This deliverable is not counted toward your grade
Project Start and Technical Implementation (site, data and investigation) pages are required practice but are not counted toward your final grade. Your engineering grade comes from Chapter 4 and Chapter 5.
Data Quality
Students apply a data quality objectives (DQO) framework to score collected data and flag items that fail the acceptance criteria.
Section progress
0% of the workflow complete
Field & Laboratory · Plan, execute and document field and laboratory data collection to a defensible quality standard.
Deliverable: Data quality evaluation report with PARCC scoring and completeness ratio.
How to complete this section
Do this next: Read the Data Quality lecture and the worked example so you know what "Data quality evaluation report with PARCC scoring and completeness ratio." has to contain.
Not sure how to start or how much depth is expected? Read the fully written model example for this deliverable first — it shows the structure, tables and level of justification your advisor grades against.
Site Investigation Lab — what this workspace teaches
Plan, execute and document field and laboratory data collection to a defensible quality standard.
- Planning a subsurface, structural or traffic field investigation
- Instrumentation selection, resolution, accuracy and calibration records
- GPS/GNSS positioning: datums, projections, RTK vs. handheld accuracy
- GIS data capture, attribute schemas and coordinate metadata
- Land surveying: traverses, levelling, closure and error adjustment
- Sampling strategy: representative sampling, spacing, depth intervals, replicates
- ASTM/AASHTO laboratory testing procedures and reporting requirements
- Chain of custody, sample labelling and preservation
- QA/QC: duplicates, blanks, repeatability and data validation rules
End-of-term milestones
- Tuesday, November 17, 2026 — Poster printed and ready. 36 in × 48 in poster finalized and printed one week before the November 24 showcase.
- Wednesday, November 18, 2026 — Final document package uploaded for scoring. Chapters 4–5, calculation package, drawings and appendices uploaded in the app for advisor scoring.
- Wednesday, November 18, 2026 — Poster presentation to faculty and industry. Wednesday poster session — printed 36 in × 48 in poster presented in person; industry reviewers score communication and impact.
- Wednesday, November 25, 2026 — Oral presentation and defense (scored). Scored oral presentation and defense held on Wednesday, November 25.
Data Quality
Students apply a data quality objectives (DQO) framework to score collected data and flag items that fail the acceptance criteria.
Section B
Engineering story
A real project situation that frames this module
The team opens week 3 believing data quality is a formality, because the proposal treated it in a single sentence. Students apply a data quality objectives (DQO) framework to score collected data and flag items that fail the acceptance criteria. The first review question is not about arithmetic — it is where the basis for data Quality Objectives (DQO) 7-step planning process came from.
Discarding an inconvenient data point as an 'outlier' without applying a documented statistical test. Because precision, accuracy, representativeness, completeness, comparability (PARCC) parameters, the error does not stay local: it is carried into the data foundation every later calculation silently depends on, and every downstream product inherits it before anyone notices.
The owner, the reviewing agency and the engineer of record carry the consequence. On this module specifically, the exposure runs through outlier screening methods (e.g., Grubbs' test) and documented rejection criteria, and the cost of correction rises every week the project record moves closer to issue.
Decisions the engineer must make
- What record establishes data Quality Objectives (DQO) 7-step planning process, and is that record in the project data inventory?
- Does EPA QA/G-4 (2006), Full standard, govern here — and is that the edition adopted by the jurisdiction?
- What is the acceptance criterion for precision, accuracy, representativeness, completeness, comparability (PARCC) parameters, and was it written before the result was known?
- Is Completeness (%) = (valid results / total planned results) × 100 valid over the parameter range this project actually occupies?
- If the check fails, does the team revise the project record or raise a change request against the locked baseline?
Photo 1. Structural framing of a pedestrian bridge: members, connections and the load path a designer must trace.
Wikimedia Commons, CC BY-SA 4.0
Section C
Why this matters
Professional
A licensed engineer defending data quality cites EPA QA/G-4 (2006), Full standard, and shows the record behind each input. Your data quality evaluation report with parcc scoring and completeness ratio. is reviewed the same way — traceability is assessed before arithmetic.
Technical
Data Quality Objectives (DQO) 7-step planning process is what makes Completeness (%) = (valid results / total planned results) × 100 usable on this project rather than a formula copied from a reference. Get it wrong and every quantity derived from it is wrong by the same factor.
Safety
The failure mode this module guards against is a decision made without a traceable basis. It reaches people through completeness ratio and its effect on statistical confidence in the sample-size calculation, which is why the safety check is recorded explicitly here rather than inferred from a passing strength or performance check.
Economic
The data foundation every later calculation silently depends on is priced from this work. Quantities, unit costs and schedule float all trace to data Quality Objectives (DQO) 7-step planning process; a late correction here is paid for as a change order, not a redline.
Environmental
Environmentally, this module fixes material use, land disturbance and the waste stream generated by rework. Choosing conservatively without justification is not free — the excess shows up as material, energy and land that the project consumes for no measurable gain.
Community
The residents and agencies who inherit the completed work inherit whatever this module decides — performance, accessibility, cost of ownership and resilience are set here, not at the ribbon-cutting.
Section D
Learning objectives
By the end of this module you will be able to:
- 1.Apply data Quality Objectives (DQO) 7-step planning process, using this project's own conditions rather than a textbook case.
- 2.Analyze precision, accuracy, representativeness, completeness, comparability (PARCC) parameters, using this project's own conditions rather than a textbook case.
- 3.Explain outlier screening methods (e.g., Grubbs' test) and documented rejection criteria, using this project's own conditions rather than a textbook case.
- 4.Interpret completeness ratio and its effect on statistical confidence in the sample-size calculation, using this project's own conditions rather than a textbook case.
- 5.Compute the governing quantity from Completeness (%) = (valid results / total planned results) × 100, with a unit audit on every term.
- 6.Apply EPA QA/G-4 (2006), Full standard, and cite the section that governs your acceptance decision.
- 7.Reproduce the worked example for a field program planned 40 water-quality samples and defend the interpretation of the result.
- 8.Produce data quality evaluation report with parcc scoring and completeness ratio. at a standard the advisor of record would accept without a second revision cycle.
Section E
Instructional content
Full lecture notes with figures and governing equations
Data Quality — what the work actually is
Students apply a data quality objectives (DQO) framework to score collected data and flag items that fail the acceptance criteria. That single sentence hides the substance of the module: data Quality Objectives (DQO) 7-step planning process, and precision, accuracy, representativeness, completeness, comparability (PARCC) parameters. Both must be established from project evidence before anything downstream is credible.
In civil engineering practice, this work is the input to the project record. Outlier screening methods (e.g., Grubbs' test) and documented rejection criteria — which is why this page asks you to record the source of every quantity, not just its value. The data foundation every later calculation silently depends on depends on it.
- Data Quality Objectives (DQO) 7-step planning process
- Precision, accuracy, representativeness, completeness, comparability (PARCC) parameters
- Outlier screening methods (e.g., Grubbs' test) and documented rejection criteria
- Completeness ratio and its effect on statistical confidence in the sample-size calculation
Photo 1. Data Quality — what the work actually is in practice — Structural framing of a pedestrian bridge: members, connections and the load path a designer must trace.
Wikimedia Commons, CC BY-SA 4.0
Governing relationships and how they are applied here
The relationships below govern data quality. Completeness (%) = (valid results / total planned results) × 100 — each is valid only inside the parameter range this project occupies, so state that range before substituting.
Precision, accuracy, representativeness, completeness, comparability (PARCC) parameters sets the values you place into these expressions. Any code-prescribed factor must match EPA QA/G-4 (2006); a factor lifted from a different edition silently changes the answer.
Completeness (%) = (valid results / total planned results) × 100
- valid results = measurements passing QA/QC review
- total planned results = number specified in the collection plan
Photo 2. Governing relationships and how they are applied here in practice — Field review: the conversation in which a scope, a constraint or a decision is actually settled.
Capstone Studio instructional photograph
Constraints, adopted standards and the safety case for data quality
EPA QA/G-4 (2006), Full standard, governs this module: Data Quality Objectives process guidance ASTM D7546 (2020), Sec. 5, adds the second constraint: Guide for statistical data quality evaluation applicable to civil field data
The safety case is explicit here. The failure mode is a decision made without a traceable basis; the people exposed are the owner, the reviewing agency and the engineer of record; the control that prevents it is completeness ratio and its effect on statistical confidence in the sample-size calculation together with an independent check by someone who did not perform the work.
- Controlling criterion for this module: data Quality Objectives (DQO) 7-step planning process.
- Adopted reference: EPA QA/G-4 (2006) — cite Full standard by number.
- Failure mode guarded: a decision made without a traceable basis.
- Evidence produced: Data quality evaluation report with PARCC scoring and completeness ratio..
Photo 3. Constraints, adopted standards and the safety case for data quality in practice — Field review: the conversation in which a scope, a constraint or a decision is actually settled.
Capstone Studio instructional photograph
Where this method stops being valid
The worked example — a field program planned 40 water-quality samples — holds only while its assumptions hold. The dataset fails the completeness DQO; the report must either recollect samples to close the gap or explicitly document reduced confidence in any conclusion drawn from this dataset. Outside that envelope the arithmetic still returns a number, and the number is wrong in a way no unit check will catch.
For this project, the boundary you are most likely to push is completeness ratio and its effect on statistical confidence in the sample-size calculation. If you cross it, say so in writing, bound the error, and carry the limitation into your results chapter. A disclosed limitation is professional practice; a silent extrapolation is not.
Photo 4. Where this method stops being valid in practice — Field review: the conversation in which a scope, a constraint or a decision is actually settled.
Capstone Studio instructional photograph
Section F
Engineering workflow
Steps
- 1. Assemble the inputs this module needs — data Quality Objectives (DQO) 7-step planning process; precision, accuracy, representativeness, completeness, comparability (PARCC) parameters — each with a unit and a source record.
- 2. Confirm EPA QA/G-4 (2006) is the adopted edition and locate Full standard.
- 3. State the assumptions and the acceptance criterion for data Quality Objectives (DQO) 7-step planning process.
- 4. Evaluate Completeness (%) = (valid results / total planned results) × 100 term by term, carrying one extra significant figure.
- 5. Test the result against outlier screening methods (e.g., Grubbs' test) and documented rejection criteria.
- 6. Audit units and run an order-of-magnitude check by hand before the number leaves your desk.
- 7. Obtain an independent check from a teammate who did not perform the work, and record their name and date.
- 8. Assemble data quality evaluation report with parcc scoring and completeness ratio. and submit it to the advisor of record for review.
Decision points
- Is every input behind data Quality Objectives (DQO) 7-step planning process traceable? If not — stop and collect the record.
- Does the result satisfy precision, accuracy, representativeness, completeness, comparability (PARCC) parameters? If not — revise the work, never the criterion.
- Would the correction change the data foundation every later calculation silently depends on? If yes — raise a change-control request before proceeding.
- Have you ruled out the most common error on this module — discarding an inconvenient data point as an 'outlier' without applying a documented statistical test?
Quality checklist
- Documented: data Quality Objectives (DQO) 7-step planning process
- Documented: precision, accuracy, representativeness, completeness, comparability (PARCC) parameters
- Documented: outlier screening methods (e.g., Grubbs' test) and documented rejection criteria
- EPA QA/G-4 Full standard cited by section number
- Units audited on every expression
- Acceptance criterion recorded before the result
- Independent check signed and dated
- Data quality evaluation report with PARCC scoring and completeness ratio. attached and named per the course convention
Section H
Interactive visualization
Data Quality — step-through
Advance one frame at a time. Each frame adds one engineering decision to the previous state.
Step 1 of 6
State the decision the data must support and the tolerable decision error.
Section I
Applicable codes and standards
EPA QA/G-4
2006 · Full standard
Adopted design/analysis reference governing this module.
Relevance: Data Quality Objectives process guidance
Reference the section number and edition in your calculation package. Do not reproduce code text.
ASTM D7546
2020 · Sec. 5
Adopted design/analysis reference governing this module.
Relevance: Guide for statistical data quality evaluation applicable to civil field data
Reference the section number and edition in your calculation package. Do not reproduce code text.
Section J
Worked examples
Full engineering solution format
Section K
Common mistakes and how to avoid them
- Discarding an inconvenient data point as an 'outlier' without applying a documented statistical test.
- Reporting a dataset as complete without checking it against the originally planned sample count.
- Treating data Quality Objectives (DQO) 7-step planning process as a given instead of establishing it from a project record.
- Producing data quality evaluation report with parcc scoring and completeness ratio. without showing how precision, accuracy, representativeness, completeness, comparability (PARCC) parameters was satisfied.
- Substituting into Completeness (%) = (valid results / total planned results) × 100 outside the range where it is valid, and reporting the number anyway.
- Missing completeness ratio and its effect on statistical confidence in the sample-size calculation, which is exactly the path to a decision made without a traceable basis.
- Collecting data before defining what decision the data has to support.
- Accepting a laboratory or field value without its method, date, operator and uncertainty.
- Using inputs that no field record, laboratory report, or published source supports.
- Stopping at output and skipping verification — an unverified number is not an engineering result.
- Confusing results (what the analysis produced) with conclusions (what the engineer decided).
Section L
Industry case study
Documented failure related to data quality
A constructed civil works project where this module's decision was made incorrectly or skipped.
Official findings
- Published investigation identified a breakdown between analysis assumption and constructed condition.
Field observations
- The controlling assumption was documented nowhere in the design record.
- No independent check existed at the stage where the error entered the work.
Engineering interpretation
- Interpretation below is student analysis for instructional purposes, not an official finding.
- Map the failure to a step in your own workflow and state where your process would have caught it.
Lessons learned
- Document the assumption, then have someone else check it before it becomes construction.
Source: Summarize the published investigation; cite it in your reference list. Do not reproduce copyrighted report text.
Section M
FE Civil exam connection
Handbook FE Reference Handbook — civil engineering practice section (record the section number from your handbook edition).
Exam topics
Handbook formulas
- Completeness (%) = (valid results / total planned results) × 100
Weak results here feed your FE Civil Academy weak-area queue for targeted practice.
Question 1 of 2
Score: 0/2In data quality, which item must be established BEFORE the analysis is run?
Section N
Apply it to your project — Data Quality
Complete this using your own capstone project data. Every field is saved to your project record and routed to your advisor with this module's submission.
Inputs and sources
Every value needs a traceable source.
| Quantity | Value | Unit | Source / record |
|---|
Assumptions and consequences
| Assumption | Basis | Consequence if wrong |
|---|
Self-check before submission
Section O
Design challenge
Consulting challenge — Data Quality
Your firm has been retained to deliver the data quality scope for a municipal client on a compressed schedule. Produce the technical position your firm would defend at a public meeting.
Client request: The client wants a defensible recommendation, the basis of design, and an honest statement of what remains unresolved.
Constraints
- Adopted local code edition governs; no exceptions without written variance.
- Budget and schedule are fixed; scope changes require change control.
- Public safety and accessibility requirements are non-negotiable.
Deliverables
- One-page basis of design
- Supporting calculation extract
- Risk and limitation statement
Evaluation
- Technical correctness
- Standard compliance
- Clarity of engineering judgment
- Honest treatment of uncertainty
Section P
Documentation workspace
Write the report section for this module in the academic editor
Section Q
File uploads
Accepted: PDF, DOCX, XLSX, CSV, PNG, JPG, ZIP
No files uploaded yet.
Section R
Deliverable and advisor review
Data quality evaluation report with PARCC scoring and completeness ratio.
Submissions route to your assigned faculty advisor and are scored independently by faculty and administrator rubrics.
Reflection
What was the hardest engineering judgment in this module, and how did you resolve it?
Section S
ABET outcome mapping
Data quality evaluation report with PARCC scoring and completeness ratio. with advisor review and dual scoring.
Assessment: Faculty rubric score and administrator rubric score on this module's submission.
Rubric: Data quality · Target: 70% of students at or above 'meets expectations'.
Data quality evaluation report with PARCC scoring and completeness ratio. with advisor review and dual scoring.
Assessment: Faculty rubric score and administrator rubric score on this module's submission.
Rubric: Data quality · Target: 70% of students at or above 'meets expectations'.
Section T
References and further study
EPA QA/G-4 (2006)
Adopted reference — cite section numbers, do not reproduce text.
ASTM D7546 (2020)
Adopted reference — cite section numbers, do not reproduce text.
Data Quality — instructor design procedure
Course template for the calculation package format expected in the final report appendix.
NCEES FE Reference Handbook
Locate the equations used here and note the handbook section for exam recall.
Advisor meeting agenda item
Bring the unresolved decision from this module to your next weekly advisor meeting.