What is a RQ?

A RQ is a one sentence (except for large studies) question to center your research around, it summarises the significant issue your research will investigate. The RQ is often considered as the most important component of research because everything is focused on answering it. It is like a guiding light, pin-pointing exactly what you want to find out, it provides clear focus and purpose.

The difference between Search & Research

Search aims to provide one answer to a question: "What was world population growth in the 20th century?" A quick Google search can give a definitive answer to this question.

Research provides multiple answers to a question: "What factors fuelled world population growth in the 20th century?" There are multiple factors, like advances in medicine, advances in nutrition, and advances in sanitation. Each of these may have a sub-list of factors, for example, specific medical advances.

Narrowing & Broadening

Sub-lists in research can help to narrow the question:
"What advances in medicine fuelled world population growth in the 20th century?"

Sometimes however, a research question may need to be broadened, for example:
Too narrow: "How did aspartame affect post-menopausal women in the 1980s who suffered from migraines?"
Broader: "How does aspartame affect women who suffer from migraines?" This will provide a list of Effect #1, Effect #2, Effect #3.

Types of RQ

Comparative RQ
For example: "Is there a difference between ... & ...?" Here we would be comparing one group to another, characterised by quantitative research.

Correlational RQ
For example: "What is the relationship between ... & ...?" How does one variable relate to another. This research could be quantitative as well as qualitative.

Causal RQ
Causal research is concerned with whether or not one variable causes another variable to change. It describes one or more factors that are causing a problem, if variable X is removed or altered in some way then variable Y is removed or altered as a consequence. For example, "What is the effect of....?", "How does perceived value affect...." This type of research can be characterised by quantitative research or experimental designs.

Descriptive RQ
Descriptive research collects data that describe characteristics of objects, e.g. people, organisations, products, brands, events, situations. Descriptive research is either quantitative or qualitative. Quantitative data could be satisfaction ratings, production figures, sales figures, demographic data. Qualitative data could be, for example, a decision making or conflict resolution processes.

Exploratory RQ
Exploratory RQ's are created when there is not much known about a phenomenon, the existing research results are unclear, the topic is highly complex, and/or there is not enough theory available to guide the development of a theoretical framework. This type of research is characterised by qualitative approaches to data gathering, e.g. informal discussions, interviews, focus groups, and case studies.

RQ Language

The language of RQs, try to organise the language with the types of RQ:

What is the relationship between...? (Correlational)
What factors affect...? (Causal, Descriptive)
How does... relate to...? (Correlational)
Why is... an issue in relation to...?
Does ... mean that ...?

6 Steps to develop a strong RQ

1. Choose a broad topic, e.g. Smart Contracts
2. Do some preliminary reading about a topic, find out what literature and data exists. Are there any interesting and concrete points arising out of the data? Do I understand the data?
3. Narrow down to a specific niche by asking open-ended 'how' and 'why' questions. Feasible scope instead of something too broad for a given time-frame. Try to figure out what you'd like to know.
4. Identify a research problem by asking questions in your niche. Poke holes in current research. Out of the reading try to identify a gap in the literature. What kind of data provide the answers to what I want to know? How will I be able to interrogate the data?
5. Write your research question. Turn your research problem into a question, aim to get to a specific, measurable question. e.g. What affect does daily use of Twitter have on the attention span of people in the age group of 16 to 20? Write 2 or 3 RQ's that come out of your preliminary research. This will help identify multiple points to approach a topic and help to narrow things down to create a really strong RQ.
6. Revise and refine your RQ: The initial question is the first guiding step in research. As you progress through the research be open to the RQ changing. Form a purpose statement (why investigating), e.g. 'The purpose of this study is to investigate / determine / establish a relationship between...' Out of the purpose statement you may need to revise the RQ.

Check list for good RQ

Focused: Must be specific, concise, to the point, no fluff or BS. e.g. Twitter / attention span / age group 16 to 20. The RQ should be understandable to readers, they should know what you are hoping to find out.

Researchable / Answerable: The RQ should be measurable, the answer found by empirical data or through existing literature. e.g. eye tracking or mouse movements to track attention span. Is your research question answerable with your research design and method?

Feasible: The RQ should be manageable, not too large in scope, nor too small. Do you have sufficient time, access to the resources, personnel, respondents, equipment, you need or the right kind of respondents?

Complex: There must be depth to the RQ, if the question can be answered by yes or no or a number then it is not complex enough. The RQ should generate a list of answers.

Relevant / Interesting: The RQ must be relevant to a particular field of interest or study, or to society as a whole. Must target a currently unanswered question and contribute knowledge that future research can build on.

Approved: make sure that your tutor, adviser has approved the RQ before diving into research.

Objective: make sure that you are not trying to convince your audience something that you believe or cherish.

Things to Avoid

• Avoid unanswerable questions, e.g. "are humans inherently good or evil?"
• Avoid opinions, for example, which national park is best? It would be better to ask what are the most popular attractions at National Parks.
• Avoid who-o-why's, why questions do not always illicit clear and specific answers. e.g. Why do companies continue to pollute despite regulations. Turn the why into a what or how question.
• Avoid something that is open ended like people who use Twitter often. Rather be specific like people who use Twitter daily.

The 'So What' Factor

Asking 'so what?' answers why the RQ is important. Interpretive 'so what?' language should always appear somewhere in academic discourse. Arguable: why does it matter? Your RQ is something that you either have to argue, defend, or explore. The RQ should set you up so that you take a position or take a stand, i.e. it should set you up to create your thesis statement.

"This shows (suggests / implies / points to) that ..."; "This is important / significant because..."; "This is worth noting as / because it..."; "This calls attention to..."; "This can be illustrated by ..."; "In doing so, it points to... / In so doing, tells us that..."; "What this means (shows / tells us / reveals / highlights / points to / implies) is ..."; "...tells us that..."; "...importantly (crucially / significantly) suggests that..."; "...which points to / suggests the need for..."; "...which is vital / crucial as it..."; "...which shows / illustrates that..."; "...which is significant as it..."; "...meaning that..."; "...illustrating / pointing to the need for..."

Developing a RQ

Let's put this into practice!! Taking the "6 steps to develop a strong RQ" above, let's go step-by-step:

Step 1: Choosing a broad topic

Here are some areas / topics of interest, some of these areas are related in some way:

• Sustainable Design
• Designing for Inclusivity
• Accessible XR
• The UX of AI
• Augmented, Virtual and Mixed Realities
• Blockchain and Smart Contracts
• UX, Privacy, Cybersecurity
• Personal Tracking of your Biometric Data

Of course these are huge areas of interest! I will need to narrow this down.

Let's choose one. This is a lot easier said than done. A little bit of Step 2 takes place in choosing a topic. Reading through the available literature changes ideas. For example, amongst others, I started looking into Smart Contracts. While I continue to be interested in this field, I feel that a lot more preliminary research would need to take place. I'd' want to create a smart contract but for that to happen I'd need to learn Solidity, the language that smart contracts are written in. It would also require further investigation into realistic applications, I though an interesting research area could be "the barriers to entry for smart contract noobs". Currently however, it appears that smart contracts are only being used in a select group of large corporations, for example, de Beers use smart contracts to trace the origin of diamonds. The barrier to entry is that they are not being used generally, the research would be more about developer experience (DX).

Other ideas:

Smart Contracts Blockchain - 4 UX problems when designing smart contracts; Blockchain UX and Decentralisation;

Research the effectiveness of displaying sustainable credentials on websites or, user experience of environmental accreditation - do these make a difference, will users choose to access these sites more, or are they ignored. What would it take for users to only access sustainable websites with low emissions. Perhaps create a specific directory of sustainable sites, how to calculate that sites make the cut. Surf the web in a sustainable way.

Sustainable AI - Ethics in technology - AI is used in monitoring and achieving sustainable solutions, but is AI sustainable in itself? An AI algorithm uses an enormous amount of energy to perform a computation. Is it not ironic that the method used to solve a problem, actually contributes to the problem itself?

Digital Twins - A Digital Twin is a digital representation of a physical entity or system, fuelled by IoT Data.

Using Extended Reality technology, this data can be visualized as a 3D model that contains both information from a physical object, as well as the data from integrated sensors.

Digital Twins are designed to optimize the operation of assets or business decisions around them. This includes, but is not limited to: Improved maintenance, upgrades and operation of the actual object.

What makes digital twin different from prototype is that they monitor the device / structure / system throughout its entire life-cycle.

Today, many organizations in the manufacturing industry have remote facilities in which equipment is measuring and collecting data. With Valorem Reply’s Digital Twin Experience, such organizations can use digital representations, AI and telemetry data to more effectively manage remote locations and train first-line workers. Thereby leveraging Extended Reality capabilities to digitally transform typically manual processes for faster, safer and more efficient business operations.

Telemetry is the in situ collection of measurements or other data at remote points and their automatic transmission to receiving equipment (telecommunication) for monitoring. The word is derived from the Greek roots tele, "remote", and metron, "measure".

an IoT, AI and Digital Twin-enabled solution for reducing operational downtime, extending the life of equipment and training front-line workers.

how device telemetry can be combined with Artificial Intelligence (AI) to derive powerful predictions and analyses for operational efficiency, bottom-line impact, and transformational outcomes. In this context, Digital Twins can be an innovative and powerful way to visualize data and provide valuable and actionable real-time information across the whole enterprise - regardless of geographic distribution.

Systems theory / thinking. How all aspects work together - collaboration. Machine learning - e.g. predicting when hardware will fail. Story telling. OT and IT together. Collaboration.

DT matchmaker
DT industry DT humans DT simulation DT healthcare DT paradigm DT User Experience / interface DT Human Computer Interaction DT Sustainability Impact

Variable Keywords
Life Cycle Sustainability Assessment (LCSA); Life Cycle Assessment (LCA); Digital Twin; Sustainability; Smart products; Industry 4.0; Cyber-physical systems; Predictive manufacturing;
Think what kind of industry, maybe data management...; Other things - remote interactions; AR; Internet of Things (IoT); Big Data Analytics; Artificial Intelligence (AI);

Mutual Relationship
Impact.... has on.... Effect.... has on.... Here go to the preliminary research.... initial research.... Look for the amount of information is available; and aligns with assignment criteria.

Ask questions
Impact - what type of impact? Effect - how is it affecting ...? Who are the people? Industry sector?

Refined working topic
Work on the top;ic further...... refine it more.....

Finalised research topic

Authentication -

Data Ownership -

Accessible AR

This is the initial idea: AR is becoming more and more prevalent in society. AR is primarily a visual experience. I was wondering what provisions are being made for accessible AR? For example, there could be an AI which could describe an AR scene, which could be listened to by a blind person. One way to test such functionality would be to get an AI to describe a scene to a sighted person, and then that person watches the scene and ranks the accuracy of the AI description.

https://www.revealio.com/top-3-ways-brands-are-using-augmented-reality-to-drive-sales/ AR influences buyer decisions by 55%....

https://www.linkedin.com/pulse/how-accessible-affordable-augmented-reality-marketing-calloway/

Step 2: Preliminary Reading

HERSKOVITZ, Jaylin et al. 2020. ‘Making Mobile Augmented Reality Applications Accessible’. In The 22nd International ACM SIGACCESS Conference on Computers and Accessibility. 1–14. Available at: http://arxiv.org/abs/2010.06035 [accessed 11 Oct 2022].

Keywords: Accessibility; augmented reality; mobile applications.

References