assignment research methodology oum

Doctor of Philosophy (Science)

Programme Synopsis

(R3-DL/421/8/0007 (A6754) 02/28)

The aim of the Doctor of Philosophy (Science) programme in OUM is to create an opportunity for developing scientific research skills among working adults in research institutions and industries. To this end, this programme will enable students to gain knowledge in research skills, develop the ability to successfully conduct research projects and hence contribute to the existing body of knowledge.

Assoc. Prof. Dr Thirumeni A/P T Subramaniam Programme Director

Email : [email protected]

Entry Requirements
PEO & PLO
Programme Structure

* Fees applicable to Malaysians and Permanent Residents only.

* The University reserves the right to revise the fees without prior notice.

Discount for the Physically Challenged Learners (OKU) and Senior Citizens

The University offers 50% discount for: 1. Senior citizens aged 60 and above on registration date; and 2. Physically challenged learners – a valid Orang Kelainan Upaya (OKU) is required. The discounts are not valid for second time registration and repeating of subjects. Learners who receive this discount are not entitled to other discounts and promotions.

Note Processing fee: RM50 Re-registration fee of RM220 per semester is chargeable for extension of the Research Thesis/Dissertation

* The University reserves the right to revise the fees without prior notice. **LATEST UPDATE: 30 August 2022

This programme is comprised of the following:

What is the aim of the Doctor of Philosophy (Science) programme at OUM? The aim of this programme is to create an opportunity for developing scientific research skills among working adults in research institutions and industries.
Demonstrate excellent ability to conduct scientific research
Possess practical knowledge and skills in scientific data analysis
Critically review ideas and issues constructively
Demonstrate exemplary leadership and practise ethical standards in intellectual discourse
Communicate ideas clearly and systematically by adhering to scholarly principles
Plan and implement solutions to solve scientific problems
Possess high ability to work independently and inculcating a lifelong learning culture
Develop and manage scientific research plans for effective implementation.
Who should apply for the Doctor of Philosophy (Science) programme? If you are interested in scientific research, passionate about creating fresh knowledge, discovering new things, seeking a greater depth of knowledge in a specific area in science and developing a new set of skills, then you should seriously consider applying for this programme. Furthermore, the transferable skills honed in this programme are highly valued by employers.
What is the fee for the Doctor of Philosophy (Science) programme? The total fee for the entire programme can be found here: https://www.oum.edu.my/all-programmes/postgraduate-programme/doctor-of-philosophy-science/#1621666338602-6a40768d-032d Fees are subject to change without prior notice.
What is the entry requirement into the Doctor of Philosophy (Science) programme? The entry requirement is a Master’s degree in Science or its equivalent. However, acceptance of a student into this programme is subject to the approval of the Postgraduate Committee.
How soon will it take me to complete the Doctor of Philosophy (Science) programme? It will take you a minimum of 4 years to complete this programme.
Is this programme recognized by the Malaysian government? PhD (Science) (A 6754 ) is fully accredited by the Malaysian Qualifications Agency (MQA).
What are my potential career options after graduating from this programme? Doctoral graduates from this programme may opt for careers in academia, consulting or research, as well as undertake specialist roles in areas related to science in universities, research institutes, and industrial R&D laboratories.
Research methodology
Proposal defence
Doctoral Seminar
Thesis writing/Dissertation. A brief write-up on each of these courses can be found under “Programme Structure” at the following link: https://oum.edu.my/demo/doctor-of-philosophy-science/
How is this programme delivered? This programme is delivered in an online mode. In addition to virtual e-tutorials for certain sessions, students are able to interact with their peers and their facilitator/supervisor though the online platform.
When can I join the Doctor of Philosophy (Science) programme? This programme accepts student intakes in the January, May and September semesters.
I’ve decided I want to pursue the Doctor of Philosophy (Science) programme at OUM. How do I apply? Apply online at http://fastrac.oum.edu.my / Alternatively, visit any of OUM’s Learning Centres (LCs) located throughout the country for guidance. For info on the LCs, go to https://www.oum.edu.my/learning-centres/
Are course materials provided by OUM? All courses materials will be provided by OUM. In addition, learners may use OUM’s physical and digital libraries to access a wide range of learning materials.
Whom should I contact if I need to find out more about the Doctor of Philosophy (Science) programme? For academic-related enquiries related to Doctor of Philosophy (Science) programme, please email Assoc. Prof. Dr. Thirumeni at [email protected] . For general enquiries on OUM programmes, email the Marketing Unit at [email protected] .

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Dissertation
What Is a Research Methodology? | Steps & Tips

What Is a Research Methodology? | Steps & Tips

Published on August 25, 2022 by Shona McCombes and Tegan George. Revised on November 20, 2023.

Your research methodology discusses and explains the data collection and analysis methods you used in your research. A key part of your thesis, dissertation , or research paper , the methodology chapter explains what you did and how you did it, allowing readers to evaluate the reliability and validity of your research and your dissertation topic .

It should include:

The type of research you conducted
How you collected and analyzed your data
Any tools or materials you used in the research
How you mitigated or avoided research biases
Why you chose these methods
Your methodology section should generally be written in the past tense .
Academic style guides in your field may provide detailed guidelines on what to include for different types of studies.
Your citation style might provide guidelines for your methodology section (e.g., an APA Style methods section ).

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How to write a research methodology, why is a methods section important, step 1: explain your methodological approach, step 2: describe your data collection methods, step 3: describe your analysis method, step 4: evaluate and justify the methodological choices you made, tips for writing a strong methodology chapter, other interesting articles, frequently asked questions about methodology.

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Your methods section is your opportunity to share how you conducted your research and why you chose the methods you chose. It’s also the place to show that your research was rigorously conducted and can be replicated .

It gives your research legitimacy and situates it within your field, and also gives your readers a place to refer to if they have any questions or critiques in other sections.

You can start by introducing your overall approach to your research. You have two options here.

Option 1: Start with your “what”

What research problem or question did you investigate?

Aim to describe the characteristics of something?
Explore an under-researched topic?
Establish a causal relationship?

And what type of data did you need to achieve this aim?

Quantitative data , qualitative data , or a mix of both?
Primary data collected yourself, or secondary data collected by someone else?
Experimental data gathered by controlling and manipulating variables, or descriptive data gathered via observations?

Option 2: Start with your “why”

Depending on your discipline, you can also start with a discussion of the rationale and assumptions underpinning your methodology. In other words, why did you choose these methods for your study?

Why is this the best way to answer your research question?
Is this a standard methodology in your field, or does it require justification?
Were there any ethical considerations involved in your choices?
What are the criteria for validity and reliability in this type of research ? How did you prevent bias from affecting your data?

Once you have introduced your reader to your methodological approach, you should share full details about your data collection methods .

Quantitative methods

In order to be considered generalizable, you should describe quantitative research methods in enough detail for another researcher to replicate your study.

Here, explain how you operationalized your concepts and measured your variables. Discuss your sampling method or inclusion and exclusion criteria , as well as any tools, procedures, and materials you used to gather your data.

Surveys Describe where, when, and how the survey was conducted.

How did you design the questionnaire?
What form did your questions take (e.g., multiple choice, Likert scale )?
Were your surveys conducted in-person or virtually?
What sampling method did you use to select participants?
What was your sample size and response rate?

Experiments Share full details of the tools, techniques, and procedures you used to conduct your experiment.

How did you design the experiment ?
How did you recruit participants?
How did you manipulate and measure the variables ?
What tools did you use?

Existing data Explain how you gathered and selected the material (such as datasets or archival data) that you used in your analysis.

Where did you source the material?
How was the data originally produced?
What criteria did you use to select material (e.g., date range)?

The survey consisted of 5 multiple-choice questions and 10 questions measured on a 7-point Likert scale.

The goal was to collect survey responses from 350 customers visiting the fitness apparel company’s brick-and-mortar location in Boston on July 4–8, 2022, between 11:00 and 15:00.

Here, a customer was defined as a person who had purchased a product from the company on the day they took the survey. Participants were given 5 minutes to fill in the survey anonymously. In total, 408 customers responded, but not all surveys were fully completed. Due to this, 371 survey results were included in the analysis.

Information bias
Omitted variable bias
Regression to the mean
Survivorship bias
Undercoverage bias
Sampling bias

Qualitative methods

In qualitative research , methods are often more flexible and subjective. For this reason, it’s crucial to robustly explain the methodology choices you made.

Be sure to discuss the criteria you used to select your data, the context in which your research was conducted, and the role you played in collecting your data (e.g., were you an active participant, or a passive observer?)

Interviews or focus groups Describe where, when, and how the interviews were conducted.

How did you find and select participants?
How many participants took part?
What form did the interviews take ( structured , semi-structured , or unstructured )?
How long were the interviews?
How were they recorded?

Participant observation Describe where, when, and how you conducted the observation or ethnography .

What group or community did you observe? How long did you spend there?
How did you gain access to this group? What role did you play in the community?
How long did you spend conducting the research? Where was it located?
How did you record your data (e.g., audiovisual recordings, note-taking)?

Existing data Explain how you selected case study materials for your analysis.

What type of materials did you analyze?
How did you select them?

In order to gain better insight into possibilities for future improvement of the fitness store’s product range, semi-structured interviews were conducted with 8 returning customers.

Here, a returning customer was defined as someone who usually bought products at least twice a week from the store.

Surveys were used to select participants. Interviews were conducted in a small office next to the cash register and lasted approximately 20 minutes each. Answers were recorded by note-taking, and seven interviews were also filmed with consent. One interviewee preferred not to be filmed.

The Hawthorne effect
Observer bias
The placebo effect
Response bias and Nonresponse bias
The Pygmalion effect
Recall bias
Social desirability bias
Self-selection bias

Mixed methods

Mixed methods research combines quantitative and qualitative approaches. If a standalone quantitative or qualitative study is insufficient to answer your research question, mixed methods may be a good fit for you.

Mixed methods are less common than standalone analyses, largely because they require a great deal of effort to pull off successfully. If you choose to pursue mixed methods, it’s especially important to robustly justify your methods.

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Next, you should indicate how you processed and analyzed your data. Avoid going into too much detail: you should not start introducing or discussing any of your results at this stage.

In quantitative research , your analysis will be based on numbers. In your methods section, you can include:

How you prepared the data before analyzing it (e.g., checking for missing data , removing outliers , transforming variables)
Which software you used (e.g., SPSS, Stata or R)
Which statistical tests you used (e.g., two-tailed t test , simple linear regression )

In qualitative research, your analysis will be based on language, images, and observations (often involving some form of textual analysis ).

Specific methods might include:

Content analysis : Categorizing and discussing the meaning of words, phrases and sentences
Thematic analysis : Coding and closely examining the data to identify broad themes and patterns
Discourse analysis : Studying communication and meaning in relation to their social context

Mixed methods combine the above two research methods, integrating both qualitative and quantitative approaches into one coherent analytical process.

Above all, your methodology section should clearly make the case for why you chose the methods you did. This is especially true if you did not take the most standard approach to your topic. In this case, discuss why other methods were not suitable for your objectives, and show how this approach contributes new knowledge or understanding.

In any case, it should be overwhelmingly clear to your reader that you set yourself up for success in terms of your methodology’s design. Show how your methods should lead to results that are valid and reliable, while leaving the analysis of the meaning, importance, and relevance of your results for your discussion section .

Quantitative: Lab-based experiments cannot always accurately simulate real-life situations and behaviors, but they are effective for testing causal relationships between variables .
Qualitative: Unstructured interviews usually produce results that cannot be generalized beyond the sample group , but they provide a more in-depth understanding of participants’ perceptions, motivations, and emotions.
Mixed methods: Despite issues systematically comparing differing types of data, a solely quantitative study would not sufficiently incorporate the lived experience of each participant, while a solely qualitative study would be insufficiently generalizable.

Remember that your aim is not just to describe your methods, but to show how and why you applied them. Again, it’s critical to demonstrate that your research was rigorously conducted and can be replicated.

1. Focus on your objectives and research questions

The methodology section should clearly show why your methods suit your objectives and convince the reader that you chose the best possible approach to answering your problem statement and research questions .

2. Cite relevant sources

Your methodology can be strengthened by referencing existing research in your field. This can help you to:

Show that you followed established practice for your type of research
Discuss how you decided on your approach by evaluating existing research
Present a novel methodological approach to address a gap in the literature

3. Write for your audience

Consider how much information you need to give, and avoid getting too lengthy. If you are using methods that are standard for your discipline, you probably don’t need to give a lot of background or justification.

Regardless, your methodology should be a clear, well-structured text that makes an argument for your approach, not just a list of technical details and procedures.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

Normal distribution
Measures of central tendency
Chi square tests
Confidence interval
Quartiles & Quantiles

Methodology

Cluster sampling
Stratified sampling
Thematic analysis
Cohort study
Peer review
Ethnography

Research bias

Implicit bias
Cognitive bias
Conformity bias
Hawthorne effect
Availability heuristic
Attrition bias

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

In a scientific paper, the methodology always comes after the introduction and before the results , discussion and conclusion . The same basic structure also applies to a thesis, dissertation , or research proposal .

Depending on the length and type of document, you might also include a literature review or theoretical framework before the methodology.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

Reliability and validity are both about how well a method measures something:

Reliability refers to the consistency of a measure (whether the results can be reproduced under the same conditions).
Validity refers to the accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

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Published Student Research

Completion of a publishable research project is an OUM graduation requirement.

While it is a long path from selecting a topic and defining a research question to having a manuscript published, OUM has invaluable resources in place to aid students in reaching their goal, namely the Research Methodology Unit, and most importantly, an experienced Research Advisor assigned to each student.

Metabolic Syndrome in Samoan Islands

Publishing student: Shaoyu Xu

Published in: Medical link issue 149 Page 16

Published: 2024

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The Gail Model and Its Use in Preventive Screening: A Comparison of the Corbelli Study

Publishing student: William R. Pruitt

Published in: The Cureus Journal of Medical Sciences

Investigating knowledge deficit in diabetes mellitus type 2 amongst young Australian adolescents, aged 18-25-A cross-sectional study

Publishing student: Vineetha Fridolin Francis-Loganathan

Published in: IP Journal of Nutrition, Metabolism and Health Science

Effects of the COVID-19 Pandemic on University Students’ Mental Health: A Literature Review

Publishing student: Beatta Kabbani

Published in: Cureus Journal of Medical Science

Measles — A Threat to Samoan Health: Literature Review

Publishing student: Anu Jose

Published in: GCMA The Medical Link

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Publishing student: Jessica L. Saenz

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Incidence and Causes of Cellulitis Among Patients at Tupua Tamasese Meaole Hospital in Upolu, Samoa in 2019

Publishing student: Addisalem Hailu Wondafrash

Can Antibiotic Stewardship Be Achieved by Utilizing the Stewardship Through Educating Patients (S.T.E.P.) Process?

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Defining your research methodology

Find out more about The Open University's Education courses and qualifications .

One of the things your proposal needs to demonstrate is that your project is feasible. Start by specifying the physical or digital context of your research. For example, is it in a classroom, lab, health, social care setting or in the field? Will it be conducted online, offline, or both?

Then, outline your research methodology, keeping in mind that your research questions will influence the methodology you choose. You will want to demonstrate that your methodology is systematic and – for certain studies - can be replicated by other researchers in similar contexts.

Research methodologies

Your methodology is the approach you will take to guide your research process and explain why you use particular methods. There are several approaches to choose from and you'll need to decide based on:

(a) Your study’s aims and objectives.

(b) Your personal perspective (including your assumptions, beliefs, values, and experiences related to the topic).

(c) The theoretical or philosophical theories you hold. For example, a positivist approach holds that knowledge is real and objective, obtainable through measurements and statistics; an interpretivist approach holds that knowledge is dependent on beliefs, values, and lived experiences; and a pragmatist approach holds that knowledge is acquired through action and doing.

Methodological approaches can be quantitative (numerical), qualitative (non-numerical), or mixed methods.

Quantitative methodologies have two main strategies: experimental and non-experimental.

The experimental strategy is used when you aim to measure the effects of a change, like comparing learning outcomes of students taught through a standard classroom book versus simulations. In this strategy, participants are randomly assigned to a control or experimental group (for example, the classroom book or the simulation group).
The non-experimental strategy is used when you don't aim to change a situation, but still measure outcomes in different populations and situations.
Descriptive or single-variable research : This type answers interesting and important questions involving one variable. For example, how many primary school students own mobile devices.
Correlational research : This type looks at the relationship between two things, by measuring two continuous variables. Researchers don't control any other factors that might be affecting the relationship. For example, assessing whether there is a relationship between students’ wellbeing and academic performance.
Q uasi-experimental research : In this type, the researcher changes an independent variable they think can make a difference but does not randomly assign participants to groups. For example, a researcher might compare the academic performance of students in a school that implements a wellbeing program with the academic performance of students in a school that does not.

Qualitative methodologies have several strategies, far too many to list them all here, they may involve:

Case study , where the focus is on a specific case like the experiences of healthcare professionals in a particular organization;
Ethnographic studies , where the focus is on describing and interpreting the culture and social structure of a group;
Grounded theory studies , where the aim is to develop a theory based on the study itself;
Narrative research , where the focus is on identifying or telling stories;
Phenomenological research , where the focus is on understanding individuals' perspectives and the world around them;
Action research , a practitioner-based approach focused on contributing to the development of a profession.

Methodologies using mixed methods combine resources and approaches to answer different research questions or different aspects of the same question. They aim at producing more in-depth findings. For example, you can explore what the levels of online engagement are of students who scored below 50% in exams using quantitative methods. You can then understand why they had these levels of online engagement using qualitative methods. We will explore the different data collection methods further down.

These are some of the well-established methodologies, but there are many others that have been developed to address specific research topics, such as visual methodologies, impact evaluations, and secondary data research.

This OU-produced open access handbook on Research Method s can help you get more insights into the different layers of research design.

Postgraduate researcher talks about research questions and methodology

Data collection techniques

There are many methods available for you to collect your data. Robson (2011, pp. 232-233) outlines the following rule of thumb for selecting which method is more suitable for you when collecting data from participants:

Use direct observation to find out what people do in public
Use interview or questionnaires to find out what they do in private
Use interviews, questionnaire or attitude scales to investigate/ explore what they think, feel and believe
Use standardised tests to determine their abilities or personality

These are some well-known data collection techniques. However, you’ve probably heard about other techniques, such as the collection of texts, documents, images and artefacts. Further, there are also some more recent approaches to data collection, such as internet-based methods, social media research, as well as data analytic platforms.

Data analysis

Once you establish which data collection methods you’ll use, you need to specify how your data will be analysed. Make sure you don’t give the impression in your proposal that you’ll gather the data and think about it later!

There are different methods for analysing quantitative and qualitative data.

For quantitative data, methods include frequency distributions and graphical displays, descriptive statistics, exploring relationships between two or more variables, and analysing differences between variables.

For qualitative data, methods include thematic analysis, discourse analysis, document analysis, and multimodal analysis.

Thematic analysis focuses on identifying and interpreting patterns of meaning in the dataset.
Discourse analysis focuses on the communication between individuals and their contextual meanings.
Document analysis evaluates electronic or physical documents to understand their meaning.
Multimodal analysis analyses data that combines multiple forms, such as a video with moving images, audio, and text.

Mixed methods can combine different data collection and analysis methods in many ways. For example, interviews can explain or triangulate findings from data analytics. Cresswell (2002) explains the different sequences with mixed method approaches. For example, the emphasis can be equal between qualitative and quantitative research, or tip in the direction of either quantitative or qualitative.

Context, timeline and limitations

As mentioned earlier, you need to have an idea about what you plan to do. You need to clarify the details of your research plan, including participants, location and settings. In your proposal, mention your current ideas about the scale of your research (e.g., sample size), access requirements (e.g., permission or gatekeepers), and the location/space of your study.

A good idea would be to produce a timeline showing what happens when. Your timeline can be textual (bullets with date intervals) or visual (an excel sheet). You can sort tasks in chronological order or in larger categories.

Finally, be transparent about any limitations that may impact the research, such as access to equipment, number of participants, time constraints, and timing issues. Acknowledging the known limitations is a strength in a proposal because it shows that you obtain a critical and overall appraisal of possible impacts on your study.

Creswell, J.W. (2002) Educational research: Planning, conducting, and evaluating quantitative and qualitative research (Vol. 7). Prentice Hall Upper Saddle River: NJ.

Robson. C. (2011) Real world research. Wiley: Cornwall.

Writing your research proposal

A doctoral research degree is the highest academic qualification that a student can achieve. The guidance provided in these articles will help you apply for one of the two main types of research degree offered by The Open University.

Level: 1 Introductory

Addressing ethical issues in your research proposal

This article explores the ethical issues that may arise in your proposed study during your doctoral research degree.

Writing your proposal and preparing for your interview

The final article looks at writing your research proposal - from the introduction through to citations and referencing - as well as preparing for your interview.

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Research method is a method or scientific technique to obtain data with specific purposes and uses. The scientific means or techniques in question are where research activities are carried out based on scientific characteristics. This is a set of rules, activities, and procedures used by the perpetrators. The methodology is also a theoretical analysis of a method or method. Research is a systematic investigation to increase knowledge, as well as systematic and organized efforts to investigate certain problems that require answers. The nature of research can be understood by studying various aspects that encourage research to do it properly. Every person has a different motivation, including influenced by their goals and profession. Motivation and research objectives in general are basically the same, namely research is a reflection of the desire of people who always try to know something. The desire to acquire and develop knowledge is a basic human need which is generally a motivation to conduct research. The validity of research data can be obtained by using valid instruments, using appropriate and adequate amounts of data sources, as well as correct data collection and analysis methods. To obtain reliable data, the instrument must be reliable and the research carried out repeatedly. Furthermore, to obtain objective data, the number of sample data sources approaches the population.Each study has specific goals and uses. In general, there are three types of research objectives, namely the nature of discovery, verification and development. The finding means that the data obtained from research is truly new data that has not been previously known.

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The practical step-by-step approach provides students with strong content and a conceptual framework. Discussions relating to concepts and theory range from simple to complex in their approach.

This clear and concise text links theory with eight practical steps central to any research process. It focuses on developing research skills by providing practical examples from both qualitative and quantitative research for a balanced and comprehensive grounding in research methodology.

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Example report in box folder, linked here

General Directions

The final report is intended to provide a comprehensive account of your collaborative course project in data science. The report should demonstrate your ability to apply the data science skills you have learned to a real-world project holistically, from posing research questions and gathering data to analysis, visualization, interpretation, and communication. The report should stand on its own so that it makes sense to someone who has not read your proposal or prototype.

The report should contain at least the parts defined below. In terms of length, it should be 5-7 pages using standard margins (1 in.), font (11-12 pt), and line spacing (1-1.5). A typical submission is around 3-4 pages of text and 5-7 pages overall with tables and figures. It is important to stay within the page limit, as practicing being succinct is an important skill. Your final report should also have a descriptive title, not “CS216 Project Report”. You should convert your written report to a pdf and upload it to Gradescope under the assignment “Project Final Report” by the due date, and assign the appropriate pages to questions in the grading rubric. Be sure to include your names and NetIDs in your final document and use the group submission feature on Gradescope. You do not need to upload your accompanying data, code, or other supplemental resources demonstrating your work to Gradescope; instead, your report should contain instructions on how to access these resources (see the Results and Methods section below for more details).

In general, your approach to this report should be to write as if you had “planned this as your project all along.” A report is not a chronological story of your project, it is a summary of what you did where the “story” serves the reader’s comprehension.

E (Exemplary, 20pts) – Work that meets all requirements in terms of formatting and sections.
S (Satisfactory, 19 pts) – Work that meets all requirements but is over 7 pages.
N (Not yet, 12pts) – Does not meet all requirements.
U (Unassessable, 4pts) – Missing at least one section.

Part 1: Introduction and Research Questions (15 points)

Your final report should begin by introducing your topic and restating your research question(s) as in your proposal. As before, your research question(s) should be (1) substantial, (2) feasible, and (3) relevant. In contrast to the prior reports, the final report does not need to explicitly justify that the research questions are substantial and feasible in the text ; your results should demonstrate both of these points. Therefore, you should remove that text to save space.

You should still explicitly justify how your research questions are relevant. In other words, be sure to explain the motivation of your research questions. Remember that relevant research questions address a subject of importance and interest within the scientific community or broader society. Additionally, we are looking for why your group believes this research project is worthwhile to your time in this course.

You can start with the text from your prototype, but you should update your introduction and research questions to reflect changes in or refinements of the project vision. You should not state specific updates, rather, write the report as the final product and the prior milestones do not exist. Pretend the readers are unaware of the prior milestones. If you feel like an explanation of changes since the prototype is warranted, place that in the appendix. Your introduction should be sufficient to provide context for the rest of your report.

E (Exemplary, 15pts) – Comprehensive introduction with clearly labeled, up-to-date research questions and a justification for how the research questions are relevant. Report introduction can stand alone without references to prior versions of the project; no text for explicit justifications for “substantial” and “feasible” are made for the research question(s).
S (Satisfactory, 14pts) – Comprehensive introduction with clearly labeled, updated research questions and a justification for how the research questions are relevant. The introduction and research questions may not have been refined from the prototype (they have still kept reasoning for why their research questions are substantial and feasible).
N (Not yet, 9pts) – Incomplete introduction where the research questions or justification are missing pieces, but at least some of it is present. Or the justification is clearly not reasonable.
U (Unassessable, 3pts) – Incomplete introduction where it is entirely missing the research questions or justification or does not demonstrate meaningful effort.

Part 2: Data Sources (15 points)

Discuss the data you have collected and are using to answer your research questions. Be specific: name the datasets you are using, the information they contain, and where they were collected from / how they were prepared. You can begin with the text from your prototype, but be sure to update it to fit the vision for your final project.

E (Exemplary, 15pts) – Origins of data are properly specified, cited, and relevant to answering the research question(s). If any significant data wrangling, cleaning, or other data preparation was done, these processes are explained.
S (Satisfactory, 14pts) – Origins of data are properly specified and cited. However, the justification is not clear why the data is relevant to the proposed research question(s). If any significant data wrangling, cleaning, or other data preparation was done, these processes are explained.
N (Not yet, 9pts) – Poorly specified data sources or the justification for using that data set or the methods to acquire the data is lacking. No discussion of preparing the dataset.
U (Unassessable, 3pts) – Data sources or methods to acquire data are missing or do not demonstrate meaningful effort.

Part 3: What Modules Are You Using? (15 points)

Your project should utilize concepts from modules we have covered in this course to answer your research question(s). We will assume you will use modules 1 (Python), 2 (Numpy/Pandas), and 5 (Probability). Your final report should state at least 3 more modules that you have utilized for your project. Each module should have a short description of how you used the knowledge in this module and a justification for that use. In addition, include what specific concepts from the module you used and at what stage of your project you mostly used this module. Potential stages include, but are not limited to: data gathering, data cleaning, data investigation, data analysis, and final report.

Module 3: Visualization
Module 4: Data Wrangling
Module 6: Combining Data
Module 7: Statistical Inference
Module 8: Prediction & Supervised Machine Learning
Module 9: Databases and SQL
Module 10: Deep Learning

Your overall report should clearly show that you used the modules discussed in this section. You should add any additional modules used and update the existing modules to be more specific to the different tasks and stages of your projects that changed since your prototype.

E (Exemplary, 15pts) – States at least 3 modules. For each module, they provide an updated (1) short description of how they used the module, (2) justification for using this module, (3) specific concepts they used, (4) what stage they used it, and (5) clearly implemented it in the final report.
S (Satisfactory, 14pts) – States at least 3 modules. For each module, they provide an updated (1) short description of how they used the module, (2) justification for using this module, (3) what concepts they used and (4) what stage they used it. Less than 3 modules are clearly implemented in the final report.
N (Not yet, 9pts) – States at least 3 modules. For each module, they provide an updated (1) short description of how they used the module, (2) justification for using this module, (3) what concepts they used and (4) what stage they used it. Only one module is clearly implemented in the final report.
U (Unassessable, 3pts) – Does not meet the Not Yet criteria.

Part 4: Results and Methods (15 points)

This is likely to be the longest section of your paper at multiple pages. The results and methods section of your report should explain your detailed results and the methods used to obtain them. Where possible, results should be summarized using clearly labeled tables or figures and supplemented with written explanations of the significance of the results with respect to the research questions outlined previously. Please note that a screenshot of your dataset does not count as a table or figure and should not be included in your final report.

Your description of your methods should be specific. For example, if you scraped multiple web databases, merged them, and created a visualization, then you should explain how each step was conducted in enough detail that an informed reader could reasonably be expected to reproduce your results with time and effort. Just saying, “we cleaned the data and dealt with missing values” or “we built a predictive model” is insufficient detail.

Your report should also contain instructions on how to access your full implementation (that is, your code, data, and any other supplemental resources like additional charts or tables). The simplest way to do so is to include a link to the box folder, GitLab repo, or whatever other platforms your group is using to house your data and code.

E (Exemplary, 15pts) – Results are thoroughly discussed using clearly labeled tables or figures followed by written descriptions. Specific explanation of how the results were generated and from what data. Link to code/data to create charts or visualizations is provided.
S (Satisfactory, 14pts) – Results are thoroughly discussed using clearly labeled tables or figures followed by written descriptions. Explanation of how the results were generated may lack some specification or it is somewhat unclear as to what data the results are from. Link provided.
N (Not yet, 9pts) – Results are discussed using tables with missing labels or lacking written descriptions. It is unclear how the results were generated and from what data.
U (Unassessable, 3pts) – Results are missing or do not demonstrate meaningful effort.

Part 5: Limitations and Future Work (10 points)

In this part, you should discuss any important limitations or caveats to your results with respect to answering your research questions. For example, if you don’t have as much data as you would like or are unable to fairly evaluate the performance of a predictive model, explain and contextualize those limitations. You may want to consider any ethical implications or potential biases of your results as well.

Finally, provide a brief discussion of future work. This could explain how future research might address the limitations you outline, or it could pose additional follow-up research questions based on your results so far. In short, explain how an informed reader (such as a peer in the class) could improve on and extend your results.

E (Exemplary, 10pts) – Comprehensive and explicit discussion of important limitations and caveats to results. Brief discussion of future work and how results could be extended and improved upon.
S (Satisfactory, 9pts) – Discussion of important limitations and caveats to results could be improved or the discussion of future work and how results could be extended and improved upon lacks some specification.
N (Not yet, 6pts) – Incomplete discussion of important limitations and caveats to results. Discussion of future work and how results could be extended and improved upon may lack some specification.
U (Unassessable, 2pts) – Limitations and future work are missing or do not demonstrate meaningful effort.

Part 6: Conclusion (5 points)

Provide a brief (one or two paragraphs) summary of your results. This summary of results should address all of your research questions.

If one of your research questions was “Did COVID-19 result in bankruptcy in North Carolina during 2020?” then a possible (and purely hypothetical) summary of results might be:

We aggregate the public records disclosures of small businesses in North Carolina from January 2019 to December 2020 and find substantial evidence that COVID-19 did result in a moderate increase in bankruptcy during 2020. This increase is not geographically uniform and is concentrated during summer and fall 2020. We also examined the impact of federal stimulus but cannot provide an evaluation of its impact from the available data.

E (Exemplary, 10pts) – Research questions are clearly and completely addressed through a summary of results.
S (Satisfactory, 9pts) – Research questions are clearly addressed through a summary of results. The results may be lacking in completely answering the research questions.
N (Not yet, 6pts) – Research questions are somewhat addressed through a summary of results. The results are lacking in completely answering the research questions. Or the results of one of the research questions is missing.
U (Unassessable, 2pts) – Conclusion is missing or does not demonstrate meaningful effort.

(Optional) Part 7: Appendix of additional figures, tables, and updates summary.

If you are struggling to keep your report within the 5-7 page limit, you may move some (not all) of your figures and tables to an optional appendix that will not count against your page limit. However, your report should stand on its own without the appendix . The appendix is for adding more nuance to your results, not to give you more space to talk about your results. Succinctness is an important skill to practice when doing data science. Your grader is not expected to look at the appendix when grading.

If you strongly feel like a summary of project updates since the proposal is required, you may put them in this appendix as well and mention they are in the appendix in the introduction.

Checklist Before You Submit:

5-7 pages in length
Standard margins (1 in.)
Font size is 11-12 pt
Line spacing is 1-1.5
Final document is a pdf
Descriptive project title
Do you feel as if this part meets the requirements of E (Exemplary) or S (Satisfactory) ?

Author Joey Scarpa

Posted March 30, 2024 — 4:30 pm

Categories Project