5.3 Multi-Source Data Synthesis & Cross-Referencing

Multi-Source Reasoning in Modern Psychometric Testing

This section provides hypothetical multi-exhibit practice: one table gives staffing, another gives costs and a third gives outputs. Combine them only because the exercise explicitly requires it. This does not describe Aon’s published numerical statement format, in which each statement relates to one data sheet. Follow your own competition’s source and navigation instructions.

For a multi-source task, identify the target quantity, the linking variables and whether the exhibits use compatible definitions, periods and units. An omitted number may be derivable elsewhere; a genuinely missing essential input limits what can be concluded.

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The Linking Variable Architecture (The "Pivot")

Cross-referencing multiple data exhibits relies on locating a linking variable (or pivot). This common metric appears in two or more separate exhibits, providing the bridge necessary to combine independent datasets.

  [ Tab 1: Staffing ]          [ Tab 2: Unit Cost ]          [ Tab 3: Outputs ]
  Region -> Staff (FTE)   -->   FTE -> Cost per FTE    -->   Region -> Cases Solved
            \________________________/                          /
                         |                                     /
             Total Regional Budget (€)                        /
                         \___________________________________/
                                           |
                            Cost per Solved Case (€)

Mathematical Formulation of Multi-Source Synthesis

Suppose an assessment requires calculating the operational cost per successful environmental inspection in a regional office:

1. Exhibit 1 provides annual-average Staffing ($S$) in Full-Time Equivalents (FTE).
2. Exhibit 2 provides full-year Average Cost per FTE ($C_{\text{FTE}}$), covering all operating costs in this exercise.
3. Exhibit 3 provides Total Inspections Completed ($I$) and the compliant fraction ($R$, expressed as a decimal). There is one initial inspection per distinct facility in this exercise.

The synthesized formula chains these parameters into a unified equation: $\text{Cost per Compliant Inspection} = \frac{\text{Total Operational Cost}}{\text{Compliant Inspections}} = \frac{S \times C_{\text{FTE}}}{I \times R}$

The denominator $I R$ must be positive for a cost per compliant inspection. Each input must be supplied or derivable on the stated basis. Do not substitute headcount for FTE without confirming their relationship, or combine partial-year cost with full-year inspections without an appropriate adjustment.

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Multi-Step Walkthrough: Environmental Regulatory Authority

To master cross-referencing under exam conditions, examine this three-part administrative case study for a fictional environmental agency. Staffing is annual-average FTE; annual salary and overhead cover the costs attributed to the listed inspections:

Exhibit 1: Regional Workforce Deployment (2024)

Exhibit 2: Operational Cost Schedule per FTE (2024)

Exhibit 3: Inspection Performance & Compliance Audit (2024)

Footnote on Exhibit 3: 'Compliance Rate represents the percentage of inspected facilities meeting the exercise’s stated compliance criteria on their single initial inspection. Each inspection covers a distinct facility.'

Executing Synthesis Scenarios

Scenario A: Productivity Benchmarking (Inspections per FTE)

Compare staff efficiency between Dublin and Galway:

• Dublin productivity: $\frac{4{,}800 \text{ inspections}}{120 \text{ FTE}} = 40.0 \text{ inspections per FTE}$
• Galway productivity: $\frac{1{,}360 \text{ inspections}}{40 \text{ FTE}} = 34.0 \text{ inspections per FTE}$
• Evaluation: Dublin staff achieved $40.0 - 34.0 = 6.0$ more inspections per FTE than Galway staff (+17.65% relative productivity advantage).

Scenario B: Total Operational Cost per Inspection

Compare the gross cost per completed inspection between Cork and Dublin:

• Dublin: Total cost = $120 \times \text{EUR }80{,}000 = \text{EUR }9{,}600{,}000$. $\text{Cost per Inspection} = \frac{\text{EUR }9{,}600{,}000}{4{,}800} = \text{EUR }2{,}000.00$
• Cork: Total cost = $60 \times \text{EUR }72{,}000 = \text{EUR }4{,}320{,}000$. $\text{Cost per Inspection} = \frac{\text{EUR }4{,}320{,}000}{2{,}160} = \text{EUR }2{,}000.00$
• Evaluation: Despite differences in scale, salaries, and total volume, both offices operate at an identical cost of €2,000 per inspection.

Scenario C: Cost per Compliant Facility

Calculate the expenditure in Limerick per compliant outcome achieved:

• Limerick total cost: $30 \times \text{EUR }64{,}000 = \text{EUR }1{,}920{,}000$.
• Limerick compliant inspections: $960 \times 0.80 = 768$.
• Unit cost per compliant outcome: $\frac{\text{EUR }1{,}920{,}000}{768} = \text{EUR }2{,}500.00$.

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Data Sufficiency & The "Cannot Say" Protocol

In an information-sufficiency exercise, use "Cannot Say" when the available evidence establishes neither the assertion nor its contradiction. A missing input may sometimes be derived or bounded; do not stop at the first blank field.

Sufficiency Checks Before Deciding

1. Sub-Category Distributions: To calculate expenditure by repair type, check for type counts or enough information to derive them. Distinct costs with an unknown, non-derivable mix leave a positive repair total insufficient to determine exact expenditure; quiz 2 applies this case.
2. Unaligned Time Horizons: Exhibit 1 reports data for Calendar Year 2024, whereas Exhibit 2 reports data for Fiscal/Tax Year 2024/2025. Without compatible component data or a justified bridging convention, a like-period comparison cannot be made.
3. Mismatched Population Denominators: Headcount (actual physical persons) versus Full-Time Equivalents (FTEs adjusted for part-time work). Do not multiply headcount by an FTE-calibrated cost unless their relationship supports that substitution.
4. Unjustified Causal Inference: Assuming that a regional disparity in performance is driven by an unmeasured variable (such as staff experience or IT systems) when no data supports it.

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Multi-Source Synthesis Framework