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Discussion Board 2: Learning Styles/Personality
After reading Chapter 7: Strategic Learning and Studying &
chapter 8: Test-Taking Skills and Strategies, and looking at
the Learning Style Youtube clip in this module, I would like for
you to answer the following questions in the Discussion Board:
1) What is your preferred learning style?
2) What is your preferred learning environment (sound,
temperature, lighting, lecture vs. hands-on vs. discussion,
working with others or not, etc.)?
3) What are some strategies (according to your learning style)
you use to study?
Preferred Leaning Styles
Please respond to the following questions, use 12 front times
new roman, proper citation 300 to 500 words
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1. Learning Styles - Discussion Board
1) What is your preferred learning style? I am definitely a
kinesthetic learner. I can hear something or study something but
will not feel comfortable with it until I have hands on
experience with it.
2) What is your preferred learning environment (sound,
temperature, lighting, lecture vs. hands-on vs. discussion,
working with others or not, etc.)? My preferred learning
environment is in a classroom setting with others. Working with
other classmates on projects really seems to help me. Good
lighting is always helpful.

3) What are some strategies (according to your learning style)
you use to study? I like to take notes during instruction. Since
that is not possible through online classes participating in the
discussions with other classmates is also a good way to study
and learn. Their perspective on a topic can be a different way at
looking at something that I may not have learned on my own.
2. Learning Styles - Discussion Board
My proffered learning style is visual and kinesthetic. I like
studying in a bright cold room because it is harder to get tired
because we all know studying is tiring. I usually just were
headphones and study alone as well. Strategies I use to study
include reading the content over and over again, writing down
notes on the material multiple times, and using flash cards to
help me.
Required Resources Week 2
Required Text
Read from the course text, Applied project: Capstone in
psychology:
a. Chapter 3: Between and Within Groups Research Designs
b. Chapter 6: Survey and Questionnaire Research
Book
American Psychiatric Association. (2013). The Diagnostic and
Statistical Manual of Mental Disorders. (5th ed.). Washington,
D.C.: American Psychiatric Publishing.
· This is the manual of psychiatric diagnostic criteria used by
mental health professionals.
Articles
Bauer, R.M. (2007). Evidence-based practice in psychology:
Implications for research and research training.Journal of

Clinical Psychology, 63(7), 685–694. Retrieved from the
EBSCOhost database.
· This article discusses the implications of evidence-based
practice (EBP) for research and research training in clinical
psychology. Bauer argues that EBP provides a useful framework
for addressing some heretofore ignored problems in clinical
research
Brendtro, L.K., Mitchell, M.L., & Doncaster, J.
(2011). Practice-based evidence: Back to the future. Reclaiming
Children and Youth, 19(4), 5-7. Retrieved from the ProQuest
database.
· This article discusses perspectives on potential flaws
associated with promoting specific treatment models as
evidence-based and then making policies based on “shaky
science.”
Dozois, D.J.A. (2013). Psychological treatments: Putting
evidence into practice and practice into evidence.Canadian
Psychology, 54(1), 1-11. Retrieved from the ProQuest database.
· This article argues that it is crucial to make practice evidence-
based and make evidence practice-based. Current issues and
opportunities with respect to evidence-based practice are
discussed. Strategies for closing the gap between research and
practice are offered.
Halter, M.J., Rolin-Kenny, D., Dzurec, L.C. (2013). An
overview of the DSM-5: Changes, controversy, and implications
for psychiatric nursing.Journal of Psychosocial Nursing &
Mental Health Services, 51(4), 30-9.
doi:http://dx.doi.org/10.3928/02793695-20130226-02. Retrieved
from the ProQuest database.
· This article provides a general overview of the DSM-5,
highlighting major aspects of the revision in comparison to the
DSM-IV-TR. The authors highlight some of the controversies
accompanying the diagnostic changes in the DSM-5.
Recommended Resources

Articles
Horner, J., & Minifie, F. D. (2011). Research ethics I:
Responsible conduct of research (RCR)—Historical and
contemporary issues pertaining to human and animal
experimentation. [Supplemental material]. Journal of Speech,
Language, and Hearing Research, 54(1), S303–S329. Retrieved
from the EBSCOhost database.
· This article provides a historical overview of the evolution of
Responsible Conduct of Research in the United States in the
context of evolution of human and animal experimentation.
Controversies, successes and present challenges by are
highlighted through real-world examples of the work of
scientists, ethicists and legal scholars.
Horner, J., & Minifie, F. D. (2011). Research ethics II:
Mentoring, collaboration, peer review, and data management
and ownership. [Supplemental material]. Journal of Speech,
Language, and Hearing Research, 54(1), S330–S345. Retrieved
from the EBSCOhost database.
· In this article, the authors discuss the Office of Research
Integrity’s (1992) definitions of mentor and trainee
responsibilities, collaborative science, peer review and, data
acquisition, management, sharing, and ownership.
Horner, J., & Minifie, F. D. (2011). Research ethics III:
Publication practices and authorship, conflicts of interest, and
research misconduct. [Supplemental material]. Journal of
Speech, Language, and Hearing Research, 54(1), S346–S362.
Retrieved from the EBSCOhost database.
· In this article, the authors provide readers with the Office of
Research Integrity’s (1992) definitions of: domain-publication
practices and responsible authorship, conflicts of interest and
commitment, research misconduct, controversies, successes and
present challenges by are highlighted through real-world
examples of the work of scientists, ethicists and legal scholars.
Warren, B.J. (2013). How culture is assessed in the DSM-5.
Journal of Psychosocial Nursing & Mental Health Services,

51(4), 40-5. doi:http://dx.doi.org/10.3928/02793695-20130226-
04. Retrieved from the ProQuest database.
· This article provides insights into the integrations of cultural
aspects in the new DSM-5 diagnostic tool.
Multimedia
Sussman, A. (Producer). (2010). Into the mind: Mind control
[Video file]. Retrieved from the Films On Demand database.
· This video provides an illustrated history of some of the most
notorious psychology experiments ever conducted in science’s
attempt to explore behavior, brainwashing and free will.
6
Survey and Questionnaire Research
Chapter Learning Outcomes
After reading and studying this chapter, students should be able
to:
• understand the decisions that are made
regarding how the population is sampled and
the
various techniques to approximate a
representative sample.
• compare and contrast different survey research
methods and comprehend what research situ-
ations match better with different research
methodologies.
• appreciatedifferent survey research designs and

the various scaling methods that can be
used to construct survey items.
• anticipate the types of errors that may occur
within the survey research project, know
how to
handle data collection issues, and begin to understand the
various approaches to analyzing
the data collected.
• construct survey items using the appropriate
scalethat helps to capture the desired behav-
iors, perceptions, and/or attitudes of the population
of interest to be surveyed.
Hemera/Thinkstock
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158
CHAPTER 6Introduction
Introduction
I f you’ve ever enjoyed the task of trying to
assem-ble a large jigsaw puzzle,
you know that different peo-
ple have different strategies.
Some people like to assemble
the edges first, and then work
toward the middle. Others like
to use the picture on the box to
assemble easily recognizable

parts of the puzzle. Some like
to find all the corners first and
work that way. Assembling a
puzzle is a complicated task,
and different strategic paths
can lead to the same solution.
When using surveys and ques-
tionnaires—the main topics of
this chapter—the same prin-
ciple applies: There are many topics to
consider, and eventually we will get to them
all,
but we have to start somewhere.
Surveys and questionnaires are similar to jigsaw puzzles in that
many pieces come together to form the final picture.
Nordic Photos/SuperStock
Voices from the Workplace
Your name: Jessica F.
Your age: 30
Your gender: Female
Your primary job title: Survey Research Specialist
Your current employer: Society for Human Resource
Management, Research Department
How long have you been employed in your present position?
15 months

What year did you graduate with your bachelor’s degree in
psychology?
2000
Describe your major job duties and responsibilities.
Produce and manage quantitative and qualitative research on HR
topics. Design survey instruments
and programs online surveys for fielding. Involved in all
aspects of data management including the
data collection process and performing data quality control.
Designs the analysis plan and conducts
the analysis using SPSS statistical software. Produces written
technical reports.
What elements of your undergraduate training in psychology do
you use in your work?
Coursework in social psychology research methods—learned
and applied the fundamentals of survey
research methodology, writing technical research reports,
running analyses in SPSS, and conducting
background research through literature reviews. I also use the
information acquired from my statistics
course in my job. Coursework in organizational behavior and
I/O (industrial/organizational) psychology
(e.g., dealing with conflict resolution, change management,
motivation, personality tests, (continued)
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159

CHAPTER 6Introduction
etc.), that are relevant in the human resource profession.
Volunteer work as a research assistant in the
department of psychology. Spent a year coding data on an
emotional experiences study.
What do you like most about your job?
Meaningfulness of the research—produce research that HR
(human resources) professionals and
other customers can utilize and apply in their organizations to
improve workforce dynamics and make
strategic business decisions. Other things that I like about my
job include variety of work, managing
research projects from beginning to end, the ability to work
independently and autonomously.
What do you like least about your job?
It can be very tedious at times (e.g., data entry, data cleaning,
writing) since a high level of accuracy is
necessary. The environment is also very structured (e.g.,
specific procedures and protocols to follow);
however, this can vary from job to job.
Beyond your bachelor’s degree, what additional education
and/or specialized training have you received?
I took several classes through SPSS—survey methodology,
survey analysis, statistical analysis, syntax,
and intermediate topics in SPSS. To design/program web-based
surveys—experience in HTML, Dream-
weaver, ColdFusion and Microsoft Access. I took classes in
most of these areas, however I picked up
most of my experience on the job. I have also taken various HR

workshops/seminars to stay current
with HR and broaden my knowledge base.
What is the compensation package for an entry-level position in
your occupation?
A research assistant position in a non-profit organization in the
Washington D.C. area: $22,000–26,000.
What benefits (e.g., health insurance, pension, etc.) are
typically available for someone in your profession?
Medical, dental and vision insurance, 401K, flexible work
schedules (e.g., telecommuting, compressed
workweek), tuition assistance, professional development
opportunities and casual dress.
What are the key skills necessary for you to succeed in your
career?
Ability to pick things up quickly (e.g., learning programming
skills, learn about a new topic), strong oral
and communication skills, research skills, analytical and
problem solving skills, attention to detail and
computer skills. I have been fortunate to progress as far as I
have in research in the non-profit sector
with a bachelor’s degree; however, I do think that at some point
in time I will need to get a masters or
a doctorate degree.
Thinking back to your undergraduate career, what courses
would you recommend that you believe are
key to success in your type of career?
Statistics, psychology research methodology class, I/O
psychology, and organizational behavior.

Thinking back to your undergraduate career, can you think of
outside of class activities (e.g., research
assistantships, internships, Psi Chi, etc.) that were key to
success in your type of career?
I believe that my research assistantship helped me to get my
first professional research position. It
made a difference to have real world research experience
outside of the classroom.
As an undergraduate, do you wish you had done anything
differently? If so, what?
I wish that I would have joined Psi Chi so that I would have
been more active in psychology. I think that
it would have helped me to learn more about the field and take
advantage of opportunities (e.g., pub-
lishing research, presenting, serving on committees, etc.).
What advice would you give to someone who was thinking
about entering the field you are in?
A bachelor’s degree in psychology provides the fundamentals to
be successful in just about any line
of work. I think that it’s important to try out different types of
jobs to see what is a good fit before
making a decision to go back to school. A masters or doctorate
in psychology is not
Voices from the Workplace (continued)
(continued)
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160
CHAPTER 6Section 6.1 Sampling the Population
6.1 Sampling the Population
The ultimate goal of sampling the population is so
that a representative portion of the population
can be studied. Thus, by studying the sample
carefully and methodi-cally, generalizations can be
drawn about the variables or behaviors of
interest in
the greater population. Two major types of
sampling approaches exist—probability sam-
plingand non-probability sampling. Why sample? If
the goal is to understand how the
population thinks, acts, feels, believes, and so
on, then why not study the entire popula-
tion?First, we oftendo not have comprehensive lists of
members of a population. Say, for
example, you wanted to survey all the citizens of
Indiana. Is therea comprehensive list
of all citizens available? The tax rolls might be a
good start,but names and addresses are
unlikely to be part of the public record. Plus,
someIndiana residents may have moved,
or others moved to Indiana. So it is
unlikely to have a complete roster of all
citizens that
is accurate. You can make the same generalization
about the students at your college or
university, all the individuals in the community
with Alzheimer’s disease, or a list of all
the skateboarders in your town. Having an

accurate roster of all the members of the
popu-
lation of interest would be unlikely.
In addition, thereare othermethodologicalissues as
well. Because of the mathematics and
probability behind sampling theory, very good
samples can be drawn from populations
with relatively small margins of error. Dillman,
Smyth, and Christian (2009) offer this exam-
ple: “one can estimate within ± 3 percentage
points the percentage of people who have a
high school education in a small county of
25,000 adults with 1,024 completes [completed
surveys] and can measure the same thingamong the
entire U.S. population of more than 300
million by obtaining only 43 more completes” (p.
59). Sampling is efficient. Lastly, survey-
ing an entire population might lead to a
greater number of non-respondents, and survey
researchers become concerned about non-respondents
because if bias is driving a person’s
choice to not complete the survey, that may weaken
the validity of the data (Dillman et al.,
2009). We are better suited to select a
sampling procedure that allows us to estimate
any
potential of sampling error in order to obtain a
representative sample while minimizing bias
and high non-response rates. Probability sampling
strives to achieve each of those goals.
Probability Sampling

There are a variety of approaches to
probability sampling, including simple random
sam-
pling, systematic sampling, stratified sampling, cluster
sampling, and multistage sampling.
always necessary, and it really depends on what you want to do
in the long run. I started out as a
research assistant and worked hard and proved that I was
capable of doing more. I was promoted
twice within about three years.
Copyright © 2009 by the American Psychological Association.
Reproduced with permission. The official
citation that should be used in referencing this material is R.
Eric Landrum, Finding Jobs With a Psychol-
ogy Bachelor’s Degree: Expert Advice for Launching Your
Career, American Psychological Association,
2009. The use of this information does not imply endorsement
by the publisher. No further reproduction
or distribution is permitted without written permission from the
American Psychological Association.
Voices from the Workplace (continued)
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161
Each is briefly described in this section. Remember,
the overarching goal of probability

sampling is that the sample drawn will be
representative of the population if all the
mem-
bers of that population have an equal probability
of being selected for the sample. Often,
you’ll hear this stated as a non-zero probability
(StatPac, 2009; StatTrek, 2009), meaning that
thereis a chance for every person to be
selected, no matter how slim that chance might
be.
Simple Random Sampling
The simple random sample is perhaps the purest
form of sampling, and probably one of
the rarest techniques used. If you had the
roster of the entire population available,
you
could assign numbers to all members in sample
frame, assign random numbers to the
possible participants, and then select the sample
through a random number table (Babbie,
1973). Random number tables are oftenfound at
the back of statistics textbooks just for
this purpose. Think of it this way—if we could
throw all the names into a largehat and
draw a certain target percentage for our survey,
in this situation everybody in the survey
population has the same probability of being
tested (Edwards & Thomas, 1993).
Systematic Random Sampling
Simply put, in a systematic random sample, every
nth person from a list is selected
(Edwards & Thomas, 1993). Let’ssay that at your
college thereare 2,000 students currently
enrolled, and you determine that you would like to

have 100 students complete your sur-
vey. Each student completing your survey would
have an equal chance of being selected;
that is, the probability of being selected is
n/N (Lohr, 2008), or in our example,
100/2,000,
or 1 out of every 20 students. So, every
20th student would be selected. After
determining
a random starting point(let’s say No. 4, for
example), every 20th student on the roster is
selected, meaning the 4th, 24th,44th,64th,84th,104th,
124th, and so forth (Chromy, 2006).
Stratified Sampling
Stratified sampling involves an approach where extra
precautions are taken to ensure rep-
resentativeness of the sample. Strata define groups
of people who share at least one com-
mon characteristic that is relevant to the topicof
the study (StatPac, 2009). The term strata is
the plural of stratum; a study can have one
stratum, or multiple strata. For example, if
you
want to ensure that your sample is representative
based on gender, then you would stratify
on gender. If you know that 55% of the
population consists of females and 45% of
the pop-
ulation consists of males, then you could use
random sampling within a gender stratum
to extract a sample that matches the gender
breakdown of the population precisely. Some-
times oversampling is used to decrease sampling

error from relatively small groups—that
is, researchers may choose to oversample from
groups less likely to respond (Edwards &
Thomas, 1993). If the percentages in the
population match the sample strata
selected (as in
the gender example above), this is proportionate
stratification; if oversampling is used, this
practice would be considered disproportionate
stratification (Henry, 1990).
Cluster Sampling
Let’ssay you were interested in studying the perceptions
of high school seniors enrolled in
Advanced Placement(AP) psychology courses throughout
the state of New York. It would
be difficult to obtain a comprehensive roster of
all students at all schools enrolled in AP
psy-
chology courses. The concept of clustering means
that rather than randomize on the level of
the individual person, you would randomize on
the level of the school where AP psychology
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162
is taught. That is, each “participant” is a school,
not an individual person. It probably would
be possible to obtain a list of all the schools

in New York that offer AP psychology; once the
students are assigned to a group or cluster, then the entire
cluster is selected or not selected
at random (Edwards & Thomas, 1993). One of
the general guidelinesabout cluster sampling
is that the researcherdesires “to have a larger
number of small clustering units than to have
a small number of larger clustering units”
(Fife-Schaw, 2000, p. 97). The cluster sample
tech-
nique is particularly useful when it is
impossible or impractical to compile an
exhaustive list
of members composing the target population
(Babbie, 1973; Henry, 1990).
Multistage Sampling
Multistage sampling describes a process that
follows after cluster sampling has been
implemented. In our AP psychology example, a
random sample of New York high schools
that offer AP psychology (clusters) is selected for
further study. Multistage sampling kicks
in once the schools to be studied are selected.
For instance, is every high school senior
within the selected school/cluster surveyed, or is a
systematic random sample drawn?
In essence, the multistage sampling approach is two-stage
sampling, involving(a) the
selection of clusters as a primary selection, and
(b) sampling members from the selected
clusters to produce the final sample (Chromy, 2006;
Henry, 1990).
Nonprobability Sampling

Nonprobability methods of sampling mean just that; it
is unknown what the probability is
of each possible participant in the population
being selected for the study. Unfortunately,
with nonprobability sampling, sampling error cannot be
estimated (StatPac, 2009). Two
key advantages to nonprobability sampling, however,
are cost and convenience (StatTrek,
2009). The main approaches utilizing the
nonprobability sampling approach are conve-
nience sampling, quota sampling, snowball sampling,
and a volunteer sample.
Convenience Sampling
Convenience samples are just
that—convenient. This tech-
nique is often used in explor-
atory research where a quick
and inexpensive method is used
to gather data (StatPac, 2009).
Psychologists have long relied
on convenience samples; for
instance, the use of introduc-
tory psychology human subject
pools represent a convenience
sample approach.
Quota Sampling
Quota sampling as a nonproba-
bility sampling technique is the
equivalent of stratified sampling
Convenience samples are a quick, low-cost method to gather
data from an available population of people. If you wanted to
have a convenience sample, where would you go?

age fotostock/SuperStock
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CHAPTER 6Section 6.2 Survey Research Methodologies
from the probability sampling world. In stratified
sampling, you identify key characteris-
tics of interest, and then you sample to ensure
that those individuals selected represent the
population of interest in a proportional
manner. In quota sampling, the researcher also
desires the strata of interest, but then recruits
individuals (non-randomly) to participate
in a study (StatPac, 2009). Thus, quotas
are filled with respect to the key
characteristics
needed for survey participants from the population.
Snowball Sampling
When using the snowball sample technique, members of
the target population of inter-
est are asked to recruit othermembers of the
same population to participate in the study.
This procedure is oftenused when thereis no roster
of members in the population, and
those members may be relatively inaccessible, such as
illegal drug users, pedophiles, or
members of a cult (Fife-Schaw, 2000). Snowball
sampling relies on referrals and may be a
relatively low-cost sampling procedure (StatPac, 2009),

but thereis a high probability that
the individuals who participate may not be
representative of the larger population.
Volunteer Sample
This is a commonly used method for soliciting
survey participation, but oftenthe results
are quite limited due to the possible motivational
differences between volunteers and
non-volunteers. When a popular website postsa
survey and invites volunteers to partici-
pate, the explanatory and predictive power of the
data gathered may be suspect (StatTrek,
2009). It is difficult to make confident
generalizations from a sample to a population
when
nonprobability samples are employed,and even less
confidence exists if a volunteer sam-
ple is utilized. With one piece of the
survey/questionnaire puzzle in place
(sampling),
the next section presents the major survey
research approaches or strategies that are com-
monly used.
6.2 Survey Research Methodologies
This section provides an overview of the choices
that survey researchers must answer concerning
how the data are collected.
Interviews
In someways, in-person interviews remain the gold
standard in survey research. Inter-
views have fewer limitations about the types
and length of survey items to be asked,

and trained interviewers can use visual aids to
assist during the interview (Frey & Oishi,
1995)—for example, the interviewee can see, feel, or
taste a product (Creative Research
Systems, 2009). Interviews are thought to be
one of the best ways to obtain detailed infor-
mation from survey participants. With an in-
person interview, the interviewer and the
participant can buildrapport through conversation
and eye contact, which might allow
for deeper questions to be asked about the
topicof interest. The drawbacks of interview-
ing include high costsand the reluctanceof individuals
to take the time to complete an
interview (Creative Research Systems, 2009; Frey &
Oishi, 1995). In addition to one-on-
one interviews that may be pre-arranged, thereare
also intercept interviews, such as those
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164
you may have seen at a mall,where an interviewer
intercepts shoppers and asks them for
an interview. The level of intimacy that can be
achieved with an in-person interview could
also be a drawback for someindividuals. There
are also group interviews, which somecall
focus groups, where a group of people

are interviewed at the same time.
Telephone Research
In someways, a growing reluctanceto participate
in in-person interviews led to the growth
of using the telephone as a modality of
conducting survey research (Tuckel &
O’Neill,
2002). The use of telephone methodology has
increased over time,but facesa number of
challenges today. For instance, thinkabout how
difficult it can be to reach someone on
the
phone who is willing to participate—Figure 6.1
(from Kempf & Remington, 2007) illus-
trates this challenge.
Potential
subject
Telephone
No
telephone
Cell phone
only
Landline
Not at
home
Screen
calls

Agree to
participate
Do not
screen calls
At home
Decline
By the time you have agreement from a possible participant in a
telephone study, a great deal of screening
has already occurred.
Source: Kempf and Remington, 2007
Figure 6.1: Example of telephone methodology
lan66845_06_c06_p157-190.indd 164 4/20/12 2:48 PM
165
Coverage has always been a concern of telephone
research as well. That is, the greater
percentage of homes with a telephone, the
better the survey coverage, and the better
the
possibility of drawing a representative sample
from the population of interest. See the fol-
lowing for how telephone coverage in the United
States has changed over time (Kempf &

Remington, 2007):
• In 1920, 65% of households did not have a
telephone.
• In 1970, 10% of households did not have a
telephone.
• In 1986, 7–8% of households did not
have a telephone.
• In 2003, less than 5% of households did
not have a telephone.
As you can see, coverage is quitegood regarding
households with a phone, but researchers
who rely on telephone surveys as their modality for
data collection face many challenges
today, such as working within the context of
Do Not Call lists. Researchers continue to
develop new strategies for improvingthe efficiency of
telephone surveys, such as by using
computer-assisted telephone interviewing (CATI)
systems, random digit dialing (RDD),
and interactive voice response systems (“press 1
if you are . . .”). But the challenges
seem
to be growing as well. The growth of cell phone
usage is changing the face of telephone
survey research. And that growth has been explosive—
from fewer than 500,000 usersin
1985 to 35 million usersin 1995, and more than
200 million cell phone usersin 2005 (Kempf
& Remington, 2007). Answering machines, Caller
ID, privacy managers, and call blocking
services all add to the increasingchallenges of
conducting survey research by telephone.
Mail Surveys

Oddsare you’ve received a survey in the mail.
Did you complete it? Did you give it to
someone else in your household to complete? As you
can see, thereare challenges to using
mailed surveys as your modality of survey data
collection. There are advantages and dis-
advantages of using a particular approach, as
explained by de Leeuw and Hox (2008). The
advantages to mail surveys include (a) relatively
low cost per survey respondent—mailed
surveys can be completedwith a relatively small
staff; (b) no time pressure on the part of
the survey respondent; (c) the mailed survey
can include visual stimuli, using different
scaling techniques and visual cues for survey
completions (such as skip patterns); (d) the
potential effect (bias) of the interviewer is
removed with a mail survey; (e) participants
have greater privacy in responding to a mail
survey; and (f) if a good sample frame is
available with a mailing list, the benefits of random
sampling techniques can be realized.
The potential disadvantages to mail surveys include
(a) potentially low response rates;
(b) limited capabilities for complex questions, and
the inability for an interviewer to clar-
ify questions being asked; (c) when mail is
delivered to a household, thereis no
guarantee
that the person for whom the survey is
intended is the person completing the survey;
and
(d) the turnaround time for receiving mailed survey
responses can be long.

Internet Surveys
Participating in a survey facilitated by the Internet could
involve invitations through list-
servs, discussion groups, advertisements on search
engine pages, email directories, pub-
lic membership directories, chat roomrosters, guest
lists from web pages, and of course
individual email solicitations (Cho & LaRose,
1999). Compared with paper and pencil
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166
surveys, online/Internet surveys offer a number of
advantages (Beidernikl & Kerschbau-
mer, 2007), including easy and inexpensive distribution
to largenumbers of individuals
via email, the participant is guided through
the survey by essentially filling out a
form
(i.e., skip patterns are hidden from view), digital
resources (e.g., video clips, sound, ani-
mation) can be incorporated into the survey design
if necessary, and questions can be
“required” to be answered as well as verified
instantly (e.g., when asked in what year
you
were born, if somethingotherthan a four digit number
is entered, the participant can be

instantly prompted to use the correct format and
prevented from proceeding until making
the correction).
A number of survey tools are available to assist
in the collection of online survey data. Two
of the more population choices are SurveyMonkey
(http://www.surveymonkey.com) and
Qualtrics (http://www.qualtrics.com); others include
QuestionPro, Zoomerang, KeySurvey,
SurveyGizmo, and SurveyMethods. Many of these
online survey websites allow you to create
an account for free and use it on a limited
basisto design a survey and then collect
data with
that survey (once you exceed a certain number
of surveys or a certain number of
responses,
then most of thesesites will want you to purchase an
annual membership). After creating
your survey, the software will create a custom
URL that you then can email to potential par-
ticipants or post on a website. You probably have
completeda number of online surveys and
are familiar with the types of questions and
formats. One of the advantages to online
survey
software is that you can usually download the
outcomes/results directly into an Excel file
for
later analysis (or othertypes of files,such as SPSS
files). Also, someof the sites can assist
with
rudimentary data analysis (and creating graphs and
charts) without even exporting the data.

Two key drawbacks of Internet surveys are
issues of coverage and nonresponse (de Leeuw
& Hox, 2008). The issue of coverage, that is,
who has Internet access and who does not,
is sometimes referred to as the digital divide
(Suarez-Balcazar, Balcazar, & Taylor-Ritzler,
2009). Coverage is a problem for Internet sur-
veys (de Leeuw & Hox, 2008), and Suarez-Bal-
cazar et al. (2009) provided somespecific exam-
ples of the possible drawbacks: (a) individuals
from low-income and working-class communi-
ties are less likely to have access to the
Internet;
(b) low-income and working-class, culturally
diverse individuals are more likely to have only
one computer, which would limit the potential
for completing Internet-based surveys; (c) lim-
ited access often translates into limited famil-
iarity with online/Internet applications, and
(d) there may be cultural barriers that make
Internet research more difficult to successfully
accomplish (more on this in a moment).
In addition to the challenge of coverage, there
is also the challenge of representativeness. An
Internet survey approach may not achieve the
level of representativeness desired (Beidernikl
The Internet can facilitate many types of
surveys, which are easier and less expensive
than regular paper and pencil surveys.
PR Newswire/Associated Press

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http://www.surveymonkey.com
http://www.qualtrics.com
167
CHAPTER 6Section 6.3 Comparisons of Methodologies
& Kerschbaumer, 2007; de Leeuw & Hox, 2008).
In fact, you can think about whether
those replying to an Internet survey are representative of the
entire population, represen-
tative of the Internet population, or even representative of a
certain targeted population
(Beidernikl & Kerschbaumer, 2007). Add in the
complexity of culture, and you can see
that well-designed Internet surveys can take a
significant amount of work. Consider this
example offered by Suarez-Balcazar et al. (2009):
For instance, in the Chicago Public Schools,
students speak over 100 dif-
ferent languages and dialects. Social scientists
planning studies in these
types of settings must consider how they are going
to communicate with
the participants’ parents. Although children of first
generation immigrants
may be able to speak, read, and participate in
Internet-based surveys in
English, information such as consent forms and
research protocols that are
sent to the parents may need to be translated into
their native language and

administered using paper-and-pencil format. (p.
99)
If not used carefully, online/Internet survey researchers are
capable of invading privacy
(Cho & LaRose, 1999), and care should be taken
to minimize that threat.
6.3 Comparisons of Methodologies
W ith all the different modalities of survey
administration, the natural ques-tion arises—which
approach is best?The answer to that complex
question is it depends. However, therehave been
somevery useful studies conducted that
compare the different methodologies, and below is a
sampling. de Leeuw and Hox (2008)
report that, on average, web-based surveys have an
11% lower response rate than mailed
and telephone surveys. In an experiment that directly
compared regular mail and e-mail
surveys, Schaefer and Dillman (1998)
found comparable response rates—57.5%
for regular mail, and 58.0% for e-mail.
When Braunsberger, Wybenga, and Gates
(2007) compared telephone surveys and
web-based surveys, a two-wave web-
based approach provided more reliable
data estimates than telephone surveys,
and at a lower cost: Each telephone survey
cost $22.75 to complete, whereas the cost
of each web-panelsurvey was $6.50.
What does the future hold for preferred
survey research modality? In addition to

the particularly useful comparison stud-
ies, a growing trendis to utilize a mixed-
mode approach (e.g., Nicolle & Lou, 2008),
where multiple modalities are accessed to
achieve the research goals. Thus, you may
see email reminders to participate in a tele-
phone survey. The mixed-mode approach
The mixed-mode approach uses several methods
to gather research. What are the benefits of this
approach? The drawbacks?
iStockphoto/Thinkstock
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168
CHAPTER 6Section 6.4 Designs for Survey Research
can also involve the collection of qualitative data as
well as quantitative data. Qualita-
tive data, such as the responses to open-ended
questions on a survey (e.g. “How do you
feel about parking on your campus?), can provide
particularly rich and useful data, and
qualitative approaches are oftenthe most helpful
when we know the least. In the Nicolle
and Lou (2008) example, faculty members were asked
about the process by which they
adopt new technologies for use in college
courses, and somefaculty completedsurveys,
whereas others were interviewed in person—thus, a

mixed-mode approach. In another
example, McDevitt and Small (2002) used both Internet
and mail to survey participants of
an annual sporting event.
If the sampling plan and survey modality puzzle
pieces are in place, another decision to
be made is the overall design of the survey
research. In someregard, theseconcepts do
overlap with topics from Chapter 8 on quasi-
experimental research designs. But a brief
review of how thesedesign decisions affect survey
research is warranted here.
6.4 Designs for Survey Research
Although different researchers may use slightly
different terminology, the major cat-egories of
survey research designs are presented in this
section.
Cross-Sectional Survey Designs
In a cross-sectional survey design, data collection occurs at
a single pointin time with the
population of interest (Fife-Schaw, 2000; Visser,
Krosnick, & Lavrakas, 2000). One way to
thinkabout a cross-sectional sur-
vey is that it is a snapshot in time
(Fink & Kosecoff, 1985). Cross-
sectional surveys are relatively
inexpensive (Fife-Schaw, 2000)
and relatively easy to do (Fink &
Kosecoff, 1985). However, if the
landscape changes rapidly, and
that amount of change is impor-
tant to your survey research, then

using a cross-sectional design
will not allow you to capture this
change over time (Fife-Schaw,
2000; Fink & Kosecoff, 1985).
Longitudinal
Survey Designs
A longitudinal survey is con-
ducted over time,but this label
alone does not give us enough details about
the type of longitudinal survey. Longitudinal
studies face unique challenges, such as keeping
trackof respondents over time and how
Cross-sectional survey design gathers data from the population
all at one time, as shown in this call center that collects survey
information for clients.
Marka/SuperStock
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169
to motivate respondents to continue to respond in
the future (Dillman et al., 2009). In
general, the key advantage of longitudinal designs is
that they allow for the study of age-
related development. However, this can be confounded
with events over time that might
influence your variables (Fife-Schaw, 2000). For

example, if you are interested in how
individuals feel about their personal safety, and the span of
your longitudinal research
includes September 11, 2001, then your research
might be affected by that historical event,
and changes may not be due only to the passage of
time.Attrition (dropping out of the
study over time) is a drawback,and participants
repeatedly tested over time can be sus-
ceptible to the demand characteristics of the
research—having participated multiple times
in the past, the participants know what is
expected and probably understand the variables
and general hypotheses being tested (Fife-Schaw,
2000).
Cohort and Panel Survey Designs
In a cohort study, new samples of individuals are
followed over time,whereas in a panel
study, the same people are tracked over time
(Jackson & Antonucci, 1994). In a panel
study, the same people are studied over time,
spanning at least two points in time (Fink
& Kosecoff, 1985; Jackson & Antonucci, 1994;
Visser et al., 2000). This type of study
can
be particularly useful for understanding why
particular changes are occurring over time,
because you are asking the same individuals to
respond over time (you also have a base-
line comparison measure from when they first entered
the study).
There are so many more variations of possible
research designs, such as trend studies,

population sampling, and even an approach called
the “multigenerational lineal panel”
approach (Jackson & Antonucci, 1994). The key to
remember for now is that there are
many pieces of this puzzle to be solved,
and the survey research design that
psychologists
select is based on a number of factors.
But the types of questions that we can answer
are
strongly governed by how we ask the question. This is
illustrated in the “Classic Studies
in Psychology” storythat follows, and much of
the remainder of this chapter is devoted
to providing helpful advice about crafting your
own survey questions, selecting the scales
of measurement, and choosing data analysis strategies to
make the most of survey data.
Classic Studies in Psychology: Loftus and Eyewitness
Testimony
(Loftus & Palmer, 1974; Loftus, 1975)
As you will see, psychologist Elizabeth Loftus cleverly
studied the relationship between the phrasing of a
question and the impact of that phrasing on the answer.
Not only is this an important consideration for survey
research, but this line of research helped Loftus to
develop expertise concerning eyewitness testimony
(and how asking questions may lead to the creation of
false memories).
In the Loftus and Palmer (1974) experiment, 45 stu-
dents were shown 7 films being used by a local Seattle
Police Department as part of their driver’s education
program. Following each film, the participants were

asked to write about the film they had just seen and to answer a
series of survey questions—the key
research question asked about the speed at which the cars were
going when the collision
Associated Press
(continued)
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170
occurred. However, for the 45 students who viewed the accident
film, groups of nine were asked dif-
ferent questions, as presented in the table below. After being
asked the particular question (note the
key word in boldface), students responded with their average
speed estimate of the two cars, in miles
per hour (mph). The results are presented in Table 6.1.
Table 6.1: Loftus and Palmer survey questions and estimates
Survey Question Average Speed Estimate
About how fast were the cars going when they smashed each
other? 40.5 mph
About how fast were the cars going when they collided with
each
other?

39.3 mph
About how fast were the cars going when they bumped each
other? 38.1 mph
About how fast were the cars going when they hit each other?
34.0 mph
About how fast were the cars going when they contacted each
other? 31.8 mph
Loftus and Palmer (1974) found these speeds to be significantly
different. Thus, even the verb used to
ask the question made a significant difference in how memories
were reported. But Loftus’ creative
thinking about these issues continued.
In a study published a year later, Loftus (1975) further explored
how survey answers were dependent
on the questions, and furthermore, how embedding false
information in the original survey questions
can lead to the embedding of false memories over time. This
classic study reports the outcomes of four
different experiments, but we’ll only describe two of those
experiments here. In Experiment 1, stu-
dents “were shown a film of a multiple-car accident in which
one car, after failing to stop at a stop sign,
makes a right-hand turn into the main stream of traffic. In an
attempt to avoid a collision, the cars in the
oncoming traffic stop suddenly and a five-car, bumper-to-
bumper collision results. The film lasts less
than 1 min., and the accident occurs within a 4-sec. period” (p.
563). The key car in the scenario (Car A)
is then presented as a part of a diagram with the other cars. Half
the students were asked, “How fast

was Car A going when it ran the stop sign?” and the other half
of students were asked, “How fast was
Car A going when it turned right?” However, in this study, the
key question of interest was not about
miles per hour but rather is “Did you see a stop sign for Car
A?” See the results in Table 6.2.
Table 6.2: Survey results
Leading Question Answer to the Next Question
“Did you see a stop sign for Car A?”
How fast was Car A going when it ran the stop sign? 53%
answer YES
How fast was Car A going when it turned right? 35% answer
YES
Just mentioning the stop sign in the question helps participants
remember that there was a stop sign.
But what if leading questions contained misinformation? What
impact would that have on memory?
Loftus addressed that issue in Experiment No. 4 in her 1975
study. She showed students a 3-minute film
of an automobile that eventually collides with man pushing a
baby carriage. After viewing
Testimony
(Loftus & Palmer, 1974; Loftus, 1975) (continued)
(continued)
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171
the film, the participants are asked 45 questions about the film,
but Loftus is only interested in 5 of the
answers. In the “Direct” condition, the participants were asked a
straightforward question, such as,
“Did you see a woman pushing the carriage?” (We know from
the description above that the correct
answer is no.) In the “False Presupposition” condition,
participants were asked, “Did the woman push-
ing the carriage cross into the road?” A third group served as
the control group and did not receive any
key questions at all (just filler questions). One week later, the
participants returned and were asked the
direct question—in this case, did you see a woman pushing the
carriage? See Table 6.3 to find out what
happens one week later.
Table 6.3: Experiment No. 4 follow-up questions and results
Experimental Condition Percentage YES Responses to “Did you
see a woman pushing the carriage?”
Direct—Did you see a woman pushing the carriage? 36% YES
False Presupposition—Did the woman who was
pushing the carriage cross into the road?
54% YES
Control (No leading question) 26% YES

Note: Remember, it was a man who was pushing the carriage in
the film. If memory were working
perfectly, the percentage of YES in all three rows should be 0%.
Note that for the control group, without any leading questions at
all, 26% remember a woman push-
ing the carriage, when in fact it was a man. But look what
happens one week later—the amount of
misremembering increases, and it can be manipulated by the
researcher. You should know that Loftus
did this with other scenarios throughout the study (1975), as
well as in other studies (e.g., Loftus &
Hoffman, 1989). These fascinating outcomes have continued to
influence Loftus’ work, and have influ-
enced the work of others as well (e.g., Crombag, Wagenaar, &
van Koppen, 1996).
If you think about it, the ability to change memories based on
the way that a question is asked has
important implications for issues such as eyewitness testimony
and repressed memories, two top-
ics that Loftus has explored throughout her career. Niland
(2007) correctly pointed out that Loftus’
research squarely puts her in the center of the controversy about
repressed childhood memories, and
that it is possible to implant a false memory. This capability (or
an accusation to some) threatens a
number of therapists and victims of abuse who have come to
believe that the memories of the abuse
have been repressed for years, and with the help of a
psychotherapist those memories can be discov-
ered (Niland, 2007).
Elizabeth Loftus has received many accolades for her work
about the formation and manipulation of
memory. When Philip Zimbardo (President of the American

Psychological Association in 2002) wrote
about “does psychology make a significant difference in our
lives,” Loftus’ research was listed as semi-
nal work in the area of eyewitness identification (Zimbardo,
2004). In 2004, Loftus was elected to the
National Academy of Sciences (a high honor); she was also
named as one of the 100 most influential
psychologists of the 20th century and the highest ranked woman
on the list (Zagorski, 2005).
Reflection Questions
1. How does the careful selection of the verb used in the
experiments by Loftus compare to the types
of verbs you might select to develop survey research questions
for a project at work? Is there a
chance that the specific wording selected might have an impact
on the results you observe?
Why or why not?
Testimony
(continued)
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172
CHAPTER 6Section 6.5 Scaling Methods
6.5 Scaling Methods

As you can surely see by now, survey research is
a complex puzzle with multiple pieces needing
to be put into place before the picture is
complete. Perhaps one of the most complicated
parts of survey research is deciding on the
scaleby which
to measure a person’s attitudes, opinions, behavior,
knowledge, etc.—in fact, there are
entire books on the subject (e.g., Netemeyer,
Bearden, & Sharma, 2003). As you read ear-
lier in Loftus’ work, how you ask the questions
does shape the answer you receive. In
fact, how you shape the possible answers can even
influence the answers you receive. For
example, Schwartz (1999) reported on someof his
previous research where he had sur-
veyed German respondents about the number of
hours per day that they watch television.
Two groups were asked the same question but given
different response categories—these
response categories are depicted in Table 6.4.
Table 6.4: How response scales can shape the results—daily TV
consumption
Low Frequency
Alternatives
Percent Reporting High Frequency
Alternatives
Percent Reporting
Up to ½ hour 7.4%
½ hour to 1 hour 17.7%

1 hour to 1 ½ hours 26.5%
1 ½ hours to 2 hours 14.7%
2 hours to 2 ½ hours 17.7% Up to 2 ½ hours 62.5%
More than 2 ½ hours 16.2% 2 ½ hours to 3 hours 23.4%
3 hours to 3 ½ hours 7.8%
3 ½ hours to 4 hours 4.7%
4 to 4 ½ hours 1.6%
More than 4 ½ hours 0.0%
2. Have you ever been in a car accident or spoken to someone
who has? Think about your memory
for that event (or ask the person about his or her memory for
that event). Is the memory like a
flashbulb memory, where every element of the scene is
remembered, or have some memories
faded over time while other “memories” seem to have been
invented? What about the effect of
an emotional reaction during a car accident, such as the rush of
adrenaline in anticipation of the
fight-or-flight response? How do these individual factors need
to be considered and combined to
better our understanding of memory for these kinds of events?
3. Eyewitness testimony has important ramifications for how
our criminal justice system works.
Eyewitness testimony can help clear some people of crimes,
whereas eyewitness testimony some-
times provides key evidence that leads to the incarceration of an

individual. Given the fallibility of
memory, does the legal system have checks and balances in
place to help prevent misremember-
ing and to minimize the fallibility of eyewitness testimony?
Testimony
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173
Lookwhat happens, dependingon the response scale.
When the scalestarts low (left side
of table), only 16.2% of respondents report
watching more than 2 ½ hours of television
per day, but when the alternatives start higher on
the scale(on the right side of the table),
37.5% of respondents report watching more than 2
½ hours of television per day. Just
by the scaledifference alone, the magnitude of
this difference makes it difficult to draw
meaningful conclusions. So what do we do about
situations where we need to design sur-
veys and items and scales? We rely on best
practices and established research that guides
the decision making necessary to select an
appropriate scale. What follows is a
brief over-
view of the major types of scales you are
likely to use.

Dichotomous Scales
When you use a dichotomous scale, there are only
two possible options. So if the possible options
are
agree/disagree, yes/no, true/false, male/female,
and so on, then you are using a binary scale.
Respondents provide nominal scale data (this is
an important consideration for later data analysis
options). Some examples of dichotomous scales
where a yes/no type of response would be
ade-
quate are:
• I am married.
• I download music illegally.
• My parents are divorced.
Some argue (e.g., Spector, 1992) that single
yes/no
questions are insufficient, because they are not sen-
sitive to subtle change over time,they dictate
that
individuals place themselves into largecategories,
and that many psychological phenomena are so
complex that a singular yes/no response may fail
to capture the complexity. As you design your
sur-
veys, keep in mind that the hypotheses you wish
to test will help to inform you if a dichotomous
scalecan yieldthe type of information you seek.

Likert Scales
Likert scales, or perhaps Likert-type scales, may be
the most famous/popular type of
scaleused by psychological researchers today. The
Likert scale is named after the psy-
chologist from the University of Michigan, Rensis
Likert (pronounced Lick-ert). Likert’s
seminal work (1932), now called a Likert scale,
called for a survey response scaleto have
a 5-point scale, measuringfrom one pole of
disagreement to the otherpole of agreement.
Each of the scalepoints has a specific verbal
description (Wuensch, 2005). A declarative
statement is made, and then the respondent selects
the appropriate answer. The low value
is strongly disagree, and the high value is
strongly agree, like this:
When using a dichotomous scale, there are
only two possible options, such as yes or no.
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1 = strongly disagree

2 = disagree
3 = neutral (neither agree nor disagree)
4 = agree
5 = strongly agree
There have been many variations and changes
suggested that are loosely based on the
above criteria, so you will oftensee “Likert-type”
scaleused rather than the very spe-
cific Likert scaleas described above. For example,
Fowler (1988) has made the argument
that Likert-type variations (shown below) might
be better suited because they would
have lesser emotional ties: 4 = completely agree, 3 =
generally agree, 2 = generally disagree,
and 1 = completely disagree or 4 = completely
true, 3 = mostly true, 2 = mostly untrue, and
1 = completely untrue. Of course, thesewould
not conform to the true Likert scalebut would
be categorized as Likert-type scales. There
have been many variations on this theme. The
following examples demonstrate many of these
variations, as presented by Vagias (2006).
Note the varying types of response anchors
possible with a Likert-type scaleapproach,
including the use of frequency, truthfulness,
probability, importance, concern, support,
usage, awareness, satisfaction, and influence. As
you thinkabout the type of scaleyou
might employ in your survey research, and you
examine the following examples, you
should begin to appreciatejust how useful and

versatile using a Likert-type scalecan be.
Level of Acceptability
1 – Totally unacceptable
2 – Unacceptable
3 – Slightly unacceptable
4 – Neutral
5 – Slightly acceptable
6 – Acceptable
7 – Perfectly Acceptable
Level of Importance
1 – Not at all important
2 – Low importance
3 – Slightly important
4 – Neutral
5 – Moderately important
6 – Very important
7 – Extremely important
Knowledge of Action
1 – Never true
2 – Rarely true
3 – Sometimes but
infrequently true
4 – Neutral
5 – Sometimes true
6 – Usually true
7 – Always true
Level of Problem

1 – Not at all a problem
2 – Minor problem
3 – Moderate problem
4 – Serious problem
Level of Awareness
1 – Not at all aware
2 – Slightly aware
3 – Somewhat aware
4 – Moderately aware
5 – Extremely aware
Likelihood
1 – Extremely unlikely
2 – Unlikely
3 – Neutral
4 – Likely
5 – Extremely likely
Level of Satisfaction – 5 point
1 – Very dissatisfied
2 – Dissatisfied
3 – Unsure
4 – Satisfied
5 – Very satisfied
Level of Appropriateness
1 – Absolutely inappropriate
2 – Inappropriate
3 – Slightly inappropriate
4 – Neutral
5 – Slightly appropriate

6 – Appropriate
7 – Absolutely appropriate
(continued)
Likert-Type Scale Response Anchors
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175
Level of Agreement
1 – Strongly disagree
2 – Disagree
3 – Somewhat disagree
4 – Neither agree or disagree
5 – Somewhat agree
6 – Agree
7 – Strongly agree
Frequency – 5 point
1 – Never
2 – Rarely
3 – Sometimes
4 – Often
5 – Always
Level of Familiarity
1 – Not at all familiar

2 – Slightly familiar
3 – Somewhat familiar
4 – Moderately familiar
5 – Extremely familiar
Level of Difficulty
1 – Very difficult
2 – Difficult
3 – Neutral
4 – Easy
5 – Very easy
Level of Quality – 5 point
1 – Poor
2 – Fair
3 – Good
4 – Very good
5 – Excellent
Level of Satisfaction – 5 point
1 – Not at all satisfied
2 – Slightly satisfied
3 – Moderately satisfied
4 – Very satisfied
5 – Extremely satisfied
Source: Vagias (2006).
Likert-Type Scale Response Anchors (continued)
Thurstone Scale and Guttman Scale
Both the Thurstone scale and the Guttman scale describe a
methodology of scaledevelop-

ment as well as measuringindividual responses. In
1928, Thurstone proposed the technique
(nowcalled the Thurstone scale) to develop a
response scaleof equally appearing intervals
by having participants make a series of
comparative judgments(Page-Bucci, 2003;
Roberts,
Laughlin, & Wedell, 1999). First, a largenumber
of attitude statements would presumably
represent the entire range of possible options,
and respondents would provide a global
eval-
uation of favorability or unfavorability toward
the topicpresented in the survey items—for
instance, a pairwise comparison could be
presented,where a respondent is forced to
choose
which statement he or she agrees with more,
and the process is repeated over and over.
From a group of individuals, this yields a
hierarchy of agreementscores for each item,and
then in the second stageindividuals re-rate the
items in terms of agreementor
disagreement
(Page-Bucci, 2003; Roberts et al., 1999). The
goal of using this multistage process is so
that
the final items retained in the survey fit the
respondents’ patterns of answering well, rather
than hoping that survey items capture what the
respondents thinkabout a particular topic.
A Guttman scaleis difficult to construct because it
is based on generating a set of items
that increase in difficulty;on a 7-item scale, if

the easiest item to agree to is Item No. 1,
and the most difficult item to agree to is Item
No. 7, and you agree with Item No. 5, that
automatically means that you agree with the first
four items as well. In otherwords, what-
ever item you agree with on the hierarchy, it is
assumed that you agree with all the items
leading up to it also. Page-Bucci indicated (2003)
that although this scalemay allow for
more complex measures than a Likert-type scale,
the scales are difficult to construct and
the scoring systems are cumbersome.
Semantic Differential Scales
The semantic differential scaletechnique, developed by
Osgood in the 1950s, is a scale
that is designed to measure affect or emotion
(Henerson, Morris, & Fitz-Gibbon, 1987),
but
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176
it can measure much more than that. Using
adjectives that are polaropposites, participants
are asked to select how they feel about the
survey topicbeing presented.For example, to
respond to the question “Thinking about this course,
how do you feel about the grading
policies being used?” the surveyed person would

be asked to place on a checkmark on
one
of the seven lines spanning the polaropposites on
the the semantic differential scalebelow:
fair ___ ___ ___ ___ ___ ___ ___ unfair
unreliable ___ ___ ___ ___ ___ ___ ___ reliable
confusing ___ ___ ___ ___ ___ ___ ___ clear
helpful ___ ___ ___ ___ ___ ___ ___ not helpful
good ___ ___ ___ ___ ___ ___ ___ bad
Based on prior research, three types
of findings tend to emerge from the
use of semantic differential scales
(Page-Bucci, 2003): an evaluative fac-
tor (good-bad), an intensity/potency
factor (strong-weak), and an activity
factor (slow-fast). Responses on these
items can be given a score of 1 to 7,
dependingon where the mark on the
scale occurred; most researchers ana-
lyze thesedata the same as they would
Likert-type agreement scale data—as
interval/ratio (scale) data. The seman-
tic differential scale is good at captur-
ing feelings and emotions, is relatively
simple to construct, and is relatively
easy for participants to use, but the
resulting analyses can be complicated
(Page-Bucci, 2003). An example of
more possible pairings appears below

(from Henerson et al., 1987):
The use of semantic differential scales reveals three
types of findings: evaluative; intensity/potency; and
activity. This type of scale is helpful in recording feelings
and emotions.
angry-calm
bad-good
biased-objective
boring-interesting
closed-open
cold-warm
confusing-clear
dirty-clean
dull-lively
dull-sharp
irrelevant-relevant
last-first
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177
not brave-brave
old-new
passive-active
purposeless-purposeful
sad-funny
slow-fast
sour-sweet
static-dynamic
superficial-profound
tense-relaxed
ugly-pretty
unfair-fair
unfriendly-friendly
unhappy-happy
unhealthy-healthy
uninformative-informative

useless-useful
weak-strong
worthless-valuable
wrong-right
Other Types of Scales
There are many more types of scales that
are used in survey research. Visual analog scales
can be used to obtain a score along a
continuum, where a participant places a
checkmark
to indicate where his or her attitude or opinion
falls along the scale. Below is an
example
of the visual analog scale:
No pain at all ———————————— The worst
pain I ever experienced
This would be an example of a subjective
continuum scale, where a checkmark is
made
along the scale to indicate how positive or
negative a respondent’s opinion is about a
particular topic:
Very positive ————————————————————
Very negative
With the advent of online survey packages, the

visual analog scalehas become digital. In
the online survey software package Qualtrics, visual
analog scales are presented as “slid-
ers,” and respondents can click on the pointer and
slide it to location along the continuum
that represents their belief. See Figure 6.2 for an
example of a series of slider questions.
lan66845_06_c06_p157-190.indd 177 4/20/12 2:49 PM
178
Completely dissatisfied
0 10 20 30 40 50 60 70 80 90 100
My co-workers
The workplace
environment
My company in
general
My direct supervisor
My annual
compensation
The opportunities for
advancement

Completely satisfied
Please rate your overall level of SATISFACTION for each of
the workplace categories
below. Move the slider to the appropriate level: 0 = completely
dissatisfied and 100 =
completely satisfied.
This is an example of a visual analog scale used in survey
research in a survey software program called
Qualtrics. Participants click on the blue arrow and drag it to the
location that indicates their answer.
Source: Qualtrics, 2011
Figure 6.2: Example of a visual analog scale
Surveys do have advantages though: They allow
for anonymity of responses and sta-
tistical analysis of largeamounts of data, they can be
relatively cost effective, sampling
mechanisms can be carefully controlled in somecases,
and by using standardized ques-
tions change can be detected over time (Seashore,
1987). Some of the limitations and
risks of the survey research approach include a
lack of control over variables of interest,
response rates may be problematical, ambiguous surveys
may lead to difficult interpre-
tation, in somecontexts participants may not believe
their data are truly anonymous
and confidential, the possibilities of bias due to non-response or
socially desirable
responding, and the inability to draw cause-and-effect
conclusions (Fowler, 1998; Sea-
shore, 1987).

Surveys are pervasive in psychology and throughout
culture. The ability to properly
design a survey and interpret its results
appropriately is a skill that well-suits psychology
majors for a future in the workplace, or
for graduate school first and then the workplace.
But it is important to remember that surveys are a
measure of self-report and not actual
behavior. There are multiple reasons why survey
data may be inaccurate; it could be that
the respondents don’t know the answer, know
the answer but can’t recall the answer,
don’t understand the question (but answer
anyway), or just choose not to answer for
whatever reason (Fowler, 1998). Because most survey
research does not share the same
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179
CHAPTER 6Section 6.6 Analysis of Survey Data
characteristics as experimental designs, it is
important not to over-interpret the results of
survey research. The survey approach is powerful in
helping psychologists identify the
relationships between variables and differences among
groups of people, but the results
are only as good as the design quality that is
necessary for this complex task.

6.6 Analysis of Survey Data
In most respects, analyzing survey data is the
same as analyzing any other type of data—your
analysis choices are based on your hypotheses,
the scales of measurement, the tools available
for data analysis, and so on. Before mentioning
specific approaches
for data analysis, let’s review at a conceptual
level the types of errors that are encountered
in survey research. Remember that errors in
this context are not mistakes but are the pos-
sible outcomes of the study that the researchercannot
account for—that is, the changes
or values of the dependent variable that are not due to the
independent variables being
manipulated, controlled, or arranged.
Types of Errors
In classic psychometric measurement theory, the
total amount of error is assumed to be
the sum of measurement error + sampling error
(Dutka & Frankel, 1993). Those who
study survey research design further categorize the types of
threats and errors that can
occur with this type of research. Although Dillman et
al. (2009) were referring specifically
to Internet panel research in this case, they present
a four cornerstone model of surveying
and errors that is useful here for our greater
understanding.
A coverage error in survey research refers to

the methodology used. For example, if an
Internet approach is used, only about 70% of
households have Internet access, so cover-
age error exists (Dillman et al., 2009). The
coverage error is much smaller with telephone
surveys, but the proportion of individuals with
landlines is decreasing whereas cell phone
subscribers are increasing(Kempf & Remington,
2007). Survey researchers need to be cog-
nizant of coverage error concerns when making
methodologicalchoices.
A sampling error occurs when not all the
potential participants from a population are
rep-
resented in a sample (Dutka & Frankel, 1993),
and this is oftendue to the sampling method
utilized by the researcher(Futrell, 1994). In fact,
this sampling procedure is so important
that it was the opening puzzle piece of this
chapter. Another related sampling issue is
volunteerism, or self-selection. When a study
relies on volunteers (for whatever reason),
thereis always a concern that volunteers may
behave differently than non-volunteers, and
if this is the case, it weakens the generalizability
of the survey results. In fact, Rosenthal
and Rosnow (1975) have reliably demonstrated that
volunteers differ from non-volunteers
in the following ways: (a) volunteers are more
educated than non-volunteers; (b) volun-
teers are from a higher social class than non-
volunteers; (c) volunteers are more intelligent
than non-volunteers; (d) volunteers are more
approval-motivated than non-volunteers;
and (e) volunteers are more sociable than non-

volunteers. However, if the only way you
can conduct your research is through volunteers,
then that is what you do. But it would
be important to remember these caveats
when drawing conclusions from your survey
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180
research (or any research) that
depends exclusively on volun-
teer participants.
Measurement error can occur
due to a number of reasons,
but measurement errors tend
to fall into the category of mea-
surement variation (the lack
of a reliable instrument) and
measurement bias (asking the
wrong questions, or using the
results inappropriately) (Dutka
& Frankel, 1993). As in any com-
plex enterprise, the potential
for mistakes can be high, and
Futrell (1994) listed some com-
mon measurement errors that
can occur in survey research:
1. Failing to assess the reliability of the survey.

2. Ignoring the subjectivity of participant
responses in survey research.
3. Asking non-specific survey questions.
4. Failing to ask enough questions to capture
the behavior, opinion, or attitude of
interest.
5. Utilizing incorrect or incomplete data analysis
methods.
6. Drawing generalizations that are not supported by
the data nor the data analysis
strategy selected.
Essentially, measurement errors address issues of
(a) did we measure what we thought we
measured, and (b) did we interpret the results
appropriately?
Non-response error is of particular concern in survey
research (Dillman et al., 2009). As
a general rule, if there is a response rate of
25% or less (or a non-response rate of 75%
or more), then the survey researchershould be
concerned with the question “Are those
responding to my survey different from those
not responding to my survey?” (Dillman et
al., 2009). There are many different approaches
for dealing with high non-response rates,
and someof those methods involve weighting the
responses that are received (Dale, 2006)
as well as specifically following up with a subset
of non-responders and asking them why
they didn’t respond. The goal here would be to
determine that therewas no systematic
bias in why people responded or did not respond to

the initial survey request. If thereis
no bias (that is, no systematic reason driving
non-response), then the non-response rate is
less of a concern to the survey researcher.
Data Handling Issues
The details and complexity of data handling issues
within survey research are beyond the
scope of this chapter, but two issues are worth
mentioning, if only briefly. After collecting
When volunteers are used in sampling, there is concern
that volunteers could change the survey results by behaving
differently than non-volunteers. How might this be addressed?
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181
your data, but prior to analysis, you will have to do
somedata “cleaning” (sometimes
called data editing). Even though every survey
researchermust do this, thereare not com-
monly accepted standards for data cleaning (Leahey,
Entwisle, & Einaudi, 2003). Some-
times it involves the elimination of outliers
(which is relatively straightforward), but other
times data decisions are more complex. For example,
someone may be hand-coding data

into an SPSS file, and on a written survey
form completedby a college student, the student
filled in “Age: ____” with 107. It would be
pretty clear from this scenario that therewas
not a 107-year-old college student in the
laboratorysetting when the data were collected,
so this value should be discarded from “age”
variable (thus, this participant has missing
data). But this brings otherissues to mind: If
this respondent reports many outliers, did
he or she take the survey seriously? Should just
the age value be discarded,or should the
entirety of the survey responses from this individual be
deleted?
Data cleaning decisions can become more complex. Let’s
say you are asking survey items
where the responses are made on a Likert-type
agreementscale, where 1 = strongly dis-
agree, 2 = disagree, 3 = neutral, 4 = agree, and 5
= strongly agree. One coded response to
the statement “I am comfortable with the
undergraduate major I have selected,” is 55.
What do you do? Do you assume the respondent
meant a 5 (strongly agree), and change
the response? Is it possible to go back and confirm
what the participant meant, or were
the data collected anonymously? You could guess
that a 55 meant a 5, but what about a
23
entry? Did the person mean 2 (disagree) or 3
(neutral)? Here’s one more: In an online
sur-
vey, where respondents directly entertheir age, a
participant enters the value 1.9. Should

that be recoded as 19 years old, or should
the data be deleted?
These data cleaning issues are also related to
how survey researchers handle missing
data, and thereare a number of complex approaches
for that (Dale, 2006; Graham, Taylor,
Olchowski, & Cumsille, 2006; Rudas, 2005). As
a psychologist/survey researcher-in-train-
ing, you should err on the side of caution. If
you cannot confirm what a participant meant
by his or her response, delete it. As you become
more savvy at performing data cleaning
and missing data analyses, you can alter this conservative
approach. Furthermore, if you
collect your survey data anonymously, you have no
method of contacting individuals to
clarify their intended response. We’ll discuss more
data cleaning issues in Chapter 7.
Data Analysis Approaches
As alluded to earlier, the possibilities for analyzing survey data
are vast, and they depend
on many of the same characteristics of otherdata
analysis situations, such as the scale of
measurement, the amount of data available, and the
hypotheses to be tested. It would not
be possible to summarize all of the options
here, as entire books are available about
the
subject (Fink, 1995). Data analytic strategies can
become more or less complicated, how-
ever. If your goal is to communicate effectively
with the public, you might not choose to

present the results of a repeated measures
ANOVA, but you might present a table of
means
or a bar graph that clearly and succinctly
communicates the storyyou want to tell. If you
are comparing two nominal scalevariables, such as
gender differences on how respon-
dents answered a categorical survey item (“Are
you married?”), then a chi-square analysis
would be appropriate. Essentially, you will need
the knowledge that you (hopefully) learn
from a statistics course to be able to analyze
your survey data. This is why somecall the
Statistics-Research Methods sequence the core of the
undergraduate psychology major.
lan66845_06_c06_p157-190.indd 181 4/20/12 2:49 PM
182
Data analyses can range from simple to complex.
Table 6.5 is an example of “complex,”
as Roelen, Koopmans, and Groothoff (2008)
reported the predictors between overall job
satisfaction and specific aspects of a job. These
researchers used survey research as their
method of data collection and a multiple regressionas
part of their data analysis strategy.
Table 6.5: Correlation between overall job satisfaction and
specific job aspects

Mean (SD) B (SE) �
Age (years) 38.1 (10.3) 0.01 (0.00) 0.05
Gender (female = 0, male = 1) −0.11 (0.09) −0.04
Educational level 1.7 (0.7)
Primary education relative to tertiary 0.15 (0.14) 0.06
Secondary education relative to tertiary 0.18 (0.13) 0.07
Physical demands (range 17) 4.0 (1.9) 0.02 (0.02) 0.03
Psychological demands (range 1–7) 4.1 (1.7) −0.04 (0.03) −0.05
Job autonomy (range 1–7) 5.4 (1.4) 0.09 (0.04) 0.09*
Decision latitude (range 1–7) 4.8 (1.7) −0.01 (0.03) −0.02
Career perspectives (range 1–7) 4.2 (1.7) 0.12 (0.04) 0.16*
Overall satisfaction (range 1–7) 5.3 (1.3)
Specific satisfaction (range 1–7) with:
Colleagues 5.6 (1.2) 0.15 (0.04) 0.14 **
Work times 5.5 (1.4) −0.02 (0.04) −0.03
Task variety 5.1 (1.5) 0.28 (0.04) 0.31**
Supervisor 4.7 (1.7) 0.06 (0.04) 0.07
Working conditions 4.7 (1.5) 0.11 (0.04) 0.13**

Workload 4.7 (1.4) 0.11 (0.05) 0.12*
Work pace 4.7 (1.5) 0.02 (0.04) 0.02
Salary 4.3 (1.6) −0.05 (0.03) −0.06
Work briefings 4.3 (1.8) −0.01 (0.04) −0.02
Mean (standard deviation, SD) calculated using age, educational
level, work-related factors, and job
satisfaction. In addition, the table presents the unstandardized
correlation coefficients B (standard
error, SE) and the standardized correlation coefficients (�),
which measure the type (positive or
negative) and relative importance of correlation. *p < 0.05 and
**p < 0.01
Source: Roelen, Koopmans, and Groothoff (2008)
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183
CHAPTER 6Section 6.7 Quick Tips for Survey Item
Construction
At first glance, this looks complicated, but the
more courses you have in statistics, and the
more survey research you do, the easier it will be
to interpret this type of data. What the
researchers found with their multiple regressiondata
analysis approach was that there
are six statistically significant predictors of a
person’s overall job satisfaction (based on
the sample that was studied by Roelen et al.,

2008). All of thesepredictors happen to have
positive beta weights, which means the higher
the value on the particular scale, the higher
the overall job satisfaction. The six significant
predictors (starting with the predictors with
highest beta weight) are task variety, career
perspectives, colleagues, working conditions,
workload, and job autonomy. Note that compared to
popular belief, salary is not a sig-
nificant predictor of overall job satisfaction—and it
is this type of insight that can make
a survey design coupled with an effective data
analysis strategy so powerful. As you do
more work in psychology, you’ll gain experience
and confidence in designing surveys
as well as analyzing the results. But just how would
you go about designing that survey,
especially if it were the first “scientific” survey
you had ever developed? We’ll discuss that
in the next section.
6.7 Quick Tips for Survey Item Construction
You determine that closed-ended items are better
suited for your research needs, and you are just
about ready to start generating your item
pool.But before you do that, it might be
beneficial to thinkbroadly for a moment about
what you are trying
to measure—that broad category of human
response you are trying to capture. Consider
thesecategories offered by eSurveyPro (2009) and
Rattray and Jones (2007): (a) attitudes,
beliefs, intentions, goals, aspirations; (b)
knowledge or perceptions of knowledge,

(c) cog-
nitions; (d), emotions; (e) behaviors and practices; (f)
skills or perceptions of skills, and
(g) demographics. Making decisions about which
broad category (or categories) you
would inquire about has implications for your
entire survey. For example, if you ask too
many knowledge questions of your respondents,
and the items are difficult, respondents
may quit your survey early, not providing you with
the data you need. Actual skills may
be difficult to capture in a survey format,
but you may be able to ask respondents about
their perceptions of their own skills. Demographics
can be tricky as well. Ask for too many
demographics, and participants may feel a sense of
intrusion.The more demographics
asked, the more identifiable a participant is,
even if the data are collected anonymously.
Ask too few demographics and you may not be able to
provide tentative answers to your
hypotheses. As you have the opportunity to
practice your survey skills over time, you
should become more comfortable in being able to
assess thesebroad areas.
lan66845_06_c06_p157-190.indd 183 4/20/12 2:49 PM
184
Construction

Use of demographics in surveys can be problematic if not
thoughtfully carried out. Sometimes, however,
demographic information is vital to research. How should these
surveys be handled?
PR Newswire/Associated Press
General advice for constructing survey items
comes from many sources. The following list
is a compilation of ideasfrom thesesources: Babbie
(1973), Cardinal (2002), Converse and
Presser (1986), Crawford and Christensen (1995),
Edwards and Thomas (1993), eSurvey-
Pro (2009), Fink and Kosecoff (1985), HR-Survey
(2008), Jackson (1970), McGreevy (2008),
and University of Texas at Austin (2007):
1. Avoid double-barreled items. That is,
each question should contain just one
thought. A tipoff to this occurring is sometimes
the use of the word “and” in a
survey item.
Example to avoid: I like cats and dogs.
2. Avoid using double negatives.
Example to avoid: Should the instructor not
schedule an exam the same weeka
paper is due?
(Answered from Strongly Disagree to Strongly Agree).
3. Try to avoid using implicit negatives—
that is, using words like control, restrict,
forbid, ban, outlaw, restrain, or oppose.

Examples to avoid: Handgun use should be banned.
All abortions should be
outlawed.
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185
Construction
4. Consider offering a “no opinion” or “don’t
know” option.
5. To measure intensity, consider omitting the
middle alternative.
Example: Strongly disagree, disagree, neutral, agree,
and strongly agree.
6. Make sure that each item is meaningful to
the individuals being asked to com-
plete the survey. That is, are the respondents
competentto provide meaningful
responses?
Example to avoid: Xanax is the best prescription
medication for clinical
depression.
7. Use simple language, standard English as
appropriate, and avoid unfamiliar or
difficult words. Depending on the sample, aim
for an eighth-grade reading level.

Example to avoid: How ingenuous are you when
the professor asks if you have
understood the material presented during a
lecture?
8. Avoid biased questions, words, and
phrases.
Example to avoid: Using clickers represents state-
of-the-art learning technology.
To what extent have clickers enhanced your learning?
9. Check to make sure your own biases
are not represented in your survey items,
such as through leading questions.
Example to avoid: Do you thinkgas-guzzling SUVs
are healthy for the
environment?
10. Do not get more personal than you need to be
to adequately address your
hypotheses. Focus on “need to know” items
and not “nice to know” items (helps
control for survey length).
11. Try to be as concrete as possible; items
should be clear and free from ambiguity.
Avoid using acronyms or abbreviation that are
not widely understood.
Example to avoid: The DSM-IV-TR is a more
accurate diagnostictool for PSTD
patients than the ICD-10.

12. Start the survey with clear instructions, and
make sure the first few questions
are non-threatening. Typically,present demographic
questions at the end of the
survey. If you ask too many demographic items,
respondents may be concerned
that their responses are not truly anonymous.
13. If the response scales change within a
survey, include brief instructions about this
so that respondents will be more likely to notice
the change.
14. If your survey is long,be sure to put
the most important questions first—in a
long survey, respondents may become fatigued or
bored by the end.
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186
Construction
15. Be sure to frame questions to minimize
response set acquiescence. Ask questions
that are reverse-scored (that is, strongly disagreeing is
a positive outcome).
Example: This course is a waste of time.(A
positive answer would be strongly
disagree.)

Case Study: Read All About It: Sampling Matters (and Dewey
Defeats Truman)
American political polling has a long history dating back to
1824 (International Directory of Company
Histories [IDCH], 2001), but perhaps the most famous blunder
that involves the sampling of opinions
from a population comes from the 1948 election where
incumbent Harry S. Truman defeated the
challenger Thomas Dewey. Although there had been some
successes with mail-in polling in predicting
presidential election outcomes in the 1930s, for the
1948 election a “perfect storm” of circumstances inter-
sected to produce one of the most famous mistaken
newspaper headlines of all times.
At the time, George Gallup was using quota sampling,
where pollsters would ask a certain number of indi-
viduals from certain categories (e.g., working females,
percentage of factory workers) their opinions about
issues, and in particular, who they intended to vote
for in the upcoming election (Jamison, 2008). After
the election (and the famous blunder where the even-
tual presidential election winner was declared the
loser), a congressional committee chastised Gallup
for not using probability sampling, which by definition would
give every eligible voter in the coun-
try an equal chance to be polled (IDCH, 2001). However, it was
not just the misstep of selecting the
wrong sampling procedure that led to this famous blunder; other
events conspired to make it so. For
instance, all the major pollsters (Gallup, Crossley, and Roper)
stopped polling weeks before the elec-
tion because major opinion changes were not expected. The

Chicago Tribune, publisher of the most
famous newspaper gaffe of all time, over-relied on its
Washington correspondent to accurately pre-
dict the outcomes. Furthermore, to get the first edition to press
on time (and due to a printer’s strike
at the time), the Tribune had to publish its first edition well-
before election returns were known,
thus preventing any last-minute changes based on early returns.
Gallup also admitted after the elec-
tion that he was a close friend of Thomas Dewey, and that
Gallup had been in contact with Dewey
throughout the campaign of 1948. All of these events coalesced
into one moment where a famous
national newspaper got it wrong in the front page headline on
November 3, 1948 (Blackwell, n.d.;
IDCH, 2001; Jamison, 2008; Walther, 2009).
Reflection Questions
1. Thinking about the polling process and presidential elections
today, what would be the impact of
declaring victory too early for the wrong candidate? To some
extent, isn’t this precisely what hap-
pened in 2000 when George W. Bush ran against Al Gore for
U.S. president?
2. Digging a bit deeper, would there be a way in which quota
sampling could be as efficient as prob-
ability sampling? What types of safeguards would need to be
put into place to prevent such egre-
gious errors to be drawn from survey results?
3. How does this famous incident in political history relate to
the types of surveys and questionnaires
that you might be asked to administer in the workplace? What
lessons can be extracted from this

type of sampling error that you can acknowledge and avoid if
survey methodology is part of your
job responsibilities someday?
Associated Press
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187
CHAPTER 6Concept Check
Chapter Summary
Of all the types of research you will be
learning about in this course as you prepare
your applied project, survey methodology may be
the most valuable, because you likely will
encounter surveys in the workplace, and you may be
in a manage-
ment position where you are asked to develop a
survey or to be a savvy consumer of
survey research results for your company or
organization. Thus, a basicknowledge of
the
key aspects of survey sampling, design, scaling,
and analysis could prove useful to your
future. It is important to be able to distinguish
between the characteristics of probability
and nonprobability sampling and to know that the
difference is oftenmeaningful depend-
ing on the types of conclusions you would
like to draw from the data. There are a variety

of approaches to survey methodology, and the
design of a survey project may involve
cross-sectional, longitudinal, cohort, or panel
survey aspects of research design. Many
scaling approaches are available, and although Likert-
type scaling is prevalent, knowing
the type of research question you want answered can
help in the selection of the survey
scalebest suited for the task. There are numerous
details to attend to regarding data analy-
sis from surveys, and key reminders are provided in
the chapter, as well as sometips for
generating your own survey questions.
Concept Check
1. Probability sampling means that
A. the sample definitely represents the population.
B. the population has multiple identifiable
characteristics.
C. all members of the population have an
equal chance of being in the sample.
D. the sample was described in sufficient detail
for individual identification of
members.
2. The non-random equivalent to stratified random
sampling is
A. cluster sampling.
B. volunteer sampling.
C. convenience sampling.
D. quota sampling.

3. What can be both an advantage and a
drawback of in-person interviews?
A. The intimacy between the interviewer and
participant.
B. The ability of the interviewer to ask
follow-up questions.
C. The ability of the participant to ask
clarification questions.
D. The cost associatedwith administration.
4. In Loftus’ (1975) experiment No. 4, people
were most likely to “recall” a woman
pushing the baby carriage if
A. the woman wore an unusual hat.
B. participants were given a false
presupposition.
C. participants were asked a direct
question.
D. the baby carriage was destroyed in the video.
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CHAPTER 6Key Terms to Remember
5. The most likely famous and popular scaleused by
psychological researchers is the
A. Likert-type scale.
B. visual analog scale.
C. Guttman scale.

D. dichotomous scale.
Answers
1. C. All members of the population have an
equal chance of being in the sample.
The answer can be found Sec-
tion 6.1.
2. D. Quota sampling. The answer can be found Section 6.1.
3. A. The intimacy between the interviewer and
participant. The answer can be found in Section 6.2.
4. B. Participants were given a false presupposition.
The answer can be found in Section 6.4.
5. A. Likert-type scale. The answer can be found in
Section 6.5.
Questions for Critical Thinking
1. Why is the survey such a prevalent
methodology that is used so frequently? Does
the prevalence of surveys have a negative effect
on individuals answering sur-
veys? Think about the number of surveys
that you have received in the past two
months, including telephone surveys, email surveys,
mail surveys, invitations to
web surveys, and so forth. How many did you
answer (completely)? How might
response rate temper one’s enthusiasm for the
survey approach?
2. Much of the variety of survey approaches

relies on stable and emerging technolo-
gies. In your workplace, you may have global
concerns where survey information
from a specific region of the world might be
valuable, but the technology infra-
structure thereis not as reliable as you would
hope. What are your otheroptions
for gaining information about cultures and
locations where technology is not so
accessible? What mistakes should be avoided
when looking at the application of
survey methodologies as described in this chapter to
otherregions of the world?
3. Every methodologicalapproach in the sciences
has limitations—no approach is
perfect, nor is any singular application of a
methodologicalapproach performed
perfectly. What types of information are surveys
good at extracting, and what
types of information should be left to other
types of research designs? Why?
Key Terms to Remember
cluster sampling The sampling practice of
“clustering” groups of a population instead
of evaluating each individual person to
gain information when it is impossible or
impractical to compile an exhaustive list of
members composing the target population.
cohort study A study design in which
new samples of individuals are followed
over time.

convenience samples The sampling
practice often used in exploratory research
where a quick and inexpensive method is
used to gather data by gathering partici-
pants who are conveniently available for
the purposes of data collection.
coverage The issueof who has Internet
access and who does not that provides a
barrier to obtaining information through
Internet surveys.
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CHAPTER 6Key Terms to Remember
coverage error An error regarding the
methodology used including access to
Internet, use of land lines, and other
methodologies.
cross-sectional survey design A study
design where data collection occurs at a
single pointin time with the population of
interest.
data analysis The process of interpreting
data through statistical analysis into mean-
ingful and accurate conclusions.
data cleaning A method of reviewing data
to ensure that it has been handled and

entered accurately.
demographics Variables used to identify
the traitsof a study population.
dichotomous scale A scalein which there
are only two possible responses, i.e., yes/
no, male/female, true/false.
Guttman scale A survey response scale
that generates a set of items that increase
in difficulty. If a participant agrees with
one scaleitem,it is assumed that they
agree with the preceding scaleitems.
in-person interviews A research method-
ology that allows an interviewer and a par-
ticipant to build rapport through conversa-
tion and eye contact, which might allow
for deeper questions to be asked about
the topicof interest. This presents fewer
limitations about the types and length of
survey items to be asked.
Likert scale A survey response scale that
has a 5-point scale, measuringfrom one
pole of disagreement to the otherpole of
agreementwith each of the scalepoints
having a specific verbal description.
longitudinal survey A study design
where data collection occurs at several
points over an extended period of time.
measurement error An error that can
occur due to a number of reasons, typi-

cally including measurement variation and
measurement bias.
mixed-mode approach A study design
where multiple research modalities are
accessed to achieve the research goals.
multistage sampling The two-stage sam-
plingpractice involving the formation of
clusters as a primary selection, then sam-
plingmembers from the selected clusters
to produce a final sample.
nonprobability sampling The sampling
practice where the probability of each
participant being selected for a study is
unknown and sampling error cannot be
estimated.See convenience sampling,
quota sampling, snowball sampling, and
volunteer sample.
non-response error An error occurring
when thereis a response rate of 25% or less
for a particular question.
panel study A study design in which the
same people are studied over time,span-
ning at least two points in time.
probability sampling The sampling prac-
tice where the probability of each partici-
pant being selected for a study is known
and sampling error can be estimated.
See simple random sampling, systematic
sampling, stratified sampling, cluster sam-
pling, and multistage sampling.

quota sampling The sampling practice
where a researcheridentifies a target
population of interest and then recruits
individuals (non-randomly) of that popu-
lation to participate in a study.
representative The assumption that a
sample will resemble all qualities of the
general population to ensure that results
of a sample can be applied to the whole
general population.
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190
CHAPTER 6Web Resources
Web Resources
Calculators for determining confidence intervals,
sample sizes, correlations, and other
research tool aids. http://www.surveysystem.com/sscalc.htm
An online survey glossary that defines important
research terms relevant to survey devel-
opment and administration. http://knowledge-
base.supersurvey.com/glossary.htm
A writing guide for survey research that assists
researchers in areassuch as survey
development, administration, and the process of
reporting results.

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