Research Methodologies

Question 1

What is qualitative research?

Answer 1

Qualitative research explores information and non-numerical data. Analysis of qualitative research can examine how people might connect meaning to their experiences or emotions.

Question 2

Pros and cons of qualitative research

Answer 2

Pros: Explores attitudes and behaviours, encourages discussion, flexible, can explain something which numbers alone may not be able to

Cons: Sample sizes can be small (may not be a true reflection) and may have bias, lack of privacy, skilled moderator or not

Question 3

What is quantitative research?

Answer 3

Quantitative research uses numbers to measure data. Analysis of statistics can help find connections and meaning in the data.

Question 4

Pros and cons of quantitative research

Answer 4

Pros: Can use larger sample sizes, accurate and impartial, fast, based on scientific data in my instance which is verified

Cons: Research typically not in a participants normal environment, un-able to follow up or expand on points

Question 5

What is a mixed method?

Answer 5

Mixed methods research integrates both qualitative and quantitative measures.

It is a holistic approach to combining statistical data with contextualised insights

Question 6

Pros and cons of mixed method approach

Answer 6

Pros: Mixing data sets can give you a better view of the overall picture, it can strengthen findings (triangulation), helps researchers develop their skills
Triangulation is the confirmation of results by different methods

Cons: A lot of resource and skills required, not suitable for all areas

Question 7

What is research design?

Answer 7

A research design is a high-level strategic plan which defines how you plan to answer your primary research question. It defines tools, techniques and methods to implement the plan.

Question 8

What are research methods?

Answer 8

Research methods are processes used to collect data. Research method types include interviews, focus groups, surveys, observation

Question 9

What are qualitative research methods?

Answer 9

Interviews, focus groups, observations, document analysis, oral history

Question 10

What are quantitative research methods?

Answer 10

Surveys, questionnaires, observation, experiments, numerical screening

Question 11

What is the Mental Capacity Act 2005?

Answer 11

Participants need a presumption of clarity to participate

Question 12

What’s the ICO?

Answer 12

Independent Commissioner’s Office (ICO) exists to provide individuals and organisations with support and empowerment through information. They police the General Data Act and GDPR!

Question 13

What is the Terrorism Act 2006?

Answer 13

I am aware that security-sensitive should be kept off personal laptops and on specifically designed servers supervised by ethics officers

Examples include research commissioned by the military, EU security call, concerns terrorist groups or involves the acquisition of security clearances

Question 14

Who creates and monitors research ethics?

Answer 14

Faculty Ethics Committee (FEC) or the Subject Ethics Panel (SEP)

Question 15

What is Innovate UK’s view on ethics?

Answer 15

Supports the UKRI ethics which include upholding the rights and dignity of participants, base partnerships on mutual respect, research conducted with integrity and transparency, participants should be informed

Question 16

Who is responsible for ethics?

Answer 16

Ethics is a collective responsibility of the researchers. Researchers are encouraged to undertake self critical ethical reflection throughout duration of a project.

Question 17

Tell us about a time when you made a methodology

Answer 17

Scope 3 example

Question 18

Tell us how you choose which research method to use:

Answer 18

It depends on the result you are pursuing. If I wish to know gain statistical clarity or a quantified answer, I would choose a quantitative approach with metrics I can measure.

If I wished to know feelings or if the topic is intangible or something which could explain what numbers cannot, then I would use a qualitative approach.

For the carbon metrics for the Regenerative Twin, we wished to measure this against 4 ‘climate change mitigation’ numerical metrics. This meant the research type was quantitative as we wished to see if it were achieved.

Question 19

Example of a sense check of data I’ve collected

Answer 19

Charred timber cladding for the regenerative twin – I might sense check this against a different EPD for charred timber cladding to triangulate the figures

Question 20

What is the difference between qualitative and quantitative research methods? When is it best to use one?

Answer 20

Quantitative research involves numerical metrics to measure data whereas qualitative research methods involve gathering behavioural data and reasoning.

The use of each depends on the project type. If measuring or analysing numerical data to achieve something, then I’d use quantitative but if wishing to understand the meaning, then I’d use qualitative.

Question 21

What is the difference between primary and secondary data? Which is more reliable?

Answer 21

Primary data is first-hand, raw collected data whereas secondary data has come from another source.

Primary data is more credible. The reliability of secondary data depends on the source. However, a good piece of research includes both primary and secondary.

Question 22

Example of a time using qualitative methods

Answer 22

Gathering feedback from the champions in London (Forms)

Strengths of this approach: Time (and cost) efficient, quantitative and qualitative as it had room for text, can be translated into graphics to digest, provide actions, good for 45+ employees, uniformity with standardised responses, can be anonymous, can be replicated with ease

Limitations of this approach: Sometimes lost in translation/opinions needs explaining, susceptible to ‘response bias’ if not anonymous, people can ignore them, questions could be misinterpreted, cannot capture complex information

Question 23

Example of a time using quantitative methods

Answer 23

Using CarboniCa on the Regenerative Twin project

Pros: Accurate data, presents data graphically for easy interpretation, does not need to be a consultant filling it out, easy optioneering

Cons: Sometimes the incorrect unit was provided and a calculation would be needed for m3, only 1 person filling it out

Question 24

Example of a time where you have recommended improvements for a methodology/data collection:

Answer 24

EPD gate system OR champions survey

Question 25

How do you project manage R&D for CarboniCa?

Answer 25

I project managed CarboniCa R&D by having a tracker, aligning objectives to the client’s strategy, and having weekly calls
* I had a CarboniCa development meeting on Thursday afternoons with MSES and ConstructSys and then a CarboniCa weekly catch up on Friday afternoon for queries
* I had a Teams channel for all stakeholders called CarboniCa R&D
* Have a group titled ‘Refining CarboniCa’ who are advanced users and provide feedback on upcoming new updates. This group meets quarterly
* Present at the carbon champions forum x3 a year on upcoming changes

Question 26

Tell us about the EPD gate system and how data is transferred?

Answer 26

Users were adding EPDs to their projects and they were going straight into the central EPD library within the carbo factor database. However, sometimes these contained inaccuracies.

I discussed and advised MSES and ConstructSys to create a mechanism whereby user added EPDs were sent to a gate, rather than into the central database.

At this ‘gate’, they were sense checked by MSC then the system would alert MSES who would do a secondary review and release the data into the EPD library.

Question 27

What data sources did you use to calculate the Recommendations section within CarboniCa?

Answer 27

CarboniCa’s carbon factor database which comprises of Bath ICE data, government convergence factors and an EPD library

Question 28

What data sources did you use to calculate the Low Carbon Flooring guide figures per m2?

Answer 28

CarboniCa’s carbon factor database which comprises of Bath ICE data, government convergence factors and an EPD library

Question 29

How did you establish the client requirements for the ICCE project?

Answer 29

These the requirements of the Innovate UK funding which we evolved into the client brief

Client wanted to develop CarboniCa into a tool for Education so I set out to find with the MoSCoW method what that would need to look like

Question 30

What data did you provide the cohort with in the ICCE project?

Answer 30

Workshop 1 – Overview on CarboniCa
Workshop 2 – Data for a typical steel frame secondary school (MDS)
Workshop 4 – App development & tutorial data

Question 31

Strengths and limitations of the MoSCoW method. Why was this selected?

Answer 31

Strengths: Facilitates discussion, good for sample size we had, interactive (post it notes on wall), simple approach, easy to use, selected as it is an efficient prioritisation method.

Limitations: Does not prioritise exactly what should come first if there are a lot of ‘Must haves’, sometimes the same item is in different categories (no qualitative understanding of why), can be team bias but we used it individually

Question 32

How has the workshop informed the proof of concept?

Answer 32

The 6 workshops hosted at NTU have informed the proof of concept by prioritising what students and academics would need in a carbon tool. This includes:
* Easy navigation and clear graphics
* Tutorials to explain data entry, and how to interpret results
* Pre-populated examples
* A vast carbon factor database

Question 33

What industry data have you collected?

Answer 33

EPDs, LCAs, reports e.g. IPCC, TCFD

Question 34

Talk me through research methodologies you have used

Answer 34

• MoSCoW example - NTU
• Delphi method – NTU
• Questionnaires – Carbon champions forum

Question 35

What is the Delphi method?

Answer 35

The Delphi method is a structured communication technique whereby forecasting is done with a panel of experts.

Example – sustainability module at NTU. The proposed module was discussed by the viewpoint of 5 departments. The process will review all current resources, agree on shared content, evaluate it. The needs of the module in the curriculum were discussed e.g. themes would be awareness, case study examples, theories and concepts, etc

Question 36

How have you provided advice based on research?

Answer 36

• Creation of the client aspirations part after questionnaire
• Advice to tailor tutorials a certain way after MoSCow feedback e.g background, data interpretation and customisation

Question 37

Tell me about CarboniCa and how new data is added?

Answer 37

Two fold approach to this:
1. User can add EPDs to their own project
2. Batch upload of new carbon factors in every release update – do 3x per year

Question 38

What problems have you encountered with CarboniCa?

Answer 38

• Being slow
• Incorrect calculations
• Perception as being difficult and an add on

Question 39

What is a sensitivity analysis?

Answer 39

A sensitivity analysis determines how different values of an independent variable can affect a particular dependent variable under a set of given assumptions. It can help you make informed choices

Question 40

What is scenario analysis?

Answer 40

A scenario analysis looks at the potential future business impacts and events in relation to one scenario to see how this is impacted and how alternatives outcomes could happen. Example – TCFD scenario analysis

Question 41

Increasing up front practical carbon to reduce whole life – explain this please

Answer 41

You often need to invest in up front embodied carbon to reduce whole life. For example, building services might be higher in up front embodied carbon but more efficient so they reduce whole life carbon

Question 42

What is data handling?

Answer 42

Data handling is the process of ensuring that research data is stored, archived or disposed of in a safe and secure way

Question 43

What are data handling techniques?

Answer 43

Data handling refers to using tables to organise information in rows and columns, making structured data easier to understand.

Question 44

What are data manipulation techniques?

Answer 44

1. Gather data from several sources
2. Organise and purify the data
3. Combine datasets and eliminate redundancies
4. Utilise data analysis to discover information

Question 45

What is data manipulation?

Answer 45

Data manipulation is the process of organising data to make it more understandable. For example, data might be alphabetically for easy comprehension.

Question 46

What are the steps of data manipulation?

Answer 46

1. Understand the data
2. Data cleaning – pre-process to remove inconsistences, missing values, etc
3. Data transformation
4. Filtering and selection
5. Aggregation and summarisation – like creating a summary table
6. Data visualisation – visualise data to identify trends, patterns, outliers, etc
7. Modelling – select appropriate algorithms to train the model to manipulate data e.g NTU MATLAB AI project
8. Iteration – repeat the process to achieve the goals
9. Testing – ensures reliable results
10. Consider ethical considerations throughout – privacy, biases, etc

Question 47

What are data manipulation tools/IT support?

Answer 47

Excel (VLOOKUP and pivot tables), the Pandas library in Python to analyse and manipulate data, R which is a statistical programming language that can manipulate data

Example – in Excel, I can filter rows and columns to see different trends e.g. who is top for 10tC total, or 10tC per £100k project value or I can create a pivot table

Question 48

How do you deal with sensitive information?

Answer 48

• Sign relevant documentation e.g NDA if external
• Keep in secure location on professional laptop only
• Examples - password protected EPD spreadsheet to avoid human error, controlling access

Question 49

Relevant legislation regarding handling and using data:

Answer 49

• Data Protection Act 2018, GDPR for personal data
• The Mental Capacity Act 2005 – participants need a presumption of clarity to participant
• The Terrorism Act (2006) & Security Sensitive Research – kept off personal laptops, ensure safe servers, etc