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Software Simulation for PhD Projects: Using MATLAB

Software simulation is the process of creating a realistic model of a system or engineering application, calculating and predicting its behaviour. It is used to test systems for reliability or performance, to identify specific problems and to predict behaviours. Software simulation is an effective tool for PhD candidates, allowing them to test and validate their theories before investing time in building physical prototypes.

It can be used to test the model and validate hypotheses, as well as to assess the performance of new designs.

It is also a good way to understand how the system operates by simulating its behaviour. Simulations are especially useful when it comes to complex systems.

Many research papers that deal with simulation use software implementation tools  that provide the necessary functionality for creating models, validating hypotheses and assessing performance for testing purposes. These tools are available for free on the web, so anyone can access them. MATLAB is one such resource tool that has been widely used in simulation research projects.


How MATLAB helps 

MATLAB is one of the best tools used for research purposes due to high graphical functionalities and advanced features which makes it one of the most useful tools for programming, and it is one of the most widely used data analysis, simulation and visualisation tools in the world. With MATLAB, you can do all of these things, and more. It’s a powerful tool for research, and it’s very flexible. It is used for applications in engineering topics such as dynamics, vibrations, systems, control, fluid mechanics, and heat transfer. 

Researchers use MATLAB for a variety of purposes, from creating complex simulations to analysing data. Some of the most common applications of MATLAB include data analysis, data visualisation, and simulation. Researchers use MATLAB to analyse data and create models that simulate their real-world counterparts. Researchers can also use MATLAB to design and test existing theories or processes. Many researchers use MATLAB to create simulations that help them understand how their research or processes function, or how they might fail in the future.

With MATLAB, researchers can have an environment to perform numerical analysis to deduct what applies to a theory or design they are developing. The primary purpose of MATLAB in research is to demonstrate that results are viable to be applied to real world problems and the MATLAB environment provides the necessary tools to verify and check this hypothesis. It is also used for complex mathematical computations. 

The research project obviously begins with the formulation of a problem that is to be solved with an innovative solution you propose in your project. This is then justified using new algorithms, methodologies and techniques. The project is then run in the MATLAB environment to evaluate its performance and the new concept is then clearly defined with future applications or limitations. 

How PhD Guidance can support researchers

PhD guidance provides for expert aid in simulation and implementation of your MATLAB needs from the field of Civil, Mechanical, Electrical, Electronics and Computer Science including computation, visualisation and programming to perform mathematical calculations and use any advanced features of the software for modelling your research project ranging from mathematics (numerical computing), development of algorithms, image processing, modelling and simulation of systems. 

The experts will guide you through the vast scope of MATLAB applications and help you in choosing the right tools to work on quantitative data analysis or other aspects of your research project that can be implemented using MATLAB. The statisticians will help you develop the necessary algorithm to simplify your analysis process and can help you explore new avenues of real-world applications of your research with our completely customised approach.

Review Standards: Cochrane Review Method for Systematic Review of 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:

  1. Chapman, S. (2022) What are Cochrane Reviews?, Evidently Cochrane. Available at: https://www.evidentlycochrane.net/what-are-cochrane-reviews/ (Accessed: 2022). 
  2. Systematic reviews: Cochrane Systematic Reviews (no date) Research Guides. Available at: https://mdanderson.libguides.com/c.php?g=384755&p=7400484 (Accessed: 2022).

What is Inferential Statistics in Data Analysis

In Inferential statistics, we make an inference from a sample about the population. The main aim of inferential statistics is to draw some conclusions from the sample and generalise them for the population data. Inferential Statistics in quantitative research works in addition to Descriptive Statistics. Where descriptive statistics helps to summarise the characteristics of a sample population, inferential statistics focuses on using that summarised data and predicting the characteristics for the larger population.

 

What is Inferential Statistics?

Given a sample of data, an inference is made to discover unknown information related to the larger population. There are various inferential statistics in research, business and economics, like hypothesis testing, sampling, and probability. In hypothesis testing, data is collected and then a null hypothesis and an alternate hypothesis are made. For example, if a researcher wants to find out the percentage of people who consume a particular food item in their country, then the researcher will have to collect data about the number of people who consume that particular food item. The researcher will then hypothesise that, the percentage of people who consume that food item is more in that country compared to other countries.

 

What is Hypothesis Testing in Inferential Statistics?

Hypothesis testing is the process, where a researcher collects data from a sample from a population and then makes a null and alternate hypothesis about the population based on the sample data. The null hypothesis is, “there is no significant difference between the sampled population and the population”. Whereas, the alternate hypothesis is, “there is a significant difference between the sampled population and the population”. Let’s consider an example to understand hypothesis testing in inferential statistics better. Suppose, a researcher visits a random shopping mall and collects data about the number of people who shop at different stores in the mall. The researcher will hypothesise that the number of people who shop at a particular store in the mall is more than the number of people who shop at other stores in the mall. The researcher can then make conclusions about the mall, that all the other stores, who don’t collect such a high number of customers, have to improve their service and make their products, which are liked by the customers, more popular in the mall.

 

Various Statistical Tests in Inferential Statistics

There are various statistical tests available in inferential statistics. These statistical tests are used to make a conclusion about the population based on the sample data. Different statistical tests in inferential statistics are explained below. – Hypothesis Test – Null and Alternative Hypothesis – Chi-Square Test – Correlation Coefficient – Regression Line – Probability in Bayes’ Theorem – Uniform Random Sampling – External Validation Methodology – Considerations – Conclusion

 

Difference Between Descriptive and Inferential Statistics?

Let’s understand the difference between descriptive and inferential statistics. Firstly, descriptive statistics helps to discover unknown information related to a particular sample. It simply describes the characteristics of the sample. On the other hand, inferential statistics makes an inference about the population based on the sample data. Let’s consider an example to understand the difference between descriptive and inferential statistics. Suppose, a researcher visits a town and calls a random sample of 100 people and asks them, “What is your profession?” and “How old are you?”. The researcher simply describes the characteristics of the sample. Now, if a researcher wants to make a conclusion about the town, then inference is made from the sample data. The conclusion can be as below. “People in this town are older than average people and their profession is more than average people”. Hence, the difference between descriptive and inferential statistics is very clear in this example.

 

Conclusion

In short, inferential statistics uses the data collected from a sample to make conclusions about the population. It is entirely distinct from descriptive statistics, where the characteristics of the sample are described. Inferential statistics is widely used in business, economics, and other quantitative fields, whereas descriptive statistics is used in qualitative research.

Conceptual Framework in Your Research: Developing A Template

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 influencing variables 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:

  1. 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).
  2. Miles, M. B. & Huberman, M.A. (1994) Qualitative data analysis: An expanded sourcebook. 2nd ed. Thousand Oaks, CA: SAGE.