming and
expensive, and can require larger sample sizes than other probability methods. Exhibit 5-7 provides an
overview of the steps involved in choosing a random sample.
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>part II The Design of Business Research
>Exhibit 5-7 How to Choose a Random Sample
Selecting a random sample is accomplished with the aid of computer software, a table of random numbers, or a
calculator with a random number generator. Drawing slips out of a hat or Ping-Pong balls from a drum serves as an
alternative if every case in the sampling frame has an equal chance of selection. Mixing the slips (or balls) and
returning them between every selection ensures that every case is just as likely to be selected as any other.
A table of random numbers (such as Appendix D, Exhibit D-10) is a practical solution when no software program is
available. Random number tables contain digits that have no systematic organization. Whether you look at rows, columns, or diagonals, you will find neither sequence nor order. Exhibit C-1 in Appendix C is arranged into 10 columns
of five-digit strings, but this is solely for readability.
Assume the researchers want a sample of 10 from a population of 95 cases. How will the researcher begin?
1
Assign each case within the sampling frame a unique number from 01 to 95.
2 Identify a random start from the random number table. Drop a pencil point-first onto the table with closed
eyes. Let’s say the pencil dot lands on the eighth column from the left and 10 numbers down from the top of
Exhibit C-1, marking the five digits 05067.
3 Determine how the digits in the random number table will be assigned to the sampling frame to choose the
specified sample size (researchers agree to read the first two digits in this column downward until 10 are
selected).
4 Select the sample cases from the sampling frame (05, 27, 69, 94, 18, 61, 36, 85, 71, and 83) using the above
process. (The digit 94 appeared twice and the second instance was omitted; 00 was omitted because the
sampling frame started with 01.)
Other approaches to selecting digits are endless: horizontally right to left, bottom to top, diagonally across columns,
and so forth. Computer selection of a simple random sample will be more efficient for larger projects.
Complex Probability Sampling
The limitations of simple random sampling have led to the development of alternative designs that are
superior to the simple random design in statistical and/or economic efficiency.
A more efficient sample in a statistical sense is one that provides a given precision (standard error of
the mean or proportion) with a smaller sample size. A sample that is economically more efficient is one
that provides a desired precision at a lower dollar cost. We achieve this with designs that enable us to
lower the costs of data collecting, usually through reduced travel expense and interviewer time.
In the discussion that follows, four alternative probability sampling approaches are considered:
(1) systematic sampling, (2) stratified random sampling, (3) cluster sampling, and (4) double sampling.
Systematic Sampling
A versatile form of probability sampling is systematic sampling. In this approach, every kth element in
the population is sampled, beginning with a random start of any case in the range of 1 to k. The kth
case, or skip interval, is determined by dividing the sample size into the population size to obtain the
skip pattern applied to the sampling frame. This assumes that the sample frame is an accurate list of the
population; if not, the number of cases in the sample frame is substituted for population size.
Population size
k = Skip interval = ___________
Sample size
The major advantage of systematic sampling is its simplicity and flexibility. It is easier to instruct
field interviewers to choose the dwelling unit listed on every kth line of a listing sheet than it is to use
a random numbers table. With systematic sampling, there is no need to number the entries in a large
personnel file before drawing a sample. To draw a systematic sample, do the following:
• Identify, list, and number the cases in the population.
• Identify the skip interval (k).
• Identify the random start.
• Draw a sample by choosing every kth entry.
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Invoices or customer accounts can be sampled by using the last digit or a combination of digits of an
invoice or customer account number. Time sampling is also easily accomplished.
Systematic sampling can introduce subtle biases. A concern with systematic sampling is the possible
periodicity in the population that parallels the sampling ratio. In sampling restaurant sales of dessert
by drawing days of the year, a skip interval of 7 would bias results, no matter which day provides the
random start. A less obvious case might involve a survey in an area of apartment buildings where the
typical pattern is eight apartments per building. A skip interval of 8 could easily over sample some types
of apartments and under sample others.
Another difficulty may arise when there is a monotonic trend in the population cases. That is, the
population list varies from the smallest to the largest case or vice versa. Even a chronological list may
have this effect if a measure has trended in one direction over time. Whether a systematic sample drawn
under these conditions provides a biased estimate of the population mean or proportion depends on the
initial random draw. Assume that a list of 2,000 commercial banks is created, arrayed from the largest to
the smallest, from which a sample of 50 must be drawn for analysis. A skip interval of 40 (2,000 divided
by 50) beginning with a random start at 16 would exclude the 15 largest banks and give a small-size bias
to the findings.
The only protection against these subtle biases is constant vigilance by the researcher. Some ways to
avoid such bias include:
• Randomize the target population before drawing the sample (e.g., order the banks by name rather
than size).
• Change the random start several times in the sampling process.
• Replicate a selection of different samples.
Although systematic sampling has some theoretical problems, from a practical point of view it is
usually treated as equivalent to a simple random sample. When similar cases are grouped within the
sampling frame, systematic sampling is statistically more efficient than a simple random sample. This
might occur if the listed cases are ordered chronologically, by size, by class, and so on. Under these
conditions, the sample approaches a proportionate stratified sample. The effect of this ordering is more
pronounced on the results of cluster samples than for other samples and may call for a proportionate
stratified sampling formula.15
Stratified Random Sampling
Most populations can be segregated into several mutually exclusive subpopulations, or strata. The process by which the sample is constrained to include cases from each of the segments is called stratified
random sampling. University students can be divided by their class level, school or major, gender, and
so forth. After a population is divided into the appropriate strata, a simple random sample can be taken
within each stratum. The results from the study can then be weighted (based on the proportion of the
strata to the population) and combined into appropriate population estimates.
There are three reasons a researcher chooses a stratified random sample: (1) to increase a sample’s
statistical efficiency, (2) to provide adequate data for analyzing the various subpopulations or strata, and
(3) to enable different research methods and procedures to be used in different strata.16
Stratification is usually more efficient statistically than simple random sampling and at worst it is
equal to it. With the ideal stratification, each stratum is homogeneous internally (cases are similar) and
heterogeneous with other strata (cases of one stratum are not similar to cases within another stratum).
This might occur in a sample that includes members of several distinct ethnic groups. In this instance,
stratification makes a pronounced improvement in statistical efficiency.
Stratification is also useful when the researcher wants to study the characteristics of certain population subgroups. Thus, if one wishes to draw some conclusions about activities in the different classes
of a student body, stratified sampling would be used. Similarly, if a restaurant were interested in testing
menu changes to attract younger patrons while retaining its older, loyal customers, stratified sampling
using age and prior patronage as descriptors would be appropriate. Stratification is also called for when
different methods of data collection are applied in different parts of the population, a research design
that is becoming increasingly common. This might occur when we survey company employees at the
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>part II The Design of Business Research
>closeup
Keynote Systems Tests the Power of Search
Twice yearly Keynote Systems evaluates the performance of five
search engines, including market leader Google, AOL Search,
Yahoo! Search, Ask.com, and MSN Search. Keynote, a “worldwide
leader in services that improve online business performance and
the bar in search site design. Is its brand that powerful that it
can influence attitudes even in the face of conflicting performance experience? If the brand is not a factor, which search
engine would produce the most satisfying and useful results,
communications technologies,” uses an online panel to perform
“interactive Web site tests to assess user experience,” profiling
not only how people use search engines, but why they search
as they do. Keynote allocates participants and experimental
the best sponsored results, and the best presentation and design? Keynote wanted to design an experiment that would show
the power of the search engine brand. To do that, it needed to
remove brand identity from the search results. Its solution was
treatments as in Exhibit CU-1: 2,000 people are randomly drawn
from more than 160,000 panel members and invited to participate via e-mail. They are assigned randomly to five groups of
400; each group is assigned a particular search engine. Whether
to design a generic-appearing search engine website and results format page, feeding actual search results into its generic
format.
For the brand power test (Exhibit CU-2) 2,000 participants
participants have any experience with that particular engine is
not a criterion for assignment. Each group is assigned a series
were again divided into five groups and assigned one search
engine. This time, however, half the participants were assigned
of search tasks, starting with a general task—Think about anything you would like to search for; go and search that—to more
specific tasks—find a local establishment, a product, an image,
and a news item. Each search engine–allocated group essentially performs the same series of tasks. From their activities, Keynote generates 250,000 metrics (including time involved in the
search, whether the search was successful, etc.). It matches these
metrics to survey data used to measure satisfaction, perceived
difficulty, and specific frustrations. From this combined data it develops several indices.
“One of the things we noted from a series of such tests was
that Google repeatedly received rave reviews, even in instances
where performance measures told a different story,” shares senior research consultant Lance Jones. With almost 60 percent
market share, Google has strong recognition and tends to set
to a branded group (n = 200) and would see the results with
a text line “Results brought to you by Yahoo/Google/Ask, etc.”;
the other half (n = 200) would see the same results but without
the brand notation line (n = 200). All five search engines were
tested using the tasks performed in the standard twice-annual
test, but all the results seen by participants were actually generated using the assigned search engine, then fed into the generic results presentation. “The results pages were delivered
live and participants would have perceived no difference in
elapsed time, as the results were delivered within milliseconds
of what the standard search would have delivered,” explained
Jones. The test produced 1, 600 queries that generated 12 distinct metrics.
Source: Lance Jones, senior research consultant.
>Exhibit CU-1 Participant Allocation in Search Engine Test
Experiment Participants
(n = 2000)
Search Engine A Group
(n = 400)
General Task
(n = 200)
Search Engine B Group
(n = 400)
Local Task
(n = 200)
Grocery
(n = 200)
Product Task
(n = 200)
Restaurant
(n = 200)
Image Task
(n = 200)
Movie
(n = 200)
New Item Task
(n = 200)
Self-Select
(n = 200)
Search Engine C Group
(n = 400)
Search Engine D Group
(n = 400)
Search Engine E Group
(n = 400)
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>chapter 5 Stage 2: Sampling Design
>closeupcont’d
>Exhibit CU-2 Participant Allocation in Brand Power Test
Experiment Participants
(n = 2000)
Branded Group
(n = 200)
Search Engine A Group
(n = 400)
Search Engine B Group
(n = 400)
Search Engine C Group
(n = 400)
Search Engine D Group
(n = 400)
Search Engine E Group
(n = 400)
Unbranded Group
(n = 200)
Is a brand powerful? Here are some sample results for Google;
keep in mind that the branded group and the unbranded group
saw the exact same results pages. On the unbranded group,
the calculated Google results satisfaction score was 732 (on a
1,000-point scale), while the branded group delivered an 800;
General Task
(n = 200)
Local Task
(n = 200)
Grocery
(n = 200)
Product Task
(n = 200)
Restaurant
(n = 200)
Image Task
(n = 200)
Movie
(n = 200)
New Item Task
(n = 200)
Self-Select
(n = 200)
General Task
(n = 200)
Local Task
(n = 200)
Grocery
(n = 200)
Product Task
(n = 200)
Restaurant
(n = 200)
Image Task
(n = 200)
Movie
(n = 200)
New Item Task
(n = 200)
Self-Select
(n = 200)
Google’s sponsored results satisfaction was 763 (unbranded) compared to 809 (branded); full design satisfaction was 753 (unbranded)
compared to 806 (branded). Evaluate the design of this sample.
www.keynote.com
home office with one method but must use a different approach with employees scattered throughout
the country or the world.
If data are available on which to base a stratification decision, how shall we go about it?17 The ideal
stratification would be based on the primary variable under study. If the major concern were to learn
how often per month patrons would use the Metro U dining club, then the researcher would stratify on
the expected number of use occasions. The only difficulty with this idea is that if we knew this information, we would not need to conduct the study. We must, therefore, pick a variable for stratifying that
we believe will correlate with the frequency of club use per month, something like days at work or class
schedule as an indication of when a sample case might be near campus at mealtimes.
Researchers often have several important variables about which they want to draw conclusions. A
reasonable approach is to seek some basis for stratification that correlates well with the major variables.
It might be a single variable (class level), or it might be a compound variable (class by gender). In any
event, we will have done a good stratifying job if the stratification base maximizes the difference among
strata means and minimizes the within-stratum variances for the variables of major concern.
The more strata used, the closer you come to maximizing inter-strata differences (differences between
strata) and minimizing intrastratum variances (differences within a given stratum). You must base the
decision partially on the number of subpopulation groups about which you wish to draw separate conclusions. Costs of stratification also enter the decision. The more strata you have, the higher the cost of the
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research project due to the cost associated with more detailed sampling. There is little to be gained in
estimating population values when the number of strata exceeds six.18
The size of the strata samples is calculated with two pieces of information: (1) how large the
total sample should be and (2) how the total sample should be allocated among strata. In deciding
how to allocate a total sample among various strata, there are proportionate and disproportionate
options.
Proportionate versus Disproportionate Sampling In proportionate stratified sampling, each
stratum is proportionate to the stratum’s share of the total population. This approach is more popular
than any of the other stratified sampling procedures. Some reasons for this include:
• It has higher statistical efficiency than a simple random sample.
• It is much easier to carry out than other stratifying methods.
• It provides a self-weighting sample; the population mean or proportion can be estimated simply by
calculating the mean or proportion of all sample cases, eliminating the weighting of responses.
On the other hand, proportionate stratified samples often gain little in statistical efficiency if the
strata measures and their variances are similar for the major variables under study.
Any stratification that departs from the proportionate relationship is disproportionate stratified
sampling. There are several disproportionate allocation schemes. One type is a judgmentally determined
disproportion based on the idea that each stratum is large enough to secure adequate confidence levels
and error range estimates for individual strata. The following table shows the relationship between proportionate and disproportionate stratified sampling.
Stratum
Population
Proportionate Sample
Disproportionate Sample
Male
45%
45%
35%
Female
55
55
65
A researcher makes decisions regarding disproportionate sampling, however, by considering how a
sample will be allocated among strata. One author states,
In a given stratum, take a larger sample if the stratum is larger than other strata; the stratum is more variable internally;
and sampling is cheaper in the stratum.19
If one uses these suggestions as a guide, it is possible to develop an optimal stratification scheme.
When there is no difference in intra-stratum variances and when the costs of sampling among strata are
equal, the optimal design is a proportionate stratified sample.
While disproportionate stratified sampling is theoretically superior, there is some question as to
whether it has wide applicability in a practical sense. If the differences in sampling costs or variances
among strata are large, then disproportionate sampling is desirable. It has been suggested that “differences of several-fold are required to make disproportionate sampling worthwhile.20
The process for drawing a stratified sample is:
• Determine the variables to use for stratification.
• Determine the proportions of the stratification variables in the population.
• Select proportionate or disproportionate stratification based on project information needs and risks.
• Divide the sampling frame into separate frames for each stratum.
• Randomize the case listing within each stratum’s sampling frame.
• Follow random or systematic procedures to draw the sample from each stratum.
Cluster Sampling
In a simple random sample, each population element is selected individually. The population can also be
divided into groups of elements with some groups randomly selected for study. This is cluster sampling.
Cluster sampling differs from stratified sampling in several ways, as indicated in Exhibit 5-8.
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>chapter 5 Stage 2: Sampling Design
>Exhibit 5-8 Comparison of Stratified and Cluster Sampling
Stratified Sampling
Cluster Sampling
1. We divide the population into a few subgroups.
1. We divide the population into many subgroups.
!
Each subgroup has many cases in it.
!
Each subgroup has few cases in it.
!
Subgroups are selected according to some criterion that is related to the variables under study.
!
Subgroups are selected according to some criterion of ease or availability in data collection.
2. We try to secure homogeneity within subgroups.
2. We try to secure heterogeneity within subgroups.
3. We try to secure heterogeneity between subgroups.
3. We try to secure homogeneity between subgroups.
4. We randomly choose cases from within each
subgroup.
4. We randomly choose several subgroups that we then
typically study in depth.
Two conditions foster the use of cluster sampling: (1) the need for more economic efficiency than
can be provided by simple random sampling and (2) the frequent unavailability of a practical sampling
frame listing individual cases.
Statistical efficiency for cluster samples is usually lower than for simple random samples chiefly because clusters often don’t meet the need for heterogeneity and, instead, are homogeneous. For example,
families in the same block (a typical cluster) are often similar in social class, income level, ethnic origin,
and so forth. Although statistical efficiency in most cluster sampling may be low, economic efficiency is
often great enough to overcome this weakness. The criterion, then, is the net relative efficiency resulting
from the trade-off between economic and statistical factors. It may take 690 interviews with a cluster
design to give the same precision as 424 simple random interviews. But if it costs only $5 per interview
in the cluster situation and $10 in the simple random case, the cluster sample is more attractive ($3,450
versus $4,240).
Area Sampling Much research involves populations that can be identified with some geographic
area. When this occurs, it is possible to use area sampling, the most important form of cluster sampling.
This method overcomes the problems of both high sampling cost and the unavailability of a practical
©Tetra Images/Alamy
A low-cost, frequently
used method, the area
cluster sample may use
geographic sample units
(e.g., city blocks).
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>part II The Design of Business Research
sampling frame for individual cases. Area sampling methods have been applied to national populations,
county populations, and even smaller areas where there are well-defined political or natural boundaries.
Suppose you want to survey the adult residents of a city. You would seldom be able to secure a listing
of such individuals. It would be simple, however, to get a detailed city map that shows the blocks of the
city. If you take a sample of these blocks, you are also taking a sample of the adult residents of the city.
Design In designing cluster samples, including area samples, we must answer several questions:
1. How homogeneous are the resulting clusters?
2. Shall we seek equal-size or unequal-size clusters?
3. How large a cluster shall we take?
4. Shall we use a single-stage or multistage cluster?
5. How large a sample is needed?1
1. When clusters are homogeneous, this contributes to low statistical efficiency. Sometimes one can
improve this efficiency by constructing clusters to increase intracluster variance. In the dining club study,
researchers might have chosen a course as a cluster, choosing to sample all students in that course if
it enrolled students of all four class years. Or maybe they could choose a departmental office that had
faculty, staff, and administrative positions as well as student workers. In area sampling to increase intracluster variance, researchers could combine into a single cluster adjoining blocks that contain different
income groups or social classes.
2. A cluster sample may be composed of clusters of equal or unequal size. The theory of clustering is
that the means of sample clusters are unbiased estimates of the population mean. This is more often true
when clusters are naturally equal, such as households in city blocks. While one can deal with clusters of
unequal size, it may be desirable to reduce or counteract the effects of unequal size. There are several
approaches to this:
• Combine small clusters and split large clusters until each approximates an average size.
• Stratify clusters by size and choose clusters from each stratum.
• Stratify clusters by size and then subsample, using varying sampling fractions to secure an overall
sampling ratio.21
3. There is no a priori answer to the ideal cluster size question. Comparing the efficiency of differing
cluster sizes requires that we discover the different costs for each size and estimate the different variances
of the cluster means. Even with single-stage clusters (where the researchers interview or observe every
element within a cluster), it is not clear which size (say, 5, 20, or 50) is superior. Some have found that in
studies using single-stage area clusters, the optimal cluster size is no larger than the typical city block.22
4. Concerning single-stage or multistage cluster designs, for most large-scale area sampling, the tendency
is to use multistage designs. Several situations justify drawing a sample within a cluster, in preference to the
direct creation of smaller clusters and taking a census of that cluster using one-stage cluster sampling:23
• Natural clusters may exist as convenient sampling units yet, for economic reasons, may be larger
than the desired size.
• We can avoid the cost of creating smaller clusters in the entire population and confine subsampling to only those large natural clusters.
• The sampling of naturally compact clusters may present practical difficulties. For example, independent interviewing of all members of a household may be impractical.
5. The answer to how many subjects must be interviewed or observed depends heavily on the specific
cluster design, and the details can be complicated. Unequal clusters and multistage samples are the chief
complications, and their statistical treatment is beyond the scope of this book.24 Here we will treat only
single-stage sampling with equal-size clusters (called simple cluster sampling). It is analogous to simple
random sampling. We can think of a population as consisting of 20,000 clusters of one student each,
or 2,000 clusters of 10 students each, and so on. Assuming the same specifications for precision and
confidence, we should expect that the calculation of a probability sample size would be the same for
both clusters.
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>chapter 5 Stage 2: Sampling Design
Double Sampling
It may be more convenient or economical to collect some information by sample and then use this information as the basis for selecting a subsample for further study. This procedure is called double sampling,
(also called sequential sampling or multiphase sampling) It is usually found with stratified and/or cluster
designs. The calculation procedures are described in more advanced texts.
Double sampling can be illustrated by the dining club example. You might use an email survey or
another inexpensive survey method to discover who would be interested in joining such a club and the
degree of their interest. You might then stratify the interested respondents by degree of interest and subsample among them for intensive interviewing on expected consumption patterns, reactions to various
services, and so on. Whether it is more desirable to gather such information by one-stage or two-stage
sampling depends largely on the relative costs of the two methods.
Because of the wide range of sampling designs available, it is often difficult to select an approach that
meets the needs of the research question and helps to contain the costs of the project. To help with these
choices, Exhibit 5-9 may be used to compare the various advantages and disadvantages of probability
>Exhibit 5-9 Comparison of Probability Sampling Designs
Type
Description
Advantages
Disadvantages
Simple Random
Each population case has an
equal chance of being selected into the sample.
Easy to implement with automatic dialing (random-digit
dialing) and with computerized
voice response systems.
Requires a listing of population cases.
Cost: High
Use: Moderate
Sample drawn using random
number table/generator.
Takes more time to
implement.
Uses larger sample sizes.
Produces larger errors.
Systematic
Cost: Moderate
Use: Moderate
Using a random start, selects
a population case and following the sampling skip interval
selects every kth case.
Simple to design.
Easier to use than the simple
random.
Easy to determine sampling distribution of mean or proportion.
Stratified
Cost: High
Use: Moderate
Divides population into
subpopulations or strata
and draws a simple random
sample from each stratum.
Results may be weighted and
combined.
Researcher controls sample size
within strata.
Increased statistical efficiency.
Provides data to represent and
analyze subgroups.
Periodicity within the population may skew the sample
and results.
If the population list has a
monotonic trend, a biased
estimate will result based
on the start point.
Increased error will result if
subgroups are selected at
different rates.
Especially expensive if
population strata must be
created.
Enables use of different methods in strata.
Cluster
Cost: Moderate
Use: High
Population is divided into
internally heterogeneous subgroups. Some subgroups are
randomly selected for further
study.
Provides an unbiased estimate
of population parameters if
properly done.
Economically more efficient than
simple random.
Often lower statistical
efficiency (more error)
due to subgroups being
homogeneous rather than
heterogeneous.
Lowest cost per sample, especially with geographic clusters.
Easy to do without a population list.
Double
(Sequential or
multiphase)
Cost: Moderate
Process includes collecting
data from any type sample.
Based on the information
found, a subsample is
selected for further study.
May reduce costs if first stage
results in enough data to stratify
or cluster the population.
Increased costs if indiscriminately used.
Use: Moderate
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>part II The Design of Business Research
sampling. Nonprobability sampling techniques are covered in the next section. They are used frequently
and offer the researcher the benefit of low cost. However, they are not based on a theoretical framework
and do not operate from statistical theory; consequently, they produce selection bias and nonrepresentative samples. Despite these weaknesses, their widespread use demands their mention here.
>Nonprobability Sampling
With a subjective approach like nonprobability sampling, the probability of selecting population cases is
unknown. There are a variety of ways to choose persons or cases to include in the sample. Often we allow
the choice of subjects to be made by field workers on the scene. When this occurs, there is greater opportunity for bias to enter the sample selection procedure and to distort the findings of the study. Also,
we cannot estimate any range within which to expect the population parameter. Given the technical
advantages of probability sampling over nonprobability sampling, why would anyone choose the latter?
There are some practical reasons for using the less precise methods.
We may use nonprobability sampling procedures because they satisfactorily meet the sampling objectives. Although a random sample will give us a true cross section of the population, this may not be
the objective of the research. If there is no desire or need to generalize to a population parameter, then
there is much less concern about whether the sample fully reflects the population. Often researchers
have more limited objectives. They may be looking only for the range of conditions or for examples of
dramatic variations. This is especially true in exploratory research in which one may wish to contact only
certain persons or cases that are clearly atypical.
Additional reasons for choosing nonprobability over probability sampling are cost and time. Probability sampling clearly calls for more planning and repeated callbacks to ensure that each selected case
is contacted. These activities are expensive. Carefully controlled nonprobability sampling often seems
to give acceptable results, so the investigator may not even consider probability sampling. While probability sampling may be superior in theory, there are breakdowns in its application. Even carefully stated
random sampling procedures may be subject to careless application by the people involved. Thus, the
ideal probability sampling may be only partially achieved because of the human element.
It is also possible that nonprobability sampling may be the only feasible alternative. The total population may not be available for study in certain cases. At the scene of a major event, it may be infeasible to
attempt to construct a probability sample. A study of past correspondence between two companies must
use an arbitrary sample because the full correspondence is normally not available.
In another sense, those who are included in a sample may select themselves. In mail surveys, those
who respond may not represent a true cross section of those who receive the questionnaire. The receivers
of the questionnaire decide for themselves whether they will participate. In web-based surveys those who
volunteer don’t always represent the appropriate cross section—that’s why screening questions are used
before admitting a participant to the sample. There is, however, some of this self-selection in almost all
surveys because every respondent chooses whether to be interviewed.
Convenience
Nonprobability samples that are unrestricted are called convenience samples. They are the least reliable
design but normally the cheapest and easiest sample to draw. Researchers or field workers have the freedom to choose whomever they find: thus, the name “convenience.” Examples include informal pools of
friends and neighbors, people responding to a newspaper’s invitation for readers to state their positions
on some public issue, a TV reporter’s “person-on-the-street” intercept interviews, or the use of employees
to evaluate the taste of a new snack food.
Although a convenience sample has no controls to ensure precision, it may still be a useful procedure.
Often you will take such a sample to test ideas or even to gain ideas about a subject of interest. In the
early stages of exploratory research, when you are seeking guidance, you might use this approach. The
results may present evidence that is so overwhelming that a more sophisticated sampling procedure is
unnecessary. In an interview with students concerning some issue of campus concern, you might talk
to 25 students selected sequentially. You might discover that the responses are so overwhelmingly onesided that there is no incentive to interview further.
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>chapter 5 Stage 2: Sampling Design
Purposive Sampling
A nonprobability sample that uses certain criteria to select cases is called purposive sampling. There are
two major types—judgment sampling and quota sampling.
Judgment sampling occurs when a researcher selects sample members to conform to some criterion.
In a study of labor problems, you may want to talk only with those who have experienced on-the-job
discrimination. Another example of judgment sampling occurs when election results are predicted from
only a few selected precincts that have been chosen because of their predictive record in past elections.
When used in the early stages of an exploratory study, a judgment sample is appropriate. When one
wishes to select a biased group for screening purposes, this sampling method is also a good choice. Companies often try out new product ideas on their employees. The rationale is that one would expect the
firm’s employees to be more favorably disposed toward a new product idea than the public. If the product
does not pass this group, it does not have prospects for success in the general market.
Quota sampling is the second type of purposive sampling. We use it to improve representativeness.
The logic behind quota sampling is that certain relevant characteristics describe the dimensions of the
population. If a nonprobability sample has the same distribution on these characteristics, then it is likely
to be representative of the population regarding other variables on which we have no control. Suppose
the student body of Metro U is 55 percent female and 45 percent male. The sampling quota would call
for sampling students at a 55 to 45 percent ratio.
In most quota samples, researchers specify more than one control dimension. Each should meet two
tests: it should (1) have a distribution in the population that we can estimate and (2) be pertinent to
the topic studied. We may believe that responses to a question should vary depending on the gender of
the respondent. If so, we should seek proportional responses from both men and women. We may also
feel that undergraduates differ from graduate students, so this would be a dimension. Other dimensions,
such as the student’s academic discipline, ethnic group, religious affiliation, and social group affiliation,
also may be chosen. Only a few of these controls can be used. To illustrate, suppose we consider the
following:
• Gender: Two categories—male, female.
• Class level: Two categories—graduate, undergraduate.
• College: Six categories—arts and science, agriculture, architecture, business, engineering, other.
• Religion: Four categories—Protestant, Catholic, Jewish, other.
• Fraternal affiliation: Two categories—member, nonmember.
• Family social-economic class: Three categories—upper, middle, lower.
In an extreme case, we might ask an interviewer to find a male undergraduate business student who
is Catholic, a fraternity member, and from an upper-class home. All combinations of these six factors
would call for 288 such cells to consider. This type of control is known as precision control. It gives
greater assurance that a sample will be representative of the population. However, it is costly and too
difficult to carry out with more than three variables.
When we wish to use more than three control dimensions, we should depend on frequency control.
With this form of control, the overall percentage of those with each characteristic in the sample should
match the percentage holding the same characteristic in the population. No attempt is made to find a
combination of specific characteristics in a single person. In frequency control, we would probably find
that the following sample array is an adequate reflection of the population:
Population
Sample
Male
65%
67%
Married
15
14
Undergraduate
70
72
Campus resident
30
28
Greek member
25
23
Protestant
39
42
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Quota sampling has several weaknesses. First, the idea that quotas on some variables assume a
representativeness on others is argument by analogy. It gives no assurance that the sample is representative of the variables being studied. Often, the data used to provide controls might be outdated
or inaccurate. There is also a practical limit on the number of simultaneous controls that can be
applied to ensure precision. Finally, the choice of subjects is left to field workers to make on a judgmental basis. They may choose only friendly looking people, people who are convenient to them,
and so forth.
Despite the problems with quota sampling, it is widely used by opinion pollsters and business
researchers. Probability sampling is usually much more costly and time-consuming. Advocates of
quota sampling argue that although there is some danger of systematic bias, the risks are usually not
that great. Where predictive validity has been checked (e.g., in election polls), quota sampling has been
generally satisfactory.
Snowball
This design has found a niche in recent years in applications where respondents are difficult to identify
and are best located through referral networks. It is also especially appropriate for some qualitative studies. In the initial stage of snowball sampling, individuals are discovered and may or may not be selected
through probability methods. This group is then used to refer the researcher to others who possess
similar characteristics and who, in turn, identify others. Similar to a reverse search for bibliographic
sources, the “snowball” gathers subjects as it rolls along. Various techniques are available for selecting
a nonprobability snowball with provisions for error identification and statistical testing. Let’s consider
a brief example.
The high end of the U.S. audio market is composed of several small firms that produce ultraexpensive components used in recording and playback of live performances. A risky new technology
for improving digital signal processing is being contemplated by one firm. Through its contacts with a
select group of recording engineers and electronics designers, the first-stage sample may be identified
for interviewing. Subsequent interviewees are likely to reveal critical information for product development and marketing.
Variations on snowball sampling have been used to study drug cultures, teenage gang activities, power
elites, community relations, insider trading, and other applications where respondents are difficult to
identify and contact.
>Define the Selection and Recruiting Protocols
Whether the researcher chooses a census or sample, selection and recruiting answers the following
questions:
• How will the researcher select cases (from a sample frame or without a sample frame)?
• How will each desired case/gatekeeper be contacted?
• How will the case/gatekeeper be convinced to participate?
• What follow-up procedures will be used to guarantee the case/gatekeeper completes the
research?
The sampling methodology often has specific protocols for drawing cases (e.g., the simple random
sample, the stratified sample, etc.); we’ve given you these in the earlier section. If the chosen method
doesn’t have such a protocol—as with nonprobability samples—you must create one. Will the data
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>chapter 5 Stage 2: Sampling Design
Percent of U.S. Households Accessible by Phone Method of Sampling Invitation
>picprofile
Landline and Cell Phone
Cell Phone Only
39.4%
50.4%
Landline Phone Only
Neither Cell nor Landline
6.5%
3.3%
Mixed-access sampling means that multiple methods are used to invite participants to a research study—phone, email, mobile or
wireless, addressed based/mail, etc. According to the CDC, approximately 97% of possible participants are reachable by phone,
while on 87% are reachable online. Mixed-access sampling reduces non-coverage error and nonresponse error. Once a participant
is recruited, regardless of the means, he or she may complete the study by a different mode (e.g., recruited by phone but take a
survey online). Sample recruitment is increasingly done by mixed access.
www.surveysampling.com, www.pewresarch.org, www.cdc.gov
collector have the latitude to choose anyone to participate, or will they need to follow specific guidelines? In the Metro U study, if we use registrar and personnel lists for accuracy, systematic or stratified
sampling protocols would be appropriate.
Research shows that participants are research weary. This means you may face difficulties gaining
compliance and cooperation. Most researchers will tell you participants need to feel that they are part
of something bigger than themselves in order to give of their time and expertise. They also may need to
be offered an incentive for sharing their ideas and experiences. In the Metro U study, we could compel
faculty to participate due to their employment, but it might backfire. It would be better to encourage participation as a means for the university to address financial issues (as they are plaguing many colleges).
For students, we will need some other means of enticement.
Some individuals actually enjoy participating in research, feeling they have much to contribute
when they are knowledgeable about the topic. These individuals often volunteer for research panels
or join communities of like-minded individuals. They have volunteered, so recruiting them is not difficult; keeping them engaged so that they follow through during a multi-activity research study is more
problematic. To keep research participants engaged, researchers need to have procedures in place (e.g,
follow-up emails or phone calls) to keep in touch with participants. For this group, an inducement
to concluding a research project is the promise to share some or all of the results; this often works
when the participant/gatekeeper is a business manager or professional. Finally, a charitable gift in the
participant’s name is gaining favor as an incentive; this might work for faculty in the Metro U study.
Food incentives often work for students. And both students and faculty would likely respond to money
as an incentive.
We often use email or phone to contact potential cases or gatekeepers. The PicProfile in this chapter
offers an example done by email. When your target population is a person, they will often inquire about
why they were chosen. If the research sponsor is willing, researchers might divulge the sample frame,
or at least its essence to the potential case as a means to recruit them (e.g., “You have posted on our
Facebook in the last two weeks” or “You have a Plenti loyalty card”). In the Metro U study, email contacts would be appropriate for both students and faculty.
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>part II The Design of Business Research
>snapshot
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