In research particularly at the PhD level, one of the most challenging tasks is predicting how systems, materials, or processes will behave under different conditions. This is where simulation tools like Ansys come into play. Ansys allows researchers to model complex scenarios in a virtual environment, saving both time and resources that would otherwise be spent on physical experiments. One of the most valuable applications of Ansys is in exploring ‘What-If’ scenarios for testing various possibilities to understand their impact on the system being studied.
‘What-If’ scenarios are critical for pushing research forward because they enable you to answer questions such as:
What happens if I change this parameter?
How will the system behave under specific conditions?
Which design modifications yield the most improvement?
These questions allow doctoral students like you to evaluate, predict, and optimize outcomes before implementing physical solutions, leading to breakthroughs in design, analysis, and problem-solving. If you already have the scenarios you need or require help identifying and executing them using Ansys, PhD Guidance is here to assist.
1. What If I Change the Material Properties?
Material selection plays a central role in engineering and scientific research. Whether you’re designing mechanical components, analyzing heat transfer, or studying structural stability, the choice of material affects everything from performance to cost.
Using Ansys, you can simulate how different material properties such as density, strength, thermal conductivity, or elasticity can impact system behavior. For example:
In structural analysis, you can test how a lightweight alloy compares to traditional steel in terms of strength and durability.
In thermal simulations, you can study how materials with varying thermal conductivity perform under high heat conditions.
This ‘What-If’ scenario is particularly useful for researchers who need to find an optimal material for specific operating conditions without conducting costly and time-consuming physical experiments. Ansys provides access to material libraries and allows for custom material definitions, giving you full flexibility to experiment with hypothetical material changes.
By iteratively testing and comparing results, researchers can identify the most suitable materials for their projects, leading to better performance, reduced costs, and innovative solutions.
2. What If I Modify the Design Geometry?
Geometry changes are one of the most common ‘What-If’ scenarios explored in research, especially in areas like mechanical engineering, fluid dynamics, and aerospace design. The physical shape of a component or structure directly influences its functionality, efficiency, and durability.
With Ansys, you can make slight or significant modifications to your design geometry and analyze their impact on performance. For instance:
In fluid dynamics (CFD) simulations, you can test how a streamlined shape improves airflow around a wing or reduces drag on a vehicle.
In structural analysis, you can experiment with design changes such as adding support structures, reducing weight, or altering thickness to achieve better load-bearing capacity.
Ansys provides parametric modeling capabilities, allowing you to systematically vary geometric parameters and analyze the corresponding results. This feature is particularly useful in optimization studies, where the goal is to identify the ideal design that meets performance criteria while minimizing material use or manufacturing costs.
By answering the ‘What-If’ question around design changes, PhD students can refine their models to achieve optimal outcomes, balancing performance, efficiency, and cost-effectiveness.
3. What If the Boundary Conditions Are Changed?
In simulations, boundary conditions define how a system interacts with its surroundings. These include factors such as temperature, pressure, forces, or velocity constraints. Changing boundary conditions in Ansys allows researchers to study how systems behave under varying external influences.
For example:
In a thermal analysis, you can model what happens to a heat exchanger when ambient temperature rises or cooling flow decreases.
In a fluid dynamics simulation, you can study how changes in inlet pressure or flow rate impact the behavior of a pipeline or nozzle.
In a structural analysis, you can examine how components respond to different loading conditions, such as increased force or uneven distribution of stress.
By simulating these ‘What-If’ scenarios, doctoral students can identify critical thresholds where performance deteriorates or failure occurs. This information is invaluable for predicting system limitations, enhancing durability, and designing for safety and reliability.
Exploring multiple boundary condition variations also provides a deeper understanding of real-world behaviors, which is essential for validating simulation models against experimental data.
4. What If I Introduce Multi-Physics Interactions?
Many real-world systems involve interactions between multiple physical phenomena, such as thermal, structural, and fluid dynamics effects. These interactions can significantly influence system performance, but they are often difficult to study through physical experiments alone.
Ansys provides advanced multi-physics capabilities, allowing you to couple different simulations and observe their combined effects. For example:
Thermal-Structural Analysis: What happens to a turbine blade when subjected to high temperatures and mechanical stress simultaneously?
Fluid-Structure Interaction (FSI): How does fluid flow through a flexible pipe affect its deformation and structural integrity?
Electro-Thermal Analysis: What is the combined effect of electrical current and heat generation on the performance of an electronic component?
By introducing multi-physics interactions in your ‘What-If’ scenarios, you can gain a holistic understanding of system behavior. This approach is particularly valuable for researchers working on problems where single-physics analysis may overlook critical interactions.
Ansys simplifies multi-physics simulations by integrating various solvers into a single workflow, making it accessible even for researchers who may not specialize in every domain.
5. What If I Optimize Input Parameters?
Parametric studies and optimization are core applications of simulation tools like Ansys. This ‘What-If’ scenario allows researchers to determine how input parameters—such as dimensions, material properties, or operating conditions—affect outputs like performance, efficiency, or failure limits.
For example:
In a design optimization study, you can test how varying the thickness of a component impacts its strength and weight.
In a process optimization scenario, you can explore how adjusting flow rates in a heat exchanger improves its thermal efficiency.
Ansys provides built-in optimization tools that automate parametric studies, allowing you to vary multiple inputs simultaneously and identify the most favorable combination of parameters. Techniques such as Design of Experiments (DOE) and sensitivity analysis help researchers pinpoint which parameters have the most significant influence on their results.
Optimization-driven ‘What-If’ scenarios are particularly valuable for PhD students because they streamline the research process. Rather than relying on trial-and-error, researchers can systematically converge on optimal solutions backed by computational data.
Exploring ‘What-If’ scenarios using Ansys can help PhD students to test hypotheses, predict outcomes, and optimize their research in a cost-effective and time-efficient manner. By using these capabilities you can gain deeper insights into the systems, identify innovative solutions, and achieve breakthroughs that may otherwise be unattainable through traditional physical experiments alone.
Integrating Ansys into your research toolkit will not only enhance the quality of your PhD work but also position you to tackle more challenging and meaningful problems in your field. Our software implementation help at PhD Guidance can help you with exactly that, reach out to us here to discuss your research requirements using Ansys and more.
Before we dig deep into understanding the difference and application of Hypothesis and Research Question, it is more important to define the exact nature of scientific inquiry. Fundamentally, scientific inquiry is a structured, formal and systematic approach to the exploration and discovery with its roots established in empirical evidence and guided by the principles of logical reasoning and critical analysis. Scientific inquiry rests on the grounds of an unbiased approach in observation and commitment towards a rigorous assessment of information. It is a formal process that seeks to generate knowledge that is verifiable and has its base in well founded theories and methodological robustness.
A very critical facet of scientific investigation is the right framing of research and the pivotal element in the framing of research are two very distinct but at the same time interrelated elements, which is the research question and the hypothesis. The research question articulates the initial inquiry or the set of inquiries that will be addressed in the study bring undertaken. The research question in other words is the “Focal point of Exploration”. Hypotheses comes next and it in fundamental terms, it is the testable prediction regarding the expected outcome of the research. The basis for creating the hypothesis is the review of the existing literature and the theoretical framework. To understand better, a hypothesis the provisional answer to the research question which is available for empirical verification.
Essentially, research question tries to identify and go in depth of potential relationships as well as patterns or trends that foster a very deep understanding of the phenomenon underlying it. In contrast to that, a hypotheses endeavors to validate the assumptions that have been already predefined by the means of testing and validation.
Knowing what is research question:
A research question is the critical first step in the research endeavor and it expects from the researcher a thorough as well as in-depth understanding of both, the topic in hand as well as the literary landscape that encapsulates it. It is a torch bearer which guides the trajectory of the investigation, giving a focal point around which the research activities pertaining to the study can be weaved.
Because of the critical value of the research question, researchers have to ensure that it is not just meaningful but also fruitful. A good research question should be not just characterized by clarity, succinctness but at the same time should be realistically aligned with the existing research. This would assist in building established foundations to develop new insights within the identified domain.
Knowing what is research question:
Crafting well defined hypothesis is an equally significant step in scholarly research. To be in a position to draft correct hypothesis, the researcher must have a very profound grasp of the subject matter and at the same time a strong awareness about the existing scholarly dialogues and theories that are relevant to the topic.
During the formulation of hypothesis, some important principles must be adhered to, so that the statements are verifiable. Hypothesis that is robust in nature comprises several attributes, including preciseness, the ability to test and at the same time alignment with established research and theories.
Research Methodology refers to the systematic process used to conduct and analyze research. It involves a set of procedures and techniques employed to gather, organize, and interpret data. Various types of research methodology, such as qualitative and quantitative methods, form the foundation for investigating and understanding diverse phenomena.
Diverse research methodology provide a spectrum of advantages in scientific exploration. Qualitative methodologies, such as interviews and observations, delve deep into understanding human behavior and motivations. Quantitative approaches, like surveys and experiments, offer precise numerical data for statistical analysis. Mixed-methods enable a comprehensive view by merging qualitative and quantitative strengths. Experimental methods establish cause-and-effect relationships, while case studies offer in-depth insights into specific instances. Each methodology caters to different research needs, fostering a nuanced understanding of complex phenomena and contributing to the richness and depth of scholarly inquiry.
This blog is your guide to the Top 5 types of research methods that haven’t been fully tapped into yet. We’re talking about different ways to do research, the kind that hasn’t been widely used or discovered. It’s crucial to stay on top of these categories of research methodology because as the world moves forward, so does the way we study and understand things. So, we’ll be checking out the latest and coolest Research methodology types, from new technologies to fresh ways of combining different fields of study.
Different types of methodology in research
Different types of methodology in research
Research methodology encompasses a variety of approaches and techniques to gather and analyze data. Here are some Different types of methodology in research:
Qualitative Methodology:
In-depth exploration of attitudes, behaviors, and motivations.
Utilizes methods like interviews, focus groups, and content analysis.
Quantitative Methodology:
Focuses on numerical data and statistical analysis.
Involves surveys, experiments, and structured observations.
Mixed-Methods Approach:
Integrates both qualitative and quantitative methods.
Offers a thorough comprehension of the research problem.
Experimental Research:
Investigates cause-and-effect relationships.
Involves controlled experiments with manipulated variables.
Survey Research:
Gathers data from a selected group through structured questionnaires.
Examines trends, attitudes, and opinions.
Case Study Methodology:
In-depth analysis of a specific instance or case.
Offers detailed insights into complex phenomena.
Action Research:
Involves collaboration between researchers and practitioners.
Aims to solve real-world problems through iterative cycles of planning, acting, observing, and reflecting.
Ethnographic Research:
Immersive study of a specific group or culture.
Requires prolonged engagement and participant observation.
Methodology 1: Neurobiological Methodology
Neurobiological Methodology stands at the forefront of methodology in research paper, bridging the realms of neuroscience and traditional research methodologies. This is one of the Research methodology types which aims to unravel the intricacies of human cognition and behavior by integrating cutting-edge brain imaging techniques with established research methods.
Key Components:
Neuroimaging Technologies: Utilizes advanced technologies such as fMRI (functional Magnetic Resonance Imaging) and EEG (Electroencephalography) for all the research methodologies including exploratory research in research methodology. Enables real-time monitoring of brain activity, offering insights into cognitive processes during various tasks.
Biometric Data Integration: Incorporates biometric data, including heart rate variability and skin conductance, to supplement neurobiological findings. Provides a comprehensive understanding of emotional responses and physiological changes related to cognitive activities.
Experimental Designs with Neural Correlates: Designs experiments that correlate specific neural activities with behavioral responses. Allows researchers to identify neural markers associated with decision-making, memory, and learning.
Cross-Disciplinary Collaboration: Encourages collaboration between neuroscientists and researchers from diverse fields. Integrates expertise from psychology, sociology, and other disciplines to ensure a holistic approach.
Applications: Neurobiological Methodology, which is a descriptive methodology in research, holds immense potential across various research domains:
In Psychology: Unraveling the neural basis of psychological disorders, emotions, and cognitive functions.
In Marketing: Understanding consumer behavior by examining the neural responses to advertisements and product choices.
In Education: Enhancing learning methodologies by identifying neural patterns associated with effective teaching strategies.
Challenges and Future Directions: Despite its promises, Neurobiological Methodology faces challenges such as data complexity and ethical considerations. Future research should focus on refining methodologies, establishing ethical guidelines, and fostering interdisciplinary collaboration to unlock the full potential of this unexplored approach. Neurobiological Methodology emerges as a groundbreaking frontier, offering a novel lens through which researchers can delve into the intricacies of human cognition and behavior. As one of the different types of methodology in research, it holds the potential to reshape our understanding of the mind and pave the way for innovative solutions across diverse fields.
Methodology 2: Augmented Reality (AR) Research Methodology
Augmented Reality (AR) Research Methodology marks an unexplored frontier, intertwining cutting-edge AR technologies with traditional research methods. This is one of the types of methodology in research which seeks to create immersive environments for data collection, offering a unique perspective on human behavior and decision-making.
Key Components:
Constructs simulated environments using AR technology to observe and analyze real-time human behavior.
Enables researchers to study reactions and interactions in controlled yet dynamic settings.
Integrates AR-generated data collection points within physical spaces.
Facilitates the gathering of diverse data sets by embedding virtual elements in real-world contexts.
Utilizes AR interfaces to track user interactions and responses.
Enhances the understanding of user engagement and decision-making processes within augmented scenarios.
Combines AR experiences with traditional research methods such as exploratory research in research methodology for a comprehensive approach.
Allows researchers to triangulate findings by comparing results obtained from both virtual and non-virtual settings.
Applications: AR Research Methodology, which is also a descriptive methodology in research, holds promise across various research domains:
Simulating scenarios to observe human responses to environmental changes in descriptive qualitative research methodology.
Analyzing consumer behavior within augmented retail environments for product placement and advertising strategies.
Creating interactive learning experiences to study the impact of AR on knowledge retention.
Challenges and Future Directions: Challenges such as technological constraints and the need for standardized protocols highlight the evolving nature of this Research methodology types. Future endeavors should focus on refining AR applications, establishing ethical guidelines, and exploring collaborative opportunities with AR developers. Augmented Reality Research Methodology stands as an exciting avenue among the types of methodology in research, offering a transformative approach to understanding human behavior within virtual and augmented spaces. As technology continues to advance, this methodology holds the potential to redefine the landscape of research methodologies across diverse disciplines.
Methodology 3: Predictive Analytics in Social Sciences
Predictive Analytics in Social Sciences emerges as a groundbreaking methodology in research papers, introducing advanced statistical models and machine learning algorithms to forecast social trends and behaviors. This type of exploratory research methodologies harnesses the power of predictive analytics to offer a new dimension to traditional categories of research methodology.
Key Components:
Advanced Statistical Models:
Applies sophisticated statistical models, including regression analysis and time-series forecasting.
Enables researchers to identify patterns and relationships within social data.
Machine Learning Algorithms:
Integrates machine learning algorithms to predict future outcomes based on historical data.
Provides a dynamic and adaptive approach to understanding social phenomena in descriptive qualitative research methodology.
Big Data Utilization:
Harnesses large datasets from diverse sources, including social media, surveys, and public records.
Facilitates the identification of trends and correlations within complex social systems.
Real-Time Analysis:
Conducts real-time analysis of social data to generate instant predictions.
Allows for timely interventions and policy adjustments based on emerging social patterns.
Applications: Predictive Analytics in Social Sciences holds immense potential across various applications:
In Sociology: Forecasting demographic shifts, social movements, and cultural trends.
In Public Policy: Informing policy decisions by predicting the potential impact of interventions.
In Market Research: Anticipating consumer behavior and market trends for strategic planning.
Challenges and Future Directions: Despite its promises, integrating predictive analytics into social sciences faces challenges such as data privacy concerns and model interpretability, which is a type of exploratory research methodologies. Future research should focus on refining models, addressing ethical considerations, and enhancing the transparency of predictive analytics methodologies.
Predictive Analytics in Social Sciences stands as a dynamic methodology, extending beyond basic research methodology to offer foresight into the complex dynamics of human societies. As we embrace the era of big data, this approach holds the potential to revolutionize how we understand and respond to social changes in real time.
Methodology 4: Quantum Research Methodology
Quantum Research Methodology represents a paradigm shift, bridging the world of quantum physics with a basic research methodology. This unexplored approach challenges the traditional classification of research methodology by harnessing the principles of quantum mechanics for data analysis.
Key Components:
Quantum Computing for Data Processing: Utilizes quantum computing’s parallel processing capabilities for handling complex datasets. Offers a quantum leap in computational efficiency, enabling the analysis of vast amounts of information.
Quantum Entanglement in Data Relationships: Applies the concept of quantum entanglement to identify interconnected relationships within datasets. Provides a unique perspective on the interdependence of variables in comprehensive research methodology.
Superposition for Multifaceted Analysis: Exploits quantum superposition to analyze data simultaneously from multiple perspectives. Enhances researchers’ ability to examine complex phenomena from various angles.
Quantum Algorithms for Pattern Recognition: Develops quantum algorithms for advanced pattern recognition within datasets. Enables the identification of subtle patterns that may go unnoticed with classical algorithms.
Applications: Quantum Research Methodology holds potential classification of research methodology across diverse fields:
Exploring quantum phenomena and complex physical systems with enhanced computational power.
Analyzing intricate biological datasets to uncover hidden relationships and patterns.
Enhancing predictive modeling and risk analysis with quantum algorithms.
Challenges and Future Directions: The integration of quantum principles into research methodologies presents challenges such as the need for quantum expertise and the development of quantum-safe data encryption. Future research should focus on refining quantum algorithms, expanding interdisciplinary collaborations, and addressing ethical considerations. Quantum Research Methodology offers a novel and comprehensive approach that transcends traditional classifications of research methodology. As quantum technologies continue to advance, this unexplored frontier holds the promise of revolutionizing how we conduct research, analyze data, and gain insights into the underlying structures of complex systems.
Methodology 5: Bibliometric Research Methodology
Bibliometric research methodology is a quantitative approach that analyzes patterns and trends within academic literature, utilizing bibliographic data to gain insights into the scholarly landscape in the comprehensive research methodology.
Key Components:
Citation Analysis:
Examines the frequency and impact of citations to understand the influence of a publication.
Identifies seminal works and measures the academic impact of research.
Co-authorship Networks:
Maps collaborations among researchers through analysis of co-authored publications.
Unveils research communities and the dynamics of collaborative efforts.
Journal Impact Factors:
Evaluates the prestige and impact of academic journals based on citation patterns.
Assists researchers in identifying reputable outlets for publication.
Keyword Co-occurrence:
Identifies prevalent themes and topics within a field by analyzing the co-occurrence of keywords.
Facilitates trend analysis and the identification of emerging research areas.
Applications:
Research Evaluation:
Assessing the impact and productivity of researchers, institutions, or journals.
Informing funding agencies and policymakers in decision-making processes.
Trend Analysis:
Identifying emerging topics and research directions within a discipline.
Assisting researchers in staying abreast of the latest developments.
Collaboration Mapping:
Facilitating the identification of potential collaborators and research networks.
Enhancing interdisciplinary research initiatives.
Challenges:
Data Quality and Availability:
Limited availability and consistency of bibliographic data.
Challenges in obtaining accurate and comprehensive citation information.
Discipline-specific Differences:
Variability in citation practices across disciplines.
Difficulty in creating standardized metrics applicable to all fields.
Self-citation Bias:
Influence of self-citations on impact metrics.
Requires careful consideration to avoid skewing results.
Future Directions:
Integration with Altmetrics:
Incorporating alternative metrics like social media mentions to provide a more comprehensive assessment of research impact.
Open Science Initiatives:
Embracing open access principles to enhance data sharing and transparency.
Facilitating broader collaboration and increasing the accessibility of research outputs.
Machine Learning Applications:
Implementing machine learning algorithms for more sophisticated analysis.
Enhancing the automation of bibliometric processes and improving accuracy.
Final Thoughts
In wrapping up our exploration of the top 5 unexplored types of research methodology for 2024, it’s like we’ve discovered a treasure chest of new ideas. These methods are like a breath of fresh air in the world of research. From understanding how our brains work to creating virtual worlds with Augmented Reality, and predicting social trends, we’re on the brink of something big.
Quantum research and Blockchain verification bring a touch of magic, making our data analysis smarter and more secure. These aren’t just fancy trends; they’re like keys to unlock a whole new era of learning. So, in 2024, researchers, buckle up and dive into these research methodology – the journey promises to be full of surprises, discoveries, and a whole lot of new knowledge!
Educba is a website that provides researchers with a comprehensive guide to different types of research methodologies. The website offers a wide range of courses and tutorials on research methodology, which can help researchers develop their research skills and knowledge. By taking these courses, researchers can learn about different research methods and techniques, such as surveys, case studies, and experiments.
This knowledge can help researchers design and conduct their research more effectively and efficiently. Additionally, the website provides a platform for researchers to connect with other researchers and experts in their field. This can help researchers build their professional network and find new opportunities for research and collaboration. Overall, educba.com is a valuable resource for researchers who are looking to develop their research skills and knowledge and build their professional network.
Frequently Asked Questions
What is the research methodology?
Research methodology is the systematic process used to conduct and analyze research.
What is literature review in research methodology?
Literature review in research methodology involves reviewing and analyzing existing literature on a specific topic.
What is qualitative research methodology?
Qualitative research methodology involves exploring and understanding complex phenomena through non-numerical data.
What are qualitative methodologies?
Qualitative methodologies encompass various approaches like interviews, focus groups, and content analysis.
What are research methodology types?
Research methodology types include qualitative, quantitative, mixed methods, experimental, and survey research.
Cochrane reviews combine the best available evidence from a number of sources and summarise it in a set of systematic reviews, syntheses and meta-analyses. They are an important way to find out what works and what doesn’t when it comes to health care. A Cochrane review looks at all the available research on a topic, taking into account the quality of the studies, the strength of the evidence, and whether or not any biases might have affected the results. The Cochrane Methodology Group develops and publishes guidelines for conducting Cochrane Reviews.
Cochrane Reviews have several advantages over other types of research. They reduce bias by drawing on a large body of evidence that can be compared across different studies. They also give us confidence that we’re getting good information because they are reviewed by experts in the field who prioritise study quality.
What is a systematic review?
A systematic review is a type of meta-analysis that seeks to summarise the results of previous research studies. It can either be narrative or quantitative, depending on how it is conducted. Both types of reviews draw on past studies to determine the strength of evidence (i.e., whether there is enough evidence to support a conclusion). However, they differ in their approach and scope. Narrative reviews take a broader perspective by including all relevant studies, while quantitative reviews focus on one specific outcome.
In general, systematic reviews are more effective than individual studies because they take into account the results of previous research studies. However, they also have some limitations: 1) the review cannot confirm relationships that might exist between two variables; 2) the review cannot identify causal relationships; and 3) it cannot rule out alternative explanations for an observed association. In summary, systematic reviews can provide useful insights about current research topics, but it is important to keep in mind their limitations before drawing conclusions from them.
Role in Medicine
A systematic review is a critical tool used in evidence-based medicine. It allows researchers to evaluate the quality of existing research and synthesise the results of multiple studies. Systematic reviews are especially important in the field of health care, where high-quality evidence can guide decisions about treatment options. They aim to improve the decision making process by combining all the available evidence from a given area into one cohesive review.
Common issues that can lead to bias and poor quality include selection bias, contamination, and poor reporting of methods. By minimising these factors, systematic reviewers can produce more accurate and reliable results.
Systematic reviews are especially useful for healthcare professionals who need to make decisions about treatment options for patients or patients with health conditions. For example, someone who wants to prescribe antibiotics to an elderly patient may want to see if there’s a systematic review comparing different treatment regimens with antibiotics.
Steps in Cochrane Review Method
A Cochrane Review is a systematic review of research in health care and health policy that is published in the Cochrane Database of Systematic Reviews. For researchers in the biomedical field conducting a systematic review, the Cochrane method of systematic reviews can be adopted as they are internationally recognized as the highest standard in evidence-based health care resources. The systematic review can be followed through the rigorous methods outlined in the Cochrane Handbook, this can also be found online at https://training.cochrane.org/handbooks.
Types of Cochrane Review
It would be helpful to understand the different types of reviews that Cochrane offers before selecting a specific guide for your research(Chapman, 2022):
Intervention reviews assess the benefits and harms of interventions used in health care and health policy.
Diagnostic test accuracy reviews assess how well a diagnostic test performs in diagnosing and detecting a particular disease.
Methodology reviews address issues relevant to how systematic reviews and clinical trials are conducted and reported.
Qualitative reviews synthesise qualitative evidence to address questions on aspects of interventions other than effectiveness.
Prognosis reviews address the probable course or future outcome(s) of people with a health problem.
Resources for systematic review
Cochrane systematic review can be aided by certain software which are available from the Collaboration:
Review Manager (RevMan) – software for preparing and maintaining Cochrane Reviews: protocols, manuscripts, characteristics of studies, comparison tables, study data, meta-analysis. In addition to reviews of studies of the effects of healthcare interventions, you can use RevMan to write reviews of diagnostic test accuracy studies, reviews of studies of methodology and overviews of reviews.
Covidence – a primary screening and data extraction tool for Cochrane authors to assess risk-of-bias, and extract data.
GRADEpro GDT – an online tool to create a Summary of Findings (SoF) table.
Summary of Findings Table:A summary of findings table presents the main findings of a review in a transparent and simple tabular format. In particular, the tables provide key information about the quality of evidence, the magnitude of effect of the interventions studied, and the quantity of data on the main outcomes. Most reviews would have just one summary of findings table.
EPPI-Reviewer – a web-based tool which helps you with all stages of the systematic review process: reference management, screening, risk of bias assessment, data extraction and synthesis.
Archie– Cochrane’s database for managing contacts and documents and delivering them for publication.
Conclusion
Cochrane reviews are used for PhD’s in some universities and medical colleges across India but most universities still require a primary study to explore the outcome of the reviews in a real world setting and to demarcate between practical applications and proposed theories.
Reference:
Chapman, S. (2022) What are Cochrane Reviews?, Evidently Cochrane. Available at: https://www.evidentlycochrane.net/what-are-cochrane-reviews/ (Accessed: 2022).
Systematic reviews: Cochrane Systematic Reviews (no date) Research Guides. Available at: https://mdanderson.libguides.com/c.php?g=384755&p=7400484 (Accessed: 2022).
A conceptual framework is a hierarchical representation of the relationships between variables under study. It can be created in a number of ways, but is most commonly displayed as a tree. The nodes at the top of the tree represent the variables being studied and may include terms such as “Condition”, “Variable”, and “Main Effect”. Each node further down the tree represents a specific relationship between the variables being studied and may include terms such as “Correlation”, “Association”, and “Time Effect”. The leaves at the bottom of the tree represent the outcomes (usually measurements) that are expected to be associated with each variable under study.
The main benefit of having a conceptual framework is that it allows researchers to think through all possible relationships between variables in order to gain a better understanding of how they might influence each other. It also helps to clarify which variables are most important to measure when designing research projects. For instance, if one variable is found to have an effect on another variable, this can help researchers better understand how to best measure both variables in future studies. However, conceptual frameworks are not always necessary in all research projects. Some may only be needed for longer-term research projects or those that involve multiple groups of participants or data collection methods (e.g., qualitative versus quantitative).
Creating a Conceptual Framework
Researchers use a conceptual framework to provide a visual or written representation of key variables, factors or concepts and their relationship with each other that will be studied in the present research.
Selecting a suitable research topic is critical in creating a conceptual framework. Before you begin your research, you must determine your topic. You must choose a research topic that interests you. Remember to check for available resources before you choose a topic. It may be beneficial to look for possible research resources before choosing a subject. It is also important to determine how much time it will take to research the case and whether you will have enough time to finish your work by the deadline.
Your research question determines exactly what you want to find out, helping to focus your research process. This is a crucial component of your conceptual framework, since this research question will determine how you will proceed throughout the course of your research.
A literature review focuses on the evaluation of current and relevant literature in a particular subject area to assess one’s knowledge and understanding of the literature. It involves the exploration and assessment of available literature on a specific topic.
The proper selection of variables is critical to the development of a research framework. Your independent and dependent variables should be determined initially after you have completed the literature review, you must find these variables relevant to your research topic and establish key relationships between them.
The conceptual framework can then be developed with reference to your problem statement and detailing out the cause and effect relationships between variables graphically.
You can also proceed to establishing other influencingvariables like the moderating variables that can affect the association between independent and dependent variables by strengthening or diminishing the relationship between them.
The mediating variable that explains the relationship between the independent and dependent variables and is affected by changes in the independent variable and resultantly affects the dependent variable.
The researcher can also establish certain control variables in this graphical representation which are presumed to be constant so that they don’t interfere with the results. They are defined in most research studies because the possibility of them occurring is high but are not studied or accounted for in the particular research.
The ‘Six Rs’ model of (Waller, 2022) is quite useful as a general guideline to develop a conceptual framework for your research.
Review – literature/themes/theory
Reflect – what are the main concepts/issues?
Relationships – what are their relationships?
Reflect – does the diagram represent it sufficiently?
Review – check it with theory, colleagues, stakeholders, etc
Repeat – review and revise it to see if something better occurs
The main idea espoused here is directly related to the elaborated steps of development as explained previously. A framework of concepts, assumptions, expectations, and beliefs is used to guide a research study when generalising from specific instances, these instances are demarcated from the literature review that the researcher carries out in pursuit of establishing connections to the research topic selected. A framework may be conceived of as a system of concepts, assumptions, expectations, and beliefs that link broad ideas or models that are created by reflecting on the main concepts or issues your research is trying to investigate. This would be an effective guide for a research study in order to establish a systematic order to the flow or logic of the study.
The researcher utilises literature to link real-world experiences or events to shape future research thoughts or methods in their research study which is used to derive the various necessary variables and establish relationships between them in order to construct a conceptual framework. The conceptual framework is then diagrammatically presented and assessed to understand if it espouses the core area the researcher is investigating with his or her research and with a feedback or reflection on this specific question, the framework can be further amended to satisfaction.
Wrapping Up
The conceptual framework acts as a link between literature, methodology, and results and mostly is used in qualitative research in the social and behavioural sciences. The framework helps the researcher visualise the research and understand the key variables that will dictate the course of the study and how the relationship between the variables must be accurately applied when investigating the phenomena under study. The conceptual framework also helps the researcher in formulating the best data collection and developing suitable tests to analyse the data to find effective results.
References:
Waller, D. (2022) Chart your research with a graphical conceptual framework, LX at UTS. Available at: https://lx.uts.edu.au/blog/2022/04/12/chart-your-research-with-a-graphical-conceptual-framework/ (Accessed: 2022).
Miles, M. B. & Huberman, M.A. (1994) Qualitative data analysis: An expanded sourcebook. 2nd ed. Thousand Oaks, CA: SAGE.
