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Local Organization is: Counseling and Resource Center of Dearborn

As your required text suggests, research is not only for those in academia. It is often a crucial consideration in leadership positions throughout organizations.

In this discussion, consider the organization you currently work for, or desire to work for, and imagine you are a leader in this organization. Consider scenarios that could potentially arise where utilizing research could be beneficial. Examples might be excessive tardiness, reduced production, low morale, and significant loss of customer base. You may use any example with the exception of those listed above.

For your initial post,

Identify an organization in your location that complements the knowledge and skills you have developed during your program. (May be hypothetical.)
Identify a leadership role that you would enjoy having within this organization. If you are not interested in a career, identify something that you think would be rewarding if you were advancing your career.
Identify the following in association with this organization and position:
Research question: What is happening in the organization that could be researched?

Narrowing a Topic and Developing a Research Question (Links to an external site.)

Hypothesis: State why you believe it is happening.
What dependent variables/factors do you believe may be affecting your chosen scenario (i.e., age, workload, work shift, ineffective communications)?

What type of factor did you identify (i.e., nominal, ordinal, interval, or ratio)?

Are there any potential extraneous variables? Why or why not. If so please list two to three.
Method: What method would best reflect an accurate analysis of the situation (e.g., quasi-experimental or phenomenology, etc.)? Include your rationale.
Lastly, discuss how an understanding of the basics of research and statistics can help a person in decision making, whether in an organization, or even in a social situation.

Discussion posts should equate to approximately 350 to 400 words.

Revista Colombiana de Estadística

Volumen 30 No.

. pp. 1 a 11. Junio 2007

Implementation of Statistics in Business and

Industry

Implementación de la estadística en los negocios y la industria

Bovas Abraham1,a

1Department of Statistics and Act. Sci., University of Waterloo, Ontario, Canada

Abstract

Statisticians have devised many tools for application and these are avail-

able to be utilized for general business improvement and industrial problem

solving. However, there is a wide gap between the available tools and what

are practiced in business and industrial organizations. Thus it is important

for statisticians to direct serious attention to bridging this gap if statistics

is to be relevant in business and industry and to the society at large. In

this paper we look at some ideas for implementation of statistical methods

in business and industry.

Key words: Statistics in Business, Industrial Statistics, Statistical Meth-

ods.

Resumen

Los estadísticos han desarrollado muchas herramientas para su aplicación

en problemas prácticos. Estas herramientas están disponibles para ser uti-

lizadas en el mejoramiento de los negocios en general y para solucionar prob-

lemas industriales. Sin embargo, existe una brecha entre las herramientas

disponibles y las que utilizan las organizaciones industriales y de negocios.

Por tanto, es importante que los estadísticos dirijan su atención a disminuir

esta brecha si se quiere que la estadística sea relevante en los negocios, la

industria y en general en la sociedad. En este artículo se presentan algunas

ideas para la aplicación apropiada de métodos estadísticos en los negocios y

la industria.

Palabras clave: estadística en negocios, estadística industrial, métodos es-

tadísticos.

aProfessor. E-mail: babraham@uwaterloo.ca

2 Bovas Abraham

1. Introduction

Implementation means different things in different contexts and to different people.
What do we mean by implementation? H.G. Wells once said ‘Statistical thinking
will one day be as necessary for efficient citizenship as the ability to read and
write’. Recently Harry & Schroeder (2000) remarked ‘We believe that statistical
knowledge is to the information and technical age what fossil fuel was to the
industrial age. In fact, the future of industry depends on an understanding of
Statistics’.

These statements imply that Statistical Thinking and Methods should become
part of the knowledge base of an organization and part of doing business. This
is the kind of implementation we are discussing. In other words a business or
industrial organization institutes Statistical Thinking in all its functions and the
use of statistical tools and data based decisions becomes a part of the every day
business.

In this context we are not thinking just about a statistician consulting with a
scientist or an engineer for a project. Although such activities are very important,
our goal is much broader. Our vision is that statistical thinking and tools should
be entrenched in the organization so that they play a prominent role in it’s daily
activities. In such organizations the roles of statisticians are quite different from
the traditional one of helping a client with some data analysis.

2. Statistics in Business and Industry

In general a typical organization has three levels: Strategic, Managerial and Oper-
ational. This classification is somewhat general and arbitrary. However, this can
help identify and emphasize different tools to be directed at the different levels.
We envision Statistics to play important roles at all these levels.

2.1. Strategic Level (Top of an Organization)

At the strategic level the most emphasis should be on Statistical Thinking which
has the following components:

i) Notion of a Process,

ii) Notion of measurement and data based decisions,

iii) Understanding and dealing with variation,

iv) Statistical tools, and

v) Systematic approach.

The notion of process thinking is fundamental to any organizational change.
Every action has some inputs and outputs which need to be identified in every

Revista Colombiana de Estadística 30 (2007) 1–11

Implementation of Statistics in Business and Industry 3

context. Decisions at the strategic level should be based on facts supported by
appropriate data and this requires an understanding of variation (Deming 1986).
Business and Industry have seen the arrival and demise of many programs such
as Total Quality Management. Embracing any program that comes along without
firm commitment and understanding is doomed to failure.

2.2. Managerial Level (Middle Level)

This is the level at which systems are devised for implementation of the directions
taken by upper management. In particular, systems for robust product and pro-
cess design, process control and improvement, and training are the responsibility
of middle management. Understanding of some statistical tools and statistical
thinking are prerequisites for those who are designing these systems.

2.3. Operational Level

At this level the methods are implemented through the system built at the mana-
gerial level. Understanding of statistical tools such as control charting, capability
analysis, design of experiments, measurement system analysis, regression analysis
etc are essential. Appropriate statistical tools need to be used by operational peo-
ple as part of their daily work. People in some areas need to know the details only
of certain statistical tools. For instance, an operator responsible for maintaining
stability of a process by charting should know the workings of a control chart but
need not know a lot about design of experiments. On the other hand an engineer
responsible for process improvement should be knowledgeable in several aspects of
statistical process control and design of experiments.

3. Implementation: General Issues

3.1. Commitment of Top Management

For the success of any program that affects an organization as a whole, full and
highly visible commitment of senior management is essential. Thus the vision
and values of the top level management is highly important. Employees must
perceive active leadership and involvement of senior people in implementation.
Top management has to asses the situation early and to decide to allocate the
needed resources. They have to decide in advance what role they can and will
play. For example the success of the Six Sigma program at General Electric and
Motorola is due to the commitment of its senior management.

Recognition of the potential benefits of implementation in the beginning can
help focus on what is needed. Top management must recognize that, in addition to
help solving problems and improving processes, statistical tools can help increase
customer satisfaction and help measure the performance of the organization. Im-
plementation of statistical tools is an ongoing process and it helps the organization

Revista Colombiana de Estadística 30 (2007) 1–11

4 Bovas Abraham

to be a learning organization and a knowledge based enterprise. Knowledge based
organizations will be the successful ones in the long run.

3.2. Role of Statisticians

The traditional role of a statistician in business or industry has been to act as a
consultant to projects, or to train some workers in certain tools such as statistical
process control and design of experiments. This role has to be broadened. Statis-
ticians need to teach at all levels of an organization (senior managers, scientists
and engineers, middle managers and operational people). Statisticians have to be
leaders, facilitators, aide to management etc. They have to identify the role of
statistics in various business functions and also have to interact with the outside
world. These require broadening statistician’s skills set. Statisticians need to ac-
quire communication skills and have to be good communicators. We have to keep
the statistical tools appropriate and simple. We also need to make sure that the
implementation adds value to the organization.

3.3. Systems Thinking

As in any other implementation, there are several components involved and these
need to be considered as part of a system for successful implementation. Some
of these components include people, technology, organizational structure and cul-
ture, methodology, etc. and these components have to work jointly. There will
be interaction between these components. Systems need to be built such that
the interaction effects are positive so that the total effect from the components is
more than the sum of the individual effects. For instance if there are two compo-
nents A and B then Effect(A + B) = Effect(A) + Effect(B) + Effect(AB)
where Effect(AB) is the ‘interaction’ between the components A and B. Sta-
tistical tools should be implemented such that Effect(AB) is positive so that
Effect(A + B) is more than Effect(A) + Effect(B). A negative ‘interaction’
can not only lower the total effect but also can cause long term problems. Some
guiding principles such as Deming’s 14 points for management can help in this
regard. Such principles help to foster positive interaction between components
such as people and technology. Some training programs use sophisticated software
to train employees without considering the background of the trainees.

3.4. Planning for Implementation

Before embarking on the implementation of statistical methods one has to have
a plan with answers to the following questions. How does it start? Who are
responsible for the tasks? When do the activities take place? What is the scope
of the system? Is there a calendar of activities? Is there a measurement system
to track progress of activities?. What are the review points, and the associated
expected results? Are resources assigned for the planned activities?

Revista Colombiana de Estadística 30 (2007) 1–11

Implementation of Statistics in Business and Industry 5

There are many project implementation systems cited in the literature. Dem-
ing’s PDSA circle (Shewhart’s wheel)-Plan, Do, Study, Act (Deming 1986) is a
well known example. Six sigma is another system which uses DMAIC -Define,
Measure, Analyze, Improve, Control. There are many others. The important fact
is that it is essential to have a systematic and disciplined approach to implementa-
tion. Implementation of statistical tools needs to be interfaced with these systems.
Most of these systems are very similar and it does not matter which one is used.
It is better to go with what is already available in the organization rather than
looking for something new. It is important to be consistent and stable with it. It
is not a good idea to change to some other system in the middle. It is also possible
to interface with quality systems such as ISO.

4. Implementation via Training and/or Consulting

All organizations have a certain set of existing knowledge base and acquiring new
knowledge may require changes in thinking and culture. An assessment of the
existing knowledge and the skills are necessary before embarking on training to
introduce new skills and knowledge into any organization. Also any plans for
training should reflect an understanding of the existing knowledge.

Training needs for different levels of an organization can be very diverse. The
distinctive needs should be recognized and the training programs should be de-
signed in such a way to suit each of the Strategic, Managerial, and Operational
levels.

Trainers should have good statistical knowledge and good business and/or in-
dustrial experience. They should be aware of the structure and culture of the
organization, the background of the trainees and the training context. It is im-
portant to make sure that the training material is relevant, appropriate, of good
quality and at the correct level. Schedule and duration of each module is also
important. In addition, presentation of the material in an understandable and
enjoyable way requires careful planning. The use of appropriate technology is also
important. For a certain level one technology may be relevant while another level
requires the use of a modified technology. Communication between the trainer and
the trainee and that between software and participants should be smooth. The
interaction between people and technology should be positive.

Statistical training can be interfaced with other training in the organization
such as ISO-9000, Six Sigma. In this context sequencing need to be carefully
planned.

5. Implementation via Education

Today’s students are tomorrow’s employees and employers. Graduates with tech-
nical and non-technical skills are needed in business and industrial organizations.
These students have to get their education from the universities. Hence it is im-
portant for universities to give more attention to the preparation of undergraduate

Revista Colombiana de Estadística 30 (2007) 1–11

6 Bovas Abraham

and graduate students for business and industry. Students need technical and non-
technical skills to be successful in the workplace and it is difficult for universities to
provide all the skills to function in the workplace. Universities are providing some
of these already. However, university statistics curriculum can be improved so
that potential employees have enough statistics and communication skills. Many
authors have discussed ideas for enhancing statistical education (for example see
ASA Committee (1980), Garfield (1995), Hoerl et al. (1993), Hogg & Hogg (1995),
Snee (1993), Vere-Jones (1995) and Wild (1995).

5.1. Undergraduate Program

It is important to design programs in such a way that a student has some min-
imum statistical and non-statistical background before he/she enters the work
place. For instance students need to understand the Scientific Method (see Box
(1976)), Problem Solving Systems, Measurement System Analysis, Sampling, Ex-
perimental Design, Regression, some Mathematical statistics, Process Control, and
Computing and Mathematics (see ASA Committee (1980)). In addition the stu-
dents should get experience in solving industrial or business problems and commu-
nicating the results to people in other areas. There are different ways of achieving
this goal. One method adopted at the University of Waterloo is to enroll the
students in a co-operative program. In this system the students cycle between
university and business or industry after each term during their undergraduate
program (Abraham 1999). This will be discussed further later. Another possi-
bility is to have joint programs such as Engineering and Statistics, Business and
Statistics, some branch of Science and Statistics, etc. Such programs help students
appreciate the role of statistics in other areas.

5.2. Graduate Education

In the graduate program also there is to be a recognition of the need for non sta-
tistical training. In addition to the typical graduate courses in Statistics (Math-
ematical Statistics, Linear Models or Regression, Forecasting, Sampling, Design,
Generalized Linear Models, Multivariate Analysis, Statistical Computing, etc.) a
student needs to have some training in communication or interpersonal relation-
ships.

One useful model to consider is to have a graduate statistics program after an
undergraduate degree in another area like Engineering. Or another idea is to have
a joint graduate program in Statistics and Business or Engineering. These require
co-operation between departments which is not that easy. It is also good to have
internships in business or industry during a graduate program. This can enhance
familiarity with working environments, hands-on-experience and communication
skills. Joint projects such as joint seminars between academia and business or
industry will also be very helpful. In such cases business leaders should be invited
to give talks in these seminar series.

Revista Colombiana de Estadística 30 (2007) 1–11

Implementation of Statistics in Business and Industry 7

6. University – Business – Industry Collaboration

Universities seek academic excellence and business and industrial enterprises re-
quire their employees to work on issues relevant to their organizations. These two
goals need not be on a collision course. With proper insight universities can pro-
vide academic excellence with relevance. Typically a university provides education
to students. It can also provide training in the workplace through its faculty. It is
difficult for a university, by itself, to provide the well rounded education required
for students to function well in the workplace. Business and industry can help by
providing contexts for relevance. They can also provide input to the universities.
Collaboration between university and business or industry is essential to produce
graduates for the future who can handle the challenges associated with the new
workplace (Brajac & MacKay (1994), Hoadley & Kettenring (1990), Snee (1990)).
Such collaboration requires carefully designed systems for implementation.

The needs of the university, and business or industry must be clearly defined
in a university -business-industry partnership. The roles of the partners should be
clearly understood. The system need to be flexible so that the students and faculty
can spend time in business or industry to enhance non-technical skills and to gain
some hands-on-experience. University courses can be modified to include project
oriented teaching. Business and/or industry should provide opportunities to gain
experience in problem formulation, planning of approach and data collection and
problem solving. Different models can be used for undergraduate and graduate
students. The systems of collaboration must make sure that the transition between
university and business or industry is smooth for students as well as faculty. Also
should accommodate the interests of students and faculty. We note that long term
commitments are required of the partners to make this partnership successful.

7. University of Waterloo and Business and

Industry

University of Waterloo is involved with business and industry in several areas and
at several levels. Here we focus on the involvement related to Statistical Methods.

7.1. Co-operative Programs

The University of Waterloo, Canada has a large (probably the largest in the world)
co-operative program involving about 11000 undergraduate students annually. In
this program a student goes to a business or industry for a four month “work
term” after every four month school term. In this program the students have
to compete to get jobs in each work term. They prepare their resumes, and
go through interviews, before they get the job. This is all managed by a large
department dedicated for this. Each student in the program has to write a report
after the co-op term regarding the work done during the work term and this
report has to be evaluated by the supervisor at work as well as by faculty in the

Revista Colombiana de Estadística 30 (2007) 1–11

8 Bovas Abraham

university. A student has to complete four to six successful work terms (depending
on the program) during his/her undergraduate program. Thus the undergraduate
program takes almost five years to complete. It is a very popular program and the
students coming through this program are highly in demand.

At the University of Waterloo, the engineering program is available only as a
co-op program while co-op is an option in other programs such as Statistics. Each
faculty (college or school) has its own special requirements. However, all co-op
placements in business and industry are administered through the Department
of Co-Operative Education, a large administrative group on campus. In general
placements in Engineering and Mathematics are well over 90%. During some
difficult years placements of first year students had some challenges, prompting
some adjustments to the timing of work terms and academic terms. The whole
Co-Op set up is a huge undertaking by the university which recognized the need
for collaboration by the University and Business or Industry.

There is a Waterloo Advisory Council consisting of people from Business or
Industry and the University. This council meets couple of times a year to discuss
various issues facing the implementation of the system. This is an opportunity
for the University to get input from Business and Industry regarding curriculum
changes, new courses and program.

7.2. Business and Industrial Statistics Research Group
(BISRG)

University of Waterloo has many centers and institutes working with business
and industry. The university recognized the importance of Statistical Methods in
Quality Improvement activities and established the “Institute for Improvement in
Quality and Productivity in 1985 as a liaison between the University and Business
and/or Industry to implement statistics in business and industry”. The goals and
objectives of this group:

• To provide a focus for multidisciplinary consulting and research in technical
and managerial methods for improving quality.

• To develop a centre offering courses and seminars for business and industry.

• To aid in developing undergraduate and graduate programs in technical and
managerial methods for quality improvement.

• To facilitate exchange programs between university faculty and business or
industry personnel

• To stimulate development of innovative training methods in quality for the
workplace.

Recently the name of this group has been changed to BISRG and now the
focus is more on research. BISRG has a program of in-company and public courses
spanning topics in variation reduction, design of experiments, process control, etc.

Revista Colombiana de Estadística 30 (2007) 1–11

Implementation of Statistics in Business and Industry 9

Third year or fourth year undergraduate, Masters, and Ph. D. students have the
opportunity of getting involved in the projects associated with the courses. They
can also serve as teaching assistants for these short courses.

By promoting closer contact between faculty members and business or industry
BISRG encourages increased applied research on topics of great interest to business
and industry. It publishes a research report series containing the results of current
research done in this group. Graduate students have greatly benefited from the
research problems generated from the collaboration with industry and business.

7.3. Campus Curriculum Changes

Faculty members involved in the group have gained valuable experience and ideas
in working with business and industry. These have helped to implement substantial
changes to the contents of Statistics courses at the University of Waterloo. The first
course (second year undergraduate) taught to some departments in engineering
now centers around continuous and process improvement. These students are
exposed to experimental design, statistical process control etc and some of them
have conducted experiments at industrial partner facilities during their work terms
which have resulted in substantial savings. Significant changes have been made
to some other courses in the Faculty of Mathematics. In some of these courses
students conduct experiments in a laboratory in groups, deal with measurement
system issues and write laboratory reports as a team. Changes have been made
to a course in experimental design to reflect the applications in industrial partner
facilities.

A variety of examples including casting, injection molding, painting, etc have
been collected from partner facilities and these appear as examples in lectures and
assignments. Students appreciate the fact that these examples are real and often
involve thousands of dollars in savings.

Thus collaboration with business and industry provided many tangible and
intangible benefits:

• Enhancements in content and delivery of courses.

• Graduate and undergraduate student involvement in real projects.

• Enhancement of applied research of faculty and graduate students.

• Professional development of the faculty members.

• Enhancement of Statistical Thinking at some partner facilities and modest
cultural changes.

• Application of newly developed methods in partner facilities.

• Savings in real dollars for business or industrial partners.

Revista Colombiana de Estadística 30 (2007) 1–11

10 Bovas Abraham

8. Concluding Remarks

Statistical Thinking and Methods need to become part of the knowledge base of an
organization. We outlined several issues related to the implementation of statistical
methods in business and industry. Well planned systems and training are necessary
for implementation. Enhancement of university education is also necessary. We
also discussed the need for University -Business- Industry collaboration. Such
collaboration will provide better opportunities for students. Some may argue that
universities should be places for education and should not be in the business of
training. It is important to keep the balance, and all such endeavors need to be
motivated by “academic excellence with relevance”.

It is important that the professional statistician is equipped with good tech-
nical and non-technical skills. This is a challenge universities have to face and
one model for successes is to form partnership with business and industry as we
have suggested. There is no need to compromise on academic excellence, however
building in ‘relevance’ to the program enhances its value.

Recibido: diciembre de 2006

Aceptado: febrero de 2007

References

Abraham, B. (1999), Implementation of Statistical Methods in Industry, Tech-
nical Report RR-99-04, Inst. for Improvement in Quality and Productivity,
University of Waterloo, Ontario, Canada N2L 3G1.

ASA Committee (1980), ‘Preparing Statisticians for Careers in industry: Report
of the ASA Section on Statistical Education Committee on Training of Statis-
ticians for Industry’, The American Statistician 34, 65–80.

Box, G. (1976), ‘Science and Statistics’, Journal of the American Statistical Asso-
ciation 71, 791–799.

Brajac, M. & MacKay, R. J. (1994), Industry-University Co-Operation: A Case
Study, Technical Report RR-94-02, University of Waterloo, Ontario, Canada
N2L 3G1.

Deming, W. E. (1986), Out of the Crisis, M.I.T., Cambridge, Massachusetts.

Garfield, J. (1995), ‘How Students Learn Statistics’, International Statistical Re-
view 63, 25–34.

Harry, M. & Schroeder, R. (2000), Six Sigma, the Breakthrough Management Strat-
egy Revolutionizing the World’s Top Corporations, Doubleday, New York.

Hoadley, A. B. & Kettenring (1990), ‘Communications between Statisticians and
Engineers/Physical Scientists’, Technometrics 32, 243–274.

Revista Colombiana de Estadística 30 (2007) 1–11

Implementation of Statistics in Business and Industry 11

Hoerl, R., Hooper, J., Jacobs, P. & Lucas, J. (1993), ‘Skills for Industrial Statisti-
cians to Survive and Prosper in the Emerging Quality Enviornment’, Ameri-
can Statistician 47, 280–292.

Hogg, R. V. & Hogg, M. C. (1995), ‘Continuous Quality Improvement in Higher
Education’, International Statistical Review 63, 35–48.

Snee, R. D. (1990), ‘A Partnership is Needed’, Technometrics 32, 267–269.

Snee, R. D. (1993), ‘What is Missing in Statistical Education’, The American
Statistician 47, 194–204.

Vere-Jones, D. (1995), ‘The Coming of Age of Statistical Education’, International
Statistical Review 63, 3–23.

Wild, C. J. (1995), ‘Continuous Improvement of Teaching: A Case Study in a
Large Statistics Course’, International Statistical Review 63, 49–68.

Revista Colombiana de Estadística 30 (2007) 1–11

Learning Objectives

After reading this chapter, you should be able to

ሁ Understand the need to evaluate published research.
ሁ Evaluate the quality of research questions and hypotheses.
ሁ Identify variables used in research.
ሁ Understand basic types of qualitative and quantitative research designs.
ሁ Interpret visualized data.
ሁ Understand basic types of statistical analyses.
ሁ Understand the basics of inferential statistics.
ሁ Demonstrate ethical research integrity.
ሁ Explain career applications involving research and statistics.

Research Methods and Statistics
Jeral Kirwan—Ashford University 5

gevende/E+/Getty Images

Section 5.1Introduction: Why Is Research Important?

Social scientists often need to collect and analyze data to test a research question or hypoth-
esis. However, professionals in many other fields also often need to solve real-world problems.
For instance, Stephon is a program evaluator who works for a non-profit organization. One
day, he is asked to evaluate several new programs that are competing for a grant offered to
small businesses. Stephon’s supervisor asks him to set up an evaluation model to assess these
proposals and to find the top three candidates for that grant. It would be easy for Stephon to
just read them and pick the three that he likes best or finds most interesting, but he knows
that would not be the most fair and equitable approach. Ins

tea

d, he follows some basic
research principles that he learned in his research methods course and designs an evaluation
that summarizes, assesses, and reports on the applications.

Stephon starts by determining the goals of the grant and using those to create a rubric with
which to evaluate the proposals. Then he uses that rubric to objectively assign a score to each
of the proposals. With that data, he creates a chart that shows how each proposal compares
to the others based on the established criteria. He presents his results to his supervisor with
his recommendations for the winners of the grant. Through this process, Stephon has used the
scientific method to collect data and make informed decisions based on that data. This shows
that research methods and statistics are valuable areas of expertise beyond the laboratory.

After considering this scenario, review the following questions:

ሁ What ethical considerations might have encouraged Stephon and his supervisor to use
an evaluation model rather than simply selecting their personal preferences?

ሁ How, in this scenario, was the creation of a rubric useful?
ሁ Are there ways that you, in your everyday life, utilize the scientific method to make

decisions? If so, when and how?

5.1 Introduction: Why Is Research Important?
Being able to understand and evaluate
published research is essential for anyone
in a psychology field. For instance, an
industrial/organizational psychologist
must keep up with current trends in orga-
nizational development by reading pub-
lished articles in that psychologist’s par-
ticular area. Even if you never conduct a
research study yourself, it is vital that you
become an active consumer of research
related to your area of interest and prac-
tice. As an informed consumer, you are
able to be evaluate whether research has
been done well and avoid falling victim to
unsubstantiated or exaggerated claims or
poorly designed research.

People in all areas of psychology use data in different ways. A clinical psychologist may read
articles about new methods for treating depression and interpret the data to help inform how
patients are treated locally. A forensic psychologist might use data on the current trends of

Shironosov/iStock/Thinkstock
ሁ It is critical for those working in psychology to

stay abreast of current research in their field.

Section 5.2Research Questions and Hypotheses

recidivism rates to better understand certain criminal behaviors. A program evaluator uses
data to determine if a program is working as it should. A research psychologist will use data
to try and prove a hypothesis. Almost every career path, including most related to psychology,
uses data to some degree.

In research, the type of design that you choose influences the type of conclusion that can be
drawn from the research. It is important to evaluate the needs and circumstances of your
research project before selecting a design. This chapter begins with a basic overview of
research questions before discussing the major types of research designs, and then the rest
of the chapter is focused on how data is visually represented and evaluated. Throughout, a
spotlight is kept on ways in which this information can be useful to you in your professional
and daily life.

5.2 Research Questions and Hypotheses
Developing a good research question and hypothesis is essential to research. They are used to
determine the methodological approach and help to focus the research design. When devel-
oping a research question or hypothesis, or evaluating a published study, consider how well
the question addresses the problem, how testable it is, and if it contributes to the body of
knowledge regarding the issue.

What Is a Research Question?
Students often confuse general philosoph-
ical questions with research questions.
Questions such as “What are the symp-
toms of ADHD?”, “What is the difference
between psychology and psychiatry?”, and
“How does one treat depression?” are
good questions but not testable with
research.

A research question must be some-
thing clear and testable such as “What is
the relationship between learning styles
and memory recall?”, “How are men and
women different with regard to perfor-
mance on math exams?”, and “Does drink-
ing coffee improve student performance on final exams?” These questions are clear and show
direct connections to the variables. Having clear, specific, and testable questions is essential
to developing a research design to assess variables.

Typically, research questions that are based on how much, to what extent, when, or who are
best tested with a quantitative approach. Qualitative approaches are better for measuring
why questions and things that are more exploratory in nature.

A research question is based on the general topic, but narrows down the scope of what the
study will seek to accomplish. You do not want the question to be too general or too narrow.
Some examples might be:

YakobchukOlena/iStock/Thinkstock
ሁ Good research questions must be testable.

Section 5.3Types of Variables in Psychology Research

1. What is the rate of depression in Colorado?
2. What are the characteristics of a good human resource manager?
3. Does education play a role in juvenile defenders’ successful rehabilitation?

When developing or evaluating a research question, consider if it is clear and testable. A ques-
tion that is too vague, or that has too many factors, cannot be easily tested. A good research
question is the guide to the rest of the research design.

What Is a Hypothesis?
A hypothesis is a testable statement that proposes relationships or differences between two
or more variables. It is your prediction of what will happen in your research study. Develop-
ing a testable hypothesis is the first step in the scientific method approach to research; a
hypothesis must be developed before data is collected. In psychological research, hypotheses
commonly focus on how some aspect of the environment affects a particular behavior. Below
are some basic hypothesis examples:

1. Students who eat breakfast score higher on statistics exams than students who do
not eat breakfast.

2. Ninth grade girls have higher memory recall on algebra exams than ninth
grade boys.

3. Medicine A will lower blood pressure more than Medicine B.
4. The personality factor conscientiousness is significantly related to self-direction

in learning.
5. The experimental group that receives a special tutorial will demonstrate better recall

than the control group that receives no intervention.

Here is an example of a more complicated hypothesis: “Conscientiousness will be uniquely,
positively related to learner self-direction after controlling for the other Big Five and narrow
traits” (Kirwan, Lounsbury, & Gibson, 2014, p. 5).

How do you develop a hypothesis? Before developing a hypothesis statement, you have to
do some background research on the topic. Look at published research and see what other
people have done and what hasn’t been done. Once you complete your literature review, con-
sider what variables interest you and how you might test a relationship between them. After
you have written a few hypotheses statements, consider which one could be readily tested.
Think about how you might go about confirming or disproving that hypothesis. Developing
a good hypothesis statement is an iterative process, and it takes time. Once you have your
hypothesis, the next step is to define your variables.

5.3 Types of Variables in Psychology Research
Imagine that you are a psychologist, and you are interested in conducting a study to deter-
mine the effects of eating breakfast on memory in young children. You assume, based on pre-
vious studies, that children who eat a healthy breakfast will perform better on exams than
children who do not eat breakfast at all (this is your hypothesis). How do you begin your
research study and test your hypothesis?

One of the first things you do as a researcher is define your variables (also commonly
described as factors). These factors are chosen by the researcher before collecting data and

Section 5.3Types of Variables in Psychology Research

they are characteristics that vary from individual to individual. For instance, with regards
to breakfast, each person may eat at different times of the morning, eat various types and
amounts of food, and perform differently on their exams (even from one day to the next).
There are four main scales for the measurement of variables, and each variable will fit into
one of them depending its characteristics.

• Nominal – The variable is divided into categories that have no relative order. For
instance, a questionnaire might ask participants to choose their gender identifica-
tion from a list.

• Ordinal – The variable is divided with categories that have a relative order. An
example might be high school levels: freshman, sophomore, junior, or senior.

• Interval – The scoring of the variable has equal distances between points on a con-
tinuum. An example would be time of day. The distance between 8 a.m. and 9 a.m.
is the same as the distance between 12 p.m. and 1 p.m. For our example, we might
measure the time between eating breakfast and taking an exam.

• Ratio – The variable represents the relationship between two numbers and is char-
acterized by an absolute zero (meaning zero is the lowest possible value). For our
example, grade point average (GPA) could be used.

It is important to understand the different types of scales of variables as that is directly con-
nected to the method of analysis. For instance, a study that only contains nominal variables
would be analyzed with non-parametric analyses such as chi-square (covered later in the
chapter).

Independent and Dependent Variables
Quantitative research designs utilize independent and dependent variables to try and explain
a phenomenon. An independent variable is a variable that the researcher controls to see
what effect it has on the dependent variable. An example would be if a researcher wants to
see if students perform differently on a
math exam depending on the tempera-
ture in the room. The researcher controls
who will be taking the tests (the students
can be chosen such that they are all the
same age, or gender, etc.) and the temper-
ature in the room to see how tests scores
may be affected (the measurable out-
come). So, the differences in the indepen-
dent variables (students and tempera-
ture) are controlled by the experimenter,
and the dependent variable (test scores)
only changes in response to those inde-
pendent variables.

Extraneous and Confounding
Variables
When investigating factors in social sci-
ence research, there are usually many
possible variables that can be connected

Wavebreakmedia Ltd/ Wavebreak Media/Thinkstock
ሁ Researchers must be aware of extraneous

variables that may impact the results of their
research.

Section 5.4Types of Research Designs

to the independent and dependent variables, and that can affect their relationship. For
instance, if we want to find out why people perform a certain way on a math test, some pos-
sible variables that affect performance are motivation, study time, IQ, personality, time of
day, and even the temperature in the room where the testing takes place. Variables that are
not a part of the research design are considered to be confounding, or extraneous, variables.
These are the unconsidered or undesirable variables that influence the relationship between
the variables the researcher is investigating. For example, take a psychologist who wants to
examine the effect of showing romantic movies to a group of men and women on how they
display affection the following week. One confounding variable could be that the women had
higher levels of romantic idealism than the men did before the study started. A comparison
of the two groups may not show an accurate picture of the outcome of the treatment (show-
ing the movie). In this instance, the extraneous variable is important because it was present
in one group and not in the other. Possible extraneous variables should be considered before
conducting such a study. While researchers cannot consider all possible variables related to
the outcome they are interested in, they should at least be aware that many possible variables
may be affecting it. A skilled researcher will consider what they can, and note limitations
when they cannot. Next, we will look at research designs that researchers use to focus in on
the variables of interest.

5.4 Types of Research Designs
As explained at the beginning of this chapter, and as the frequently cited research expert Cre-
swell (2013) explains, research scientists and people working with data in all fields must
understand that the research question and hypothesis guides the research design. In the next
section, we will examine several different research designs and strategies for both quantita-
tive and qualitative approaches.

Quantitative Research Methods
In the natural and social sciences, quantitative research is an empirical investigation of phe-
nomena based on mathematical models and theories. Hypotheses are developed and mea-
sured based on statistical questions that address aspects of an observed phenomenon such as
what, where, when, how much, and to what extent? Before data can be collected and analyzed,
a representative sample of a population must be collected based on a variety of sampling
strategies.

Sampling Strategies
Psychology researchers seek to examine traits, behaviors, and other human characteristics
(as do marketing and advertising analysts). However, it can be difficult to collect data if the
populations being investigated are large or difficult to reach. Sampling strategies are used to
find a subset of the population that accurately represents the population. Random sampling
is when everyone in the population has the same chance of being selected. In experimental
research, the sample must be randomly selected. Stratified sampling occurs when a popula-
tion is divided into subgroups (the strata) and then each subgroup is randomly sampled from.
Systematic sampling involves a set protocol for selecting participants, such as every 20th
person to enter a clinic, for example. Cluster sampling involves dividing the population into
clusters and then randomly choosing a certain number of people in each cluster. Each of these
fall under the category of probability sampling because they use randomization.

Section 5.4Types of Research Designs

Non-probability sampling does not
require the use of randomization and
includes several strategies, such as the fol-
lowing. A convenience sample is one in
which the participants are chosen based
on availability. An example would be giv-
ing undergraduate students extra credit to
participate in a local study. Purposive
sampling involves selecting participants
based on some characteristic they may
have, such as individuals diagnosed with a
rare disease. Proportional sampling
divides participants into subgroups and
samples each subgroup randomly; it might
be used when the researcher needs to
have a particular number of people with a
certain characteristic, such as having equal numbers of participants that identify as women or
men. The strategies go beyond these few examples and greatly depend on the nature of the
research design.

Experimental Research Designs
Experimental designs are scientific studies that rely on random assignment of subjects and
strict control over the research environment and variables of interest. They are the only
approaches that are considered rigorous enough to address cause-and-effect relationships
between variables. While there are many experiments in psychology research, they are less
common than other approaches due to the nature of most studies in the social sciences and
the cost and limitations of the laboratory settings that experiments require.

Here is an abstract from a study that used an experimental design:

We present a novel experimental design to measure honesty and lying. Par-
ticipants receive a die which they roll privately. Since their payoff depends
on the reported roll of the die, the subjects have an incentive to be dishon-
est and report higher numbers to get a higher payoff. This design has three
advantages. First, cheating cannot be detected on the individual level, which
reduces potential demand effects. Second, the method is very easy to imple-
ment. Third, the underlying true distribution of the outcome under full hon-
esty is known, and hence it is possible to test different theoretical predictions.
We find that about 20% of inexperienced subjects lie to the fullest extent
possible while 39% of subjects are fully honest. In addition, a high share of
subjects consists of partial liars; these subjects lie, but do not report the pay-
off-maximizing draw. We discuss different motives that explain the observed
behavioral pattern. (Fischbacker & Föllmi-Heusi, 2013, p. 525)

Quasi-Experimental Designs
Quasi-experimental designs involve studies that are close to experimental in nature but do
not have participants that have been randomly selected.

bogdandreava/iStock/Thinkstock
ሁ Types of random sampling include stratified,

systematic, and cluster sampling.

Section 5.4Types of Research Designs

Here is an abstract from a study with a quasi-experimental design:

Many job redesign interventions are based on a multiple mediator–multiple
outcome model in which the job redesign intervention indirectly influences a
broad range of employee outcomes by changing multiple job characteristics. As
this model remains untested, the aim of this study is to test a multiple mediator
–multiple outcome model of job redesign. Multilevel analysis of data from a
quasi-experimental job redesign intervention in a call center confirmed the
hypothesized model and showed that the job redesign intervention affected
a broad range of employee outcomes (i.e., employee well-being, psychological
contract fulfillment, and supervisor-rated job performance) through changes
in 2 job characteristics (i.e., job control and feedback). The results provide fur-
ther evidence for the efficacy and mechanisms of job redesign interventions.
(Holman & Axtell, 2016, p. 284)

Observational Research
Observational research (often called field research) is a type of non-experimental design
in which the researcher observes and records behaviors in the context of the study’s param-
eters. For instance, a researcher may want to observe aggressive behaviors in children ages
2–4, so he gets permission to sit at a preschool playground and watch the children interact.

Here is an abstract from an observational research study:

Background
Regular physical activity is associated with a range of physical and psychologi-
cal health benefits. In North America the majority of adolescents are insuffi-
ciently active.

Purpose
The purpose of this study was to examine the prospective relationship
between adolescents’ perceptions of transformational leadership displayed
by their school physical education teachers and their own physical activity
behaviors, both with respect to within-class physical activity (WCPA) and also
leisure time physical activity (LTPA).

Method
The study used a prospective observational design. Using multilevel structural
equation modeling (MSEM), we examined the extent to which adolescents’
affective attitudes mediated the effects of teachers’ behaviors on adolescents’
physical activity responses. Two thousand nine hundred and forty-eight ado-
lescents (Mage = 14.33, SD = 1.00, Nfemale = 1,641, 55.7 %) from 133 Grade 8–10
classes in British Columbia (Canada) provided ratings of their physical educa-
tion teachers’ behaviors midway through the school year. Two months later,
students completed measures of affective attitudes, WCPA, and LTPA.

Results
The results indicated that adolescents’ perceptions of transformational teach-
ing explained significant variance in both WCPA and LTPA, and these effects

Section 5.4Types of Research Designs

were fully mediated by adolescents’ affective attitudes (total indirect effect:
b = 0.581, p < 0.001).

Conclusion
The findings suggest that transformational leadership behaviors displayed
by physical education teachers may be an important source of adolescent
enjoyment of physical education as well as health-enhancing physical activ-
ity involvement within school and outside of school. (Beauchamp et al., 2014,
p. 537)

Qualitative Research Methods
Qualitative research in the social sciences
is a method of inquiry where the goal is to
gather information to achieve a more in-
depth understanding of human behavior.
Popular approaches include interviews,
case studies, ethnographies, narratives,
and empirical data collection based on
grounded theory. Qualitative research
uses non-probability sampling strategies.
Samples are typically convenience or pur-
posive samples as the goal is to look at a
particular person or group and does not
follow the quantitative goal of generaliz-
ing to a larger population. Some common
designs and examples can be seen below.

Interpretive Study
An interpretive study is used when the researcher wants to see how individuals make mean-
ing of a particular situation or phenomenon. Data is collected by conducting interviews,
observations, or reviewing documents (such as patient records or personal diaries). Analysis
of interpretive data focuses on themes or common patterns to form a descriptive exposition
connecting the data to the published literature. For example, if a researcher wanted to study
how people in poverty make decisions about what food to buy, that researcher might collect
information on a sample group’s food purchases over a certain period of time and also con-
duct interviews with the sample population to understand their thought processes and lived
experiences.

Grounded Theory
Grounded theory is a systematic qualitative method used in social science research that starts
with general questions to develop a theory based on emerging patterns in collected data.
The early phase of this type of research is intended to be somewhat vague and leads into the
development and verification of a theory based on the data collected.

Here is an abstract of a study that used grounded theory:

KatarzynaBialasiewicz/iStock/Getty Images
ሁ Interpretive studies involve interviewing and

collecting qualitative data.

Section 5.4Types of Research Designs

Currently, a relatively small number of studies have employed qualitative
methods to rigorously examine the experiences of health care professionals
enrolled in mindfulness-based stress reduction (MBSR). This study devel-
oped a working model of how participants may experience change during
an adapted MBSR program for health care professionals. The model derived
from the data demonstrated that participants echoed themes similar to those
described by clinical populations engaged in MBSR, such as the salience of
the group experience and support, discovery of acceptance as well as the
realization that some degree of frustration and/or distress is part of learning
and establishing a mindfulness practice. Unique themes highlighted included
becoming aware of perfectionism, the automaticity of “other focus” and the
“helping or fixing mode”. Findings illustrated the nuanced change processes
undertaken by participants and the implications such change held across pro-
fessional and personal domains. (Irving et al., 2014, p. 60)

Phenomenology
Phenomenology is a qualitative research approach looking at human consciousness and self-
awareness of experiences. A phenomenological approach emphasizes the subjective experi-
ences people have and how they interpret the world in particular situations. The main goal
of this approach is to better understand how others view their surroundings. For example,
a researcher might use a phenomenological method to study the experiences of children at
Disneyland by interviewing children and their parents and then analyzing those interviews to
find the essential themes of their experiences.

Ethnography
Ethnographic research is a broad approach in field research that focuses on participant obser-
vation and involves the researcher being immersed in the culture being studied as one of the
participants. Ethnography is a very broad area with many different methods associated with
it. The benefits include learning about a particular culture or group from the inside, rather
than being an impartial observer.

Here is an abstract from an ethnographic study:

Threat of supernatural punishment can promote prosociality in large-scale
societies; however, its impact in smaller societies with less powerful deities is
less understood. Also, while perceived material insecurity has been associated
with increasing religious belief, the relationships between insecurity, super-
natural punishment beliefs, and prosocial behavior are unclear. In this study,
we explore how material insecurity moderates the supernatural punishment
beliefs that promote different expectations about distant, anonymous strang-
ers among a sample of villagers living in Yasawa, Fiji. We examined this rela-
tionship by employing an economic game designed to measure local recipient
favoritism vs. egalitarian, rule-following behavior. Using indices of three dif-
ferent “punishing” agents – the Christian God (“Bible God”), the deified ances-
tors (Kalou-vu), and the police – we find that increased belief in Bible God
punishment predicts less local recipient favoritism at low and moderate but
not high material insecurity. Punishing Kalou-vu also predicts less favoritism
at low and moderate insecurity, but more favoritism at high insecurity. Police

Data Visualization

punishment poorly predicts favoritism, suggesting that secular authority has
less impact on isolated communities. We discuss implications for understand-
ing how different kinds of supernatural and secular agent beliefs impact pro-
social behavior. (McNamara, Norenzayan, & Henrich, 2016, p. 34)

Case Study and Single-Subject Designs
Case study research in the social sciences is focused on a specific group, environment, or
situation and can include large groups or even single subjects. This research strategy is com-
monly used when the topic of interest is not common, or when a particular local problem is
the focus. The value of investigating a real-world phenomenon within a particular context
cannot always be done using inferential designs. For instance, a researcher may want to look
at why a person committed mass murder, which is not a common enough situation to be
generalizable.

Here is an abstract from a case study:

When a serial killer is present, the media and local authorities often claim
that public attitudes and behaviors change sharply, usually becoming more
fearful and less outgoing. Unfortunately, much of the evidence on this score is
either speculative or anecdotal. This study reports quantified results from a
series of surveys conducted over the course of a serial killing spree in Baton
Rouge, Louisiana. The temporal trend in fear of crime is punctuated by a mod-
erate increase during the serial killing spree, and a sharp decline after the
apprehension of the serial killer. Moreover, post apprehension data reveal
that nearly 56% of respondents report experiencing an increase in their fear
of crime specifically in response to the serial killer. This was fairly evenly
distributed across races and marital statuses, but, as expected, females and
younger people were more likely to report increases in fear. Additionally, 46%
of respondents took the extra step of implementing some sort of protective
measure, with the most frequent being carrying mace or pepper spray or add-
ing a security device to their home. In the latter case, respondents were moti-
vated by a mix of concern over their own safety and that of their family. (Lee
& DeHart, 2007, p. 1)

5.5 Data Visualization
Data can be represented in graphs and figures to help show its characteristics. Data visu-
alization can be helpful to researchers as they check to see whether the data are normally
distributed or independent and whether other assumptions are being met. For people in the
business world, data visualization can also be a quick way to show a summary of results in
a business presentation, for example a pie chart can show a company’s total sales by month.
Some examples of ways to visualize data are included below.

Frequency Distribution
A frequency distribution shows how scores of a variable are distributed in a sample. They
can be helpful in seeing the characteristics of a group in relation to some category of interest.
For example, if you want to see how many people a certain disease affects based on age, you
might use a frequency distribution. Another example is shown in Figure 5.1.

Section 5.5

Section 5.5Data Visualization

Figure 5.1: Frequency distribution
ሁ A frequency distribution shows the frequency with which certain data points appear.

Cumulative Frequency Distribution
A cumulative frequency distribution (see Figure 5.2) adds the frequency of data points
at a certain score together with all of the previous data points to show the percentage as it
increases from the lowest score on the variable to the highest. This might be useful for under-
standing the number of observations that lie above and below a particular value. For example,
in Figure 5.2 one can see that around 500 students scored 100 points or fewer on the text,
while the remaining students scored above 100 points.

Figure 5.2: A cumulative frequency distribution
ሁ This cumulative frequency distribution shows the proportion of students who scored at or below

each data point.

Section 5.6Statistical Analyses

Pie Chart
A pie chart (see Figure 5.3) is divided into “slices” to illustrate the numerical proportions of
the variable of interest. This type of illustration is often used to show different responses to a
survey question or proportions of a population with a certain characteristic, such as race or
age. One particular strength of pie charts is that they show proportions very clearly. Figure
5.3 shows the proportion of students receiving a particular grade on a test.

Figure 5.3: A pie chart
ሁ This pie chart notes the distribution of grades in a group of students.

5.6 Statistical Analyses
Descriptive statistics are simple ways of describing the characteristics of a sample or popula-
tion. For example, if you want to see which major city in the U.S. treats the most people for
addiction-related issues, you could look at the records of all major cities and compare. By
looking at the rates of treatment, you could make decisions about where resources could be
better spent. See the Career Spotlight feature on Paul Randall Gesn to learn more about sta-
tistical analysis.

D, 26%

F, 10% A, 9%

B, 17%

C, 38%

Career Spotlight: Paul Randall Gesn

Name: Paul Randall Gesn

Primary job title: Statistical Analyst

Type of employer: State Health Agency

What degrees do you hold? A doctorate in experimental psychology with a specialization
in social and personality psychology.

(continued on next page)

Section 5.6Statistical Analyses

Measures of Central Tendency: Mean, Median, and Mode
The average is a general term that can refer to all three measures of central tendency, but
typically indicates the arithmetic mean. However, it is important to have an understanding of
all three measures: mean, median, and mode.

The mean (often notated as M, µ, or x̄) is the sum of all values in a group of scores divided by
the number of scores (this is also known as the arithmetic average). For example: (5 + 10 +

Career Spotlight: Paul Randall Gesn (continued)

Describe your major job responsibilities. I provide research and data analytic support
for a program that oversees the provision of services to women and children. Specifically,
I help to develop indicators to monitor for fraud and other noncompliant behavior among
the external vendors who actually provide those services. I also conduct analysis on trends
in program participation and expenditures.

Provide a general overview of your career background. I have held positions, in both
government and the private sector, related to healthcare, education, and the development
of nationally representative standardized tests and assessments. The common theme
among all my nonacademic positions has been a focus on research and data analysis. I have
also taught psychology courses online and in a campus-based setting. Although my full-
time work is in a nonacademic/applied setting, I enjoy maintaining my ties to academia.
Many colleges and universities are hiring adjunct instructors rather than full-time instruc-
tors, so the opportunity to teach at least one course is generally available.

Are there any undergraduate activities that you think helped to influence success
in your type of career? I was involved in conducting my own research project during my
junior and senior undergraduate years, and this really helped to solidify my interest in
research and analyzing data. This experience helped to influence the direction I took in
selecting a graduate program and then deciding on a career path to pursue.

What advice would you give to someone thinking about a field similar to yours? In
graduate school, take as many statistics, research methods, and psychometrics courses
as you can. If possible, become involved with an actual research project. You should enjoy
working with numbers, extracting meaningful information from large datasets, and pro-
viding that information to a variety of audiences, some of whom do not have a background
in research and statistics. Research- and statistics-intensive job positions often require at
least a master’s degree, but some employers will hire at the bachelor’s level, provided the
applicant has previously gained the required knowledge and experience. Also, you need to
have a good working knowledge of at least one statistical analysis software package, such
as SAS, SPSS, Stata, or R.

What do you like most about your job? I really enjoy starting with a large database of
raw data and determining if there is any meaningful information to be gleaned from that
data. I think every dataset tells a story, and it is the job of the analyst to determine what
that story is. I also enjoy conducting research and writing up the results in formal research
reports. Finally, I enjoy communicating results to audiences who might not have a back-
ground in research or statistics. The communication is generally two-way: they can also
provide me with information on the policy implications of the results.

Section 5.6Statistical Analyses

15 + 20) / 4 = 12.5. The median (often notated as Mdn or Mdn) is the number that lies at the
midpoint of a distribution of numbers when the numbers are arranged in numerical order. An
example would be 1, 2, 3, 4, 5, 5, 5, 6, 6, 7, 8, 8, 8, 9, 20. The value 6 is in the exact middle, so it
is the median. When there are an even number of values, the average of the two middle values
is the median, such as: 1, 2, 3, 4, 5, 5, 5, 6, 7, 8, 8, 8, 9, 20. In this case, since (5 + 6) / 2 = 5.5,
the median is 5.5. The mode (often notated as Mo) is the most frequently occurring number in
a distribution. For instance: 1, 2, 3, 4, 5, 5, 5, 6, 6, 7, 8, 8, 9. Five is the mode as it occurs more
than any other value.

As you critique articles, or any type of research results (such as is commonly found in news
reports), be sure to pay close attention to which measure(s) of central tendency were used in
the study and be careful about assuming what the “average” is.

Measures of Dispersion
The measures of central tendency tell us where the relative center of the distribution lies.
Researchers also want to measure how spread out the values are in the sample; this is where
measures of dispersion come into play. Some of the measures of dispersion are the range,
interquartile range, variance, and standard deviation. The range tells us the distance between
the lowest and highest values in the distribution. The interquartile range (see Figure 5.4)
tells us the difference between the lowest and highest values in the two middle quartiles (the
middle 50% of the scores) when the distribution is divided into four sections, or quartiles.

Figure 5.4: The interquartile range
ሁ When the data are divided into four equal quartiles, the interquartile range is composed of the

middle two quartiles, or the middle 50% of the data, as shown here.

The standard deviation tells us how much the scores deviate from the mean scores and is
just the square root of the variance. Another way of putting it is the average amount of vari-
ability in a set of scores. The standard deviation is usually notated as s, SD, or σ. You often
see distributions expressed as the mean ± the standard deviation, such as 14.83 ± 2.51. A
standard deviation can tell us how varied a data set is. For instance, if the standard deviation
of a set of grades on a particular test is small, then we can conclude that the grades were very
similar. If the grades were mostly high, this might mean the test was too easy; if they were
mostly low, it might have been too difficult. Looking at the distributions of scores can help
teachers adjust their tests over time to be appropriately challenging.

Section 5.7Inferential Statistics

Standard Normal Curve: The Bell Curve
In a normally shaped distribution, half of the population will be above the mean and half will
be below it. In psychology research, most populations are normally distributed and the bell-
shaped curve helps us understand how this might look. If we averaged the math test scores of
all the students in the 8th grade at a school, we might find that the average score is 50. Half of
the students would have higher scores, and half lower, in a normally distributed sample (see
Figure 5.5).

Figure 5.5: The bell curve
ሁ This normal curve is based on the distribution of grades in a sample.

In a normal curve, about 68% of the data is within one standard deviation of the mean, about
95% is within two standard deviations, and about 99.7% is within three standard deviations.
This fact makes it easy to quickly assess ranges and frequencies of certain outcomes across
a population. Additionally, the standard normal distribution (a distribution with a mean of 0
and a standard deviation of 1) can be used to calculate the probabilities involved in any other
normal distribution. Due to these benefits and others, normal distributions are a popular
statistical analysis tool.

5.7 Inferential Statistics
Researchers often want to see if two variables are connected. For instance, we might want to
investigate if the amount of time a person studies is connected to how well they do on a math
exam. In this case, we want to use an inferential test called a test of relationships to measure
that connection. Tests of relationships are designed to show how groups or variables are
related. Non-researchers also need to have an understanding of how variables are connected,
and how those connections are verified with scientific methods. For example, say the results
of the previously suggested research on studying show that more studying is related to a

Section 5.7Inferential Statistics

higher grade, but only to a point; after a
certain number of hours of studying, there
is nothing more to be gained. Being able to
interpret these results might influence
your study habits.

Correlation
Correlation analyses are used to look at
the mutual relationship between two vari-
ables, such as how IQ is related to GPA. A
positive correlation indicates the extent to
which the two variables move up together
or down together. IQ and GPA are posi-
tively correlated, so as IQ gets higher, GPA
gets higher. A negative correlation indi-
cates the extent to which one variable moves up while the other variable moves down. If that
were the case with IQ and GPA, as IQ moved lower, GPA would move higher. When there is no
clear relationship, the variables are considered to have zero correlation. For instance, IQ may
not be a factor in the grades in a subject such as wood shop. While in cases of positive and
negative correlations, the movement of one variable can reliably predict a similar move in
another variable, that correlation does not imply causation. There may be an unknown factor
that influences both variables similarly. For instance, amount of ice sold in grocery stores may
have a positive correlation with sunburns, but it is obvious that ice does not cause sunburns,
nor do sunburns cause people to buy ice. The root of the relationship may be that a third fac-
tor, weather, influences both sunburns and ice consumption in similar ways, causing them to
be correlated.

In a similar vein, it is important to note that a correlation coefficient does not address the
issue of cause and effect. Do not make the mistake of thinking that a high correlation implies
that one variable causes variation in the other variable. For example, there might be a nega-
tive correlation between the number of pirates in the world over the last 200 years and the
pounds of ice cream sold each year (the number of pirates has gone down while the amount
of ice cream sold has gone up). However, it is not likely that the reduction in pirates each
year has caused ice cream sales to go up. This is often referred to as a spurious relation-
ship or spurious correlation. This shows how essential it is to carefully interpret results from
correlational research and to not make causal assumptions without considering all possible
variables that affect a relationship. For those not in the research field, this skill can come in
handy when reading the news; for example, when you hear about some connection between
eating a certain food and experiencing a certain health concern, it is important to be critical
of assumptions that one caused the other to happen. Cause-and-effect assertions cannot be
substantiated outside of true experimental designs (see Figure 5.6).

Some examples of variables that are correlated include:

• Height and time to run a mile
• Hours of studying and grade on a statistics exam
• Income and happiness
• Caffeine intake and alertness

Dmitrii_Guzhanin/iStock/Thinkstock
ሁ Tests of relationships measure how two

variables interact.

Section 5.7Inferential Statistics

Figure 5.6: Scatter diagram of a positive correlation
ሁ This plot shows the correlation between the monthly price of car insurance and the annual

income of a new college graduate.

The scatter diagram in Figure 5.6 is a visual display of data which allows you to see an asso-
ciation between two variables. For example, you can see there is a strong, positive relation-
ship between the annual income of new college graduates and the price they pay monthly
for car insurance. The scatter diagram illustrates the strength of the correlation between the
variables along the slope of a line; the straighter the line, the stronger the correlation. This
correlation can point to, but does not prove, a causal relationship. It may be that people who
make higher wages purchase more expensive cars, and that is why their insurance premiums
are higher.

The Correlation Coefficient
While a scatter diagram is an excellent way to look at the relationship of two variables, many
journals do not publish graphs due to the large amounts of space they take up. Numerical
summaries of results appear in research reports much more frequently than do visual rep-
resentations. Correlations are also often presented in the news and on social media in the
following manner: “a new study suggests there is a correlation between heart health and
consuming meat.” Claims like that need to be carefully evaluated on the nature of the research
design.

The correlation coefficient (see Figure 5.7) is the numerical summary of a bivariate (two-
variable) relationship. It is usually symbolized as r. The correlation coefficient has a range of
–1.0 (a perfect inverse relationship) to +1.0 (a perfect positive relationship), with 0 being no
correlation at all.

Section 5.7Inferential Statistics

Figure 5.7: Correlation coefficient range
ሁ The correlation coefficient ranges from –1.0 to 1.0.

When an r falls on the right side of the continuum (from 0.0 to 1.0), it indicates that there is
a “direct” or positive relationship between the two variables. As the score on one variable
increases, so does the score on the other variable. Here is an example of a positive correlation
from a real research project:

Results indicated that increases in self-compassion were significantly related
to the number of days a week that participants meditated (r = .42, p < .05), as well as the number of times per day they informally practiced self-compas- sion (r = .43, p < .05). (Neff & Germer, 2013)

When an r falls on the left side of the continuum (from –1.0 to 0.0) it indicates an “indirect”
or “inverse” relationship between the variables. As the score on one variable increases, the
score on the other variable decreases. For example, a hypothetical study might have looked at
school attendance and GPA:

Results indicated that there is a significant inverse relationship between the
number of days a student misses in school and their GPA (r = .72, < .01). So, the more days a student misses of school, the lower their GPA tends to be, compared to the average.

When a correlation is strong, it is said to be more predictive than when a correlation is weak.
How do we know if a correlation is strong? The common conventions for the effect size of a
Pearson correlation coefficient are (note that these are based on absolute values) based on
Cohen (1992, p. 157):

Trivial: 0 to < .1 Small to medium: .10 to .30 Medium to large: .30 to .50 Large to very large: .50 to 1.0

Be aware that though an effect size can tell us the general strength of the relationship between
the two variables, the practical value greatly depends on the research conducted. For instance,
when looking at human behavior, there can be many variables that contribute to a particular
outcome, so a relatively small effect size can be significant. But a medical study looking at the
effects of a particular medicine may require a much higher effect to be considered viable.

When at or close to r = 0.0, there is no evident correlation, the pattern of association is ran-
dom, and it shows non-systematic variation. No line, straight or otherwise, can be fit to the

– 1.0 0.0 1.0

Positive correlationNegative correlation

Section 5.7Inferential Statistics

relationship between the two variables. The two variables are said to be uncorrelated (see
Figure 5.8).

Figure 5.8: Scatterplot
ሁ This scatterplot shows no evident correlation between the variables income and shoe size.

One issue to watch out for when analyzing data for correlation is the possibility of outliers.
You have outliers when one or more of the data points are located away from the bulk of the
scores. These can cause the size of a correlation coefficient to underestimate or exaggerate
the strength of the relationship of two variables. Often when performing statistical analyses,
tests will be done to identify whether or not a data set contains outliers. If outliers are pres-
ent, the researcher will note that in the findings and potentially run another analysis on the
data without the outliers to compare the results against the previous findings. Outliers should
be carefully considered in correlational research.

Regression Analysis
In some situations, we may want to use more than one variable (often called predictor vari-
ables) to predict some other variable (often called the criterion variable); this is called regres-
sion analysis. Using more than one predictor variable can usually help increase the amount
of variance accounted for in the criterion variable. Put another way, we can usually increase
predictive validity by using multiple predictors rather than just one.

As an example, suppose that the variables are as follows:

Y Annual salary as a college professor

X1 Number of years since PhD was obtained

X2 Number of publications

Section 5.7Inferential Statistics

X3 Age (in years)

X4 Student ratings of teaching performance

Suppose that we do a correlational study and obtain the following correlation matrix (Table 5.1):

Table 5.1: Correlation matrix

Y X1 X2 X3 X4

Y ––– .62 .46 .26 .51

X1 ––– .68 –.15 .46

X2 ––– .05 .30

X3 –– –.01

X4 ––

Next we run a multiple regression analysis, and obtain the following summary of results
(Table 5.2):

Table 5.2: Summary of results

Step Variable R R2 R2 change

1 Years since PhD (X1) .62** .38 .38**

2 Student ratings (X4) .67** .45 .07**

3 Age (X3) .70** .49 .04*

4 # of publications (X2) .71** .50 .01

*p < .05 **p < .01

This can be interpreted as follows: The four predictors X1 – X4 display a multiple correlation
of .71 (R; significant at the p < .01 level) which accounts for 50% of the variance in Y (R2). The most important variable in predicting annual salary appears to be the number of years since the professor got his or her PhD (X1), which accounts for 38% of the variance in annual salary. After controlling for X1, student ratings of the professor’s teaching (X4) account for an additional 7% of the variance in annual salary. After controlling for X1 and X4, we find that the professor’s age (X3) accounts for an additional 4% (p <.05), and after controlling for the other 3 predictors, number of publications (X2) adds an additional 1% to the predictable variance of annual salary. The moral of this regression example is if you want to make more money as a professor, get your PhD early, teach well, and age.

Statistical Tests of Differences
Sometimes researchers want to test a hypothesis that groups are different from each other.
Different from the tests of relationships, the tests of differences look to see how variables
or groups are different from each other. This set of tests include t tests and ANOVA analyses.

Section 5.7Inferential Statistics

The t Test
We use t tests to assess if the means of two groups are statistically different from each other.
For instance, a researcher might want to see if a group that gets a depression medicine (the
experimental group) is statistically different from the group that gets no medical treatment
for depression (the control group). There are two main types of t tests, dependent and inde-
pendent. A dependent t test, also known as a “within-subjects” or “repeated measures” test,
is used when the same sample is measured twice. The goal is to evaluate if there is a change
from the first measurement to the second. The other type of t test is the independent samples
t test. This is used when comparing two different samples on only one measurement.

Here is an example of a study using a t test:

Children in our sample were generally very familiar with electronic games.
Of our sample, 84.4 % reported playing video games on a computer, 81.2 %
on a console and 50.4 % on a handheld device in the previous 6 months. Only
6.1 % reported playing no games at all during that time. Similarly, only 11.4 %
of our sample had no exposure to violent video games. Boys had consider-
ably more exposure to violent video games than did girls [t(189.24) = 9.07,
p < .001, r = .46, 95 % CI = .38, .54] (Ferguson & Olson, 2014).

Analysis of Variance (ANOVA)
A one-way analysis of variance (ANOVA) makes it possible to compare data for three or
more groups, similar to running all possible t tests between each pair of groups at the same
time. Let’s say you want to do a study to compare three study groups to see how study style
affects grades on a final exam. The first group studies each day for two weeks, the second
group studies each day for one week, and the third group only studies on the day before the
exam. The ANOVA looks at the differences between the grades of the three groups (called
between-groups variation), as well as the variance between the grades of each individual in
each group (called within-groups variation). If the variances between the three teams of stu-
dents are significantly larger than the average variation found in each person’s performance,
then you may have a significant difference in student grade by group. For instance, the aver-
age score for three groups might be 90, 85, and 72, making the overall average 82.33 (the
three numbers added together and divided by three). In the end, the ANOVA (also commonly
referred to as an F test) gives you one value that quantifies whether or not there is a signifi-
cant difference between your populations, such as F = 3.43, p = 0.012. In this case, because
the p-value is less than 0.05, we know that one of the groups is significantly different from
the others.

Post Hoc Analysis
Let’s say that you ran an ANOVA and the results suggest that there are some differences
between the groups—how will you know which of the groups are different from the average?
You have to run a post hoc (meaning “after the fact”) analysis. This analysis dives deeper into
the single-value result of the ANOVA so that you can figure out which group study strategy
is better than the others, rather than just saying that there is definitely a difference between
the three groups. We know from our ANOVA analysis that the average score for the three
groups is 82.33, and we know that one group is significantly different from the others, but not
which one. A post hoc analysis can then be performed to find out that the third group, with an

Section 5.7Inferential Statistics

average score of 72, is significantly different from the others (different meaning, in this case,
they performed significantly worse on the test).

Non-Parametric Tests: Chi-Square Tests
The ANOVA procedures we discussed in the previous section allow researchers to compare
group means, but sometimes we might want to simply compare the frequencies associated
with different groups or with some expected values. Suppose, for example, that we choose to
study the number of men and women that are managers in a major company. If the distribu-
tion of managers is random with respect to gender (based only on the two categories of male
and female), we would expect close to half males and half females. However, if we observe
that 85 of the 100 managers are female and only 15 are male, we would be tempted to con-
clude that this distribution is non-random and that the company has an unfair distribution of
women to men based on our expectation of a 50/50 ratio. A chi-square analysis would be
used to try and verify that assumption that the ratio is 50/50.

The chi-square tests reviewed below are called nonparametric tests, because we do not have
to make the assumption that the data are normally distributed. They test nominal data, which
are measured in terms of frequencies. The key question asked in a chi-square test is whether
the frequencies observed in a group (or levels of a variable) are significantly different from
what is expected.

One Sample Chi-Square Goodness of Fit Test
We sometimes want to compare observed frequencies to see if they are a good fit compared
to the frequencies we expect to see. For example, we might want to compare the observed
frequencies of extroverts versus introverts in a classroom of 100 undergraduates to see if fits
a 50/50 distribution. An alternative hypothesis might be that there are a significantly unequal
number of introverts and extroverts in the class. Suppose that there were five rows of 20 seats
each and we used the Myers Briggs Type Indicator (MBTI) to determine if each of the 100
students in class is an extrovert or introvert. The expected frequencies of extroverts for each
row would be .5 × 20 = 10. The following distribution is observed where the column marked
O refers to the observed frequencies of extroverts (Table 5.3).

Table 5.3: Frequencies of extroverts

Row O

1 19

2 17

3 15

4 9

5 5

There are a total of 65 extroverts in the class. A calculation of the chi-square would show that
this is significantly different from the expected value of 50 extroverts. So, it appears there is
not an equal distribution of extroverts and introverts in the classroom.

Section 5.7Inferential Statistics

Chi-Square Test of Independence
We may want to compare several groups on some type of classification variable. For example,
we might want to gather data on Ashford University psychology majors who indicate that
they are a) going to graduate school after getting their bachelor’s degree, b) getting a job
after getting their degree, and c) not sure what they are going to do after graduation. Then,
perhaps, we want to compare them based on a four-category variable indicating the degree
status of their father: 1) high school or lower, 2) some college, 3) bachelor’s degree, or 4)
some graduate work or graduate degree. The results from a hypothetical study of 200 Ashford
University psychology majors are shown below (Table 5.4):

Table 5.4: Data from hypothetical survey of psychology majors

Degree of education
of father

High
school

Some
college

Bachelor’s
degree

Graduate
school

Row
total

Post-
graduate
plans

Grad school 4 14 22 40 80

Job 25 25 25 5 80

Not sure 10 15 10 5 40

Column total 39 54 57 50

What trends do you see in these data? A statistical analysis of these data would show that
there is a significant difference between the father’s degree status and what the child may do.
But, further analysis would be needed to determine which factor level is most significant.
However, a look at the observed table will often help the researcher determine what is signifi-
cant. For instance, you can see just looking at the table that a student whose father went to
graduate school is more likely to go to graduate school (the value is 40) than other students.

T E S T Y O U R K N O W L E D G E : D E S C R I P T I V E V E R S U S
I N F E R E N T I A L T E S T S
Which of the following is a good example of using inferential statistics?

1. Calculating the average age of each person in the U.S. for the Census.

2. Maintaining a spreadsheet of all the NFL player scores for each year.

3. Asking random people at a local mall about their shopping preferences to inform store
owners about current trends.

The first two are examples of descriptive statistics in that the data describes everyone in
a population. The third choice is inferential, as data from a random sample is used to make
inferences about a larger population.

Section 5.8Ethics in Research

5.8 Ethics in Research
What is research integrity and why is it
important from an ethical standpoint?
What are the costs of poor integrity in
research? Research integrity refers to
using honest and valid methods in design-
ing, conducting, and reporting research
based on ethical standards. These meth-
ods are often based on rules, regulations,
guidelines, and professional norms that
are regulated by professional organiza-
tions. For instance, all research in the
United States that involves human sub-
jects must adhere to the guidelines of the
Office of Human Research Protections of
the Department of Health & Human Ser-
vices, which can be found at https://www
.hhs.gov/ohrp/. Poor conduct in research can result in negative outcomes such as harm to
participants, loss of credentials (such as a professional license), and legal issues related to
fraud and scientific misconduct.

Researchers must consider everyone involved in the process of collecting and analyzing data,
from the researchers to the participants. Having strong research integrity is vital to ensuring
all people (and sometimes animals) involved are carefully considered and protected from
undue harm. For instance, if a researcher provides false data regarding a new medicine to
treat schizophrenia, patients that are prescribed the medicine could be exposed to harm-
ful side effects, or even death. Psychologists and researchers in this area are governed by
the guidelines proposed by the American Psychological Association, as discussed in prior
chapters.

While there are overarching ethical guidelines involved in the practice of psychology, there
are some considerations that are specific to data and research methods. Some ethical pitfalls
to avoid in research include the following:

• Data falsification: This occurs when researchers falsify data that they collected to
show the results that they are looking for even when nothing significant was found.

• Data fabrication: This occurs when researchers claim to have data even when none
was actually collected.

• Plagiarism: Unintentional plagiarism occurs when a researcher utilizes too much
information gathered from previous studies during the literature review stage.
Intentional plagiarism occurs when researchers publish others’ results as their own.

• Conflict of interest: A conflict of interest can occur when a researcher has another
obligation related to a study. For instance, a researcher that works for a pharmaceu-
tical company may choose to show only positive gains when reporting the results so
that the company (and the researcher) can receive financial benefit.

skynesher/E+/Getty Images
ሁ Research integrity requires conscientiousness

and an adherence to established ethical guidelines.

https://www.hhs.gov/ohrp/

Section 5.9Career Applications Involving Research and Statistic

5.9 Career Applications Involving Research
and Statistics

Many students ask, “why do I need to take research methods and statistics?” Statistics is not
most people’s favorite topic, but it is essential to practice in many areas of psychology, even
in non-research settings. To be viable in any field, it is essential that one is able to keep up-to-
date with current practices and research. For instance, a clinical psychologist that works with
veterans must understand the newest research on PTSD to be able to utilize the latest treat-
ments. To do that, he or she must read the current research articles on that treatment, which
requires an understanding of the research methods and principles of how the statistical test-
ing was applied. An industrial/organizational psychologist utilizes many different tests of fac-
tors like personality and career strengths, and a marketing analyst may be required to mine
user data to develop a report about the demographics of the people that have used a certain
social media tool and looked at their product. An understanding of statistical processes, such
as testing for reliability and validity, is essential to effectively utilize research tools. But indi-
viduals who do not intend to conduct research still need to know the main ideas behind the
most common strategies.

There are also many careers that are research-focused, even for individuals with only a bach-
elor’s degree. Most universities employ graduates that have a bachelor’s degree as analysts
in institutional research and assessment. These individuals are responsible for data entry,
designing studies, and analyzing data using basic and advanced statistical procedures. Gradu-
ates with skills in research design and statistics can also find work in many fields, such as
marketing, clinical research, and military organizations. As data becomes more and more
available in online modalities, there is also a need for graduates who are skilled at analyz-
ing information from social media sites and online behavior. Whether you want to conduct

Career Spotlight: Sandra G.

Sandra Gibson is a human resources analyst for a Fortune 500 company. She uses research
and data to help find the best job candidates for certain positions in the organization.
This may start with a job analysis for a particular position to determine the most desir-
able characteristics and qualifications for that specific job. For instance, if the company is
looked to hire a sales manager, her research may show that the best candidate would have
previous sales experience and be outgoing, conscientious, versatile, and able to work under
tight deadlines. Once the profile is created, the job is posted and people apply. Sandra can
use psychometric analyses, such as personality tests, along with performance measures,
to help find the best candidate for the sales manager position. While Sandra is not a tradi-
tional researcher, she uses research methods and data to perform her job and to find the
best candidates for her organization.

Additionally, Sandra must keep to an ethical code set by the government and by the com-
pany she works at, as well as her own moral compass. Every day she handles employees’
and applicants’ personal data, and it is her duty to keep that information confidential, even
from her other coworkers in human resources. She also has to remain impartial during
the job selection process, and refrain from letting bias interfere with her decision making.
Keeping these ethical considerations in mind, Sandra is always looking for new, data-based
ways to better predict who would be the best person for a certain position.

Summary

research or work in a particular area related to psychology, you need to have a good under-
standing of basic research methods and statistical analyses.

Summary
We began this chapter discussing why an understanding of research is essential to many dif-
ferent career paths psychology graduates might pursue. An understanding of how research
is conducted and reported is vital to being able to keep up with current trends in any field.
For instance, a person that works in marketing may need to learn about new methods of
utilizing social media to promote a product based on psychological factors. This chapter also
described some basic qualitative and quantitative methods that are common in psychological
research. In this chapter, common methods of visualizing data were reviewed, and a simple
overview of statistical analysis was given. This should be familiar from your previous statis-
tics class, but hopefully this served as a useful refresher. Lastly, the chapter discussed ethical
considerations, as being able to understand the ethical implications of research affords you
the chance to critically evaluate research and professional practices in many of the areas you
may find yourself in.

Concept Check
1. Which of the following might a research psychologist use research data for?

a. to create a new medicine
b. to learn more about other fields
c. to attempt to prove a hypothesis
d. to evaluate a program

2. A hypothesis does not have to be clear or specific to be testable.
a. True
b. False

3. Grouping people by their ethnic group membership constitutes data of what scale?
a. nominal
b. ordinal
c. interval
d. ratio

4. In statistics, a population refers to which of the following?
a. all of the values in a particular sample
b. all possible members of a defined group
c. a theoretical group
d. any group based on a large, random selection

5. Pie graphs have what particular strength?
a. they indicate each value individually
b. they list individual data points in order
c. they make proportions very clear
d. they make calculating descriptive statistics easy

6. Which of the following is not a measure of central tendency?
a. mean
b. range
c. median
d. mode

Summary

7. Studying the characteristics of a population by analyzing a sample describes which?
a. descriptive statistics
b. inferential statistics
c. statistical analysis
d. parameter estimation

8. Which of the following is not considered an ethical pitfall related to research?
a. data falsification
b. plagiarism
c. conflict of interest
d. making a lot of money

9. An understanding of research methods and results is important in many careers,
even if the professional is not conducting research.
a. True
b. False

Answers
1. c. The answer can be found in Section 5.1.
2. b. The answer can be found in Section 5.2.
3. a. The answer can be found in Section 5.3.
4. b. The answer can be found in Section 5.4.
5. c. The answer can be found in Section 5.5.
6. b. The answer can be found in Section 5.6.
7. b. The answer can be found in Section 5.7.
8. d. The answer can be found in Section 5.8.
9. a. The answer can be found in Section 5.9.

Questions for Critical Thinking
1. Your colleague asks you to be an author on her research article that she is submit-

ting for publication, but you have not been a part of that research project. Discuss
some ethical considerations that might be related to this situation.

2. An understanding of published research is important for a researcher, but it can
also be valuable to non-researchers. Describe some ways that you might need to
review and evaluate published research in the career that interests you.

3. A colleague approaches you with a research idea that involves investigating the
relationship between job satisfaction and employee selection at the organization
you work for. Describe what type of sampling methods and inferential statistics
might be most appropriate for this study.

Key Terms
analysis of variance (ANOVA) A set of
statistical models used to test the mean dif-
ferences on a variable of interest when there
are more than two groups.

average A general term that can refer to
the mean, median, or mode, but is most
often used to indicate the arithmetic mean.

chi-square analysis A set of statistical
analyses used to test if there are statistical
differences between expected and observed
frequencies.

cluster sampling A sampling strategy that
involves dividing the population into clus-
ters and then randomly choosing a certain
number of people in each cluster.

Summary

confounding variable Variables that
influence the independent and dependent
variables of a study but are not a part of the
research design. Also referred to as extrane-
ous variable.

convenience sample A sampling strategy
in which the participants are chosen based
on availability. See also non-probability
sampling.

correlation analyses A statistical test
to determine the strength of relationship
between two variables.

correlation coefficient The numerical
summary of a bivariate (two-variable) rela-
tionship. It is usually symbolized as r. The
correlation coefficient has a range of –1.0
(a perfect inverse relationship) to +1.0 (a
perfect positive relationship), with 0 being
no correlation at all.

cumulative frequency distribution A
table of frequencies of a data class that is
the sum of the data elements in that class
and all the previous classes.

dependent variable In scientific research,
it is the variable that represents the out-
come a researcher is attempting to measure;
it is directly affected by the independent
variable.

frequency distribution A statistical table
that displays the frequency of outcomes in a
sample.

hypothesis A testable statement that pro-
poses relationships or differences between
two or more variables.

independent variable In scientific
research, it is the stand-alone variable that
the researcher controls.

interquartile range The difference
between the lowest and highest values in
the two middle quartiles (the middle 50% of
the scores) when the distribution is divided
into four sections, or quartiles.

interval scale A scale of measurement
where the distance between data points is
equal and known.

nominal scale A level of measurement that
is categorical and has no inherent numerical
value.

non-probability sampling A method of
gathering a sample from participants who
are chosen based on the judgement of the
researcher rather than at random.

observational research A type of non-
experimental design in which the researcher
observes and records behaviors in the con-
text of the study’s parameters. Also referred
to as field research.

ordinal scale A level of measurement
where the order of responses is important,
but the difference between them is not
quantifiable.

outliers Data points that are located away
from the bulk of the scores.

pie chart A circular statistical graphic
which is divided into slices to illustrate the
numerical portions of a variable.

probability sampling Any method of
gathering a sample from a larger population
using some form of randomization.

proportional sampling A sampling
method that divides participants into
subgroups and samples each subgroup
randomly.

purposive sampling A sampling strategy
that involves selecting participants based on
some characteristic they may have that is of
interest to the researchers. See non-proba-
bility sampling.

quartile In descriptive statistics, the
quartiles of a ranked set of data values are
the three points that divide the data set into
four equal groups, each group comprising a
quarter of the data.

Summary

random sampling The sampling method
where everyone in a population has an
equal chance of being selected.

range The distance between the lowest and
highest score in a set of values.

ratio scale A scale representing the math-
ematical relationship between two numbers
indicating the number of times the first
number contains the second. This scale of
measure is also characterized by having an
absolute zero as the lowest possible value.

regression analysis A set of statistical
measures to assess the strength of the rela-
tionship between several variables.

research question A specific, clear, and
testable question that guides the research
design.

spurious relationship An assumed causal
relationship between two correlated vari-
ables that are almost certainly not caus-
ally related. Also referred to as spurious
correlation.

standard deviation A statistical value of
the variation in a set of data values.

stratified sampling The sampling strategy
where a population is divided into sub-
groups (the strata) and then each subgroup
is randomly sampled from.

systematic sampling A sampling strategy
that involves a set protocol for selecting
participants.

tests of differences Tests that examine
how variables or groups are different from
each other.

tests of relationships Tests that exam-
ine the relationships between groups or
variables.

t test A statistical test to assess if the means
of two groups are significantly different
from each other.

variable Any factor, in relation to research,
that varies in quantity or quality.

Professional Resources
Professional Organizations that Post Research Careers in Psychology
http://www.apa.org/careers/index.aspx

http://www.siop.org/

Research and Ethics Training
https://about.citiprogram.org/en/homepage/

Psychological Research Blog
http://bps-research-digest.blogspot.com/

http://www.apa.org/careers/index.aspx

http://www.siop.org/

https://about.citiprogram.org/en/homepage/

http://bps-research-digest.blogspot.com/

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