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How to Collect Reliable Engineering PhD Data When Your Fieldwork Plan Falls Apart in Canada

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Engineering PhD fieldwork can be disrupted by laboratory access, equipment availability, site access, partner changes or measurement constraints. Laboratory access may be delayed, specialised equipment may become unavailable, an industry partner may withdraw, or field conditions may prevent the planned measurements. When this happens, changing the data-collection approach does not necessarily mean changing the entire research project.

For Engineering PhD researchers in Canada, the key is to identify what evidence is required to answer the research question and then select a reliable alternative data-collection method. This may involve laboratory experiments, simulations, sensor measurements, case studies, or existing datasets, depending on the research design.

This guide explains how to adapt an Engineering PhD data-collection plan while maintaining data quality, methodological consistency, and research validity. The structured PhD Data Collection Services in Canada can support researchers in selecting appropriate data sources, refining collection methods, and maintaining methodological alignment.

What Will You Learn from This Blog?

  • How to respond when planned engineering fieldwork becomes unavailable.
  • How to select alternative PhD Data Collection Methods.
  • How to maintain data quality after changing your collection approach.
  • How Canadian engineering research examples use different forms of evidence.

Challenges of Data Collection in Engineering PhD Research:

Engineering research may depend on conditions that researchers cannot fully control.

Common problems include:

  • Laboratory or equipment access delays
  • Equipment failure or maintenance
  • Difficulty accessing field sites
  • Changes in industry collaboration
  • Insufficient participant or sample availability
  • Seasonal or environmental limitations
  • Safety or ethical restrictions
  • Insufficient or inconsistent measurements

The important question is not simply “How can I replace my data?” but:

“What type of evidence is required to answer my research question?”

This distinction helps researchers select an appropriate alternative rather than collecting data that are convenient but scientifically unsuitable.

Article - Recreation Image 1 - PA

 

Strategies for collecting reliable PhD data

1. Start With the Research Question

When fieldwork fails, return to the research question before changing the methodology.

Identify:

  • What needs to be measured?
  • Which variables are required?
  • What population, material, system, or environment is being studied?
  • What evidence is needed to support the expected findings?

A useful sequence is:

Research Question → Required Evidence → Data Source → Collection Method → Analysis

PhD Research Example:

A 2020 McGill University study investigated 3D-printed wood-fibre-reinforced architected cellular composites and evaluated their thermomechanical performance experimentally (Estakhrianhaghighi et al., 2020).

This example shows why researchers should define the engineering problem and required evidence before selecting a data-collection method.

2. Identify Exactly What Has Been Lost

Do not assume that the entire research design has failed.

Determine whether the problem affects:

  • The entire dataset
  • One study location
  • Specific variables
  • A particular experiment
  • Participant recruitment
  • A specific measurement period
  • Equipment-dependent observations

This allows the researcher to identify what can still be collected and what needs an alternative.

For example:

Original Plan:
Field measurements from three Canadian sites.

Problem:
Access to one site becomes unavailable.

Possible Response:
Retain the available sites and evaluate whether another suitable site or validated secondary dataset can address the missing evidence.

Research Example:

A University of Waterloo doctoral thesis by Ning Qin investigated the hydrodynamics and mass transfer of dense carbon dioxide segments in water. The research focused on obtaining specific experimental evidence about the behaviour of the system, illustrating the importance of identifying the measurements (Qin, 2017).

This example supports the principle that researchers should identify which variables, measurements, or experimental observations are affected before redesigning the entire data-collection plan.

3. Select an Alternative Engineering PhD Data Collection Method

Different engineering research questions require different forms of evidence.

Research Need

Possible Alternative

Material performance

Laboratory testing

System behaviour

Simulation or modelling

Structural monitoring

Sensor or instrumentation data

Industrial process research

Industry or secondary datasets

Infrastructure research

Case study or field data

Environmental measurements

Existing monitoring datasets

Process optimisation

Experimental + computational data

The alternative should be methodologically justified, not selected simply because it is easier to obtain.

Research example:

Yiju Zhao’s PhD thesis developed a numerical simulation framework for two-dimensional material MOSFETs operating at cryogenic temperatures. The research used computational modelling to investigate device behaviour and incorporated experimental data, including HfS₂ MOSFET data, to evaluate the framework (Zhao, 2024).

This example demonstrates how computational modelling can provide an alternative or complementary source of evidence when direct experimental investigation alone does not fully address the research objective.

When selecting alternative sources, the data-collection strategy should remain focused on the original research objectives, required evidence, and methodological requirements.

4. Combine Experimental and Computational Evidence Where Appropriate

When direct field measurements become difficult, engineering researchers may be able to combine different forms of evidence.

For example:

Experimental Data + Simulation → Model Validation

or

Field Measurements + Existing Dataset → Comparative Analysis

Research example:

Ning Qin’s University of Waterloo doctoral research on dense carbon dioxide segments in water provides an example of engineering research involving experimental investigation of hydrodynamics and mass transfer. The thesis was conducted within Mechanical and Mechatronics Engineering and focused on obtaining evidence about the physical behaviour of the system (Qin, 2017).

This example shows that the appropriate data strategy depends on the research problem and the evidence needed to investigate it.

5. Validate the Replacement Data

Alternative data should not automatically be treated as equivalent to the original data.

Check:

  • Does it measure the same variable?
  • Is the measurement method sufficiently comparable?
  • Is the dataset reliable and documented?
  • Does it represent the intended research setting?
  • Can the data be analysed using the planned methodology?
  • What limitations does the alternative introduce?

Where appropriate, researchers can conduct method comparison, calibration, validation experiments, or sensitivity analysis to establish confidence in the revised approach.

Research Example:

Piotr Wiciak’s PhD thesis investigated ultrasonic non-destructive testing of composite materials. The research compared ultrasonic evaluation with destructive shear testing and numerical simulations. In Wiciak’s thesis, the comparison reported a maximum error of 7% for the specific GFRP-bar application studied (Wiciak, 2020).

This demonstrates why alternative measurements should be validated against other evidence rather than automatically assumed to be equivalent.

Consider PhD Assistance Research Lab for customised PhD Data Collection Support in Canada and identifying an alternative method for your research.

6. Document Every Change to the Data Collection Plan

If the original fieldwork method changes, document:

Original Method → Problem → Alternative Method → Justification → Validation → Limitations

This creates a transparent record of why the research design changed.

For a Canadian Engineering PhD, the researcher should also consider whether the change affects research ethics approval, data permissions, participant consent, collaboration agreements, or institutional requirements. Where human participants or identifiable human data are involved, researchers should check their institution’s REB requirements and applicable TCPS 2 guidance (Government of Canada, 2022).

Research example:

A University of Toronto engineering PhD project by Meagan Flus used 24-hour observation at hackathons to study conflict and dynamics within engineering design teams. The project illustrates how researchers can define the research setting, observation period, and data-collection approach around a specific research question (University of Toronto, 2026).

This example demonstrates why researchers should clearly document the research setting, data sources, collection procedures, and methodological decisions so that changes to the research plan remain transparent and traceable.

 

Quick PhD Data Quality Checklist

Check

Question

Completeness

Are required observations available?

Consistency

Were similar procedures followed?

Accuracy

Are measurements reliable?

Relevance

Does the data answer the research question?

Comparability

Can different data sources be compared?

Documentation

Are collection procedures recorded?

Validation

Has the alternative approach been checked?

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Conclusion

If Engineering PhD fieldwork in Canada fails to meet expectations, it does not mean that researchers should abandon their research objectives. A practical response is to assess the evidence required, identify the missing data, evaluate viable alternatives, and validate the revised method.

If the evidence is derived from experiments, sensors, simulations, case studies, or available databases, its appropriateness should be determined by the research problem and methodology involved.

Need Support with Your Engineering PhD Data Collection in Canada? Talk to our experts to improve your research question, data sources, collection methods, alternative datasets, validation, and methodological alignment. PhD Assistance Research Lab can help you review and strengthen your data-collection approach so your evidence remains relevant, reliable, and defensible.

Book a Free Expert Consultation and get support for your PhD data collection.

Frequently Asked question

Identify what evidence the site was expected to provide and whether the remaining sites can answer the research question. If a gap remains, consider another suitable site or a validated secondary dataset.

Only if the difference does not undermine the research objective. Compare the variables, measurement methods and research context, then use validation or sensitivity analysis where appropriate.

Not necessarily. First identify which part of the research design is affected. If the research question and required evidence remain supported, only the affected data-collection component may need to change.

Check whether the data measure the required variables, follow sufficiently comparable procedures and represent the intended research setting. Validation or method comparison may also be needed.

Record the original method, the fieldwork problem, the alternative method, its justification, validation approach and limitations. This helps maintain methodological transparency.

They can provide alternative or complementary evidence when appropriate for the research question. Their assumptions, comparability and limitations should be assessed before using them as replacement evidence.

Reference

  1. Estakhrianhaghighi, E., Mirabolghasemi, A., Zhang, Y., Lessard, L. and Akbarzadeh, A. (2020), 3D-Printed Wood-Fiber Reinforced Architected Cellular Composites. Adv. Eng. Mater., 22: 2000565. https://doi.org/10.1002/adem.202000565
  2. Qin, N. (2017). Micro-scale studies on hydrodynamics and mass transfer of dense carbon dioxide segments in water [Doctoral dissertation, University of Waterloo]. https://uwspace.uwaterloo.ca/bitstream/10012/11940/1/QIN_NING.pdf.
  3. Zhao, Y. (2024). Development of a simulation framework for 2D-material MOSFETs: Investigating cryogenic behaviors and enhancing performance optimization [Doctoral dissertation, University of Waterloo]. UWSpace. https://uwspace.uwaterloo.ca/items/e4aff851-ea47-4767-9904-3fd1045dd30e/
  4. Wiciak, P. (2020). Quality assessment of composite materials using ultrasonic non-destructive testing methods [Doctoral dissertation, University of Waterloo]. https://uwspace.uwaterloo.ca/items/fd610a0d-a6cc-4c6b-b95d-524ec5bdbc51
  5. University of Toronto, Faculty of Applied Science & Engineering. (2026, July 3). How hackathons can serve as a “living laboratory” for studying engineering design team dynamics. https://www.mie.utoronto.ca/how-hackathons-can-serve-as-a-living-laboratory-for-studying-engineering-design-team-dynamics/
  6. Government of Canada. (2022). Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans – TCPS 2 (2022). Panel on Research Ethics. https://ethics.gc.ca/eng/documents/tcps2-2022-en.pdf