Need a Topic/Title and Research Proposal outline in Power Point on research to be carried out in the Field of Managerial Psychology. The research context should be Nigeria. I will be providing guidelines and additional readings to serve as a basis to come up with the requested material.
Week 6: How to Write a Research Proposal
1.1. Purpose of the proposal
The purpose of the proposal is to ensure that the candidate has done sufficient preliminary reading/research in his/her area of interest, that he/she has developed a well-grounded research project and that he/she is familiar with the methodology of his/her subject.
The proposal will have to convince members of the academic community or other possible funders that you have identified a relevant scientific problem and a methodical approach to solve the research problem within a set time period. A research proposal should have a proper layout and a logic structure, as clear and concise proposals have more likely to be accepted.
1.2 Steps for writing a research proposal:
• What is my research question? (Hypotheses)
• What do I wish to study?
• The problem/research questions needs to be clear and specific
STEP 1: Read
Before putting together a research project, it is important that the researcher knows the state of research in their field.
This requires a lot of reading, starting from the latest publications as they allow for an initial understanding of the most relevant literature around the research topic. Then a deeper idea of the subject will be acquired through a more comprehensive literature review. It is also useful in social sciences and humanities to read a book on theories and research traditions and methods on the topic under study. In addition, it might be useful to search for other researchers working in the same field and familiarize with their research profile and on-going projects.
STEP 2
• Literature review
• Identify the gaps in the literature
• What’s new?
• What this study will add?
• Specifically, in social sciences, research topics and are commonly built on a hypothesis that is generated from everyday experiences and observations.
• Research projects are usually built on a hypothesis which again is often built on researcher’s observations and experiences inside or outside academia.
STEP 3
· Decide what methods will be used
· Decide what will be the participant characteristics
How to draft a research proposal
A research proposal should include the following parts:
TITLE
The title should be clear and specific, but not too detailed. For example, assume we had done an experiment in which we had examined whether or not having breakfast affected people’s ability to conctrate later in the day.
A good title would be ‘The effects of breakfast on mid-morning concentration levels’.
Avoid using titles that are:
• Too vague
• Detailed enough
· The American Psychological Association suggests the title to be about 10-12 words long.
· INTRODUCTION AND LITERATURE
This section introduces the reader to the topic
• Give an introduction to the area of interest
• Provide a rationale for the study using previous studies
• Show how the current study fits in with existing literature
Literature review will be around your chosen topic -should summarize and discuss the most recent and most relevant research findings related to the current research project.
· This part should highlight the research gap
· The review provides the background of the problem
· Critical thinking is essential
State the problem: For example:
• Why this study needs to be conducted
• What issues this study raise that have not been raised in other studies?
• What is the purpose of this study?
•What is the research question and the hypothesis
In sum:
· Give an introduction to the area of interest
· Provide a rationale for the study
· Show how the current study fits in with existing literature
· The hypothesis, research question and objectives of the study should be stated at the end of this section.
· METHODOLOGY
It explains each step the researcher will take in order to conduct the research.
This is a very important part of a research project outline and should receive a lot of attention. In this section the intended methods of data gathering and data analysis, ethical issues as well as difficulties in gathering data and other material should be described. It is important to describe this section in detail.
If, for example, a study will use questionnaires to collect data, the procedure of developing the questionnaires, the type of questions and the reasons for using those specific questions, and the procedure of analysing the data gathered from these questionnaires should be explained clearly.
A draft of the questionnaires used (if copyright terms allow) should be added in an appendix.
Sampling
Describe the population under consideration (provide a brief description on age, sex, education etc.), inclusion and exclusion criteria, number of participants. Describe how participants will be recruited and from where.
Research design
• Qualitative/quantitative study
• Analysis & its justification
Ethics
• Name the ethics committee
• What efforts will be taken to protect participants?
• The right to withdraw
• Participation is voluntary
• Issues of confidentiality
• How they will receive a copy of results when study is finished?
· Proposals should address research ethics adequately, or which have serious ethical problems. Please include examples of how others have considered ethical issues within existing published research in your chosen area. Refer to relevant documents in appendices (e.g. consent form, information sheet, debrief).
Instruments used to collect the data
What instruments you will use?
For example, interviews, questionnaires (i.e. describe what each measurement assess –how reliable and valid are your questionnaires?
For qualitative study, describe the process of designing the interview schedule (refer to appendices). Provide some (2) examples of the questions asked.
Procedure
Complete description on what will happen to a participant.
Describe how you will contact the participants.
Data analysis
How the data will be collected and what statistical measures will be used?
This section should explain in some detail how you will manipulate the data that you assembled to get at the information that you will use to answer your question.
Select a statistical test to analyse your data depending on your research question (i.e. testing for a correlation between variables, a predictive relationship or to compare values?). It will include the statistical or other techniques and the tools that you will use in processing the data.
Expected Results
This section should give a good indication of what you expect to get out from your research.
It should join the data analysis and possible outcomes to the theory and questions that you have raised.
Reference section
This is the list of the relevant works.
Use APA referencing style.
Plagiarism
Note:
A research proposal is an academic text, and thus it should follow the rules of academic integrity. It is important to highlight that in the academic world there is zero tolerance towards plagiarism. Thus the authors of research proposal should make sure that they are aware and follow the international citation rules i.e. The exact words of another author, should be presented in quotation marks (even if this quotation comprises only one sentence or less) and should be followed by the reference (author, date and page). Other people’s thoughts or findings should always be referenced.
Research Proposal Presentation
Please, complete all the relevant readings before preparing your research proposal presentation.
You should use the APA Reference style for in-text citations, use a dedicated “References” section at the end, and be consistent throughout the work.
General guidelines for the Research Proposal Presentation:
The main points of your Research Proposal should be presented in a PowerPoint presentation consisting of 7-8 slides and accompanying text. In your presentation you should provide a brief background to your research topic and the rationale of your study, describe the methodology that will be applied, the design of your study and explain how you are planning to analyse your data. Also, the possible practical implications of your study should be outlined.
Under the slides (in the notes section) text should be provided. This text should be reflecting in general what you would say if the presentation was orally presented. The presentation should not just be copying and pasting from your research proposal, but selections should be made and presented briefly.
Make sure that apart from the content, the appearance of your presentation should be considered (e.g. not very long text in each slide, font size, colours etc.). Also, any sources used should be acknowledged and cited in the slides and reference list accordingly.
Please note that plagiarism is not accepted in any form.
Preparing for Presentation
Practice makes perfect
What is a presentation?
A presentation is a visual communication tool
People can present their work, ideas, research work, advertised products, etc.
Such practises are highly used in professional settings
Hess, Tosney, & Liegel (2009)
What is a presentation?
It is primarily conducted with visual displays of data
Includes the most important information from your work
It summarizes the sections of your work
Preparing your
presentation
Thinking about your audience
Planning your presentation
Putting your materials together
Structure of the presentation
Visual aids and slides
Practising your presentation (for oral only)
Planning
Purpose – why and what?
Audience – who and where?
e.g. Students, lecturers, different professionals etc
Planning
Decide your purpose
Define your objectives
Develop ideas
Design the structure
Prepare notes and visual support material
Have a logical structure
As with written assignments, a well thought-out structure will assist in making the presentation a coherent piece of work with your aims easily-understood by the audience.
Research Proposal Presentation
The research title
A brief introduction to your topic/ Background theory
The rational of your study
The research questions
The methodology
Research design
Ethical considerations
Practical implications of your research
Visual Aids
Using Visual Aids (e.g. PowerPoint)
highlight key points –using bullet points
display images or diagrams (if possible)
Bullet points can often be more effective, your audience will be able to concentrate on what you are saying rather than reading the slide
4-5 bullet points are an acceptable amount per slide –keep it simple
Font size should be large enough to be legible to be seen from all parts of the room (e.g. 22 – 28 for the text and 34-40 for titles.)
*In this case, you can use the box bellow and add your notes
Tips for effective PowerPoint presentation
Keep focused
Keep it brief, simple and clear (e.g. No more than 6 lines per slide )
Keep it visual
Keep your audience in mind
Be consistent
Week 13: Conducting
Critical Literature
Review
CONDUCTING CRITICAL LITERATURE REVIEW
1.1. Introduction
What is a literature review?
A literature review is:
“… a systematic method for identifying, evaluating and interpreting the …work produced by
researchers, scholars and practitioners.” (Fink, 1998, p.3)
“An examination of the research that has been conducted in a particular field of study”
(Ferfolja & Burnett, 2003, p.1)
A literature review
• surveys scholarly articles, books and other sources (e.g. dissertations, conference
proceedings) relevant to a particular issue, area of research, or theory.
• provides a short description and critical evaluation of work critical to the topic.
• offers an overview of significant literature published on a topic.
(Lyons, 2005)
Literature reviews is defined as:
Hart (2003, p.13) further adds that “literature is the selection of available documents…and
the effective evaluation of these documents in relation to the research being proposed.
1.2 Why to conduct a literature review?
“…without it you will not acquire an understanding of your topic, of what has already been
done on it, how it has been researched, and what the key issues are.” (Hart, 1998, p.1)
Conducting a literature review is important because:
• It determines the need for actually doing the proposed research
For example, even if there are similar studies published, the researchers might
suggest the need for similar studies or replication
•It narrows down the problem.
It can be overwhelming getting into the literature of a field of study. Thus, a literature
review is important to understand where you need to focus your efforts.
•It generates hypotheses or questions for further studies (Mauch & Park, 2003)
Literature Reviews are conducted for Various Reasons.
Some of them are:
1. For a review paper
2. For the introduction (and discussion) of a research paper, master’s thesis or dissertation
3. To embark on a new area of research
4. For a research proposal
Conducting a literature review is a helpful way to:
• Get background knowledge of the field of inquiry
• Understand Facts
• To search for Eminent scholars
• To read about Parameters of the field
• To understand the most important ideas, theories, questions and hypotheses.
• To gain Knowledge of the methodologies common to the field and a feeling for their
usefulness and appropriateness in various settings.
(Mauch & Park, 2003)
1.3. The process of Literature review
The process consists of four basic steps as these are presented in figure 1 below:
Figure 1: The literature review process
Steps for conducting a literature review
• Choose a topic of your interest
• Define your research question
• Decide on the scope of your review
• Select the databases you will use to conduct your searches
• Conduct your searches and find the literature
• Review the literature
Step 1: Choose a topic
a) First, you need to choose a research interest. This might be based on your professional and
personal experience, your current interest or background knowledge.
b) Select a research interest from the everyday interest:
c) Use the research interest to choose the research topic:
Important note: Remember that you need to formulate and define the gap in the
knowledge or the problem. Which topic or field is being examined and what are its
component issues?
Step 2: Search and Choose the Literature
a) Find materials relevant to the research subject.
Use keywords and key phrases
Searching the literature involves reading and refining your problem. So you need to define
what you need to know.
Where can the appropriate literature be found?
Mostly from primary sources of literature such as:
• Academic journals
• Conference proceedings
• Theses and dissertations and
• Government pamphlets
Important note: You need to evaluate the data you search: determine which literature
makes a significant contribution to the understanding of the topic
Step 3: Analyze and interpret the literature:
• Discuss the findings and conclusions of the relevant literature
• Develop your own argument and critique of the literature to ensure that it support your own
thesis/ideas/points of view.
Step 4: Write the Review
• During this step, you need to compose and refine the literature you read.
• The written literature review becomes a work that accurately carries the research that can be
understood by the intended audience
Begin your literature review …
• Introduce your Literature Review by defining or explaining your research problem
Note: In the introduction, explain why the topic is important and give the reader an idea of
where you are going in your paper.
In order to construct the literature review:
• Summarize individual studies or articles
Use as much or as little detail as each merits according to its comparative importance
in the literature
Length demonstrates significance
Don’t need to provide a lot of detail about the procedures used in other studies
Most literature studies only describe the main findings, relevant methodological
issues, and/or major conclusions of other research
• Discuss major areas of agreement of disagreement
• Make logical interpretations from the literature reviewed
If there is no discussion of the relevance of the overview to other work in the field, or
if there is no interpretation of the literature it may signal that the author has not
thoroughly examined the topic of interest
Table 1: Organization of the Review
Introduction to the literature review
Content – what is covered
Structure –how is organized
Boundaries- what is outside of its scope
Body of the literature review
SECTION 1
The most important topic or a key concept
Discussed and evaluated
Summarized and related to your research
project
SECTION 2:
The next important topic or a key concept
Discussed and evaluated
Summarized and related to your research project
ADDITIONAL SECTIONS
Follow the same pattern
Conclusion
From each of the section summaries,
Highlight the most relevant points
Related these back to the need for research
Reiterate what these mean for the research design
1.4. Important tips:
1) Generally speaking write in the past tense, expect when you need to discuss their
significance- then use the present tense.
2)An organizational scheme should be used to arrange the literature:
Organizational Schemes:
• Topical order
• Chronological order
• Problem-cause-solution order
• General-to-specific order
• Known-to- unknown order
• Comparison and contrast order
• Specific to general order
Summary:
An effective Literature review:
• Places each work based on its contribution to the understanding of the subject under review
• Describes the relationship of each work to the others under consideration
• Identifies new ways of interpretation and shed light on any possible gaps in previous
research studies
• Resolves the conflicts amongst seemingly contradictory previous studies
• Identifies areas of prior scholarship to prevent duplication of effort
• Points the way forward to further research
• Places one’s original work (in the case of theses or dissertations) in the context of existing
literature.
Note: Make sure that you are accurate and thorough
• Your review acts as a guide to the audience and the reader
• Thus, it is very important to make your review:
• Accurate: e.g. citations correct, findings attributed to authors correct.
Ensure that someone is able to track down the article and that you have provided a reliable
presentation
Complete: for example try to include all important papers and not every paper which is
written around the topic of interest.
Finally, make sure that you manage the references you find and use in your review and you
create a full reference list by the end of the review.
• Organize and store your references so as to save a significant amount of work
• Make in-text citations based on required referencing styles (e.g. APA or Harvard) according
to the University rules and regulations
• Create a list of references based on the required reference style.
References
Burge, C. (2005) Experimental Molecular Biology: Biotechnology II, Spring 2005.
(Massachusetts Institute of Technology: MIT OpenCouseWare), Available at:
http://ocw.mit.edu. License: Creative Commons BY-NC-SA
Fink, A. (1998). Conducting literature research reviews: from paper to the internet.
Thousand Oaks, CA: Sage.
Hart, C. (1998). Doing a literature review: Releasing the social science research
imagination. Sage.
Lyons, K. (2005). UCSC library – how to write a literature review. Available at
http://library.ucsc.edu/ref/howto/literaturereview.html
Mauch, J., & Park, N. (2003). Guide to the successful thesis and dissertation: A handbook for
students and faculty . CRC Press.
Ridley, D. (2012). The literature review: A step-by-step guide for students. Sage.
Additional Reading
Randolph, J. J. (2009). A guide to writing the dissertation literature review. Practical
Assessment, Research & Evaluation, 14, 1-13.
UU-PSY705 – ZM Project and Research Management
Course Code Course Title Department
UU-PSY 705 – ZM Project and Research Management
Field Type of Course Mode of Delivery
Psychology Master’s in Managerial Psychology Online/ Blended
Course Duration Language of Instruction Lecturer
Seven weeks English
Objectives of the Course:
This module consists of seven weeks and aims to provide a broad understanding of the main
topics related to project and research management.
The content of this module is closely related to research and sampling techniques., focusing on
student’s understanding and knowledge of qualitative and quantitative research methods.. The
module will explore the essential principles of research design and alternative research
strategies. It will also provide an understanding of research methods for data collection, data
management and analysis. It will consider the use of computer programs to perform qualitative
and quantitative analysis of data.
Learning Outcomes:
After the completion of the course students should be able to:
1. Define and discuss the key elements of research and the common methods of
quantitative and qualitative data collection.
2. Apply the main statistical tests used to analyse quantitative data.
3. Critically analyze the different methods used to collect and analyse qualitative data.
4. Understand the ethics in research in regards to the protection of the participant’s rights
data, privacy and autonomy.
5. Critically analyse the theory and practice of project management and how to manage
research in applied settings.
6. Demonstrate thorough understanding of the main guidelines on how to prepare a formal
research proposal.
UU-PSY 705 – ZM Project and Research Management Page 1
Course Contents
Week 1: Introduction to Research and Sampling Techniques
Reading: Introduction to Research
Further reading: Sampling Techniques
Task 1- Quantitative and Qualitative research
Task 2- Sampling Techniques
Week 2: Quantitative Research Reading: Quantitative Research Methods
Further Reading: Quantitative Data Analysis Task 1- Quantitative research : IV and DV Task 2-
Quantitative Analysis
Week 3: Qualitative research Reading: Qualitative research methods Further reading: Qualitative
Data
Task 1- Qualitative Research Methods
Task 2- Qualitative Methods of Data Analysis
Week 4: Ethical Considerations
Reading: Ethical Considerations
Task 1- Conducting Ethical Research
Week 5: Project Management Skills Reading: Project Management Skills.
Task 1- Key factors of a successful Project
Week 6: How to Write a Research Proposal Reading: Literature Review.
Further reading: How to write a research proposal
Preparation for summative assignment – Guidelines Effective Presentation
Week 7: Final Assessment Point
Reading: Assessment Brief- Research Proposal (3500 words).
Learning Activities and Teaching Methods
Weekly topic overviews, power point presentations, video links, weekly formative and
summative tasks and discussion forums.
UU-PSY 705 – ZM Project and Research Management Page 2
Assessment Methods
Assignment 1 – This assignment is worth 100% of the module marks and is due at the end of
Week 7; Sunday 11.59 pm (23:59 hours) UTC time on the due date at the latest.
Course Requirements:
Assessment Criteria: Students will be evaluated based on the grade of the summative
assignment. In order to pass this course they need to achieve an overall minimum grade
of 70%. The tasks and discussion forum included in this course are formative, this
means that they are not graded but students are encouraged to participate in each one as
they are designed to help them and provide a good understanding of the main concepts
that are included in each of the assignments.
UU-PSY 705 – ZM Project and Research Management Page 3
Week 6: Conducting
Critical Literature
Review
CONDUCTING CRITICAL LITERATURE REVIEW
1.1. Introduction
What is a literature review?
A literature review is:
“… a systematic method for identifying, evaluating and interpreting the …work produced by researchers, scholars and practitioners.” (Fink, 1998, p.3)
“An examination of the research that has been conducted in a particular field of study”
(Ferfolja & Burnett, 2003, p.1)
A literature review
· surveys scholarly articles, books and other sources (e.g. dissertations, conference proceedings) relevant to a particular issue, area of research, or theory.
· provides a short description and critical evaluation of work critical to the topic.
· offers an overview of significant literature published on a topic.
(Lyons, 2005)
Literature reviews is defined as:
Hart (2003, p.13) further adds that “literature is the selection of available documents…and the effective evaluation of these documents in relation to the research being proposed.
1.2 Why to conduct a literature review?
“…without it you will not acquire an understanding of your topic, of what has already been done on it, how it has been researched, and what the key issues are.” (Hart, 1998, p.1)
Conducting a literature review is important because:
• It determines the need for actually doing the proposed research
· For example, even if there are similar studies published, the researchers might suggest the need for similar studies or replication
•It narrows down the problem.
· It can be overwhelming getting into the literature of a field of study. Thus, a literature review is important to understand where you need to focus your efforts.
•It generates hypotheses or questions for further studies
(Mauch & Park, 2003)
Literature Reviews are conducted for Various Reasons.
Some of them are:
1. For a review paper
2. For the introduction (and discussion) of a research paper, master’s thesis or dissertation
3. To embark on a new area of research
4. For a research proposal
Conducting a literature review is a helpful way to:
· Get background knowledge of the field of inquiry
· Understand Facts
· To search for Eminent scholars
· To read about Parameters of the field
· To understand the most important ideas, theories, questions and hypotheses.
· To gain Knowledge of the methodologies common to the field and a feeling for their usefulness and appropriateness in various settings.
(Mauch & Park, 2003)
1.3. The process of Literature review
The process consists of four basic steps as these are presented in figure 1 below:
Figure 1: The literature review process
Steps for conducting a literature review
· Choose a topic of your interest
· Define your research question
· Decide on the scope of your review
· Select the databases you will use to conduct your searches
· Conduct your searches and find the literature
· Review the literature
Step 1: Choose a topic
a) First, you need to choose a research interest. This might be based on your professional and personal experience, your current interest or background knowledge.
b) Select a research interest from the everyday interest:
c) Use the research interest to choose the research topic:
Important note: Remember that you need to formulate and define the gap in the knowledge or the problem. Which topic or field is being examined and what are its component issues?
Step 2: Search and Choose the Literature
a) Find materials relevant to the research subject. Use keywords and key phrases
Searching the literature involves reading and refining your problem. So you need to define what you need to know.
Where can the appropriate literature be found?
Mostly from primary sources of literature such as:
· Academic journals
· Conference proceedings
· Theses and dissertations and
· Government pamphlets
Important note: You need to evaluate the data you search: determine which literature makes a significant contribution to the understanding of the topic
Step 3: Analyze and interpret the literature:
• Discuss the findings and conclusions of the relevant literature
• Develop your own argument and critique of the literature to ensure that it support your own thesis/ideas/points of view.
Step 4: Write the Review
· During this step, you need to compose and refine the literature you read.
· The written literature review becomes a work that accurately carries the research that can be understood by the intended audience
Begin your literature review …
• Introduce your Literature Review by defining or explaining your research problem
Note: In the introduction, explain why the topic is important and give the reader an idea of where you are going in your paper.
In order to construct the literature review:
· Summarize individual studies or articles
· Use as much or as little detail as each merits according to its comparative importance in the literature
· Length demonstrates significance
· Don’t need to provide a lot of detail about the procedures used in other studies
· Most literature studies only describe the main findings, relevant methodological issues, and/or major conclusions of other research
· Discuss major areas of agreement of disagreement
· Make logical interpretations from the literature reviewed
· If there is no discussion of the relevance of the overview to other work in the field, or if there is no interpretation of the literature it may signal that the author has not thoroughly examined the topic of interest
Table 1: Organization of the Review
Introduction to the literature review
· Content – what is covered
· Structure –how is organized
· Boundaries- what is outside of its scope
Body of the literature review SECTION 1
The most important topic or a key concept
· Discussed and evaluated
· Summarized and related to your research project
SECTION 2:
The next important topic or a key concept
· Discussed and evaluated
· Summarized and related to your research project
ADDITIONAL SECTIONS
· Follow the same pattern
Conclusion
From each of the section summaries,
· Highlight the most relevant points
· Related these back to the need for research
· Reiterate what these mean for the research design
1.4. Important tips:
1) Generally speaking write in the past tense, expect when you need to discuss their significance- then use the present tense.
2)An organizational scheme should be used to arrange the literature:
Organizational Schemes:
· Topical order
· Chronological order
· Problem-cause-solution order
· General-to-specific order
· Known-to- unknown order
· Comparison and contrast order
· Specific to general order
Summary:
An effective Literature review:
· Places each work based on its contribution to the understanding of the subject under review
· Describes the relationship of each work to the others under consideration
· Identifies new ways of interpretation and shed light on any possible gaps in previous research studies
· Resolves the conflicts amongst seemingly contradictory previous studies
· Identifies areas of prior scholarship to prevent duplication of effort
· Points the way forward to further research
· Places one’s original work (in the case of theses or dissertations) in the context of existing literature.
Note: Make sure that you are accurate and thorough
· Your review acts as a guide to the audience and the reader
· Thus, it is very important to make your review:
· Accurate: e.g. citations correct, findings attributed to authors correct.
Ensure that someone is able to track down the article and that you have provided a reliable presentation
Complete: for example try to include all important papers and not every paper which is written around the topic of interest.
Finally, make sure that you manage the references you find and use in your review and you create a full reference list by the end of the review.
· Organize and store your references so as to save a significant amount of work
· Make in-text citations based on required referencing styles (e.g. APA or Harvard) according to the University rules and regulations
· Create a list of references based on the required reference style.
References
Burge, C. (2005) Experimental Molecular Biology: Biotechnology II, Spring 2005. (Massachusetts Institute of Technology: MIT OpenCouseWare), Available at: http://ocw.mit.edu. License: Creative Commons BY-NC-SA
Fink, A. (1998). Conducting literature research reviews: from paper to the internet.
Thousand Oaks, CA: Sage.
Hart, C. (1998). Doing a literature review: Releasing the social science research imagination. Sage.
Lyons, K. (2005). UCSC library – how to write a literature review. Available at http://library.ucsc.edu/ref/howto/literaturereview.html
Mauch, J., & Park, N. (2003). Guide to the successful thesis and dissertation: A handbook for students and faculty . CRC Press.
Ridley, D. (2012). The literature review: A step-by-step guide for students. Sage.
Additional Reading
Randolph, J. J. (2009). A guide to writing the dissertation literature review. Practical Assessment, Research & Evaluation, 14, 1-13.
Week 4: Research
Ethics and
Professional Issues
Research Ethics and Professional Issues
Learning objectives:
• 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 study1) the experimental nature
of the treatment; (2) the services that will or will not be available to the control group(s) if
appropriate; (3) the means by which individuals will be assigned to treatment and control
groups; (4) treatment alternatives that are available to individuals who do not wish to
participate in the research or wish to withdraw once a study has begun; and (5) compensation
for or monetary costs of participating including, if appropriate, whether reimbursement from
the participant or a third-party pay or will be sought.
Informed Consent for Recording Voices and Images in Research
In research that involves recording voices and images psychologists are required to obtain
informed consent from research participants prior to recording their voices or images for data
collection purposes unless (1) the research consists merely of naturalistic observations in
public places, and it is not anticipated that the recording can lead to personal identification or
harm, or (2) the research design includes deception, and consent for the use of the recording
is obtained at a later stage i.e. during debriefing.
Client/Patient, Student, and Subordinate Research Participants
(a) When research is conducted with clients/patients, students, or subordinates as participants,
psychologists take steps to protect the prospective participants from adverse consequences of
declining or withdrawing from participation.
(b) When taking part in a study is a course requirement or an opportunity for extra credit, the
prospective participant is given the option to choose between other equitable alternatives.
Dispensing with Informed Consent for Research
Psychologists may omit informed consent only (1) where research would not reasonably be
assumed to create distress or harm and involves (a) the study of normal educational practices,
curricula, or classroom management methods conducted in educational settings; (b) only
anonymous questionnaires, naturalistic observations, or archival research for which
disclosure of responses would not place participants at risk of criminal or civil liability or
damage their financial standing, employability, or reputation, and confidentiality is protected;
or (c) the study of factors associated with job or organization effectiveness conducted in
organizational settings for which there is no risk for participants’ employability, and
confidentiality or (2) where otherwise permitted by law or federal or institutional regulations.
Offering Inducements for Research Participation
(a) Psychologists avoid offering excessive or inappropriate financial or other inducements for
research participation as such inducements are likely to force participation.
(b) When professional services are offered as an inducement for research participation,
psychologists make clear the nature of the services, as well as the risks, obligations, and
limitations.
Deception in Research
(a) Psychology research should not involve deception unless the use of deceptive techniques
is justified by the study’s significant prospective scientific, educational, or applied value and
that effective non deceptive alternative procedures are not feasible.
(b) Psychologists do not deceive potential participants about research that is reasonably
expected to cause physical pain or severe emotional distress.
(c) Psychologists explain any deception involved in a study as early as possible, preferably at
the completion of their participation, and inform participants of their right to withdraw their
data.
Debriefing
(a) Upon the completion of the study, researchers are required to provide participants with
accurate and appropriate information about the nature of the experiment or study.
Researchers also share with participants any information related to what the purpose of the
research was, as well as what the findings indicate.
(b) If scientific or human values justify delaying or withholding this information,
psychologists take reasonable measures to reduce the risk of harm.
(c) When psychologists become aware that research procedures have harmed a participant,
they take reasonable steps to minimize the harm.
1.3. Human Care and Use of Animals in Research
(a) Psychologists acquire, care for, use, and dispose of animals in compliance with current
federal, state, and local laws and regulations, and with professional standards.
(b) Psychologists who are trained in research methods and experienced in the care of
laboratory animals supervise all procedures involving animals and are responsible for
ensuring that the necessary measure are taken for their comfort, health, and human treatment.
(c) Psychologists ensure that all individuals under their supervision who are using animals in
their studies have received appropriate instructions regarding research methods and the
carrying, maintenance, and handling of the species being used, to the extent appropriate to
their role.
(d) Psychologists take actions to minimize the discomfort, infection, illness, and pain of
animal subjects.
(e) Psychologists use a procedure subjecting animals to pain, stress, or privation only when
an alternative procedure is unavailable and the goal is justified by its prospective scientific,
educational, or applied value.
(f) Psychologists perform surgical procedures under appropriate anesthesia and follow
techniques to avoid infection and minimize pain during and after surgery.
(g) When it is appropriate that an animal’s life be terminated, psychologists proceed rapidly,
with an effort to minimize pain and in accordance with accepted procedures.
1.4. Reporting Research Results
(a) Psychologists do not fabricate data.
(b) If psychologists discover significant errors in their published research, they take
reasonable actions to correct such errors in a correction, retraction, erratum, or other
appropriate publication means.
Plagiarism
Psychologists do not present portions of another’s work or data as their own, even if the other
work or data source is cited occasionally.
Publication Credit
(a) Psychologists take responsibility and credit (e.g. authorship credit), only for work they
have actually performed or to which they have significantly contributed.
(b) Principal authorship and other publication credits accurately correspond to the relative
scientific or professional contributions of the individuals involved, irrespective of their
relative status. Just the possession of an institutional position (e.g. department chair), does not
justify authorship credit. Minor contributions to the research or to the writing for publications
are acknowledged appropriately, such as in footnotes or in an introductory statement.
(c) With the exception of some exceptional circumstances, a student is listed as the main
author on any multiple-authored article that is considerably based on the student’s doctoral
dissertation. Faculty advisors/supervisors discuss publication credit with students at an early
stage and throughout the research and publication process as appropriate.
Duplicate Publication of Data
Psychologists do not publish, as original data, data that have been previously published. This
does not preclude republishing data when they are accompanied by proper acknowledgment.
Sharing Research Data for Verification
(a) After the publication of the results, psychologists do not withhold the data on which their
conclusions are based from other competent professionals who seek to verify the results
through reanalysis and who intend to use the data only for that purpose, provided that the
confidentiality of the participants can be protected and unless legal rights concerning
proprietary data preclude their release. This does not preclude psychologists from requiring
that such individuals or groups be responsible for costs associated with the provision of such
information.
(b) Psychologists who request data from other psychologists to verify the substantive claims
through reanalysis may use shared data only for the declared purpose. Requesting
psychologists obtain prior written agreement for all other uses of the data.
Reviewers
Psychologists who review material submitted for presentation, publication, grant, or research
proposal review respect the confidentiality of and the proprietary rights in such information
of those who submitted it.
References
American Psychological Association (2003). Ethical Principles of Psychologists and Code of
Conduct. Retrieved from http://www.apa.org/ethics/code/
The British Psychological Society (2009). Code of Ethics and Conduct Guidance. Retrieved
from https://beta.bps.org.uk/sites/beta.bps.org.uk/files/Policy%20-
%20Files/Code%20of%20Ethics%20and%20Conduct%20%282009%29
American Psychological Association (2002). Ethical principles of psychologists and code of
conduct. Retrieved https://memforms.apa.org/apa/cli/interest/ethics1.cfm
http://www.apa.org/ethics/code/
https://beta.bps.org.uk/sites/beta.bps.org.uk/files/Policy%20-%20Files/Code%20of%20Ethics%20and%20Conduct%20%282009%29
https://beta.bps.org.uk/sites/beta.bps.org.uk/files/Policy%20-%20Files/Code%20of%20Ethics%20and%20Conduct%20%282009%29
https://memforms.apa.org/apa/cli/interest/ethics1.cfm
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_Qualitative_Research
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Week 2:Quantitative Data
Analysis
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Quantitative Research Data Analysis
Learning objectives:
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
Understand statistical terms related to quantitative research (confidence intervals and p-values)
Learn the basic statistical tests
Define sampling, data distribution and randomization
Explain probability and non-probability sampling and describe the different types of each
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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.
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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:
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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,
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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.
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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.
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Figure 5. Regions of rejection at 95% and 99% confidence interval
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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/
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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.
Independent Samples t-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.
Dependent t-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.
Pearson product moment correlation: Pearson’s correlation is used to test the linear relationship between at
least two continuous variables.
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Spearman correlation: Spearman’s correlation evaluates the 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:
A group of individuals with elevated anxiety are trying three 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.
A manufacturer has two different processes to make light bulbs. They want to know if one process is better than
the other.
Students from different colleges take the same exam. 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
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*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 http://www.statsmakemecry.com/smmctheblog/stats-soup-anova-ancova-manova-mancova
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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 http://www.statsmakemecry.com/smmctheblog/stats-soup-anova-ancova-manova-mancova
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?
Simple Regression: The simplest regression models involve a single response variable Y and a single predictor
variable X.
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Multiple Regression: 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.
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*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.
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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.
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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.
Week 5: Project
Management
Project Management
Learning objectives:
· 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 management processes
· 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 the customers’ 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.
References
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.pd
f
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 Network Security, 9, 211-215. Retrieved from
http://paper.ijcsns.org/07_book/200905/20090528.pd
f
Week3: 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 needs of 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. It is 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 research in 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. International journal 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 research in 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 handbook of 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. Nurse researcher, 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. International journal 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. Nurse researcher, 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=DRL4PF2u9X
A
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 a
the
research questions
.
are interested in examining a
research
qualitative 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?
Prompts (if necessary)
· 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?
Prompts (if necessary)
· (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 directing observations 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. Nurse researcher, 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. International journal 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 medical journal, 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_sLWl8
8
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 Educational Research (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 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 researcher puts 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 the
statistical 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 Quantitative Research? Retrieved from
https://www.snapsurveys.com/blog/what-is-the-difference-between
–
qualitative-research-and-quantitative-research
/