Book Review: "Research Methods: The Basics" by Nicholas Walliman
Introduction:
Nicholas Walliman's "Research Methods: The Basics" is an essential primer for anyone starting out in research across multiple fields. Walliman's thorough examination of research methodologies provides readers with the fundamental skills required to conduct scholarly inquiries with confidence and proficiency.
Summary:
In this basic yet informative guide, Walliman demystifies the research process, beginning with the fundamental concepts of developing research questions and hypotheses. He then goes on to explain various research designs, methodologies, and data collection approaches, providing useful insights into their applications and implications. Walliman uses illustrative examples and case studies to show how researchers can manage ethical considerations, evaluate data effectively, and articulate their findings persuasively.
Key Themes:
Research Fundamentals: Walliman explains the fundamentals of research, such as how to formulate research questions, establish hypotheses, and choose relevant procedures.
Research Design and Methodologies:The book explores a range of research designs, from experimental to qualitative approaches, giving readers a thorough grasp of their strengths and limits.
Ethical Considerations: Walliman emphasizes the significance of ethical integrity in research, guiding readers through ethical decision-making processes and exposing ethical quandaries frequently in scholarly investigation.
RESEARCH METHODS THE BASICS
Research Methods: The Basics is an easy-to-understand introduction to several aspects of research theory, methodology, and practice. This book is divided into two sections: the first addresses the nature of knowledge and the motivations for research, and the second includes the specific methods employed to conduct good research.
- structuring and planning a research project
- the ethical issues involved in research x different types of data and how they are measured
- collecting and analysing data in order to draw sound conclusions
- devising a research proposal and writing up the research.
This book is a necessary work for anyone starting out in research, and it is generally relevant across the social sciences and humanities. It includes a glossary of keywords and recommendations for further reading.
Nicholas Walliman is Senior Lecturer in the Department of Architecture at Oxford Brookes University, UK
CONTENTS
Introduction
Research methods:
Definition and Purpose: Research methods are tools and techniques used to undertake investigations to discover new facts or insights. The quality of research has a direct impact on the quality of outcomes across a wide range of fields, including natural sciences, social sciences, anthropology, psychology, and politics.
Scope: Research methods include a diversified set of tools customized to different types of inquiries across many subjects, similar to how unique tools are used for different practical activities. Understanding the right tools for the job and how to use them effectively is critical for conducting successful research.
Book Overview: The book is separated into two major portions. Part I covers research theory and practice, while Part II focuses on specific research methods for data collecting, analysis, and presentation.
Additional Resources: Each chapter proposes additional reading to increase awareness of the issues covered. Technical terms, which are highlighted upon their initial use, are explained and gathered into a glossary for future reference.
Benefits: Understanding research methods makes it easier to not only undertake research projects but also assess the reliability of claims made in a variety of industries. The book functions as both a reference guide and a step-by-step tutorial for performing research projects.
Importance: In a world flooded with information and difficult challenges, understanding research methodologies is vital. These strategies promote clear thinking, methodical research, evidence-based conclusions, and critical analysis of arguments, so increasing overall knowledge and understanding of one's surroundings.
PART I
Research theory and practice
Scope of Part I:
Part I of the book presents an overview of research, including its theoretical foundations and the wider context in which research methods are used. While it may not go into specific strategies for data collecting and analysis, knowing the nature of research is critical for making good use of research methods.
Chapter 2: Theoretical Basis of Research:
This chapter delves into many philosophical approaches on knowledge acquisition and comprehending the universe. It addresses the historical history of human thought, as well as the ongoing argument over individual autonomy in their environment and society.
Chapter 3: Structure of Research Projects:
This chapter examines the common features shared by research projects, such as defined aims, argumentation leading to conclusions based on evidence, and formulation of aims and arguments.
Chapter 4: Ethics in Research:
Ethical considerations play a pivotal role in research to ensure the honesty of researchers and the protection of participants from harm. This chapter explores the principles of ethical research, emphasizing the importance of causing no harm and striving for potential gains for participants and society.
Chapter 5: Reviewing Literature:
Research is embedded in a larger context of existing knowledge and previous work. This chapter highlights the significance of examining literature on the chosen subject, advising readers on where to locate information and how to evaluate its relevance to their research. Developing skills in literature review has been identified as important for critical thinking in a variety of areas of life.
These chapters offer the framework for understanding the concepts and ethical considerations that underpin research, which are critical for determining the selection and application of research methodologies in later sections of the book.
Research BASICS
Research methods and designs:
Research Methods Definition:
Research is the systematic discovery of new knowledge, whereas research methods are the processes of collecting, sorting, and analyzing data to reach accurate conclusions. Using suitable procedures improves the validity of research results and provides a solid foundation for new information.
Practical Application:
Learning about research methods is similar to gaining tools for practical application. To provide hands-on experience, research techniques courses frequently include assignments in which these approaches are applied to real-world research projects, such as dissertations or reports.
Research Theory and Practice:
Research requires both theoretical understanding and practical practice. Historical philosophical advances have influenced research techniques, highlighting the significance of knowing the context in which research methods are used.
Utilizing Research:
Research serves a variety of functions, including identifying, describing, explaining, assessing, comparing, correlating, forecasting, and controlling phenomena. Each aim necessitates a distinct research strategy designed to properly solve complicated topics.
Research Designs:
Different research designs are appropriate for different research topics, each having its own set of characteristics and regularly used research methods. Common research designs include historical, descriptive, correlational, comparative, experimental, simulation, evaluative, action, ethnological, feminist, and cultural research.
Choosing Research Design:
The nature of the study challenge determines the proper research design. Once the research objectives have been established, the research design chosen serves as a framework for data collecting and analysis, indicating which research methodologies are appropriate. Researchers can use numerous study designs to answer multifaceted research topics.
Research Methods Compatibility:
Specific study designs frequently necessitate the use of research methods that are adapted to the design's inherent challenges. However, some research methodologies are widely transferable across different research designs, making them useful instruments in the research process.
Where to Find Out More
Aside from continuing to study this book, you may want to look at additional introductions to research. Most books on this subject cover the entire process of conducting research. The following books are geared toward undergraduate and postgraduate research, and a careful study of the preliminary chapters will provide additional instruction on research fundamentals. Each provides a slightly different perspective on the topics, so refer to as many as possible. You can probably accomplish this at the library without borrowing any books.
Blaxter, L., Hughes, C. and Tight, M. (2006) How to Research (third edition). Buckingham: Open University Press.
The first chapter gives an entertaining review of what research is about.
Rudestam, K. E. and Newton, R. (2007) Surviving Your Dissertation: A Comprehensive Guide to Content and Process (third edition). Thousand Oaks, CA: Sage.
Again, the first couple of chapters provide an introduction to research.
David, M. and Sutton, C. (2004) Social Research: The Basics. London: Sage.
A good chapter on getting started.
Swetnam, D. (2000) Writing Your Dissertation: How to Plan, Prepare and Present Successful Work (third edition). Oxford: How To Books.
Chapter 1 gives some simple advice on how to get started.
Biggam, J. (2008) Succeeding with Your Master’s Dissertation: A Step-by-Step Handbook. Basingstoke: Palgrave.
A useful, simple and easy to read book for a person that has not done a dissertation before.
Research theory
RESEARCH THEORY
Purpose of Research: Research aims to acquire knowledge and understanding by collecting facts and interpreting them to construct a comprehensive view of the world, including both external and internal realities.
Role of Philosophy: Philosophy underpins our understanding of reality and knowledge. Philosophical stances influence research practices and methodologies, shaping how researchers perceive and interpret information.
Importance of Philosophy in Research: Despite skepticism towards the relevance of philosophy in research, understanding philosophical perspectives is crucial. Everyone operates with a philosophical framework, whether conscious or unconscious, which impacts their approach to research and interpretation of findings.
Critical Evaluation: Developing sensitivity to philosophical issues enables researchers to critically evaluate research reports, discern underlying assumptions, and assess the appropriateness of methodologies and validity of conclusions. This skill is essential for both analyzing existing research and conducting one's own research.
Influence of Philosophy on Research: Research approaches differ depending on philosophical assumptions about reality (metaphysics) and knowledge acquisition (epistemology). Research methodology and interpretations fluctuate depending on philosophical perspectives.
Overall, appreciating the role of philosophy in research is crucial for understanding the underlying concepts that guide research techniques and critically evaluating the validity and implications of research findings.
Metaphysics Overview: Metaphysics explores fundamental questions about existence, identity, knowledge, and the nature of reality. It encompasses contrasting views such as idealism and materialism, which represent opposite ends of a spectrum with varying degrees of intermediate positions.
Idealism vs. Materialism: Idealism posits that reality exists within the mind, influenced by mental and social factors, leading to constant change. In contrast, materialism asserts that only physical entities and interactions are real, independent of social factors, resulting in stability.
Epistemology Definition: Epistemology focuses on theories of knowledge, including validation and methods used to acquire knowledge. It investigates the reliability of sensory experiences and the power of reasoning, presenting two primary approaches: empiricism and rationalism.
Empiricism and Rationalism: Empiricism relies on sensory experiences and inductive reasoning, starting from specific observations to develop general conclusions. Rationalism, on the other hand, employs deductive reasoning, beginning with general premises to derive specific conclusions.
Hypothetico-Deductive Method: A pragmatic approach that combines elements of both empiricism and rationalism, commonly used in scientific inquiry. It involves formulating hypotheses based on deductive reasoning and testing them through empirical observation, reflecting the scientific method.
Strengths and Weaknesses: Inductive reasoning, while widely used, faces challenges regarding the reliability and generalizability of conclusions. Deductive reasoning is susceptible to the accuracy of premises and the potential for falsification, requiring hypotheses to be falsifiable and subject to empirical testing.
Scientific Progress: Science advances through a process of theory formulation, empirical testing, and falsification. Theories are continually refined or replaced based on observations and experiments, with the survival of the fittest theory.
HYPOTHETICO-DEDUCTIVE REASONING OR SCIENTIFIC METHOD
Hypothetico-Deductive Reasoning: The hypothetico-deductive method integrates both inductive and deductive reasoning, involving the following steps:
Identification or clarification of a problem
Development of a testable hypothesis through inductive reasoning
Deductive charting of implications
Practical or theoretical testing of the hypothesis
Refinement or rejection of the hypothesis based on results.
Foundation of Scientific Method: This method, crucial to modern scientific research, was formulated by Popper in the 1960s. It allows for the exploration of complex theories through systematic testing and refinement.
Complexity of Testing: Real-life testing poses challenges due to the intricate nature of theories and testing methods. Testing relies on underlying assumptions and can be influenced by various conditions, potentially leading to the rejection of theories based on faulty premises.
Assumptions of Scientific Method: The scientific method operates under certain assumptions, including:
Order: The universe follows discoverable rules.
External reality: A shared reality exists independently of individual perception.
Reliability: Senses and reasoning produce reliable interpretations of reality.
Parsimony: The simplest explanations are preferred.
Generality: Discoveries apply universally across relevant contexts.
Opposing Views in Metaphysics and Epistemology: Idealists and relativists challenge these assumptions, emphasizing the subjective nature of reality and the social dimension of knowledge creation. This ideological clash remains unresolved.
Positivism vs. Relativism: Positivism asserts an objective reality and values-free inquiry, aiming to discover universal laws. Relativism emphasizes subjective interpretations and the role of human perception and values in knowledge creation.
Postmodernism: Postmodernism challenges traditional views of knowledge and truth, questioning the validity of grand narratives and highlighting the role of power and discourse in shaping knowledge.
Critical Realism: Critical realism offers a reconciliatory approach, recognizing a natural order in social events while acknowledging the interpretive nature of understanding. It seeks to bridge the gap between positivism and relativism by emphasizing the importance of interpretation and context in knowledge creation.
KEY FIGURES : Key figures that have influenced thinking about research
Plato (427–347 BC) and Aristotle (348–322 BC) – these represent the two contrasting approaches to acquiring knowledge and understanding the world (epistemology). Plato argued for deductive thinking (starting with theory to make sense of what we observe) and Aristotle for the opposite, inductive thinking (starting with observations in order to build theories).
René Descartes (1596–1650) – provided the starting point for modern philosophy by using a method of systematic doubt; that we cannot rely on our senses or logic, and therefore he challenged all who sought for the basis of certainty and knowledge. His famous maxim is ‘I think, therefore I am’, that is – I can only be sure of my own existence, the rest must be doubted.
John Locke (1632–1704) – made the distinction between bodies or objects that can be directly measured, and therefore have a physical existence, and those abstract qualities that are generated by our perceptions and feelings.
George Berkeley (1685–1753) – argued that all things that exist are only mental phenomena. They exist by being perceived. This is ‘our’ world.
David Hume (1711–1776) – made a distinction between systems of ideas that can provide certainty – e.g. maths – and those that rely on our perceptions (empirical evidence) which are not certain. He recognized the importance of inductive thinking in the advancement of scientific knowledge, but highlighted its restrictions in finding the truth.
Immanuel Kant (1724–1804) – held that our minds organize our experiences to make sense of the world. Therefore ‘facts’ are not independent of the way we see things and interpret them.
Karl Popper (1902–1994) – formulated a combination of deductive and inductive thinking in the hypothetico-deductive method, commonly known as scientific method. This method aims to refine theories to get closer to the truth.
Auguste Compte (1789–1857) – maintained that society can be analysed empirically just like any other subjects of scientific enquiry. Social laws and theories are based on psychology and biology.
Karl Marx (1818–1883) – defined moral and social aspects of humanity in terms of material forces.
Emil Durkheim (1858–1917) – argued that society develops its own system of collectively shared norms and beliefs – these were ‘social facts’.
Max Weber (1864–1920) – insisted that we need to understand the values and meanings of subjects without making judgements – ‘verstehen’ was the term he coined for this which is German for ‘understanding’.
Thomas Kuhn (1922–1995) – revealed that scientific research cannot be separated from human influences and is subject to social norms.
Michel Foucault (1926–1984) – argued that there was no progress in science, only changing perspectives, as the practice of science is shown to control what is permitted to count as knowledge. He demonstrated how discourse is used to make social regulation and control appear natural.
Jacques Derrida (1930–2004) – stated that there is no external or fixed meaning to text, nor is there a subject who exists prior to language and to particular experiences. You cannot get outside or beyond the structure. This approach led to the movement called Deconstruction.
WHERE TO FIND OUT MORE
There has been a lot written on the philosophy of knowledge and research, and it is recommended that you have a good basic understanding of the argument over the philosophy of scientific knowledge and its detractors in order to place your study within the philosophical background. When putting together this chapter, I found the following books to be quite useful and worth looking into. The titles provide an indication of the topic covered. I've listed the more approachable ones first.
Two good introductory books to start with:
Thompson, M. (2006) Philosophy. London: Hodder (Teach Yourself). This is a simple introduction to philosophy which explains the main terminology and outlines the principle streams of thought.
Warburton, N. (2004) Philosophy: The Basics. (fourth edition). London: Routledge. A book in the same series as this one.
The following concentrate on scientific approaches and dilemmas: Okasha, S. (2002) Philosophy of Science: A Very Short Introduction. Oxford: Oxford University Press.
A clear, non-technical introduction to the philosophy of science.
Chalmers, A. (1999) What Is This Thing Called Science? (third edition). Milton Keynes: Open University Press.
And three influential books for the enthusiast!:
Kuhn, T. S. (1970) The Structure of Scientific Revolutions (second edition). Chicago: Chicago University Press.
Popper, K. (1992) The Logic of Scientific Discovery. Routledge Classics. London: Routledge. Feyerabend, P. (1993)
Against Method: Outline of an Anarchistic Theory of Knowledg (third edition). London: Verso.
STRUCTURING THE RESEARCH PROJECT
Research Problem Definition
Clarity and Conciseness: Clearly state the research problem in a concise manner to provide direction and focus.
Significance: Ensure the problem is significant and not trivial, addressing unresolved controversies or gaps in knowledge.
Scope: Delimit the scope of the problem to make it manageable and practical for investigation.
Feasibility: Ensure the problem is feasible by confirming the availability of necessary information and the possibility of drawing conclusions.
Formulation: Formulate the research problem based on questions, unresolved controversies, gaps in knowledge, or unmet needs within the subject area.
Expressing the Research Problem
Question Formulation: Express the research problem as a main question, followed by sub-questions to break down the main problem into manageable components.
Example: "Are school exam results a true test of a student’s intelligence?"
Exploratory Statements: For smaller-scale studies, express the subject and scope of exploration in a statement of intent.
Example: "This study examines the career development of women engineers in the automotive industry in Britain, focusing on identifying barriers and exploring effectiveness of initiatives."
Sub-Question Development
Investigation of Aspects: Split the main question into different aspects that can be investigated separately.
Exploration of Perspectives: Consider different personal or group perspectives to gain a comprehensive understanding.
Concept Exploration: Investigate different concepts used within the problem area to clarify understanding.
Scale Consideration: Analyze the problem at different scales, such as individual, group, or organizational levels.
Comparison of Outcomes: Compare outcomes of different aspects to enrich the analysis and provide comprehensive answers to the main question.
HYPOTHESES
Hypothesis Development
Purpose: Hypotheses are formulated to test specific statements or explanations regarding phenomena.
Everyday Analogies: Hypotheses are rational guesses similar to those made in everyday life to explain occurrences.
Testability: A good hypothesis must be testable, allowing for empirical or experimental verification.
Organizational Aid: Hypotheses aid in organizing research efforts by specifying factors (variables) and methods for data collection and analysis.
Hypothesis Formulation
Statement: Express the hypothesis as a clear and testable statement.
Example: "School exam results are a true test of a student’s intelligence."
Abstract to Operational: Convert abstract hypotheses into operational ones for direct testing, a process known as operationalization.
Sub-Hypotheses: Break down main hypotheses into sub-hypotheses to address different components, each implying distinct research methods.
Propositions
Focus: Propositions allow the study to concentrate on specific relationships between events without strict adherence to hypothesis characteristics.
Interrelationship: Propositions consist of a series of statements indicating relationships between events, leading to a conclusion.
Example: "Specifically designed public sector housing provided for disabled people continues to be designed according to government recommendations, resulting in a mismatch between housing and accommodation requirements."
Argumentation
Logical Reasoning: Arguments support research conclusions through logical reasoning, based on premises and conclusions.
Types of Reasoning: Inductive reasoning moves from observations to general conclusions, while deductive reasoning starts from general principles to specific conclusions.
Construction and Scrutiny: Construct arguments using evidence to support or refute statements, employing sound logic and avoiding fallacies.
Identifying Fallacies
Definition: Fallacies are flaws or errors in reasoning that weaken arguments or lead to incorrect conclusions.
Types: Fallacies can be categorized into formal and informal types, each affecting the logical structure or content of arguments, respectively.
Formal Fallacies
Logical Structure: Formal fallacies occur when the logical structure of an argument is flawed.
Example: Consider the fallacy of affirming the consequent:
If A, then B.
B.
Therefore, A.
Detection: Recognize when premises do not logically support conclusions, often due to missing or false premises.
Informal Fallacies
Content Flaws: Informal fallacies arise from the content of the argument, often through misleading or irrelevant claims.
Examples:
False Analogy: Drawing inaccurate comparisons between unrelated subjects.
Appeal to Emotion: Manipulating emotions to sway opinions rather than relying on logical reasoning.
Detection: Look for misleading analogies, emotional appeals, or irrelevant evidence that weakens the argument's validity.
Testing Arguments for Fallacies
Recognition: Identify fallacies by examining the structure and content of arguments for flaws.
Scrutiny: Scrutinize arguments for logical consistency and relevance of evidence to avoid fallacious reasoning.
Refutation: Challenge fallacious arguments by highlighting logical errors, false analogies, or emotional appeals that undermine their validity.
Constructing Fallacy-Free Arguments
Logic and Evidence: Construct arguments based on sound logic and credible evidence to avoid fallacies.
Precision: Use clear and precise language to convey arguments effectively and minimize the risk of fallacious reasoning.
Critical Analysis: Engage in critical analysis to detect and eliminate fallacies, ensuring the strength and validity of arguments.
WHERE TO FIND OUT MORE
Brink-Budgen, R. (2009) Critical Thinking for Students: Learn the Skills of Critical Assessment and Effective Argument (fourth edition). Oxford: How To Books.
Bonnett, A. (2001) How to Argue. Harlow: Pearson Education.
logic and argument
Hodges, W. (2001) Logic: An Introduction to Elementary Logic (second edition). London: Penguin.
Salmon, M. H. (2007) Introduction to Logic and Critical Thinking (fifth edition). Belmont, CA: Wadsworth.
Gensler, H. J. (1989) Logic: Analyzing and Appraising Arguments. London: Prentice-Hall International.
Fisher, A. (1998) The Logic of Real Arguments. Cambridge: Cambridge University Press
two amusing books about fallacy
Pirie, M. (2007) How to Win Every Argument: The Use and Abuse of Logic. London: The Continuum. Well-written and entertaining.
Thouless, R. H. (1974) Straight and Crooked Thinkin (revised edition). London: Pan Books.
Old, but still entertaining and thought-provoking.
Research ethics
Ensuring Research Integrity: Ethical Considerations
Research undertakings, regardless of novelty, are only valuable when carried out with honesty and integrity. Trust in research outcomes is based on the certainty that researchers performed ethically throughout the entire process. Here's a breakdown of the ethical issues required for ensuring research integrity:
Plagiarism and Citation
Plagiarism: Unethical appropriation of others' work undermines research integrity.
Citation Virtue: Proper acknowledgment of sources through citation is imperative, demonstrating a breadth of knowledge and respect for intellectual contributions.
Ethical Treatment of Participants
Respectful Conduct: Ethical research with human participants demands respect, encompassing pre-, during-, and post-research interactions.
Adherence to Guidelines: Educational and professional bodies provide stringent ethical guidelines, mandating compliance and consultation with advisors for complex ethical dilemmas.
Transparency in Data Handling
Honest Reporting: Transparency in data collection, analysis, and interpretation prevents accusations of misconduct.
Ethical Responsibilities: Researchers bear ethical responsibilities towards fellow researchers, respondents, and the academic community, ensuring accurate and accountable reporting.
Intellectual Ownership and Bias Mitigation
Plagiarism Avoidance: Researchers must refrain from presenting others' work as their own, acknowledging sources appropriately.
Guarding Against Bias: Maintaining scientific objectivity and acknowledging potential biases are crucial for research integrity.
Ethical Aims and Language Use
Research Aims: Clearly stating research aims and their ethical implications is essential, ensuring alignment with ethical standards.
Language Sensitivity: Researchers must use neutral language to avoid bias, stereotyping, or discrimination, maintaining ethical standards in communication.
Participant Consent and Protection
Informed Consent: Participants must be provided with clear and understandable information to make informed decisions about participation.
Protection Measures: Ensuring participants' rights, especially those vulnerable, and obtaining consent at all organizational levels is paramount.
Conclusion
Adhering to ethical standards is foundational for research integrity. From plagiarism prevention to participant protection, ethical conduct at every stage of research ensures credibility and trustworthiness in research outcomes.
CARRYING OUT THE RESEARCH
Conducting Ethical Research: Mitigating Harm and Maximizing Gain
Ethical research is guided by the principle of causing no harm and, ideally, facilitating benefits for both participants and the broader field. Here are key considerations for conducting research ethically:
Risk Assessment and Method Selection
Risk Evaluation: Researchers must assess potential risks associated with chosen methods and outcomes, striving to minimize harm to participants' reputation, dignity, and privacy.
Harm Mitigation: Methods should prioritize minimizing risks to participants, avoiding any revelations that could be detrimental.
Data Recording
Avoiding Distortion: Researchers should be cautious of simplifying transcripts during data organization, ensuring nuances and subtleties are preserved to avoid misinterpretation.
Guarding Against Assumptions: Imposing personal interpretations based on assumptions risks distorting data, undermining research integrity.
Participant Interaction
Maintaining Rapport: Close communication with participants requires transparency to ensure mutual understanding and prevent deceptive practices.
Setting Realistic Expectations: Researchers should refrain from misleading participants or raising unrealistic expectations to extract information.
Handling Sensitive Material
Confidentiality Assurance: Sensitive information must be presented in a manner that protects individuals' privacy and dignity, ensuring confidentiality and anonymity.
Providing Guidance: In cases of sensitive information, researchers should offer guidance on seeking appropriate support without direct involvement.
Avoiding Deception and Covert Methods
Commitment to Honesty: Ethical research mandates honesty, ruling out deception or covert methods, which can have unforeseen and potentially harmful consequences.
Questioning Usefulness: Researchers should critically evaluate the necessity of such methods, considering the risks and ethical implications.
Ensuring Data Security
Legal Compliance: Adherence to data protection regulations is essential, ensuring secure storage and transmission of personal data.
Confidentiality Measures: Robust storage systems and secure transmission methods are necessary to safeguard data from unauthorized access.
Conclusion
Ethical research practices are critical to sustaining integrity and credibility. Researchers sustain their work's ethical basis by promoting participant well-being, assuring transparency, and adhering to legal and ethical requirements.
Finding and reviewing the literature
Navigating the Sea of Information: Finding and Evaluating Literature
The major purpose of research is to contribute to the field by generating new knowledge and insights. To avoid repetition and maintain relevance, a thorough literature evaluation is essential. Here's a guide to successfully finding and reviewing literature:
Importance of Literature Review
Preventing Redundancy: Before embarking on research, assess the existing knowledge landscape to avoid duplicating efforts.
Identifying Gaps: Literature reviews reveal areas of controversy or gaps in knowledge, guiding researchers toward fruitful avenues for exploration.
Accessing Information Sources
Libraries: University libraries offer a wealth of resources, including books, journals, and electronic databases. Utilize online catalogs and electronic resources for efficient searching.
Information Services: Government departments, research establishments, and professional organizations provide valuable information relevant to various fields.
Museums and Galleries: These institutions not only house exhibits but also produce informative materials and may offer access to archived artifacts.
Experts: Seek guidance from subject matter experts, including university faculty members and local professionals, for specialized insights.
Online Resources: Leverage the vast expanse of the internet, but exercise caution to discern credible sources from unreliable ones.
Evaluating Web Sources
Accuracy: Verify the accuracy of information by cross-referencing with other reliable sources.
Authority: Assess the credibility of authors and organizations behind the content. Look for academic affiliations or reputable publishers.
Bias: Be wary of biased presentations influenced by vested interests. Scrutinize the motives of authors or sponsoring organizations.
Detail and Relevance: Evaluate the depth and relevance of information provided. Avoid overly general or overly specialized content.
Currency: Check for the currency of information and the last update date. Outdated content may lack relevance or accuracy.
Cross-Checking: Compare information from web sources with data from other reliable sources to validate accuracy.
Subject Gateways: Utilize pre-evaluated subject gateways vetted by experts for high-quality, reliable information.
Conclusion
A meticulous literature review serves as the foundation of any research endeavor. By effectively navigating information sources and critically evaluating web content, researchers can ensure the integrity and relevance of their work.
DOING A LITERATURE REVIEW
Doing a Literature Review: A Comprehensive Guide
You will need to assess the document's relevance to your personal dissertation question and study objectives. The literature review must be conducted in four key directions, rather than simply inside your own subject area. Here they are, sorted from general to particular, and their relative importance depends on the nature of your subject:
x Research theory and philosophy – to establish the intellectual context(s) of research related to your subject.
x History of developments in your subject – to trace the background to present thinking.
x Latest research and developments in your subject – to inform about the current issues being investigated and the latest thinking and practice, to discuss the conflicting arguments, and to detect a gap in knowledge.
x Research methods – to explore practical techniques that have been used, particularly those that might be relevant to your project.
Here is a checklist of useful points for your review:
Compile an overview of the literature to illustrate the interplay of ideas and major steps in the development of your subject.
x Introduce the important issues of your research problem through the analysis of the literature. x Explain the general theoretical background to help the reader understand the attitudes behind the reviewed literature and your own philosophical stance.
x Make links across discipline boundaries when doing an interdisciplinary review, rather than keeping each separate and examined in turn. You may even suggest some new links that need to be investigated.
x Include some account of how the previous research was done, so that you have a precedent for your own approach to methodology.
Q.How many references should you have? This depends on the subject and extent of the review.
Ans. As the literature review part of a research proposal has to be very short and compact due to limitation of space, you are unlikely to be able to cite more than 15–20 authors, 5–10 might even be sufficient in a narrowly defined field. For a literature review chapter of a dissertation or research project, 20–35 references are more likely. The important thing is to select those that are really significant for your work.
Critical appraisal involves several key steps:
Evaluate Relevance: Thoroughly read the research article and determine its relevance to your study. Assess the clarity of the research purpose, methods, data collection, analysis, findings, and conclusions.
Identify Assumptions: Uncover the underlying assumptions behind the writing and arguments. Recognize theoretical positions, such as feminist, Keynesian, Modernist, or Freudian approaches, which shape the discourse.
Trace Logical Argumentation: Analyze the logical progression of the argument to ensure it leads from evidence to conclusions. Identify conclusion indicators and assess the credibility of evidence provided to support the conclusions.
Compare with Other Work: Conduct comparative analysis with other literature to highlight different approaches, levels of thoroughness, contradictions, strengths of arguments, implications of theoretical stances, and types of conclusions.
PRESENTING YOUR ANALYSIS
A review of the literature should provide an introduction to the most recent notions and breakthroughs in thinking in the chosen area, citing pertinent papers, publications, and authors to support the description. However, a complete critical evaluation of the publications under consideration will necessitate a more methodical approach. You must write a summary that briefly summarizes your critical appraisal and assessment of the quality of each research work. It is advisable to standardize the form of your appraisals using standard headings, such as:
Study design and assumptions
x Methods of data collection
x Analytical methods
x Main findings
x Conclusions
x The study’s strengths and limitations – clarity – logic – scope
Why Conduct a Literature Review?
Introduction to Research: The literature review serves as an essential introduction to your research project, providing a foundation for its significance.
Understanding the Landscape: It helps you understand the current state of knowledge in your field, identifying gaps and areas for further exploration.
Positioning Your Work: By analyzing existing research, you can position your own study within the context of previous findings and theoretical frameworks.
Key Steps in Conducting a Literature Review
Identify Relevant Literature: Conduct a thorough search of academic databases, libraries, and online resources to gather relevant literature.
Critically Analyze Sources: Assess the quality and relevance of each source, considering factors such as methodology, theoretical framework, and credibility of authors.
Synthesize Information: Synthesize key findings from the literature to provide an overview of existing knowledge and highlight areas of consensus or controversy.
Evaluate Research Design: Evaluate the design, methods, and conclusions of each study to assess its strengths and limitations.
Presenting Your Analysis: Structure your literature review with clear headings, summarizing key points and providing a critical appraisal of each source.
Checklist for Conducting a Literature Review
Overview of Literature: Provide an overview of relevant literature, highlighting major themes and developments.
Introduction of Research Problem: Introduce the research problem or question through the analysis of existing literature.
Theoretical Background: Explain the theoretical background to help readers understand the underlying concepts and your philosophical stance.
Interdisciplinary Connections: Make connections across disciplines, identifying links and suggesting areas for further investigation.
Research Methods: Explore the research methods used in previous studies, particularly those relevant to your own project.
Citation Selection: Select a balanced number of citations that are significant and relevant to your research objectives.
Analyzing and Presenting Your Findings
Study Design and Assumptions: Evaluate the study design and underlying assumptions guiding the research.
Methods and Data Collection: Assess the methods of data collection and analysis employed in each study.
Main Findings and Conclusions: Summarize the main findings and conclusions of each source, considering their implications for your research.
Strengths and Limitations: Critically appraise the strengths and limitations of each study, including clarity, logic, and scope.
Conclusion
Conducting a literature review requires meticulous attention to detail and critical analysis of existing research. By following these steps and utilizing a systematic approach, you can effectively synthesize the literature and position your own research within the broader scholarly conversation.
WHERE TO FIND OUT MORE
Ridley, D. (2008) The Literature Review: A Step-by-Step Guide for Students. London: Sage.
Useful strategies are described for efficient reading, conducting searches, organizing information, and writing the review itself. Examples of best and worst practice drawn from real literature reviews are included throughout to demonstrate how the guidance can be put into practice
Hart, C. (2001) Doing a Literature Search: A Comprehensive Guide for the Social Sciences. London: Sage.
A practical and comprehensive guide to writing a literature review which takes the reader through the initial stages of an undergraduate dissertation or postgraduate thesis.
Machi, L. (2009) The Literature Review: Six Steps to Success. London: Corwin/Sage.
A compact reference that offers master’s and doctoral-level students in education and the social sciences a roadmap to developing and writing an effective literature review for a research project, thesis, or dissertation.
Finke, A. (2010) Conducting Research Literature Reviews: From the Internet to Paper (third edition). London: Sage.
An accessible but in-depth look at how to synthesize research literature that presents nearly a hundred new online examples and references from the social, behavioural, and health sciences.
Dochartaigh, N. (2007) Internet Research Skills: How To Do Your Literature Search and Find Research Information Online (second edition). London: Sage.
The open web is becoming central to student research practice, not least because of its accessibility, and this clear text describes search strategies and outlines the critical skills necessary to deal with such diverse and disorganized materials.
PART II
The main research methods
Understanding Data
Define data and its characteristics.
Explore different levels of abstraction in data representation.
Discuss methods for collecting and analyzing data.
Accessing and Analyzing Existing Data
Explain the accessibility of existing data in the information age.
Discuss sources for accessing data collected by others.
Describe methods for analyzing existing data and testing its reliability.
Collecting Original Data
Present various methods for collecting original data.
Include techniques such as questioning, observation, experiments, and simulations.
Analyzing Data
Focus on quantitative data analysis using statistical methods.
Address qualitative data analysis, highlighting diverse analytical methods to detect patterns and trends.
Writing About Research
Emphasize the importance of clarity in research objectives.
Seek guidance on structuring a research proposal.
Learn techniques for effectively writing research reports, papers, dissertations, or theses.
Data in the Information Age:
The key points regarding the nature and hierarchy of data in research
Information is rapidly expanding in the modern era.
Data, or bits of information, serve as raw material for research.
The volume of data is increasing exponentially, challenging researchers to manage and interpret it effectively.
Nature of Data:
Data are not permanent truths but rather ephemeral and subject to change.
They can be influenced by various factors, leading to inconsistencies and inaccuracies over time.
Memory fades, details are lost, and interpretations may distort the original information.
Cautious Interpretation:
Researchers acknowledge the fallibility of their data and findings.
Conclusions are often expressed with cautious language, indicating likelihood rather than certainty.
This approach reflects the uncertainty inherent in research outcomes.
Hierarchy of Information:
Information follows a hierarchy from theory to values.
Theory represents abstract statements about the world.
Concepts are abstract building blocks of theory.
Indicators point to the existence of concepts.
Variables are measurable components of indicators.
Values represent concrete data, the most specific form of information.
Example of Hierarchy:
Theory: "Poverty leads to poor health."
Concepts: Poverty, poor health.
Indicators of poverty: Low income, poor living conditions, restricted diet, etc.
Variables of poor living conditions: Levels of overcrowding, provision of sanitary facilities, infestations of vermin, etc.
Values of levels of overcrowding: Numbers of people per room, floor areas of dwellings, etc.
Theory:
Refers to statements making claims about phenomena.
Can vary from well-researched claims to informal guesses.
Research often challenges, refines, or extends existing theories or develops new ones.
Concepts:
Represent particular phenomena, abstract or concrete.
Used for understanding and communication.
Should be clearly defined for mutual understanding.
Indicators:
Perceivable phenomena indicating the presence of abstract concepts.
Examples include trembling, facial expressions, or pacing for anxiety.
In scientific fields, indicators are usually well-defined; in humanities and social sciences, definitions may vary.
Variables:
Measurable components used to gauge the extent of indicators.
Allow for quantification of abstract concepts.
Examples include breathing rate for anxiety.
Values:
Units of measurement for variables.
Precision depends on the nature of the variable.
Can range from highly precise measurements to broad categories.
Levels of Abstraction in Research:
Research starts at a theoretical level, moves to concrete measurements, and returns to abstraction in conclusions.
Research structure progresses from theoretical questions to concepts, measures, types of measurements, and actual data.
Diagrams like Figure 6.1 illustrate these levels of abstraction in research structure.
PRIMARY AND SECONDARY DATA
Primary Data:
Data observed, experienced, or recorded close to the event.
Considered closest to the truth.
Four basic types: measurement, observation, interrogation, participation.
Provides immediate recording of situations and phenomena.
Time-consuming to collect, not always possible, but crucial for understanding and communication.
Secondary Data:
Interpreted and recorded data.
Found in various forms like news bulletins, magazines, documentaries, etc.
Quality depends on the source and presentation methods.
Assessment of quality involves reviewing evidence, validity of arguments, and reputation/qualifications of the source.
Comparing data from different sources helps identify bias, inaccuracies, and interpretations.
QUANTITATIVE AND QUALITATIVE DATA AND LEVELS OF MEASUREMENT
quantitative and qualitative data and levels of measurement:
Quantitative Data:
Measured using numbers, allowing for mathematical analysis.
Examples include census figures, economic data, performance data.
Can range from simple counts to more complex statistical analyses.
Even seemingly qualitative data can be quantified for analysis.
Qualitative Data:
Descriptive in nature, expressed in words rather than numbers.
Examples include observation notes, interview transcripts, historical records.
Relies on careful definition of concepts and variables.
Human interpretation and evaluation are essential.
Reliability checks often involve triangulation with multiple data sources.
Levels of Measurement:
Nominal Level: Basic categorization, no specific order.
Ordinal Level: Order based on a particular property, no precise measurement.
Interval Level: Precise measurement on a regular scale, without a meaningful zero.
Ratio Level: Precise measurement with a true zero, allows for true ratios.
Each level permits different types of statistical analysis.
Additional Resources:
Seale, C. (ed.) (2004) Researching Society and Culture (second edition). London: Sage.
Leedy, P. D. and Ormrod, J. (2009) Practical Research: Planning and Design (ninth edition). Harlow: Pearson.
Blaxter, L., Hughes, C. and Tight, M. (2006) How to Research (third edition). Buckingham: Open University press.
Collecting and analysing secondary data
Importance of Secondary Data:
Secondary data provides the background and context for research questions and problems.
It allows researchers to explore current theories and ideas.
Utilizing existing data can save time and resources, especially for students.
Secondary data can facilitate longitudinal studies and comparisons with primary data.
Advantages of Secondary Data:
Produced by expert researchers with extensive resources.
Allows for longitudinal studies and comparisons with primary data.
Publicly available data provides transparency and permanence.
Cuts down on the need for time-consuming fieldwork.
Disadvantages of Secondary Data:
Misses out on the experiential learning gained from primary data collection.
May not align perfectly with research needs or focus.
Costly to acquire certain datasets.
Mismatch in terminology and collection methods can complicate data aggregation.
Challenges in Accessing and Assessing Secondary Data:
Locating and accessing relevant data can be challenging.
Authenticating sources and assessing credibility is crucial.
Evaluating representativeness and suitability for the research project is essential.
Changes in data collection and analysis methods over time can affect data reliability.
Types and Sources of Secondary Data:
Written materials: Organizational records, publications, government documents.
Non-written materials: Television and radio programs, artworks, historical artifacts.
Survey data: Census data, economic surveys, organizational surveys.
Libraries, museums, and archives: Catalog systems facilitate data search.
Commercial and professional bodies: Trade organizations, professional associations, government agencies.
List of examples of documentary data from a wide range of sources:
Personal documents
Oral histories
Commentaries
Diaries
Letters
Autobiographies
Official published documents
State documents and records
Official statistics
Commercial or organizational documents
Mass media outputs
Newspapers and journals
Maps
Drawings, comics, and photographs
Fiction
Non-fiction
Academic output
Journal articles and conference papers
Lecture notes
Critiques
Research reports
Textbooks
Artistic output
Theatrical productions – plays, opera, musicals
Artistic critiques
Programmes, playbills, notes, and other ephemera
Virtual outputs
Web pages
Databases
These examples cover a diverse range of documentary sources that researchers can utilize for secondary data analysis.
SUITABILITY OF DATA FOR YOUR PROJECT
Before selecting to use secondary data in your research project, you should perform various checks to ensure that the data matches with your research objectives and can effectively address your research questions:
Do measures match those you need?: Assess whether the data available include the necessary measures relevant to your research, such as economic indicators, demographic characteristics, or social statistics. Ensure that the data provide the variables required to answer your research questions adequately.
Coverage: Evaluate the extent of coverage provided by the secondary data. Determine if there is a sufficient amount of data available of the required type and if any unwanted data can be excluded. Consider whether the available data adequately represent the phenomena or population you are studying.
Population: Verify whether the population covered by the secondary data matches the population relevant to your research. Ensure that the characteristics of the population in the data align with those of your target population. Assess whether the secondary data provide information that is applicable to your research context.
Variables covered: Examine the variables included in the secondary data and assess whether they align with the variables necessary for your research. Consider whether the precise nature of these variables is crucial for your research objectives, particularly for statistical tests or explanatory research.
Costs vs. benefits: Evaluate whether the benefits of using the secondary data outweigh the associated costs. Consider factors such as the time and resources required to access, process, and analyze the data compared to the potential insights gained from using the data.
Access: Determine whether you will have access to the secondary data required for your investigation. Consider any limits or permissions required to access the data and confirm that all applicable approvals are in place.
By carrying out these checks, you may guarantee that the secondary data you wish to use is appropriate for your research project and will effectively contribute to meeting your study objectives.
AUTHENTICATION AND CREDIBILITY
Authentication and credibility of secondary data are crucial aspects to consider when utilizing data collected by external sources. Here are key points to ensure the reliability and suitability of the data:
Source Reputation: Evaluate the reputation of the organization providing the data. Government statistics and data from well-known organizations are often more authoritative due to their commitment to maintaining credibility.
Assess Data Collection Methods: Examine the methods used for data collection and analysis. Look for information on sampling methods, survey response rates, and the context in which data were collected. Internet-based data sets may provide this information through hyperlinks or detailed reports.
Historical Data Authentication: Authentication of historical data may involve complex techniques such as textual analysis, carbon dating, or cross-referencing. Expert assessment is often required to ensure the reliability of historical data.
Credibility and Bias: Consider the potential for error or bias in the data, especially when acquired from particular interest groups or sources with specific agendas. Contextualize the data and assess its representativeness to make informed conclusions.
Consistency Over Time: When using data collected over time, ensure that data collection and analysis methods have remained consistent. Changes in methods can affect the reliability of the data, particularly for longitudinal studies.
By considering these factors, researchers can verify the reliability and credibility of secondary data and make informed decisions about its suitability for their research projects.
ANALYSING SECONDARY DATA
Content Analysis:
Definition: A quantitative analysis method examining text or media to count occurrences of specific phenomena.
Purpose: To gauge the importance of certain aspects within a body of content and develop theories.
Basic Stages:
State the Research Problem: Define what is to be counted and why, relating to the study's subject and content.
Employ Sampling Methods: Choose representative publications or media and select relevant sections for investigation.
Devise Units of Analysis: Determine the aspects of content to be retrieved and recorded.
Create Coding Manual: Describe and number the codes representing units of analysis.
Retrieve Coded Fragments: Use manual or computer-based systems to extract coded content.
Quality Checks on Interpretation: Ensure consistency in units of analysis, classification, and combination of data.
Data Analysis: Determine methods for interpreting the data.
Data Mining:
Definition: Extracting patterns or knowledge from large datasets using computational methods.
Purpose: To uncover hidden patterns, correlations, or trends in data.
Methods: Utilize algorithms and statistical techniques to analyze large volumes of data.
Applications: Commonly used in market research, fraud detection, and scientific discovery.
Meta-Analysis:
Definition: Statistical analysis combining results from multiple studies to produce a single quantitative summary.
Purpose: To synthesize findings across studies and assess the consistency and magnitude of effects.
Steps:
Literature Review: Identify relevant studies and gather data.
Effect Size Calculation: Quantify the magnitude of effects in each study.
Weighting Studies: Assign weights based on study quality or sample size.
Aggregation: Combine effect sizes to produce an overall estimate.
Assessment of Heterogeneity: Evaluate variability among study results.
Publication Bias Analysis: Examine potential bias in the published literature.
These methods offer valuable tools for analyzing secondary data, allowing researchers to extract meaningful insights and draw conclusions from existing sources.
CONTENT ANALYSIS
CONTENT ANALYSIS Content analysis is a quantitative form of analysis that consists of an examination of what can be counted in text of any form (articles, advertisements, news items etc.) or other media such as pictures, television or radio programmes or films, and live situations such as interviews, plays, concerts. The analysis is done very often, but not necessarily, from secondary sources. The method was developed from the mid-1900s chiefly in America, and is a rather positivistic attempt to apply order to the subjective domain of cultural meaning. It is done by counting the frequency of phenomena within a case in order to gauge its importance in comparison with other cases. As a simple example, in a study of racial equality, one could compare the frequency of different races in the illustrations in fashion magazines in various European countries. Much importance is given to careful sampling and rigorous categorization and coding in order to achieve a level of objectivity, reliability and generalizability and the development of theories.
There are several basic stages to this method:
State the research problem i.e. what is to be counted and why? This will relate to the subject of the study and the relevant contents of the documentary source.
s Employ sampling methods in order to produce representative findings. This will relate to the choice of publications or other media, the examples selected and the sections within the examples that are investigated.
s Devise the units of analysis. These are the aspects of the content that will be retrieved and recorded in the form of a coding schedule.
s Describe and number the codes that are a measure of the units of analysis in the form of a coding manual.
Retrieve the coded fragments. This can be done manually, but computer based search systems are more commonly used when the text can be digitized.
Do quality checks on interpretation. This covers issues of:
– the units of analysis (can the selected aspects or themes really be divided from the rest of the text?);
– classification (are the units counted really all similar enough to be counted together?);
– combination of data and formation of ‘100 per cents’ (how can the units counted be weighted by length/detail/authoritativeness and how is the totality of the elements to be calculated?).
Analyse the data (what methods of interpretation will you use?).
CODING SCHEDULE, CODING MANUAL AND TABULATION OF RESULTS
Coding Schedule:
Definition: A structured table outlining units of analysis and corresponding codes for content analysis.
Purpose: To organize and categorize data for systematic analysis.
Steps:
Preliminary review to identify units of analysis.
Setup of coding schedule table with columns for each unit of analysis.
Breakdown of units of analysis to establish codes.
Creation of coding manual listing descriptions or measurements as numbered codes.
Tabulation of Results:
Definition: Presentation of numerical data from content analysis in tabular form.
Purpose: To facilitate analysis and comparison of coded content across cases.
Steps:
Produce separate tables for each case based on the coding schedule.
Fill in columns with corresponding codes.
Count frequency of code occurrences across all cases.
Data Mining:
Definition: Technique for extracting patterns or knowledge from large datasets.
Purpose: To discover meaningful relationships, trends, and behaviors in data.
Methods:
Statistical tools for pattern discovery and trend prediction.
Data visualization techniques for clear understanding of data.
Utilization of algorithms like fuzzy logic and genetic algorithms for complex analysis.
Meta-Analysis:
Definition: Statistical analysis of accumulated data from multiple research studies.
Purpose: To synthesize and compare results across studies on a specific topic.
Steps:
Define the research issue.
Collect studies with similar methods and quality.
Identify common methods of measurement.
Select purpose of analysis (comparison or tracking).
Conduct statistical analysis and report results.
Discuss limitations and recommend further research.
Despite challenges like methodological differences and publication bias, meta-analysis provides a valuable means to assimilate results from multiple studies on a particular topic.
WHERE TO FIND OUT MORE
Heaton, J. (2004) Reworking Qualitative Data: The Possibility of Secondary Analysis. London: Sage.
Kiecolt, J. and Nathan, L. (1986) Secondary Analysis of Survey Data. A Sage University Paper. Newbury Park, CA: Sage.
Stewart, D. and Kamins, M. (1993) Secondary Research Information Sources and Methods (second edition). Thousand Oaks, CA: Sage.
Collecting primary data
There are several basic methods used to collect primary data; here are the main ones:
x asking questions
x conducting interviews
x observing without getting involved
x immersing oneself in a situation
x doing experiments
x manipulating models.
COLLECTING PRIMARY DATA
Introduction:
Primary data collection requires careful planning to identify needed data, their sources, and appropriate collection methods.
Various methods are used, including asking questions, conducting interviews, observing, immersing in situations, experiments, and using models.
Sampling:
Definition: The process of selecting a subset of cases from a larger group to represent the whole.
Purpose: To gather data efficiently and cost-effectively while ensuring representativeness.
Types of Sampling:
Individuals: Selecting a subset of individuals from a larger population.
Groups/Case Studies: Studying dynamics within different groups or systems.
Comparative Approach: Selecting diverse cases for comparison when individual cases are unique.
Sampling Frame: The defined population from which the sample is selected.
Representativeness: Ensuring that the sample accurately reflects the characteristics of the population.
Characteristics of Populations:
Homogeneous: All cases are similar.
Stratified: Contains layers or strata with different characteristics.
Proportional Stratified: Strata with known proportions.
Grouped by Type or Location: Contains distinctive groups based on type or location.
Considerations in Sampling:
Knowledge of population characteristics.
Accessibility to all sectors of the population.
Non-representative samples hinder accurate generalizations about the population.
Primary data collection demands strategic planning and meticulous consideration of sampling methods to ensure the validity and reliability of the collected data.
DATA COLLECTION METHODS
Sampling Considerations:
Conclusions drawn from larger samples are generally more convincing but must balance against research resources.
Sample size should align with population characteristics, study detail, and statistical requirements.
Types of Sampling Procedures:
Probability Sampling: Based on random selection methods, ensuring each element has an equal chance of selection. Examples include simple random, stratified, and cluster sampling.
Non-Probability Sampling: Relies on non-random means, offering limited generalizability. Techniques include accidental, quota, and snowball sampling.
Asking Questions:
Method: Questionnaires, a common tool for collecting both quantitative and qualitative data.
Advantages: Structured format, convenience, cost-effectiveness, and potential for anonymity.
Delivery Methods: Personal, postal, or online.
Question Types: Closed format (predefined options) and open format (free-text responses).
Pre-Testing: Commonly conducted through pilot studies to refine questionnaires.
Accounts and Diaries:
Method: Soliciting firsthand accounts or diary entries from participants.
Application: Provides qualitative insights into actions and feelings.
Authenticity Check: Cross-checking with other sources and participants.
Data Transformation: Accounts are coded and analyzed for patterns and themes.
Conducting Interviews:
Types: Structured, unstructured, and semi-structured interviews offer flexibility in questioning.
Application: Particularly useful for qualitative data collection and probing responses.
Delivery: Face-to-face, telephone, or focus groups.
Recording: Audio recording facilitates accurate data retention and analysis.
Observing Without Getting Involved:
Method: Observing phenomena without direct interaction or questioning.
Application: Useful for recording events, activities, and conditions.
Types: Visual surveys, preliminary assessments, and detailed observations.
Data Collection: Involves both qualitative and quantitative data recording.
Immersing Oneself in a Situation:
Method: Observing and experiencing phenomena firsthand within their natural context.
Application: Commonly used in anthropology and qualitative research.
Approach: Often based on grounded theory, evolving theory from continuous data collection.
Doing Experiments:
Method: Controlled manipulation of variables to investigate cause-effect relationships.
Application: Used across various disciplines to study interactions and phenomena.
Design: Involves identifying essential factors, manipulating variables, and validating assumptions.
Control Group: Essential for validating assumptions and comparing against manipulated groups.
Internal and External Validity
Generalization of Results:
For broader applicability, experiments should exhibit both internal and external validity.
Internal Validity: Ensures that cause-and-effect relationships are supported within the study.
External Validity: Determines the extent to which findings can be generalized to populations or other settings.
Factors Affecting Validity:
Internal Validity Undermined by:
Faulty sampling.
Unnoticed factors.
Deterioration or change in materials.
Faulty instruments.
External Validity Compromised by:
Faulty sampling and unnoticed factors.
Poor description hindering replication.
Changes in subject behavior due to artificiality.
Laboratory and Field Experiments
Characteristics:
Laboratory experiments offer control over variables but may appear artificial.
Field experiments occur in real-life settings, promoting natural behavior but lacking control.
Experiment Types:
True Experimental Designs: Utilize random selection and control groups for reliable outcomes.
Quasi-Experimental Designs: Employ matched control and experimental groups when random selection is unfeasible.
Pre-Experimental Designs: Lack control groups and random selection, affecting reliability.
Ex Post Facto: Investigate events post-occurrence without control, relying on retrospective data analysis.
Manipulating Models or Simulations
Purpose and Types:
Models simplify events for detailed inspection.
Types include diagrammatic, physical, and mathematical models.
Model Characteristics:
Diagrammatic models show relationships on paper, aiding understanding of complex systems.
Physical models provide three-dimensional representations for qualitative or quantitative analysis.
Mathematical models predict outcomes based on inputs, aiding in simulations of real-world phenomena.
Model Limitations:
Models are constructed based on specific purposes and assumptions.
Limitations include incomplete understanding of variables and inaccuracies in modeling interactions.
WHERE TO FIND OUT MORE
This is a big subject and there are innumerable books that deal with every aspect of primary data collection. All the general research methods books will have sections on data collection.
Meadows, D. (2008) Thinking in Systems: A Primer. London: Chelsea Green. An introduction to systems thinking and modelling used to understand complex phenomena
QUANTITATIVE DATA ANALYSIS
Quantitative Data Analysis
Quantitative analysis involves using mathematical operations to investigate numerical data, with statistics being the primary tool for this purpose. Here's an overview of some key points:
Purposes of Quantitative Analysis:
Measurement
Comparison
Relationship examination
Forecasting
Hypothesis testing
Concept and theory construction
Exploration
Control
Explanation
Data Sources:
Surveys often yield quantitative data, but other methods like content analysis can also provide numerical information.
Data Compilation:
Data need to be organized in an easily readable form for analysis.
Spreadsheets are commonly used, with each row representing a case and each column representing a variable.
Data can be in the form of integers, real numbers, or categories (nominal units).
Levels of Measurement:
Nominal, ordinal, interval, and ratio levels influence the choice of analysis methods.
Statistical Tests:
There are various statistical tests available, with selection based on factors like the number of cases and the nature of the data.
Descriptive tests reveal data properties, while inferential tests make inferences about populations from samples.
Parametric vs. Non-Parametric Statistics:
Parametric statistics rely on assumptions about population parameters, often assuming a normal distribution.
Non-parametric statistics are used when data do not follow a normal distribution or when assumptions cannot be made.
Types of Analysis:
Univariate analysis examines one variable at a time.
Bivariate analysis explores the relationship between two variables.
Multivariate analysis considers relationships between more than two variables.
Statistical Significance:
Tests like chi-square are used to determine if results are statistically significant and representative of the population.
Analysis of Variance:
Tests like ANOVA assess differences between groups under different conditions.
Regression Analysis:
Multiple regression assesses the effects of multiple independent variables on a single dependent variable.
Logistic regression is used for dependent variables measured in nominal scales.
Non-Parametric Tests:
Used when assumptions of parametric tests are not met, such as with small sample sizes or non-normal data distributions.
Test Selection:
It's crucial to choose appropriate tests based on data characteristics to avoid misleading results.
Quantitative data analysis offers a robust framework for exploring and understanding numerical data, providing insights into relationships, patterns, and trends within datasets.
WHERE TO FIND OUT MORE
Hoy, W. (2009) Quantitative Research in Education: A Primer. London: Sage.
Seale, C. (ed.) (2004) Researching Society and Culture (second edition). London: Sage.
Qualitative Data Analysis
Qualitative data analysis is a dynamic and iterative process that involves collecting and interpreting non-numerical data, such as descriptions, opinions, and feelings. Here are some key points:
Reciprocal Process:
In qualitative research, data collection and analysis often occur simultaneously, with each informing the other in an iterative manner.
Preliminary data analysis guides further data collection, leading to a deeper understanding of the subject.
Data Characteristics:
Qualitative research deals primarily with data expressed in words rather than numbers, especially when studying people or social phenomena.
Process Overview:
The research process involves defining research questions, collecting background information, interpreting data, selecting subjects, collecting data, and disseminating results.
In qualitative research, the focus is on understanding the context and exploring concepts and theories in a tentative and exploratory manner.
Steps in Qualitative Research:
Bromley outlines ten steps for qualitative research, including stating research questions, collecting background information, suggesting interpretations, and cross-examining evidence.
Bromley usefully suggests a list of ten steps you should take when carrying out qualitative research (1986: 26):
Clearly state the research issues or questions.
x Collect background information to help understand the relevant context, concepts and theories.
x Suggest several interpretations or answers to the research problems or questions based on this information.
x Use these to direct your search for evidence that might support or contradict these. Change the interpretations or answers if necessary.
x Continue looking for relevant evidence. Eliminate interpretations or answers that are contradicted, leaving, hopefully, one or more that are supported by the evidence.
x ‘Cross examine’ the quality and sources of the evidence to ensure accuracy and consistency.
x Carefully check the logic and validity of the arguments leading to your conclusions.
x Select the strongest case in the event of more than one possible conclusion.
x If appropriate, suggest a plan of action in the light of this.
x Prepare your report as an account of your research.
Theory Building:
There is a strong connection between data collection and theory building in qualitative research.
Theoretical ideas may develop from collected data, but often researchers start with a theoretical standpoint to guide data collection.
Data Collection Methods:
Various methods are used in qualitative research, such as qualitative interviewing, focus groups, participant observation, discourse analysis, and analysis of texts and documents.
Art and Science:
While qualitative analysis involves an element of artistry, it also requires a systematic and scientific approach to ensure the validity and reliability of conclusions.
Researchers must present strong arguments supported by high-quality evidence and sound logic, akin to presenting a case in a legal setting.
Qualitative data analysis offers a nuanced understanding of complex phenomena, allowing researchers to explore meanings, contexts, and relationships in depth. Through iterative processes and systematic approaches, qualitative research generates rich insights that contribute to knowledge and understanding across various disciplines.
Steps in Analyzing Qualitative Data
Analyzing qualitative data, which includes words, pictures, and sounds, requires a structured approach to make sense of complex information. Here are the key steps:
1. Data Reduction:
Simplify the initial data by coding, clustering, and summarizing.
Convert extended text into manageable units to facilitate analysis.
2. Data Display:
Organize the simplified data into diagrams and tables for easier exploration of relationships and significance.
Use graphical representations to visualize patterns and trends.
3. Conclusion Drawing/Verification:
Draw conclusions based on the analyzed data.
Verify findings through continuous comparison and validation.
Preliminary Analysis During Data Collection:
Continuously process raw data (field notes, interview tapes) to identify gaps and develop hypotheses.
Summarize results using standardized formats to maintain coherence and identify emerging patterns.
Typologies and Taxonomies:
Organize data into categories and subgroups based on types or properties.
Classify data to identify patterns and behaviors, aiding in further analysis.
Coding System Development:
Develop a coding system to label and categorize data units.
Ensure codes are discrete and unambiguous to effectively classify data.
Pattern Coding:
Identify patterns and themes in the coded data.
Condense coded information into meaningful groupings to develop a comprehensive understanding.
Interim Summary:
Review collected data and produce an interim summary of findings.
Reflect on data quality, gaps, and emerging themes.
Analysis During and After Data Collection:
Use graphical methods such as matrices and networks to summarize, explore, and compare data.
Employ matrices to record variables and relationships systematically.
Utilize networks to illustrate processes, relationships, and roles effectively.
Different Types of Displays:
Time-ordered displays for sequencing events.
Conceptually ordered displays for illustrating abstract concepts and relationships.
Role-ordered displays for depicting roles and relationships within organizations.
Partially ordered displays for analyzing complex situations without imposing strict internal order.
Case-ordered displays for comparing cases based on important variables.
Meta displays for amalgamating and contrasting data across cases.
By following these steps and employing appropriate graphical methods, researchers can effectively analyze qualitative data and derive meaningful insights and conclusions.
Qualitative Analysis of Texts, Documents, and Discourse
Analyzing qualitative data from texts, documents, and discourse involves various methods to uncover underlying meanings and patterns. Here's a breakdown of key qualitative analysis techniques:
1. Interrogative Insertion:
Implies questions within the text to reveal the logic and intended message of the discourse.
Helps understand how the text is structured to appeal to specific audiences.
2. Problem-Solution Discourse:
Analyzes statements to uncover underlying problems and proposed solutions.
Investigates the sequence of argumentation, including situation, problem, response, and evaluation.
3. Membership Categorization:
Examines how individuals perceive social norms and roles portrayed in the text.
Reveals assumptions and pre-judgments about societal organization and behavior.
4. Rhetorical Analysis:
Studies language and argumentation techniques used to persuade readers.
Identifies credibility markers and persuasive strategies employed by authors.
5. Narrative Analysis:
Extracts themes, structures, and interactions from stories or accounts.
Analyzes storytelling techniques and the underlying meanings conveyed.
6. Semiotics:
Studies signs and symbols to interpret meanings within text, visuals, and media.
Distinguishes between denotation (perceived meaning) and connotation (interpreted meaning).
7. Discourse Analysis:
Studies language use within social contexts to understand power dynamics and rhetorical strategies.
Analyzes both the interpretive context and the rhetorical organization of discourse.
By applying these qualitative analysis methods, researchers can gain deeper insights into textual data, uncovering implicit meanings and societal dynamics embedded within language and discourse.
WHERE TO FIND OUT MORE
Flick, U. (2009) An Introduction to Qualitative Research (fourth edition). London: Sage.
Part Six: From Text to Theory deals particularly with analysis of qualitative data.
Seale, C., Gobo, G., Gubrium, J. and Silverman, D. (2004) Qualitative Research Practice. London: Sage.
An edited book with chapters by almost 40 leading experts in the field and covering a diversity of methods and a variety of perspectives. Pick the bits that are relevant for you.
Miles, M. B. and Huberman, A. M. (1994) Qualitative Data Analysis: An Expanded Sourcebook. London: Sage.
This has a lot of examples of displays that help to explain how they work, but is technically sophisticated so you might find it difficult initially to understand the terminology in the examples.
Silverman, D. (1993) Interpreting Qualitative Data: Methods for Analysing Talk, Text and Interaction. London: Sage.
Holliday, A. (2007) Doing and Writing Qualitative Research (second edition). London: Sage. A general guide to writing qualitative research aimed at students of sociology, applied linguistics, management and education.
Schwandt, T. (2007) Qualitative Enquiry: A Dictionary of Terms (third edition). Thousand Oaks, CA: Sage.
To help you understand all the technical jargon.
Coffey, A. and Atkinson, P. (1996) Making Sense of Qualitative Data: Complementary Research Strategies. London: Sage.
The authors use a single data set which they analyse using a number of techniques to highlight the range of approaches available to qualitative researchers.
Writing the Proposal and Writing up the Research
Writing plays a crucial role in research projects, both at the beginning and the end. Here's how to approach the tasks of formulating a research proposal and writing up the research findings:
Formulating a Successful Research Proposal:
Define the aims, objectives, and methodology of the research clearly.
Justify the need for the research and explain its significance.
Outline the resources required, including time, funding, equipment, and personnel.
Follow a defined pattern: introduction, aims and objectives, methodology, resources required.
Ensure clarity and coherence to convey intentions effectively.
Consider the research proposal as a contract, setting the basis of agreement between all involved parties.
For educational research exercises, use proposals as learning opportunities to understand theoretical and methodological aspects.
Writing up the Research:
Summarize the research findings, methods used, and conclusions drawn.
Present the research in a structured and coherent manner.
Provide sufficient detail for others to understand and potentially replicate the study.
Use word processing software to organize, edit, and format the text effectively.
Adhere to any formatting guidelines provided by the institution or funding body.
Aim for clarity, accuracy, and conciseness in presenting the research outcomes.
Consider the audience and tailor the writing style accordingly, whether academic, professional, or general.
By approaching the writing tasks systematically and following established patterns, researchers can effectively communicate their research goals, methods, and findings to various audiences.
THE MAIN INGREDIENTS AND SEQUENCE
Academic research proposals are usually composed of the following elements:
x the title;
x aims of the research;
x the background to the research – context and previous research;
x a definition of the research problem;
x outline of methods of data collection and analysis;
x possible outcomes;
x timetable of the project and description of any resources required;
x list of references.
Writing a Research Proposal: Key Components
The Title
Encapsulate the essence of the research.
Include essential keywords related to the study's focus, variables, and scope.
Exclude unnecessary phrases like "an investigation into" or "a study of."
Aims of the Research
Clearly state the main aim and any subsidiary aims.
Ensure precision to avoid vagueness and impracticality.
Context: Background and Previous Research
Provide background information that explains the emergence of the research problem.
Review previous research to identify gaps in knowledge or contentious issues.
Clarify physical, conceptual, and theoretical factors shaping the research context.
The Research Problem
Clearly define the research problem and its significance.
Express the problem in abstract terms initially, then outline practical investigation strategies.
Outline of Methods
Describe the research approach and methods briefly.
Tailor methods to efficiently collect and analyze data relevant to the research problem.
Differentiate between data collection and analysis methods, ensuring clarity on their application.
Possible Outcomes of the Research
Specify the nature and scope of potential outcomes.
Relate outcomes directly to the aims of the research.
In PhD proposals or funded research, indicate the potential contribution to knowledge.
Timetable of the Project and Resource Description
Allocate time limits to research tasks to ensure timely completion.
List essential resources such as equipment, skills, and software needed for the project.
Ensure practicality in achieving project aims within the specified timeframe and with available resources.
List of References
Meticulously record all cited work to prevent plagiarism and demonstrate awareness of relevant literature.
Keep references relevant to the research topic rather than providing an exhaustive bibliography.
Writing up the Research: Key Considerations
Summarize research findings, methods, and conclusions clearly and concisely.
Structure the research report logically and coherently.
Use word processing tools effectively for organization and formatting.
Tailor the writing style to the intended audience, whether academic or professional.
Adhere to any formatting guidelines provided by institutions or funding bodies.
By addressing these key components, researchers can effectively communicate their research intentions, methods, and findings, ensuring clarity and coherence throughout the proposal and research write-up process.
Writing up a Dissertation or Research Project
When you're embarking on writing a dissertation or a research project, structuring your work effectively is essential. Here's a guide to help you navigate through the process:
When to Start Writing Up:
Begin by preparing a structure for your writing once you have clarity on your research objectives, possibly after finalizing your proposal.
Gradually accumulate notes, observations, and data related to your study, using them as the basis for your first draft.
Expect both the framework and the text to evolve as your work progresses, with refinements and revisions along the way.
Frame and Fill:
Create a framework by listing possible chapter or section headings, drawing from your proposal and plan of work.
Develop a conventional format with sections like Introduction, Background and Previous Research, Main Issues and Research Problem, Research Methods, Description of Research Actions and Results, and Conclusions.
Insert sub-headings within each section to elaborate on various aspects of your study.
Don't feel constrained to write sequentially from beginning to end; insert relevant notes and observations within the framework as you go.
Aim for a balanced result by allocating appropriate word counts to each section based on the overall length requirement.
Coming to Conclusions:
Collect and analyze data to derive conclusions relevant to your research problem and project aims.
Build a logical argument based on evidence to support your conclusions.
Explain how your analysis provides new insights into the subject and addresses the research problem.
Summarize the main conclusions concisely in the concluding chapter, tying together the various strands of your research.
Revisions:
Use a word processor to make revisions easily, taking off the pressure of getting everything right on the first attempt.
Revise at different levels: structural, paragraph sequence, and detailed (grammar, punctuation, vocabulary, spelling).
Keep track of revisions by saving each iteration as a new file, labeled clearly with a revision number or date.
By following these steps and maintaining a disciplined approach to writing and revising, you can effectively structure and refine your dissertation or research project to convey your findings convincingly.
WHERE TO FIND OUT MORE
Greetham, B. (2008) How to Write Better Essays (Palgrave Study Skills). Basingstoke: Palgrave.
Redman, P. (2005) Good Essay Writing: A Social Sciences Guide (third edition). London: Sage, in association with The Open University.
This shows you how to approach different types of essay questions, provides detailed guidelines on the various ways of supporting and sustaining key arguments, addresses common worries, and provides extensive use of worked examples including complete essays which are fully analysed and discussed.
Shields, M. (2010) Essay Writing: A Student’s Guide. London: Sage.
This offers practical, in-depth guidance on each of the stages of the essay writing, reading academic texts, how to get the most out of lectures, referencing and citations, fluency and appropriateness of style and language.
how to do dissertations
Locke, L. F. (2007) Proposals that Work: A Guide for Planning Dissertations and Grant Proposals (fifth edition). London: Sage.
How to write effective proposals for dissertations and grants, covering all aspects of the proposal process.
Punch, K. (2006) Developing Effective Research Proposals. London: Sage.
A straightforward and helpful guide with a good collection of examples of proposals to illustrate the qualities looked for, organized around three central themes: What is a research proposal, who reads proposals and why?; How can we go about developing a proposal?; and What might a finished proposal look like?
Vithal, R. (2010) Designing Your First Research Proposal: A Manual for Researchers in Education and the Social Sciences (second edition). Cape Town: Juta
a dissertation or research project
Thody, A. (2006) Writing and Presenting Research. London: Sage.
A practical, example-driven approach that shows you how to write up, report and publish research findings, not just as dissertions, but also as papers, books, articles and teaching presentations.
Wolcott, H. (2009) Writing Up Qualitative Research (third edition). London: Sage.
Good, down to earth, and easy to understand for both undergraduates and graduates.
Monippally, P. and Shankar, B. (2010) Academic Writing:Guide for Management Students and Researchers. Delhi: Sage.
Three main aspects are focused on: understanding existing research, documenting and sharing the results of the acquired knowledge, and acknowledging the use of other people’s ideas and works in the documentation.
the whole process of doing dissertations and theses
Walliman, N. (2004) Your Undergraduate Dissertation: The Essential Guide for Success. London: Sage.
An overview aimed at undergraduates, with helpful guidance at each stage of the process.
Walliman, N. (2005) Your Research Project: A Step-by-Step Guide for the First-Time Researcher (second edition). London: Sage.
This is aimed more at PhD or MPhil level students, and aims to lead you through the process of formulating a proposal and getting started with the work.
GLOSSARY
a selection of relatively unknown terms:
Algorithm: A set of rules or procedures used for calculations or problem-solving, often expressed as mathematical formulas.
Bell Curve: Also known as the normal or Gaussian curve, it represents the distribution of values in a population, with the majority clustering around the mean and fewer towards the extremes. This distribution is crucial for parametric statistical analysis.
Causal Process Theory: An interrelated set of definitions and statements that not only define a theory but also describe when and where causal processes are expected to occur, explaining the mechanisms by which independent variables affect dependent variables.
Descriptive Statistics: Methods for quantifying the characteristics of numerical data, including measures of central tendency (mean, median, mode) and measures of dispersion (range, variance, standard deviation).
Ethics: The rules of conduct governing interactions with other individuals or organizations, particularly in research, aimed at avoiding harm and providing benefits where possible.
Falsification: The process by which a hypothesis is rejected based on true observational statements conflicting with it, a key aspect of scientific inquiry proposed by philosopher Karl Popper.
Generality: The assumption that valid relationships observed in specific cases investigated by researchers can be generalized to other similar cases in the broader context.
Informative Language: Language used to convey information or communicate factual statements.
Meta-analysis: An analysis that integrates the results of multiple individual studies on a particular topic to provide a comprehensive overview and potentially uncover patterns or trends across studies.
Operational Definition: A set of actions defining how to detect or measure a theoretical concept, typically independent of time and space, crucial for ensuring clarity and consistency in research methodology.
Quota Sampling: A sampling technique attempting to balance the sample by selecting responses from equal numbers of different types of respondents, used when representative sampling is desired but random sampling is impractical.
Relational Statements: Statements that convey information about relationships between two concepts, forming the foundation of scientific knowledge by explaining, predicting, and providing understanding of the surroundings.
Simulation: The recreation of a system or process in a controlled form, often using computers, to manipulate variables and study their effects.
Typology: An organization of cases or data into types based on specific characteristics, facilitating understanding and analysis of phenomena.
Reflections: Broader significance of the content
As I delved into the complexities revealed in the pages of this book, I couldn't help but notice its significant implications for the changing environment of academic study. The thorough examination of research approaches, procedures, theoretical frameworks, and epistemological perspectives serves not only as a guide for prospective researchers, but also as evidence of the dynamic nature of knowledge creation. In an era of growing multidisciplinary collaboration and the advent of new research paradigms, this book is a light of intellectual rigor and critical inquiry. Its emphasis on methodological diversity and the examination of underlying assumptions emphasizes the importance of reflexivity in scholarly activities, encouraging readers to engage in nuanced reflections on knowledge formation and the intricacies of the research process. By navigating the maze of research methodologies and philosophical orientations, this book provides researchers with the tools they need to manage the ever-changing currents of academia, encouraging an environment of intellectual curiosity and scholarly brilliance.
Evaluation:
"Research Methods: The Basics" stands out for its accessibility and clarity, making it an excellent resource for new researchers looking to learn the fundamentals of research methodology. Walliman's clear writing style and pedagogical approach ensure that complicated ideas are presented in a way that is both understandable and engaging. Furthermore, the book's emphasis on real-world examples and practical applications makes it more relevant and useful for researchers from a variety of fields.
Conclusion:
In conclusion, Nicholas Walliman's "Research Methods: The Basics" is an invaluable resource for anyone embarking on a research project. Whether pursuing academic studies, conducting professional research, or looking to improve their research skills, readers will find Walliman's advice essential in navigating the complexity of the research process. By laying a firm basis in research methodology, this book enables researchers to conduct rigorous and ethical studies, thereby contributing to the growth of knowledge in their particular domains.
[Rating: ⭐⭐⭐⭐⭐ (5/5)]
