ME290Global Engineering Professional SeminarGlobalizat.docx

ME290
Global Engineering Professional Seminar
Globalization, Cultural Difference and
Collaborating Across Cultural Boundaries
Lecture-1
Globalization, Cultural Difference and
Collaborating Across Cultural Boundaries
Globalization the process by which
businesses or other organizations
develop international influence or
start operating on an international
scale.
Cultures
• Culture is the dominant set of behaviors, values,
beliefs, and thinking patterns we learn as we grow and
develop in our social groups.
In all but one of the following countries it is expected that
you bring a gift to a business meeting. In which country is it

not expected?
A. China B. Czech Republic
C. Japan D. Denmark
The correct answer is D. Denmark
Which of the following is associated with dead and should
not be given as a gift in the Chinese culture?
A. Clocks B. Straw sandals
C. Handkerchief D. Crane
The answer is - All of the above!
Typical Dimensions of Culture
Tree Model of CULTURE
Surface Culture
Deep Culture
Language
Dress
Art & Music

Food
Gestures Formality
Gender Roles
Religion
Holidays
Personal Space
Communication & Learning Styles
Time
Methods of Decision Making
Relationships
Attitudes
Work Ethic
Negotiating Styles
Values
Cultural Differences
Some main indicators of cultural differences are:
•Behavioral patterns: appearance vs. reality

•Non-verbal behavior: Gestures, signs, mimics
•Distance behavior: personal space vs. closeness
Behavioral Pattern
•If we don’t know anything about other cultures, we
tend to use stereotypes as our knowledge base –
Is this a right approach?
•What are stereotypes?
•Negative labeling of a certain group or culture
based on the actions and/or appearances of a
few individuals.
Non-Verbal Behavior
• Understood as the process of communication through
sending and receiving wordless messages.
• Language is not the only source of communication;
there are other means, including:
• Gestures and touch
• Body language or posture, facial expression and eye contact
• Object communication such as clothing, hairstyles or even
architecture and symbols
• Problems and conflicts can occur when expressing
gestures or facial expressions in other cultures –
messages can often be misinterpreted.

Distance Behavior
• The right personal distance when conducting business
shows respect and acceptance.
• Too much distance gives the impression of dislike and
discomfort.
• Too little will make the person draw back.
Diversity & Inclusion
Diversity refers to differences (such as age, gender,
ethnicity physical appearance, thought styles, religion,
nationality, socio-economic status, belief systems etc.)
Inclusion refers to the creation of opportunities and the
elimination of barriers that allow all people to
participate in and contribute to ideation, planning,
projects, programs, processes, teams, organizations,
social activities, fun or any other opportunity that helps
achieve successful outcomes.
Etiquette is the code of behavior that defines
expectations for social behavior according to
contemporary conventional norms within a society,
social class, or group
Global Business Etiquette
• With the “shrinking world” effect of globalization, a

critical element of success is the demonstration of
respect and appreciation for cultural difference.
• it is a necessity to be able to manage and do business
with people from different countries
• Future engineers should be prepared to work across
cultural and geographic differences.
Communication differences (Example)
Perceived Western Norm
• Shaking head from side
to side = "No".
• Personal space needed is
45 to 60 cm.
• Touching less common,
considered
inappropriate.
Perceived Indian Norm
• Shaking head from side to
side = Yes
• Personal space needed is 15

to 45 cm.
• Touching during
communication is common.
Interaction at work difference (Example)
Perceived Western Norm
• "Take charge" personality is
valued.
• A frank debate is OK.
• Disagreement with superior is
considered okay.
• Advancement is based on
performance and demonstrated
command of skills.
Perceived Indian Norm
• Defer to superiors
• Courteous & polite
communication

• Disagreement with superior
is considered disrespectful.
• Advancement is based in
large part on seniority and
longevity.
5 Keys to Doing Business Globally
1. Be aware of your own culture and its impact on you.
2. Don’t expect others to think the same way you do.
3. Accept local customs and norms and try to adapt
your behavior, but don’t try to imitate or act against
your own norms and values.
4. Be open, flexible, self critical, tolerant, sensitive and
show willingness to get involved in another culture.
5. Open your senses--be attentive and stay patient!
Listen, observe and try to understand before judging
and evaluating other behaviors.
Working on Cross-Cultural Teams
• Working with foreign colleagues should not be
regarded as a burden, but as an enrichment.

• Be helpful when recognizing language barriers, but
don’t be arrogant or correct every sentence.
• Take an interest in other cultures. Encourage
colleagues to share their unique experiences.
• Be careful not to mock or joke about culturally
sensitive issues. Humor is highly subjective and
varies widely across cultures.
Conclusions
• Rules of conduct and business etiquette exist in every
culture and help us to know how to behave in each situation.
• We know how to behave in our culture of origin, but we
don’t know how to behave in a foreign culture – there are no
general international rules of etiquette.
• Global etiquette can make the difference between opening
or closing doors to business opportunities.
• We need to build cultural awareness and seek specific
knowledge of other cultures to navigate global business and
partnership opportunities.

Week 2: Quantitative
Research Methods
Quantitative Research Methods
Learning objectives:
· Define Quantitative Research
· Learn the methods of data collection in Quantitative Research
· Explain key terms related to Quantitative Research
1.1 What is Quantitative Research?
Quantitative Research is used to quantify the problem via
generating numerical data or data that can be transformed into

useable statistics. It is used to quantify behaviors, attitudes,
opinions, and other defined variables, and generalize results
from large sample populations (Wyse, 2011). The main aim of
a quantitative research study is to classify features, calculate
them, and construct statistical models in an attempt to explain
what is observed.
1.2 The main characteristics of quantitative research (Earl,
2010):
· The data is usually collected using structured research tools.
· The results are based on larger sample sizes that are
representative of the population.
· The research study can usually be replicated, given its high
reliability.
· Researcher has a clearly defined research question to which
objective answers are sought.
· All aspects of the study are carefully designed before data is
gathered.
· Data are in the form of numbers and statistics, usually
arranged in tables, charts, figures, or other non-textual forms.
· Project can be used to generalize concepts more widely,
predict future results, or investigate causal relationships.
· Researcher uses tools, such as questionnaires or computer
software, to collect numerical data.
1.3 When to Use Quantitative Methods (Creswell, 2002):
Researchers should begin by asking themselves the following

questions:
· What type of question am I asking?
· What type of data will I need to collect to answer the
question?
· What type of results will I report?
For instance, a researcher may want to explore the association
between income and whether or not families have health
insurance. This is a question that asks “how many” and seeks to
confirm a hypothesis. Hence, the methods will be highly
structured and consistent during data collection (e.g. a
questionnaire with closed-ended questions). The results will
generate numerical data that can be analyzed statistically as the
researcher looks for a correlation between income and health
insurance. This is an example where quantitative research
should be applied. A quantitative approach will allow the
researcher to test the relationship between the two factors (i.e.
income and health insurance). The data can be also used to look
for cause and effect relationships and therefore, can be used to
make predictions.
On the other hand, another researcher might be interested in
exploring the reasons that people choose not to have health
insurance. This researcher is interested in the various reasons
why people make that choice and what the possible barriers may
be when people choose not to get insurance. This is an open-
ended question that will not provide results that can be
statistically analysed. Qualitative methodology would best
apply to this research problem.

Examples of research questions:
Are females more likely to be teachers than males?
Is the proportion of males who are teachers the same as the
proportion of females?
Is there a relationship between gender and becoming a teacher?
In the example above, you can see that there are different ways
of approaching the research problem, which is concerned with
the association between males and females in teaching.
1.4 Data collection in Quantitative Research:
Data Collection is an important part of any type of research
study. Inaccurate data collection can influence the results of a
study and ultimately lead to invalid results.
Sources of Quantitative Data (Leedy and Ormrod, 2001):
The most popular sources of quantitative data include:
· Experiments/clinical trials.
· Observing and recording well-defined events. These may
either involve counting the number of times that a particular
phenomenon/behavior occurs (e.g. how often a specific word is
used in interviews, counting the number of patients waiting in
emergency at specified times of the day), or coding
observational data to translate it into numbers and secondary
data (e.g. company accounts).

· Obtaining relevant data from management information
systems.
· Administering online, phone or face-face surveys with closed-
ended questions. These require that the same questions are
asked in the same way to a large number of people.
Prior to designing a quantitative research study, researchers
needs to decide whether it will be descriptive or experimental,
as this will specify how they gather, analyze, and interpret the
results. A descriptive study is based on three basic rules: 1)
subjects are usually measured once 2) the intention is to merely
establish associations between variables and 3) the study may
include a sample population of hundreds or thousands of
subjects to ensure that a valid estimate of a generalized
relationship between variables has been obtained. An
experimental design includes: 1) subjects measured before and
after a particular treatment 2) the sample population may be
very small and purposefully chosen, and 3) it is intended to
establish causality between variables. Quantitative researchers
try to identify and isolate specific variables involved within the
study framework, seek correlation, relationships and causality,
and take actions to control the environment in which the data is
gathered to avoid the risk of other variables, besides the one
being studied, accounting for the relationships identified.
1.5 Some of the strengths of using quantitative methods to study
research problems include
(Earl,2010):
· Enhances the generalization of the results, as it allows for
broader studies to be contacted, involving a greater number of
people.

· Increased objectivity and accuracy of results. Quantitative
methods are typically designed to provide summaries of data
that can be generalized. In order to accomplish this, quantitative
studies usually involve few variables and many cases, and uses
prescribed techniques to ensure validity and reliability.
· Applying well established standards allows for replication,
and the comparison with similar studies
· Allows for summarizing a vast amount of information and
making comparisons across categories and over time
· It decreases personal bias can by keeping a 'distance' from
participants and using established computational techniques
1.6 Some limitations associated with using quantitative methods
include (Earl, 2010):
· Although quantitative data is more efficient and allows to test
hypotheses, it can miss contextual detail
· Uses a static and rigid approach, and hence employs a process
of discovery that tis not very flexible
· There is high risk for "structural bias" and false representation
due to the development of standard questions by researchers
(i.e. the data actually reflects the view of the researcher instead
of the participant)
· Results provide less detail on behavior, attitudes, and
motivation

· Researcher may collect a dataset that is much narrower and
sometimes superficial
· Results provide only numerical descriptions (but not detailed
narrative) and less elaborate accounts of human perception
· The research is usually conducted in an unnatural, artificial
environment (i.e. laboratory) to increase the level of control
applied to the exercise. However, this level of control might not
normally be applied in real world settings thus providing
"laboratory results" as opposed to "real world results"
· Preset answers will not necessarily reflect how people really
feel about a topic and, in some cases, might just be the closest
match to the preconceived hypothesis.
1.7 Quantitative Data (Abramson & Abramson 2008):
Before analyzing quantitative data, researchers must identify
the level of measurement associated with the quantitative data.
The level of measurement can affect the type of analysis that
will be used. There are four levels of measurement:
· Nominal data: Data has no logical order. It is basic
classification data Example: Male or Female
There is no order associated with male or female
· Ordinal data: Data has a logical order, but the differences
between values are not constant Example: T-shirt size (small,

medium, large)
Example: Military rank (from Private to General)
· Interval data: Data is continuous and has a logical order, data
has standardized differences between values, but no natural zero
Example: Fahrenheit degrees
Remember that ratios are meaningless for interval data. You
cannot say, for example, that one day is twice as hot as another
day.
· Ratio (scale): data is continuous, ordered, has standardized
differences between values, and a natural zero
Example: height, weight, age, length
Having an absolute zero allows you to meaningful argue that
one measure is twice as long as another. For example, 10 inches
is twice as long as 5 inches
Remember that there are various ways of approaching a research
question and how the researcherputs together a research
question will determine the type of methodology, data
collection method, statistics, analysis and presentation that will
be used to approach the research problem.
In another research problem the relationship between gender
and smoking is explored. In this case there are two categorical
variables (i.e. gender and smoker), with two or more groups in

each. For example:
· Gender (male/female)
· Smoker (yes/no)
The researcher investigates whether or not there is a significant
relationship between these variables.
1.8 Variables:
An experiment has three characteristics:
1. A manipulated independent variable (often denoted by x,
whose variation does not depend on that of another).
2. Control of other variables i.e. dependent variables (a variable
often denoted by y, whose value depends on that of another.
3. The observed effect of the independent variable on the
dependent variables.
In science, the term observer effect means that the act of
observing will influence the phenomenon being observed.
Example of Variables in Scientific Experiments
If a scientist conducts an experiment to test the theory that a
vitamin could extend a person’s life-expectancy, then:

The independent variable is the amount of vitamin that is given
to the subjects within the experiment. This is controlled by the
experimenting scientist.
The dependent variable, or the variable being affected by the
independent variable, is life span.
Table 1.
Key terms associated with quantitative research (Field, 2013)
Hypothesis/Null hypothesis:
A hypothesis is a logical assumption, a reasonable guess, or a
suggested answer to a research problem.
A null hypothesis states that minor differences between the
variables can occur because of chance errors, and are therefore
not significant.
*Chance error is defined as the difference between the predicted
value of a variable (by thestatistical model in question) and the
actual value of the variable.
In statistical hypothesis testing, a type I error is the incorrect
rejection of a true null hypothesis (a "false positive"), while a
type II error is incorrectly retaining a false null hypothesis (a
"false negative"). Simply, a type I error is detecting an effect
(e.g. a relationship between two variable) that is not present,
while a type II error is failing to detect an effect that is present.
Randomised, controlled and double-blind trial:
Randomised - chosen by random.

Controlled - there is a control group as well as an experimental
group. Double-blind - neither the subjects nor the researchers
know who is in which group.
References
Creswell, J. W. (2002). Educational research: Planning,
conducting, and evaluating quantitative. Prentice Hall.
Earl B.R. (2010). The Practice of Social Research. 12th ed.
Belmont, CA: Wadsworth Cengage.
Kultar, S. (2007). Quantitative Social Research Methods. Los
Angeles, CA: Sage
Wyse, S.E. (2011). What is the Difference between Qualitative
Research and QuantitativeResearch? Retrieved
fromhttps://www.snapsurveys.com/blog/what-is-the-difference-
between- qualitative-research-and-quantitative-research/

Week 3: Qualitative
Research Methods
Qualitative Research Methods
Learning Objectives
· To introduce students to the main concepts related to
qualitative research
· To gain an understanding of the main methods of qualitative
data collection
· To introduce the ethical considerations and other related to
qualitative research particularly
1.1 Introduction to Qualitative research methods
Qualitative research is a form of social inquiry that focuses on
the way people interpret and make sense of their experiences
and the world in which they live.” (Holloway, 1997, p.2).
"Qualitative" is an umbrella term used for a wide range of
methods that have always been widely used by modern social

sciences. These ways of investigating and analysing social
aspects of life, in business and in other sectors are useful for all
fields of international research, whether at the level or
individuals, organisations or groups, during periods of routine
or crisis, and regarding past or present times. This know-how
can be of great use in many professional settings, beyond
academic research, including: business organisations, profit and
non-profit organisations, expertise and consultancy at local,
national and international levels, education settings and more
generally any position necessitating a deep understanding of
social phenomena.
1.2 Investigating a research problem:
When we are investigating a social phenomenon, we problem. In
order to examine it, we need to design athe research questions.
are interested in examining a researchqualitative research study
and address
For instance:
Ø Research problem:

Every year, students following compulsory research training
units on the MSc in Research Methods online programme come
from a wide range of backgrounds – from different countries;
different education systems; and different professional
backgrounds.
Recently many students from non-European countries join the
course. Course tutors on the MSc need to be able to tailor their
teaching to meet the needs and expectations of this diverse
group. This makes it important to research new students’
backgrounds, their motivations for joining the MSc, and what
they hope to get out of the programme.
To do so, the following research questions need to be addressed:
· Why do students choose to study for the MSc in Research
Methods through the online programme?
· What do students hope to get out of the programme?
· What backgrounds do students come from?
Ø Designing your own qualitative study
Considering the above research problem here are some things
you might want to think about
before you design your own qualitative study:
· What, specifically, do you want to find out from the students?
· What methods could you use to generate this data?
· How will you chose which students to involve?

· How will you make sure the students you involve can tell you
the things which are most important to them?
· What problems might you encounter?
1.3 Qualitative methods of data collection
a) Interviews
The interview is a flexible tool for data collection, enabling
multi-sensory channels to be used such as verbal, non-verbal,
spoken and heard (Cohen, Manion & Morrison, 2013).
Interviewing is about creating a dynamic situation where you
can access information which is not otherwise available and
which illuminates your research questions.
· Why interview?
The purposes of the interview in the wider context of life are
varied (Examples below):
· To evaluate or assess a person in some respect
· To select or promote an employee
· To effect therapeutic change, e.g a psychiatric interview
· To test or develop hypotheses
· To gather data, as in surveys or experimental situations
· To sample respondents’ opinions

As a distinctive research technique, the interview serves three
main purposes:
1) To be used as the principal means of collecting information
having direct bearing on the research objectives.
2) To be used to test hypotheses or to suggest new ones or as an
explanatory device to help identify variables and relationships.
3) The interview to be used in conjunction with other methods
in a research undertaking
(Cohen et al., 2007, p.351)
· Planning interview-based research procedures:
a) Designing an interview schedule
Before the actual interview
b) Setting up an interview
– A few practical and ethical considerations
c) Conducting an interview
– What’s it like being interviewed? Feedback from our
interviewees

– Comparing our experiences as interviewers
– Listening to interviews: what issues are raised?
Your role as an interviewer:
· Learner & listener
– non judgmental
· Facilitator & manager
It’s your job to:
– manage the interview
– make the interview a positive experience for the interviewee
Structuring your Interview
· Questions
– clear, simple, non-leading, open
– Finding an easy way in
– general to the specific and vice versa
· Schedules and Interview guides
Example 1: Excerpt from a semi-structured interview schedule
· Questions can be prepared ahead of time

· Open-ended questions encourage communication
· Participants express their views in their own terms
· Provides a clear set of instructions
· reliable, comparable qualitative data
Semi-Structured Interviews
· Formal interview
· Follows an 'interview guide.‘
· List of questions and topics that need to be covered.
· Identified from prior research or unstructured interview or
focus groups
· Questions are open-ended
· Flexible
· Interviewer can follow-up interesting points made
· Even if they deviate from guide
Topic: Government sponsored study
Fred: it’s a regeneration area and obviously we’ve got to knock
some houses down in order to build some ones to regenerate the
area and one of the residents who was a lady said everybody
else seems to be getting everything and we’re getting nothing.
Well outside there is a brand new Learning Centre and she said
that’s not for us. You’re going to move us out and move new
people in and they’ll get the benefit of that new Learning

Centre.
Mel: It’s too good for us you mean?
Fred: No I think, well yes in a sense I think she was saying that,
but I also think in another way what she was saying was that
you decided it’s not for us because you’re knocking our houses
down and you’re moving us somewhere else and you’re bringing
new people and you’re bringing new people on who have got
jobs and got money and that’s for them, not for our kids.
Example 2:
Topic: What your ITT programme involves and why
Q1. Can you tell me about the way your ITT programme is
designed?
Prompts (if necessary)
· What are the key activities trainees engage in and how is their
time spent in different tasks/locations?
· Who are the different people involved with the trainees’
programme and how are they involved?
Q2. (If not mentioned above) Can you talk about why the
programme has been designed in this way?
· Are there any principles or other underlying ideals guiding the

programme design?
· Are there any practical influences/shapers? {funding
constraints, staff/trainee retention, requirements of Standards}
· {Unpack what interviewee means by theory and practice if
they refer to these concepts}
Q3. Do you have any ideas about how the programme might
develop in the future?
· (If applicable) are any of these plans presently in motion?
· (If applicable) what might help or hinder the development of
these plans?
Ethical considerations
– Putting your interviewee first
– Confidentiality
– Permission to record
Creating a suitable environment
– Friendly seating
– Encouragement (&management) through body language

b) Focus Group Interview
What is focus group interview (FGI)?
“A research technique which allows the collection of qualitative
data through group interaction, on a topic determined by the
researcher” (Morgan, 1996).
“The dialogic nature of the Focus Group Discussions allows the
co-construction of meaning between the different interviewees
on the topic investigated” (Overlien, 2005)
Why focus groups interviews?
· Useful methodology in exploring and examining what people
think, how they think, and why they think the way they think
about the issues of importance. No pressure to them into making
decisions or reaching a consensus.
· “ideal” approach for examining the stories, experiences, points
of views, beliefs of individuals.
· The participants can develop their own questions and
frameworks and seek their own needs
· The research can access different communication forms which
people use to their
everyday life (joking, teasing etc.) à gain access to diverse
forms of communication à valuable as it may not be possible or
difficult to capture the knowledge of individuals by asking them

to respond to more direct questions such as in surveys and
questionnaires.
· Focus groups permit researchers to enter the world of
participants which other research methods may not be able to
do.
· Focus group discover how “accounts are articulated, opposed
and changed through social interaction and how this relates to
peer communication and group norms”
· Offer the researchers a means of obtaining an understanding
(insight) of a wide range of views that people have about a
specific issue and how they interact and discuss the issue (eg in
the paper p.5).
· A focus group interview is useful when the research does not
have a depth of knowledge about the participants (thoughts,
feelings, understandings, perceptions and impressions of people
in their own words).
· Obtaining in-depth understanding of the numerous
interpretations of a particular issue of the research participants.
· Particularly suitable for exploring issues “where complex
patterns of behaviour and motivation are evident, where diverse
views are held”
· Explore the gap between what people say and what they do.
· Ideal for many people from ethic minority group.
· FGI as a basis for empowering marginalised people.

· Ability to cultivate people’s responses to events as they
involve.
(Liamputtong, 2011)
When do we use FGI?
· Exploratory phase – opening up issues
· Main phase
· as an alternative to interviews
· as a precursor to interviews
· Testing findings (especially implications)
Advantages of focus group methodology:
Some criticism about the focus group
methodology:
· Researchers are provided with a great

· Focus group discussions may not be
opportunity to appreciate
the way
sufficiently in depth to allow the
people see their own reality
à they get
closer to data
researchers to gain a good
understanding of the participants’
experiences.
·
Intended
individuals
and
groups

à
· The participants may not actively
more
people
are
involved
in
the
take part in group discussions.
research projectà the research will
meet their needs.

·
Enables
in
–depth
discussions and
· Some research topics are unsuitable
involves
a
relatively small
number
of
for focus group environments (such
people.

as living with HIV/AIDS). Such
interviews can be carried out by other

methods (personal interviews).
· Focused on a specific area of interest
· The quality of data generated will be
à allows participants to
discuss
the
affected by the characteristics and
topic in greater detail

context of the focus group.
· Group
processes assist people to
· Certain personalities (such as
explore and clarify their points of view
dominant) may influence the group
à the “group effect”

discussion.
· There
is a moderator, researcher that
· Personal info and experience may not
guides
and
assists
the participants
to
be discussed.
discuss

the
topic.
Encourages
interaction and guides conversation.
·
Moderator
à obtaining good and
· Criticised for offering a shallower
accurate info from the focus group à
understanding of an issues than those
crucial role. Can be more than one

moderator in one FG.
obtained from individual interviews.
· The participants can share social and
· In institutional workplaces
cultural
experiences
(age,
gender,
(workplace or schools) people may
educational background) or other areas
of concern (divorce, marriage etc)

be reluctant to express their opinions
or discuss their personal experiences
in front of their colleagues.

Virtual Focus Groups :
· Reduction of costs and time of research fieldwork
· Feasibility of bringing together individuals who are located in
geographically dispersed areas
· Availability of a complete record of the discussion without the
need of transcription
· Anonymity secured by the research setting
Issues to consider in focus groups:
Group size (6-10 individuals)
· Group composition (homogeneous vs. heterogeneous groups)
· Discussion schedule with appropriate questions
· Methods of data recording (difficult to rely only on field-
notes; consent for tape recording)
· Ethics (consent forms; anonymity; confidentiality of results)

· Rules of engagement
· Role of the group moderator (personal and leadership skills;
setting rules of engagement) (Liamputtong, 2011)
c) Using observations
Why do I want to observe?
· “The distinctive feature of observation as a research process is
that it offers the investigator the opportunity to gather ‘live’
data from naturally occurring social situations. In this way the
researcher can look directly at what is taking place in situ rather
than relying on second-hand accounts” (Cohen et al., 2007, p.
397).
· What people do differ from what they say they do and
observation provides a reality check (Robson, 2002, p. 310)
When doing observations . . .
· Do I want to look, or listen, or hear or both?
· Do I want to observe in real time, or later, or both?
– If in real time, do I record on paper or electronically?
– If later, do I capture events by voice recorder, or video, or
stills camera, or in text (e.g. chat rooms)
· Do I want to observe continuously, or for fixed periods, or at
fixed intervals?

· Do I want to observe the whole arena, or just part of it?
· Do I want to the same events/people all the time, or to
change?
· Do I want to do the observation myself or involve others?
Diagram 1: Decisions involved when conducting research using
observations
Developing a focus:
LeCompte and Preissle (1993, p. 199-200) provide a useful set
of guidelines for directingobservations of specific activities,
events or scenes and they suggest that they should include
answers to the following questions:
· What sorts of events do I want to observe? Do I know:

– Precisely? Can I develop a set of codes to categorise events?
– To some extent? Can I specify the types of event but not the
detailed events?
– Only in general terms? Do I have a broad focus, but little idea
what will happen in detail?
· Is my main focus:
– What happens?
– When it happens (how often, in what sequence)?
– Who is involved?
– Some combination?
· What matters most:
– Quantification?
– Description?
· Do I want to/can I be:
– Completely out of the picture (e.g. behind a screen)?
– Present but not participating?
– Present, participating & recording?
– Present, fully involved, & not recording?
· What is the impact of my role on events?

· What are the ethical implications of my role?
· What aspects of context do I need to record:
– Who’s present?
– What they are there for?
– Where people/objects are?
– How people move?
– What time it is?
– How long the observation lasts?
– What changes?
– What happens before/after?
· Field notes
– Record what is important in the light of a ‘sensitising
framework’
– Rely heavily on the observer’s sensitivity
· Semi-structured schedules
– Tight-loose structure, e.g.:
· broad categories tight – detailed description loose

· timing tight – focus loose
· Structured schedules
– Tight structure, especially for coding events
– Systematic and enables the researcher to generate numerical
data from the observations and they can facilitate to make
comparisons between settings and situations. The observer
adopts a passive, non-intrusive role (Cohen et al., 2007)
Further reading:
Boyce, C. and Neale, P., (2006). Conducting in-depth
interviews: A guide for designing and conducting in-depth
interviews for evaluation input.
Doody, O., & Noonan, M. (2013). Preparing and conducting
interviews to collect data. Nurseresearcher, 20, 28-32.
Fereday, J. and Muir-Cochrane, E., (2006). Demonstrating rigor
using thematic analysis: A hybrid approach of inductive and
deductive coding and theme development. Internationaljournal
of qualitative methods, 5(1), pp.80-92.
Jacob, S. A., & Furgerson, S. P. (2012). Writing interview
protocols and conducting interviews: Tips for students new to
the field of qualitative research. The Qualitative Report,
17, 1-10.

Kitzinger, J. (1994). The methodology of focus groups: the
importance of interaction between research participants.
Sociology of health & illness, 16(1), 103-121.
Kitzinger, J. (1995). Qualitative research. Introducing focus
groups. BMJ: British medicaljournal, 311, 299.
Liamputtong, P. (2011). Focus group methodology: Principle
and practice. Sage Publications.
Smithson, J. (2000). Using and analysing focus groups:
limitations and possibilities.
International journal of social research methodology, 3, 103-
119.
Videos:
Fundamentals of Qualitative Research:
https://www.youtube.com/watch?v=wbdN_sLWl88
References:
Cohen, L., Manion, L. and Morrison, K., (2007). Research
methods in education. Routledge.
LeCrompte, M. and Preissle, J. (1993) Ethnography and

Qualitative Design in EducationalResearch (second edition).
London: Academic Press
Liamputtong, P. (2011). Focus group methodology: Principle
and practice. Sage Publications.
Robson, C., (2002). Real world research. 2nd. Edition.
Blackwell Publishing. Malden.
Week 3: Qualitative
Data Analysis
Qualitative Data Analysis

Learning Objectives
· To discuss some of the theoretical models within which
qualitative data can be analysed, and select the most appropriate
one for a particular piece of research
· To understand the stages involved in qualitative data analysis
(coding procedures and developing themes
· To assess how rigour can be maximised in qualitative data
analysis
1.1 Introduction to Qualitative Data Analysis
You are probably familiar with the basic differences between
qualitative and quantitative research methods based on the
previous weeks and the materials provided and the different
applications those methods can have in order to deal with the
research questions posed.
Qualitative research is particularly good at answering the ‘why’,
‘what’ or ‘how’ questions, such as:
· “What are the perceptions of carers living with people with
learning disability, as regards their own health needs?”
· “Why do students choose to study for the MSc in Research
Methods through the online programme?
Qualitative researchers are not generally interested in the
discovery of cause and effect relationships.

1.2 What do we mean by analysis?
· Qualitative Data Analysis (QDA) is the range of processes and
procedures whereby we move from the qualitative data that have
been collected into some form of explanation, understanding or
interpretation of the people and situations we are investigating.
· QDA is usually based on an interpretative philosophy. The
idea is to examine the meaningful and symbolic context of
qualitative data
· A generous amount of words is created by interviews or
observational data and needs to be described and summarised.
· The questions asked may require the researchers to seek
relationships between various themes that have been identified,
or to relate behaviour or ideas to biographical characteristics of
respondents such as age or gender.
· Implications for policy or practice may be derived from the
data, or interpretation sought of puzzling findings from previous
studies.
· Ultimately theory could be developed and tested using
advanced analytical techniques.
1.3 Approaches in Analysis
a) Deductive approach
· Using your research questions to group the data and then look
for similarities and differences

· Used when time and resources are limited
· Used when qualitative research is a smaller component of a
larger quantitative study
b) Inductive approach
· Used when qualitative research is a major design of the
inquiry
· Using emergent framework to group the data and then look for
relationships
In summary:
There are no ‘quick fix’ techniques in qualitative analysis
(Lacey & Luff, 2007).
• There are probably as many different ways of analysing
qualitative data as there are qualitative researchers doing it!
• It is argued that qualitative research is an interpretive and
subjective exercise is intimately involved in the process, not
aloof from it (Pope & Mays 2006).
However there are some theoretical approaches to choose from
and in this week we will explore a basic one. In addition there
are some common processes, no matter which approach you
take. Analysis of qualitative data usually goes through some or
all of the following stages (though the order may vary):

· Familiarisation with the data through review, reading,
listening etc
· Transcription of tape recorded material
· Organisation and indexing of data for easy retrieval and
identification
· Anonymising of sensitive data
· Coding (may be called indexing)
· Identification of themes
· Re-coding
· Development of provisional categories
· Exploration of relationships between categories
· Refinement of themes and categories
· Development of theory and incorporation of pre-existing
knowledge
· Testing of theory against the data
· Report writing, including excerpts from original data if
appropriate (e.g. quotes from interviews)
Adapted from Pacey and Luff (2009, p. 6-7)

1.4 What do you want to get out of your data?
It is not always necessary to go through all the stages above,
but it is suggested that some of them are necessary in order to
go in-depth in your analysis!
Let’s take an example based on the research question provided
above about the health needsof the carers:
Research question:
“What are the perceptions of carers living with people with
learning disability, as regards their own health needs?”
· You may be interested in finding out the community services
that needs to be provided in order the perceived needs of the
carers to be met.
· You might also be interested to know what kind of services
are needed or are valued by most of the carers.
· Maybe several respondents mention that they struggle with
depression and loneliness
In order to explore this, three broad levels of analysis that could
be pursued are as follows:

· One approach is to simply count the number of times a
particular word or concept occurs
(e.g. loneliness) in a narrative. Such approach is called content
analysis. It is not purely qualitative since the qualitative data
can then be categorised quantitatively and will be subjected to
statistical analysis
· Another approach is the thematic analysis from which we
would want to go deeper than this. All units of data (e.g.
sentences or paragraphs) referring to loneliness could be given a
particular code, extracted and examined in more detail. Do
participants talk of being lonely even when others are present?
Are there particular times of day or week when they experience
loneliness? In what terms do they express loneliness? Are those
who speak of loneliness are also those who experience depress?
Such questions can lead to themes which could eventually be
developed such as ‘lonely but never alone’.
· Finally, for theoretical analysis such as grounded theory we go
further in depth. For example, you may have developed theories
when you have been analysing the data with regard to
depression as being associated with perceived loss of a ‘normal’
child/spouse. The disability may be attributed to an accident, or
to some failure of medical care, without which the person cared
for would still be ‘normal’. You may be able to test this
emerging theory against existing theories of loss in the
literature, or against further analysis of the data. You may even
search for ‘deviant cases’ that is data which seems to contradict
your theory, and seek to modify your theory to take account of
this new finding. This process is sometimes known as ‘analytic
induction’, and is use to build and test emerging theory. (Lacey
& Luff,
2009, p.8)

In the following sections we will explore two approaches for
qualitative data analysis: a) grounded theory approach and b)
thematic analysis.
1.5 Grounded Theory
· Glaser & Strauss (1967)
· Aim = to generate/discover a theory
· Systematic
· Based on observations
· Focus on social processes
Developed out of research by sociologists Glaser and Strauss
(1967). Glaser and Strauss were concerned to outline an
inductive method of qualitative research which would allow
social theory to be generated systematically from data. As such
theories should be ‘grounded’ in rigorous empirical research,
rather than to be produced based in the abstract.
Grounded theory is a methodology; it is a way of thinking about
and conceptualising data. Itis an approach to research as a
whole and as such can use a range of different methods.
Grounded Theory analysis is inductive, in that the resulting
theory ‘emerges’ from the data through a process of rigorous
and structured analysis.

1.6 Procedure and the Rules of Grounded Theory approach
1. Data Collection and Analysis are Interrelated Processes. In
grounded theory, the analysis begins as soon as the first bit of
data is collected.
2.Concepts Are the Basic Units of Analysis. A theorist works
with conceptualizations of data, not the actual data per se.
Theories can't be built with actual incidents or activities as
observed or reported; that is, from "raw data." The incidents,
events, and happenings are taken as, or analyzed as, potential
indicators of phenomena, which are thereby given conceptual
labels. If a respondent says to the researcher, "Each day I
spread my activities over the morning, resting between shaving
and bathing," then the researcher might label this phenomenon
as "pacing." As the researcher encounters other incidents, and
when after comparison to the first, they appear to resemble the
same phenomena, then these, too, can be labeled as "pacing."
Only by comparing incidents and naming like phenomena with
the same term can a theorist accumulate the basic units for
theory. In the grounded theory approach such concepts become
more numerous and more abstract as the analysis continues
3. Categories Must Be Developed and Related. Concepts that
pertain to the same phenomenon may be grouped to form
categories. Not all concepts become categories. Categories are
higher in level and more abstract than the concepts they
represent. They are generated through the same analytic process
of making comparisons to highlight similarities and differences
that is used to produce lower level concepts. Categories are the
"cornerstones" of a developing theory. They provide the means
by which a theory can be integrated.
4. Sampling in Grounded Theory Proceeds on Theoretical
Grounds. Sampling proceeds not in terms of drawing samples of
specific groups of individuals, units of time, and so on, but in

terms of concepts, their properties, dimensions, and variations.
5. Analysis Makes Use of Constant Comparisons. As an incident
is noted, it should be compared against other incidents for
similarities and differences. The resulting concepts are labelled
as such, and over time, they are compared and grouped as
previously described.
6. Patterns and Variations Must Be Accounted For. The data
must be examined for regularity and for an understanding of
where that regularity is not apparent.
7. Process Must Be Built Into the Theory. In grounded theory,
process has several meanings. Process analysis can mean
breaking a phenomenon down into stages, phases, or steps.
Process may also denote purposeful action/interaction that is
not necessarily progressive, but changes in response to
prevailing conditions
8. Writing Theoretical Memos Is an Integral Part of Doing
Grounded Theory. Since the analyst cannot readily keep track of
all the categories, properties, hypotheses, and generative
questions that evolve from the analytical process, there must be
a system for doing so. The use of memos constitutes such a
system. Memos are not simply about "ideas."
(adapted from Corbin and Strauss, 1990, pp.7-10)
1.7 Thematic Analysis approach (Braun & Clarke, 2006, p.79)
· Flexible

· Interview data-Categorised into themes
· Surface analysis
· Reflects reality
· Acceptance of what is said
Thematic analysis is a method for identifying, analysing, and
reporting patterns (themes) within data. It minimally organises
and describes your data set in (rich) detail. However, it also
often goes further than this, and interprets various aspects of
the research topic (Boyatzis, 1998).
· Boyatzis (1998) defines the 'unit of coding' as the most basic
segment or element of the raw data of information that can be
assessed in a meaningful way regarding the phenomenon (pxi)
· A good thematic code 'captures the qualitative richness of the
phenomenon' (Boyatzis 1998, p.31) and has 5 elements:
· A label
· A definition of when the theme occurs
· A description of how to know when the theme occurs
· A description of any qualifications or exclusions to the theme
· Examples to eliminate possible confusion when looking at the
theme
Braun and Clarke (2006) identify some "potential pitfalls" to be
avoided in qualitative analysis:

1. A failure to actually analyse the data
2. Using data collection questions as themes that are reported
3. A weak or unconvincing analysis
4. A mismatch between the data and the analytic claims that are
made about it.
Phases of thematic analysis (inductive and deductive) (Braun &
Clarke, 2006)
Phase
Description of the Process
1.
Development of
Determining important theoretical
a priori codes
areas that can be used as initial

codes to organize the data
(Boyatzis, 1998). Use of theory-
driven coding that links to the
theoretical framework of the
study.
2.
Familiarization with the
Transcription of data and field

data
notes, reading and re-reading the
data, noting down initial ideas
(Braun & Clarke, 2006)
3.
Carrying out theory-driven coding
Coding data in a systematic
fashion within each interview and
the field notes and across the

entire data collating data relevant
to each a priori code (Boyatzis
1998; Braun & Clarke, 2006).
4. Reviewing and revising codes and
Reviewing and revising theory-
Carrying out additional data-driven coding
driven codes in the context of the
data (Boyatzis, 1998). Additional

coding is done at this stage, which
is not confined by the a priori
codes and inductive (data-driven)
codes are assigned to the data
(Fereday
&
Muir-Cochrane,

2006).
5.
Searching for themes
Collating
codes
into potential
themes, gathering all data relevant
to each potential theme (Braun and Clarke, 2006; Fereday and
Muir-Cochrane, 2006)
6.
Reviewing themes
Checking if the themes produced
are related to the coded extracts
(Level 1) and the entire data set
(Level 2) as well as developing the

thematic ‘map’ of the analysis
(Braun & Clarke, 2006) so as to
determine credibility of the themes
(Fereday and Muir-Cochrane,
2006).
7.
Producing the report
The final
opportunity
for
the
analysis in which vivid compelling
extract examples are
selected,
final analysis of selected extracts,
relating back the analysis to the

research
questions
and
the
relevant literature and
producing
a scholarly report of the analysis
(Braun and Clarke, 2006).
1.8 Example of qualitative data analysis using thematic analysis
Question: “how do you feel about your student
accommodation?”
Participants: 10 Master’s students living in student
accommodation an open question
• You have coded three data segments using the code
‘satisfactory accommodation’. You have defined ‘satisfactory’
as instances when students indicate that their accommodation
generally meets their needs, but they report mixed views,
balancing positive opinions with critical comments. You have
decided not to include views which are almost exclusively
positive or negative. The data segments you have coded as

‘satisfactory’ are:
‘It’s okay – it’s not my home, my house at home in my country,
but I have the things I need, desk, bed, arm chair, clean and
warm, not damp or anything.’ (Student 3)
‘It could be nicer – the decoration is a bit old, and it can be a
little bit noisy at night sometimes – but overall it’s fine just for
students. When I graduate and get a job, I want to rent a more
modern apartment, fashionable with lots of technology.’
(Student 9)
‘The only thing is it’s a bit small… I can’t invite all my friends
to my room to watch television or chat, so we have to go to the
coffee shop, cinema… it’s a bit
expensive always going out. That’s the main problem, but I
quite like it, it’s quite good, I feel quite safe.’ (Student 2)
Is it okay to say ‘3 students reported that their accommodation
was satisfactory’?
In qualitative studies, we are interested in individual’s feelings,
thoughts, beliefs and unique contributions. It is ok to say that 3
students reported that about their accommodation.
1.9 Producing the report of the data
Several students suggested their accommodation, while having
some limitations, was generally satisfactory, being ‘okay’
(student 2) or ‘fine for students’ (student 9). Their
accommodation appeared to meet many of their needs, for
instance, student 3 commented ‘I have the things I need, a desk,

bed, arm chair, clean and warm, not damp or anything’, while
student 2 reported she ‘feels quite safe’. However, they also
noted some limitations, for example, about the limited space:
‘it’s a bit small… I can’t invite all my friends to my room’
(student 2), and the décor: ‘it could be nicer – the decoration is
a bit old’ (student 9).
Nonetheless, the students seemed to be quite accepting of these
limitations – notably, student
2 still said ‘I quite like it, it’s quite good’ even though she
found it quite expensive going out to see friends because her
room was too small to invite them over.
There was also some suggestion that the students tended to
think of their accommodation as temporary; student 3 is clear ‘it
is not my home, my house’, while student 9 is already planning
to rent a more modern apartment which suits his tastes better on
graduating. This might be considered to have made them more
accepting of their accommodation’s limitations, as long as their
accommodation generally meets their main needs as students.
Summary:
· The words in bold and underlined fond indicate how we
suggest possible conclusions from the data as in qualitative
research we talk about interpretations and how ‘reality’ is
constructed by other people’s point of view.
· Therefore we tend not to say that e.g. ‘students are not
satisfied’ we prefer to report ‘students seem not to be satisfied’

1.10 Interpretative Phenomenological Analysis (IPA)
· The IPA has conceptually derived from the philosophical
principles of phenomenology that views a person’s own
perception of the world as primary.
· To preserve fully that validation of people’s perceptions of the
world.
· Any attempt to report on another individual’s experience will
be necessarily be distorted.
· The reflexive role of the researcher in the interpretation is to
the fore.
IPA DATA and interpretation
· Raw data for IPA
· Interview transcripts
· Diaries
· Autobiographies
· IPA interests in mental process and tries to record what is real
in participant's mind.
· Knowledge is uniquely constructed by researcher.
· Analysis involves identifying recurring themes and make sense
together.
IPA steps (Smith, 2008)

· Read the transcripts several times and note associations or an
early interpretation.
· Identify themes
· Re-order and organise themes into more primary themes.
· Draw a table of organised themes with best clustering and
hierarchy.
· IPA approach can be reflected by thematic analysis.
References
Boyatzis, R. E. (1998). Transforming qualitative information:
Thematic analysis and code development. sage.
Braun, V., & Clarke, V. (2006). Using thematic analysis in
psychology. Qualitative researchin psychology, 3, 77-101.
Corbin, J. M., & Strauss, A. (1990). Grounded theory research:
Procedures, canons, and evaluative criteria. Qualitative
sociology, 13, 3-21.
Fereday, J. and Muir-Cochrane, E., (2006). Demonstrating
rigour using thematic analysis: A hybrid approach of inductive
and deductive coding and theme development.
Internationaljournal of qualitative methods, 5,80-92.
Glaser, B., & Strauss, A. (1967). The discovery of grounded
theory. Weidenfield & Nicolson, London, 1-19.
Lacey A. and Luff D. (2009) Qualitative Research Analysis. The
NIHR RDS for the East Midlands / Yorkshire & the Humber.

Further reading:
Aronson, J. (1995). A pragmatic view of thematic analysis. The
qualitative report, 1-3.
Braun, V., & Clarke, V. (2006). Using thematic analysis in
psychology. Qualitative researchin psychology, 77-101.
Boyce, C. and Neale, P., 2006. Conducting in-depth interviews:
A guide for designing and conducting in-depth interviews for
evaluation input.
Charmaz, K. (2011). Grounded theory methods in social justice
research. The Sage handbookof qualitative research, 4, 359-380.
Corbin, J. M., & Strauss, A. (1990). Grounded theory research:
Procedures, canons, and evaluative criteria. Qualitative
sociology, 13, 3-21.
Doody, O., & Noonan, M. (2013). Preparing and conducting
interviews to collect data. Nurseresearcher, 20, 28-32.
Fereday, J. & Muir-Cochrane, E.(2006). Demonstrating rigour
using thematic analysis: A hybrid approach of inductive and
deductive coding and theme development. Internationaljournal
of qualitative methods, 5, 80-92.
Jacob, S. A., & Furgerson, S. P. (2012). Writing interview
protocols and conducting interviews: Tips for students new to
the field of qualitative research. The Qualitative Report, 17(42),
1-10.

Lacey, A., & Luff, D. (2001). Qualitative data analysis (pp.
320-357). Sheffield: Trent Focus.
Smith, J., & Firth, J. (2011). Qualitative data analysis: the
framework approach. Nurseresearcher, 18, 52-62.
Smithson, J. (2000). Using and analysing focus groups:
limitations and possibilities.
International journal of social research methodology, 3, 103-
119.
Strauss, A., & Corbin, J. (1994). Grounded theory methodology.
Handbook of qualitative research, 17, 273-85.
Video: https://www.youtube.com/watch?v=DRL4PF2u9XA

Week 5: Project
Management
Project Management
· Learn the key stages of project development and
implementation
· Understand the producers involved in each stage of a project
1.1 Introduction
The Project Life Cycle consists of 4 main stages; these are
initiation, plan, execution and closeout. The planning and
execution stages are monitored and controlled by the project
managers. The project manager and project team have one
common goal: to carry out the work of the project with the
purpose of meeting the project’s aims. Every project has a
beginning, a middle period during which activities move the
project toward completion, and an ending (either successful or
unsuccessful).

Figure 1. Project management phases
1.2 Project management phases
1.2.1 Project initiation
The project initiation stage is the first phase in the Project Life
Cycle and involves starting up the project, a business case,
feasibility study, terms of reference, appointing the team and

setting up a Project Office. A project is initiated by defining its
purpose and scope, the justification for initiating it and the
solution to be implemented. Recruiting a skilled project team,
setting up a Project Office and performing an end of Phase
Review are also necessary in the first phase of the project life
Cycle.
Figure 2. Project initiation activities
1.2.2 Project Planning
The project initiation phase is followed by the project planning
phase. This involves creating a suite of planning documents to
help guide the team throughout the project delivery. The
planning phase involves completing the following key steps:

Figure 3. Project planning activities
· Resource Plan: to identify the staffing, equipment and
materials needed
· Financial Plan: to quantify the financial expenditure required
· Quality Plan: to set quality targets and specify quality control
methods
· Risk Plan: to identify risks and plan actions needed to
minimise them
· Acceptance Plan: to specify criteria for accepting deliverables
Finally, a phase review is carried out to assess the deliverables
produced to date and approve the start of the project execution
phase. During the project execution phase the project team
produces the deliverables while the project manager monitors
and controls the project delivery by undertaking:
· Time Management: tracking and recording time spent on tasks
against the Project Plan
· Cost Management: identifying and recording costs against the
project budget
· Quality Management: reviewing the quality of the deliverables
and managementprocesses

· Change Management: reviewing and implementing requests for
changes to the project
· Risk Management: assessing the level of project risk and
taking action to minimize it
· Issue Management: identifying and resolving project issues
· Acceptance Management: identifying the completion of
deliverables and gaining thecustomers’ acceptance (if
applicable)
· Communications Management: keeping stakeholders informed
of project progress,risks and issues
1.2.3 Project Execution
With a clear definition of the project and a suite of detailed
project plans, the execution plan can be initiated.
While each deliverable is being constructed, a number of
management processes take place to monitor and control the
deliverables being output by the project. These include
managing time, cost, quality, change, risks, issues, suppliers,
customers and communication.
Once all the deliverables have been produced, the project is
ready for closure.

Figure 3. Project execution activities
1.2 4 Project Closure

The project closure phase involves releasing the final
deliverables, handing over project documentation and/ or results
to the business or public, terminating supplier contracts,
releasing project resources and communicating project closure
to all stakeholders. The last remaining step
is to undertake a post implementation review, to identify the
level of project success and note any lessons learned for future
projects.
In order to assess the level of project success, an assessment is
made of the level of conformity to the management processes
outlined in the quality plan. These results, as well as a list of
the key achievements and learning outcomes, are documented
within a post implementation review and presented to the
customer and/or project sponsor for approval.

Westland, J. (2006). The project management life cycle. ISBN,
749445556, 5-37.
Additional Reading
Archibald, R., Di Filippo, I. and Di Filippo, D. (2012). The six-
phase comprehensive project life cycle model including the
project incubation/feasibility phase and the post-project
evaluation phase. PM World Journal, 1, 1–40. Retrieved from
http://www.dphu.org/uploads/attachements/books/books_5917_0
.pdf
Suri, P.K, Bhushan, Bharat and Jolly, Ashish (2009). Time
estimation for project management life cycles: A simulation
approach. International Journal of Computer Science and
NetworkSecurity, 9, 211-215. Retrieved
fromhttp://paper.ijcsns.org/07_book/200905/20090528.pdf
1

Week 2:Quantitative Data
Analysis
2

Quantitative Research Data Analysis
1. Quantitative Data Analysis:
1.1 Statistics and Descriptive statistical analysis:
Statistics is concerned with the systematic collection of
numerical data and its interpretation.
Descriptive statistics are used to describe the basic features of
the data that have been collected in a
study. They provide simple summaries about the sample and the
measures (e.g. mean, mode,
median, range, standard deviation etc). Together with simple
graphics analysis, they form the basis
of virtually every quantitative analysis of data. It should be
noted that with descriptive statistics no
conclusions can be extended beyond the immediate group from
which the data was gathered.
Mean: The average value of the entire set of numbers. The Mean
or average is probably the most

commonly used method of describing central tendency. To
compute the mean all the values are
added up and divided by the number of values. For example, the
mean or average quiz score is
determined by summing all the scores and dividing by the
number of students taking the exam.
Example:
15, 20, 21, 20, 36, 15, 25, 15
The sum of these 8 values is 167, so the mean is 167/8 =
20.875.
Mode: The number that appears most often in a set of numbers.
To determine the mode, you might
again order the scores as shown above, and then count each one.
The most frequently occurring
value is the mode. In our example, the value 15 is the mode as it
occurs most frequently (three
times).
Median: The middle value between the largest and smallest in a
set of numbers. One way to
(confidence intervals and p-values)

-probability sampling and
describe the different types of each
3
calculate the median is to list all scores in numerical order, and
then locate the score in the center of
the sample. For example, if there are 500 scores in the list,
score #250 would be the median. If we
order the 8 scores shown above, we would get:
Example:
15,15,15,20,20,21,25,36
There are 8 scores and score #4 and #5 represent the halfway
point. Since both of these scores are
20, the median is 20. If the two middle scores had different
values, they should be added and
divided by two to calculate the median.
Dispersion: Dispersion refers to the spread of the values around
the central tendency. There are two
common measures of dispersion, the range and the standard

deviation.
Range: The difference between the largest and smallest in a set
of numbers i.e. the highest value
minus the lowest value. In our example distribution, the high
value is 36 and the low is 15, so the
range is 36 - 15 = 21.
Standard deviation: A quantity expressing by how much the
members of a group differ from the
mean value for the group.
1.2 Visual aid:
A set of data on its own is very hard to interpret. There is a lot
of information contained in the data,
but it is hard to see. Eye-balling your data using graphs and
exploratory data analysis is necessary
for understanding important features of the data, detecting
outliers, and data which has been
recorded incorrectly.
Outliers
Outliers are extreme observations which are inconsistent with
the rest of the data. The presence of
outliers can significantly distort some of the more formal
statistical techniques, and hence there is a

high need for preliminary detection and correction or
accommodation of such observations, before
further analysis takes place. Usually, a straight line fits the data
well. However, the outlier “pulls”
the line in the direction of the outlier, as demonstrated in the
lower graph in Figure 2. When the line
is dragged towards the outlier, the rest of the points then fall
farther from the line that they would
otherwise fall on or close to. In this case the “fit” is reduced;
thus, the correlation is weaker.
Outliers typically occur from an error including a mismarked
answer paper, a mistake in entering a
score in a database, a subject who misunderstood the directions
etc. The researcher should always
seek to understand the cause of an outlying score. If the cause is
not legitimate, the researcher
should eliminate the outlying score from the analysis to avoid
distorts in the analysis.
4

Figure 1. A demonstration of how outliers can be identified
using graphs
Figure 2. The two graphs above demonstrate data where no
outliers are observed (top graph) and data where an outlier
is observed (bottom graph).
Data distribution (Langley & Perrie, 2014):
Data can be "distributed" (spread out) in different ways:
5
Figure 3. Distribution of Data
The Normal Curve (Bell Curve):
The graph of the normal distribution depends on two factors i.e.
the mean (M) and the standard

deviation (SD). The location of the center of the graph is
determined by the mean of the
distribution, and the height and width of the graph is determined
by the standard deviation. When
the standard deviation is large, the curve is short and wide;
when the standard deviation is small, the
curve is tall and narrow. Normal distribution graphs look like a
symmetric, bell-shaped curve, as
shown above. When measuring things like people's height,
weight, salary, opinions or votes, the
graph of the results is very often a normal curve.
2. Statistical Analysis (Burns & Grove, 2005):
2.1 One-tailed versus two-tailed test:
One-tailed test: A test of a statistical hypothesis, where the
region of rejection is on only one side of the sampling
distribution, is called a one-tailed test. For example, suppose
the null hypothesis states that the mean is less than or
equal to 10. The alternative hypothesis would be that the mean
is greater than 10.
Two-tailed test: When using a two-tailed test, regardless of the

direction of the relationship you
hypothesize, you are testing for the possibility of the
relationship in both directions. For example,
6
we may wish to compare the mean of a sample to a given value
x using a t-test. Our null hypothesis
is that the mean is equal to x.
Figure 4. One- tailed and two-tailed test
2.2 Alpha level (p value)
In statistical analysis the researcher examines whether there is
any significance in the results.
The acceptance or rejection of a hypothesis is based upon a
level of significance – the alpha (a) level
This is typically set at the 5% (0.05) a level, followed in
popularity by the 1% (0.01) a level

These are usually designated as p, i.e. p =0.05 or p = 0.01
So, what do we mean by levels of significance that the 'p' value
can give us?
The p value is concerned with confidence levels. This states the
threshold at which you are prepared to accept the
possibility of a Type I Error – otherwise known as a false
positive – rejecting a null hypothesis that is actually true.
The question that significance levels answer is 'How confident
can the researcher be that the results have not arisen
by chance?'
Note: The confidence levels are expressed as a percentage.
So if we had a result of:
p = 1.00, then there would be a 100% possibility that the results
occurred by chance.
7

p = 0.50, then there would be a 50% possibility that the results
occurred by chance.
p = 0.05, then we are 95% certain that the results did not arise
by chance
p = 0.01, then we are 99% certain that the results did not arise
by chance.
Clearly, we want our results to be as accurate as possible, so we
set our significance levels as low as
possible - usually at 5% (p = 0.05), or better still, at 1% (p =
0.01)
Anything above these figures, are considered as not accurate
enough. In other words, the results are not significant.
Now, you may be thinking that if an effect could not have arisen
by chance 90 times out of 100 (p = 0.1), then that
is pretty significant.
However, what we are determining with our levels of
significance, is 'statistical significance', hence we are much
more strict with that, so we would usually not accept values
greater than p = 0.05.
So when looking at the statistics in a research paper, it is
important to check the 'p' values to find
out whether the results are statistically significant or not.

8
Figure 5. Regions of rejection at 95% and 99% confidence
interval

9
Table 1
Statistical Symbols
Accessed: http://www.statisticshowto.com/statistics-symbols/
2.3 Statistical tests (Field, 2013)
There are a number of tests that can be used to analyse
quantitative data, depending on what the researcher is
looking for, what data were collected and how the data were
collected.
Below are a few of the most common tests used to analyse
quantitative data:
t-Test

http://www.statisticshowto.com/statistics-symbols/
10
A t-Test is used to compare whether two groups have different
average values (for example, whether men and
women have different average heights).
A difference is more likely to be meaningful and “real” if:
(1) the difference between the averages is large
(2) the sample size is large
(3) responses are consistently close to the average values and
not widely spread out (the standard deviation is
low).
Example where a t-Test can be used:
A researcher hypothesizes that individuals who are allowed to
sleep for only four hours will score significantly
lower than individuals who are allowed to sleep for eight hours
on a cognitive skills test. Sixteen participants are
invited into a sleep lab and are randomly assigned to two
groups. One group sleeps for eight hours and the other

group sleeps for four hours. The next morning all participants
complete the SCAT (Sam's Cognitive Ability Test).
The researcher wants to find out whether the average SCAT
scores differ between the two groups.
-Test: The Independent Samples t- Test
compares the means of two independent groups
in order to determine whether there is statistical evidence that
the associated population means are significantly
different.
-test: The dependent t-test (also called the paired
t-test or paired-samples t-test) compares the
means of two related groups to determine whether there is a
statistically significant difference between these
means.
Correlation analysis
Correlation analysis is a statistical test used to study the
strength of a relationship between two, numerically
measured, continuous variables (e.g. height and weight). A
sample correlation coefficient is estimated (denoted r)
and can range in value from −1 to +1 and quantifies the
direction and strength of the linear association between

the two variables.
As correlation is a measure of association, you can also think of
the results in terms of effect size:
.00-.19: very weak.
.20-.39: weak.
.40-.59: moderate.
.60-.79: strong.
.80-1.0: very strong.
used to test the linear relationship between at
least two continuous variables.
11
monotonic relationship between two continuous
or ordinal variables. In a monotonic relationship, the variables

tend to change together, but not necessarily at a
constant rate. The Spearman correlation coefficient is based on
the ranked values for each variable rather than the
raw data.
ANOVA (Analysis of Variance)
ANOVA is one of a number of tests (ANCOVA - analysis of
covariance - and MANOVA - multivariate analysis
of variance) that are used to describe/compare a number of
groups.
Examples of when you might want to test differences between
groups:
different interventions: Cognitive behavioral
therapy (CBT), Attention bias modification (ABM) and the
Mindfulness-based therapy (MBT). You want to see
whether one therapy is more effective than the others.
bulbs. They want to know if one process is better than
the other.
xam. You

want to see if one college outperforms the other.
One way and two way ANOVA
One-Way ANOVA has one independent variable (1 factor) with
> 2 conditions
– conditions = levels = treatments
e.g., for a brand of cola factor, the levels are:
Coke, Pepsi, RC Cola
Two-Way ANOVA has 2 independent variables (factors)
– each can have multiple conditions
e.g. Two Independent Variables (IV’s)
– IV1: Brand; and IV2: Calories
– Three levels of Brand:
• Coke, Pepsi, RC Cola
– Two levels of Calories:
• Regular, Diet
*When a factor uses independent samples in all conditions, it is
called a between subjects factor i.e. between-

subjects ANOVA
12
*When a factor uses related samples in all conditions, it is
called a within-subjects factor i.e. within-subjects
ANOVA (referred to as repeated measures).
ANCOVA - analysis of covariance
Analysis of covariance (ANCOVA) blends ANOVA and
regression that allows to compare one variable in 2 or
more groups considering (or correcting for) variability of other
variables, called covariates.
Retrieved from
http://www.statsmakemecry.com/smmctheblog/stats-soup-
anova-ancova-manova-mancova
MANOVA - multivariate analysis of variance

A MANOVA is an ANOVA with two or more continuous
response variables. Like ANOVA, MANOVA has both
a one-way flavor and a two-way flavor. The number of factor
variables involved distinguish a one-way
MANOVA from a two-way MANOVA.
Retrieved from
13
MANCOVA
Both a MANOVA and MANCOVA feature two or more response
variables, but the key difference between the
two is the nature of the IVs. While a MANOVA can include
only factors, an analysis evolves from MANOVA to
MANCOVA when one or more covariates are added to the mix.

Retrieved from
Regression Analysis
In statistical modeling, regression analysis is a statistical
process used to estimate the linear relationship between
two or more variables. It includes many techniques for modeling
and analyzing several variables, when the focus
is on the relationship between a dependent variable and one or
more independent variables (or 'predictors'). More
specifically, regression analysis shows how the typical value of
the dependent variable changes when any one of
the independent variables is varied, while the other independent
variables are held fixed.
Example scenario: Suppose you are a sales manager and want to
predict next month’s numbers. A number of
factors from the weather to a competitor’s promotion to the
rumor of a new and improved model can impact the
number of sales. e.g. The more the rain, the more the sales.”
Regression analysis is a way of mathematically sorting out
which of those variables does indeed have an impact

on sales. It answers the questions: Which factors matter most?
Which can we ignore? How do those factors
interact with each other? And, perhaps most importantly, how
certain are we about all of these factors?
single response variable Y and a single predictor
variable X.
14
gression: The Multiple Regression procedure fits
a model relating a response variable Y to multiple
predictor variables X1, X2, ... . The user may include all
predictor variables in the fit or ask the program to use a
stepwise regression to select a subset containing only
significant predictors.
3. Parametric and Nonparametric Tests (Frost, 2015)
A parametric statistical test makes assumptions about the
parameters (defining properties) of the

population distribution(s) from which one's data are drawn,
whereas a non-parametric test makes no
such assumptions. Nonparametric tests are also called
distribution-free tests because they do not
assume that your data follow a specific distribution.
It is argued that nonparametric tests should be used when the
data do not meet the assumptions of the parametric
test, particularly the assumption about normally distributed
data. However, there are additional considerations
when deciding whether a parametric or nonparametric test
should be used.
3.1 Reasons to Use Parametric Tests
Reason 1: Parametric tests can perform well with skewed and
nonnormal distributions
Parametric tests can perform well with continuous data that are
not normally distributed if the
sample size guidelines demonstrated in the table below are
satisfied.

15
*Note: These guidelines are based on simulation studies
conducted by statisticians at Minitab.
Reason 2: Parametric tests can perform well when the spread of
each group is different
While nonparametric tests don not assume that your data are
normally distributed, they do have other assumptions
that can be hard to satisfy. For example, when using
nonparametric tests that compare groups, a common
assumption is that the data for all groups have the same spread
(dispersion). If the groups have a different spread,
then the results from nonparametric tests might be invalid.
Reason 3: Statistical power
Parametric tests usually have more statistical power compared
to nonparametric tests. Hence, they
are more likely to detect a significant effect when one truly
exists.
3.2 Reasons to Use Nonparametric Tests

Reason 1: Your area of study is better represented by the
median
The fact that a parametric test can be performed with nonnormal
data does not imply that the mean is the best
measure of the central tendency for your data.
16
For example, the center of a skewed distribution (e.g. income),
can be better measured by the median where 50%
are above the median and 50% are below. However, if you add a
few billionaires to a sample, the mathematical
mean increases greatly, although the income for the typical
person does not change.
When the distribution is skewed enough, the mean is strongly
influenced by changes far out in the distribution’s
tail, whereas the median continues to more closely represent the
center of the distribution.
Reason 2: You have a very small sample size
If the data are not normally distributes and do not meet the

sample size guidelines for the parametric tests, then a
nonparametric test should be used. In addition, when you have a
very small sample, it might be difficult to
ascertain the distribution of your data as the distribution tests
will lack sufficient power to provide meaningful
results.
Reason 3: You have ordinal data, ranked data, or outliers that
you cannot remove
Typical parametric tests can only assess continuous data and the
results can be seriously affected by
outliers. Conversely, some nonparametric tests can handle
ordinal data, ranked data, without being
significantly affected by outliers.
4. Experimental Design (McLeod, 2007)
Experimental design refers to how participants are allocated to
the different conditions (or IV levels) in an
experiment.
Three types of experimental designs are commonly used:

1. Independent Measures:
This type of design is also known as between groups. Different
participants are assigned to a different condition
of the independent variable. This means that each condition of
the experiment includes a different group of
participants. In this experimental design random allocation
should be used, to ensure that each participant has
an equal chance of being assigned to one group or the other.
17
Example:
2. Repeated Measures:

This type of design is also known as within subjects. The same
participants are exposed to all the experimental
conditions (i.e. each condition of the experiment includes the
same group of participants).
* Control: To control for order effects in repeated measures
design the researcher counter balances the order of
the conditions for the participants i.e. alternating the order in
which participants perform in different conditions
of an experiment.
Counterbalancing
Suppose that in a repeated measures design all of the
participants first learned words in 'loud noise' and then
learned it in 'no noise'. We would expect the participants to
show better learning in 'no noise' simply because
of order effects, such as practice (i.e. the same words are
repeated in both conditions). A researcher can control
for order effects using counterbalancing.
Example:

18
3. Matched Pairs:
Different participants are used in each condition, but
participants are ‘matched’ as far as possible on relevant
variables in terms of any important characteristic which might
affect performance, e.g. gender, age, intelligence
etc.
One member of each matched pair must be randomly assigned to
the experimental group and the other to the
control group.
5. Power of the study:
There is increasing criticism about the lack of statistical power
of published research in sports and exercise science
and psychology. Statistical power is defined as the probability
of rejecting the null hypothesis; that is, the

probability that the study will lead to significant results. If the
null hypothesis is false but not rejected, a type 2
error is incurred. Cohen suggested that a power of 0.80 is
satisfactory when an alpha is set at 0.05—that is, the risk
of type 1 error (i.e. rejection of the null hypothesis when it is
true) is 0.05. This means that the risk of a type 2 error
is 0.20.
The magnitude of the relation or treatment effect (known as the
effect size) is a factor that must receive a lot of
attention when considering the statistical power of a study.
When calculated in advance, this can be used as an
indicator of the degree to which the researcher believes the null
hypothesis to be false. Each statistical test has an
effect size index that ranges from zero upwards and is scale
free. For instance, the effect size index for a correlation
test is r; where no conversion is required. For assessing the
difference between two sample means, Cohen's d ,
Hedges g, or Glass's Δ can be used. These divide the difference
between two means by a standard deviation.
Formulae are available for converting other statistical test
results (e.g. t test, one way analysis of variance, and χ2
results—into effect size indexes (see Rosenthal, 1991).
Effect sizes are typically described as small, medium, and large.
Effect sizes of correlations that

equal to 0.1, 0.3, and 0.5 and effect sizes of Cohen's that equal
0.2, 0.5, and 0.8 equate to small,
medium, and large effect sizes respectively. It is important to
note that the power of a study is
linked to the sample size i.e. the smaller the expected effect
size, the larger the sample size required
to have sufficient power to detect that effect size.
For example, a study that assesses the effects of habitual
physical activity on body fat in children might have a
medium effect size (e.g. see Rowlands et al., 1999). In this
study, there was a moderate correlation between
habitual physical activity and body fat, with a medium effect
size. A large effect size may be anticipated in a study
that assesses the effects of a very low energy diet on body fat in
overweight women (e.g. see Eston et al, 1995). In
Eston et al’s study, a significant reduction in total body intake
resulted in a substantial decrease in total body mass
and the percentage of body fat.
The effect size should be estimated during the design stage of a
study, as this will allow the researcher to determine
the size required to give adequate power for a given alpha (i.e.
p value). Therefore, the study can be designed to

19
ensure that there is sufficient power to detect the effect of
interest, that is minimising the possibility of a type 2
error.
Table 2.
Small, medium and large effect sizes as defined by Cohen
When empirical data are available, they can be used to assess
the effect size for a study. However,
for some research questions it is difficult to find enough
information (e.g. there is limited empirical
information on the topic or insufficient detail provided in the
results of the relevant studies) to
estimate the expected effect size. In order to compare effect
sizes of studies that differ in sample
size, it is recommended that, in addition to reporting the test
statistic and p value, the appropriate
effect size index is also reported.

20
References
Abramson, J. H., Abramson, Z. H. (2008). Scales of
Measurement. Research Methods in Community Medicine:
Surveys, Epidemiological Research, Programme Evaluation,
Clinical Trials, Sixth Edition, 125-132.
Blaikie, N. (2003). Analyzing quantitative data: From
description to explanation. Sage
Publications.

Burns N., Grove S.K. (2005). The Practice of Nursing Research:
Conduct, Critique, and Utilization (5th Ed.). St.
Louis, Elsevier Saunders.
Creswell, J. W. (2013). Research design: Qualitative,
quantitative, and mixed methods approaches. Sage
Publications, Incorporated.
Eston, RG, Fu F. Fung L (1995). Validity of conventional
anthropometric techniques for estimating
body composition in Chinese adults. Br J Sports Med, 29, 52–6.
Field, A. (2013).Discovering Statistics Using IBM SPSS
Statistics. (4th Ed).
Publications Ltd.
Frost J. (2015). Choosing Between a Nonparametric Test and a
Parametric Test. Retrieved from
http://blog.minitab.com/blog/adventures-in-statistics-
2/choosing-between-a-nonparametric-test-and-a-parametric-
test
Langley C, Perrie Y (2014). Maths Skills for Pharmacy:
Unlocking Pharmaceutical Calculations. Oxford
University Press.

Lyons, R. (2010). Best Practices in Graphical Data Presentation.
Ohio, USA.
Saul McLeod (2007). Simply Psychology. Retrieved from
https://www.simplypsychology.org/experimental-
designs.html
Rosenthal R. (1991.). Meta-analytic procedures for social
research (revised edition). Newbury Park, CA: Sage
Rowlands A.V, Eston R.G, Ingledew D.K. (1999). The
relationship between activity levels, body fat and aerobic
fitness in 8–10 year old children. J Appl Physiol, 86, 1428–35.
Sampling Techniques

Sampling Methods for Qualitative and Quantitative Research
Learning Objectives
· To understand the difference between population and sampling
· To gain an understanding of the main sampling techniques
used in research
1.1 Sample vs Population
A critical consideration in any research process is the
researcher’ choice for a representative sample from which
certain inferences can be drawn later on based on the collection
of data.
Researchers commonly investigate traits or characteristics of
populations in their studies. A population is a group of
individual units with some shared characteristics. For example,
a researcher may want to explore characteristics of female
smokers in the United Kingdom. This would be the population
being analyzed in the study. However, it would be impossible to
collect data from all female smokers in the UK. Thus, the
researcher would select some individuals from who data will be
collected. This is called sampling. If the group of individuals
from who the data is gathered is a representative sample of the
population, then the results of the study can be generalized to
the population as a whole.
It should be noted that the sample will only be representative of
the population if the researcher uses a random selection
procedure to select participants. The group of units or
individuals who have a legitimate chance of being selected to
participate in a study are commonly referred to as the sampling

frame. If a researcher, for instance, explored the cognitive
ability of preschool children and target licensed preschools to
collect the data, the sampling frame would be all preschool aged
children in those preschools. Students in those preschools could
then randomly select through a systematic process to participate
in the study. However, such recruitment procedure can lead to a
discussion of biases in research. For example, low-income
children may be less likely to be enrolled in preschool and
therefore, have fewer chances to be involved in the study. Extra
care must be taken to control biases when determining sampling
techniques.
‘A sample is a finite part of a statistical population whose
properties are studied to gain information about the whole’
(Webster, 1985).
1.2 Sampling techniques
There are two main types of sampling: probability and non-
probability sampling.
Probability sampling
A probability sampling method is any method of sampling that
utilizes some form of random selection. In order to have a
random selection method, researchers must set up some process
or procedure that assures that the different units in their
population have equal probabilities of being selected.
Probability sampling is divided into:
Simple random sampling – it is the basic sampling technique,
where a group of subjects (a sample) for study are selected from
a larger group (a population). Each individual is selected totally
by chance and each member of the population has an equal
chance of being included in the sample.

Stratified sampling – population is divided into subgroups
(strata) and members or units are randomly selected from each
group
Systematic sampling – uses a specific method to select members
or units e.g. every 10th person on an alphabetized list
Cluster random sampling – divides the population into clusters.
Clusters are randomly selected and all members of the cluster
selected are sampled
Multi-stage random sampling – a combination of one or more of
the above methods is applied
Non-probability sampling
A basic characteristic of non-probability sampling techniques is
that samples are selected based on the subjective judgement of
the researcher, rather than random selection (i.e., probabilistic
methods). Non-probability sampling is divided into:
Convenience or accidental sampling – members or units are
selected based on availability
Purposive sampling – members of a specific group are
purposefully selected to participate in the study
Modal instance sampling – members or units are the most
common within a defined group and therefore are sought after
Expert sampling – members considered to be of high quality are
selected for participation
Proportional and non-proportional quota sampling – members
are sampled until exact proportions of certain types of data are
collected or until sufficient data in different categories is
obtained
Diversity sampling – members are selected intentionally across
the possible types of responses to capture all possibilities
Snowball sampling – existing study subjects recruit future
subjects from among their acquaintances, and this process
continues until enough subjects are collected

Table 1.
Probability and Non-probability Sampling
Table 2.
Key Differences between Probability and Non-probability
Sampling
References
Bryman, A., & Cramer, D. (1994). Quantitative data analysis for
social scientists (rev. Taylor & Frances/Routledge.
Creswell, J. W. (2002). Educational research: Planning,
conducting, and evaluating quantitative. Prentice Hall.
Creswell, J. W. (2013). Research design: Qualitative,
quantitative, and mixed methods approaches. Sage Publications,
Incorporated.
Additional Reading:
Marshall, M. N. (1996). Sampling for qualitative research.
Family practice, 13(6), 522-526. Retrieved from
http://mym.cdn.laureate-
media.com/2dett4d/Walden/COUN/8551/09/Sampling_for_Quali
tative_Research.pdf

Week 4: Research
Ethics and
Professional Issues
Research Ethics and Professional Issues
• Gain an understanding of the ethical issues and concerns in
research
• Learn the principles to follow when conducting research

1.1. Introduction
Research Ethics is concerned with the ethical issues involved in
the conduct of research, the
regulation of research, the procedures and process of ethical
review as well as broader ethical
issues associated with research (i.e. scientific integrity and the
end uses of research).
Research ethics are the guidelines researchers follow to protect
the rights of humans, animals
who participate in studies. Universities, government, and
organizations often have
Institutional Review Boards (IRBs).
Ethical guidelines are published by professional organizations
such as American
Psychological Association.
Ethical approval is required for all research carried out by staff
and/or students. This includes
research where there is no face to face interaction between
researcher and participant
(including internet surveys).
Ethics Committee is an independent body in a member state of
the European Union,

consisting of healthcare professionals and non-medical
members, whose there main
responsibility is to protect the rights, safety and well-being of
participants involved in a
research.
Institutional Approval
When institutional approval is necessary for a research project,
psychologists are required to
provide accurate information about their research proposals and
obtain approval before
conducting the research. Then research is conducted in
accordance with the approved
research protocol.
Both the British Psychological Society (BPS) and the American
Psychological Association
(APA) have agreed guidelines on the ethical issues involved in
psychological research.

1.2. Four Ethical Principles
• Respect
• Competence
• Responsibility
• Integrity
Informed Consent: For any research to be ethical, the researcher
must have gained informed
consent from the participants. If the participant is under 16
years old, the informed consent is
provided by their parents or carers.
Deception: Have the participants been deceived in any way? If
so, could this have been
avoided? Deception includes: misleading the participants in any
way and the use of stooges
or confederates.
Debriefing: is conducted with the participants after the study
has taken place. It has a number
of aims:
•To ensure that none of the participants have been harmed
•To make sure that the researchers have informed consent

•To make sure that the researcher allowed the participants an
opportunity to remove their
results from the study.
•To make sure that participants had the opportunity to ask
question
Withdrawal from the study: Participants have the right to
withdraw from the research at
any point. They should also be allowed to withdraw their data.
Anonymity and Confidentiality: Participants have a right to
remain anonymous in
publication of the research and confidentiality should be
maintained except in exceptional
Note that this is not an extensive list of the ethical
considerations involved in research and
professional settings. For a more detailed description of the
ethical issues in psychology you
can refer to BPS ethical code (2009).
circumstances where harm may arise to the participant or
someone associated with the
research or participant. No names must be used in a research

report.
Protection of participants: researcher must protect participants
from both physical and
psychological harm
Informed Consent
(a) When obtaining informed consent psychologists are required
to inform participants about
(1) the purpose of the research, expected duration, and
procedures; (2) their right to refuse to
participate and to withdraw from the research once a study has
begun; (3) the foreseeable
consequences of declining or withdrawing; (4) reasonably
foreseeable factors that may be
expected to affect participants’ willingness to participate in the
research (e.g. potential risks,
discomfort, or adverse effects); (5) any prospective research
benefits; (6) limits of
confidentiality; (7) incentives for participation; and (8) whom
to contact for questions about
the research and research participants' rights.
(b) Psychologists conducting intervention research that involved
the use of experimental
treatments clarify to participants prior to the beginning of the

ME290Global Engineering Professional SeminarGlobalizat.docx

ME290Global Engineering Professional SeminarGlobalizat.docx

Recommended

Recommended

More Related Content

Similar to ME290Global Engineering Professional SeminarGlobalizat.docx

Similar to ME290Global Engineering Professional SeminarGlobalizat.docx (20)

More from endawalling

More from endawalling (20)

Recently uploaded

Recently uploaded (20)

ME290Global Engineering Professional SeminarGlobalizat.docx