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Analytic rigour<br />

The previous pages stressed how important it is that we as analysts are honest <strong>in</strong> our th<strong>in</strong>k<strong>in</strong>g, analysis and communicat<strong>in</strong>g the<br />

threat to our clients. A few scholars and practitioners are now develop<strong>in</strong>g methods and tools to enhance m<strong>in</strong>dfulness and rigour <strong>in</strong><br />

our quest to provide <strong>in</strong>sight and foresight to clients. In this issue we look at Daniel Zelik et al’s rigour model which tries to answer<br />

the question:<br />

“How can <strong>in</strong>telligence analysts ensure that they have been rigorous <strong>in</strong> the analytical process?”<br />

How far beyond simply collect<strong>in</strong>g and<br />

list<strong>in</strong>g data did analyst go?<br />

Low: Compiled a unified form<br />

High: Integrated with thorough consideration<br />

of diverse <strong>in</strong>terpretations<br />

8. Explanation critiqu<strong>in</strong>g<br />

7. Information synthesis<br />

Were the perspectives of doma<strong>in</strong> experts<br />

<strong>in</strong>cluded <strong>in</strong>to the assessments?<br />

Low: No effort to seek out expertise<br />

High: Experts have been consulted<br />

How many different perspectives were <strong>in</strong>corporated<br />

<strong>in</strong> exam<strong>in</strong><strong>in</strong>g the primary hypotheses?<br />

Low: Little use of other analysts<br />

High: Peer & expert review on<br />

cha<strong>in</strong> of reason<strong>in</strong>g with strong<br />

and weak <strong>in</strong>ferences clearly <strong>in</strong>dicated<br />

Does the analyst consider and<br />

understand the assumptions<br />

and limitations of their analysis?<br />

1. Hypothesis exploration<br />

5. Sensitivity analysis<br />

Were multiple hypotheses considered<br />

<strong>in</strong> expla<strong>in</strong><strong>in</strong>g data?<br />

Low: m<strong>in</strong>imum weigh<strong>in</strong>g of alternatives<br />

High: Multiple perspectives to<br />

identify the best & most probable<br />

explanations<br />

toolbox<br />

3. Information validation<br />

4, Stance analysis<br />

6. Specialist collaboration2. Information search<br />

Low: Explanation seems valid on<br />

surface<br />

High: Analyst has strategy to consider<br />

strengths of explanations if support<strong>in</strong>g<br />

sources were to prove <strong>in</strong>valid<br />

Dalene Duvenage<br />

The depth and breadth of the search process<br />

used <strong>in</strong> collect<strong>in</strong>g data.<br />

Low: Rout<strong>in</strong>e and readily available data sources<br />

High: Exhaustively explore all data<br />

The levels at which <strong>in</strong>formation sources are corroborated<br />

and cross-validated.<br />

Identify the stance or perspective<br />

of the source and<br />

plac<strong>in</strong>g it <strong>in</strong>to a broader<br />

context for understand<strong>in</strong>g<br />

Low: Analyst notice bias <strong>in</strong> source<br />

High: Research <strong>in</strong>to source background to understand<br />

how their perspective might <strong>in</strong>fluence<br />

their stance<br />

Low: No effort to verify source accuracy<br />

Based on Zelik, Daniel et al 2007. Understand<strong>in</strong>g Rigor <strong>in</strong> Information Analysis papers <strong>here</strong>, <strong>here</strong> and <strong>here</strong><br />

High: Systematic approach to verify<br />

<strong>in</strong>formation and w<strong>here</strong> possible use<br />

source closest to issue<br />

October 2012 <strong>Foreknowledge</strong> 13

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