Net valence and dominance gap: meaning and application

You have measured a list of associations: some positive and some negative, some strong and some weak. How do you compare them fairly, and how can you tell whether the negative side may dominate the overall judgement? Net valence and dominance gap are two Neurofactor calculation conventions that help, provided you know their limits.

Net valence is a Neurofactor working concept that combines the valence and the strength of an association into a single value between −100 and +100. The dominance gap is the difference between the strongest negative and the strongest positive association within a group.

Both are working definitions, not validated scientific scales. They make associations comparable within one measurement design and show whether the negative side may weigh more. They do not predict individual behaviour.

An association is a meaning that people connect with something, such as a product or an organisation (see Association: meaning and application). Two properties matter for the judgement:

• Valence: is the association positive, neutral or negative?
• Strength: how strongly are the subject and the association connected? How to understand and measure strength is explained in Association strength: meaning and application.

A strongly negative association that is barely connected to the subject weighs differently from a mildly negative one that everyone mentions at once. Net valence, also called weighted valence, therefore combines valence and strength into one number, making associations additive and comparable. It is a Neurofactor working concept: a calculation convention, not a term from the scientific literature and not a validated measurement scale.

Neurofactor uses the following convention:

1. Valence is coded on a five-point scale: strongly negative (−2), mildly negative (−1), neutral (0), mildly positive (+1) and strongly positive (+2).
2. Strength is expressed on a scale from 0 (no connection) to 100 (maximum connection within the chosen measurement).
3. Net valence = valence × strength ÷ 2.

Dividing by two keeps the result between −100 and +100. A strongly positive association at maximum strength scores +100 (2 × 100 ÷ 2). A mildly negative association with strength 60 scores −30 (−1 × 60 ÷ 2). A neutral association always scores 0.

Decide in advance whether you calculate per participant and then average, or average valence and strength first and then multiply. The mean of products is not the product of means. Report the spread and the number of participants alongside the mean.

The dominance gap is the difference between the strongest negative and the strongest positive association within a group, both expressed as net valence. Neurofactor calculates it as follows:

Dominance gap = |strongest negative net valence| − strongest positive net valence

If the strongest negative association is −52 and the strongest positive is +38, the dominance gap is 52 − 38 = +14. A positive result means that the negative side weighs more at the top; the larger that difference, the more that negative association may dominate the judgement. A negative result means that the positive side weighs more.

The dominance gap deliberately looks at the extremes: a group can be slightly positive on balance while one heavily negative association drives the choice. This, too, is a working definition: there is no validated threshold above which a difference counts as "large".

Building a judgement from weighted, summed components is not new. In 1963 Fishbein investigated the relationship between beliefs about an object and the attitude towards that object (Fishbein, 1963). This work was developed in Fishbein and Ajzen's book on belief, attitude, intention and behaviour (Fishbein & Ajzen, 1975). Busemeyer and Jones summarise that model as: attitude equals belief times evaluation, summed across salient beliefs (Busemeyer & Jones, 1983). Such models are known as expectancy-value or multi-attribute models.

Net valence borrows that principle: valence is weighted by strength, after which you can add. It is not an application of those models. The components are measured differently, the purpose is descriptive, and the theory does not validate the Neurofactor calculation.

Negative information often weighs more than comparable positive information. Baumeister and colleagues reviewed research from many domains and concluded that bad events, feedback and impressions generally have more impact than good ones (Baumeister et al., 2001).

Within this negativity bias, Rozin and Royzman distinguished negativity dominance among other aspects: the overall impression of a combination of negative and positive elements is more negative than the algebraic sum of the separate values (Rozin & Royzman, 2001). A simple sum of net valences may therefore underestimate the negative side. This is why Neurofactor reports the dominance gap alongside the sum. It does not measure negativity bias or show that it occurs in your target group; it is a practical flag.

Treating ordinal answers as numbers. The five-point valence scale is essentially ordinal: you know that "strongly negative" is more negative than "mildly negative", but not whether the distances are equal. Liddell and Kruschke showed that analysing ordinal data as if they were metric can produce false, missed and even reversed effects, and that averaging across items does not solve this (Liddell & Kruschke, 2018).

Multiplying scales. A product of two scales depends on where the zero point lies. Code valence as 1 to 5 instead of −2 to +2 and even a negative association gets a positive product. Analysing such multiplicative composites has long been recognised as a problem in the methodological literature (Evans, 1991). Hardeman and colleagues note that the chosen scoring of such products can affect correlations with other variables. They advise first establishing that a multiplicative model fits and choosing the scoring on theoretical and empirical grounds (Hardeman et al., 2013).

The consequence: the −100 to +100 scale makes comparison within one measurement design easier, but −40 is not "twice as negative" as −20.

Read more about association salience and deal-breaking associations. A high dominance gap is not proof of a deal-breaking association, but it is a reason to investigate.

The measures are mainly an ordering aid after an association measurement (see How do you measure associations?):

• Prioritising: which associations weigh most and deserve closer study?
• Comparing groups: does the balance differ between groups measured with the same design?
• Tracking repeat measurements: does a net valence shift after a change?
• Focusing communication: a high dominance gap points to a negative association you want to understand first.

They do not replace research into why an association exists and whether it affects choices.

1. Define the question: which subject, which group and which decision?
2. Fix the measurement design: how you collect associations, ask for valence and convert strength to 0–100.
3. Choose the calculation level in advance: per participant or per group.
4. Report per association the net valence, the spread and the number of participants who mentioned it.
5. Determine the dominance gap and state which associations sit at the extremes.
6. Look at the distribution behind means close to zero and at rarely mentioned associations.
7. Test with follow-up research whether a striking association is relevant to the choice.

This example is fictional. All figures were invented to show the calculation and are not research results.

Situation and question. A fictional producer of meat alternatives wants to know which associations flexitarians, people who deliberately eat less meat, have with "plant-based meat". Which associations deserve attention before it adjusts its communication?

Calculation for one participant. A participant mentions "highly processed", rates it as strongly negative (−2) and gives a strength of 80. The net valence is −2 × 80 ÷ 2 = −80. The same participant mentions "sustainable" as mildly positive (+1) with strength 70, which gives +35.

Fictional group results (net valence calculated per participant and then averaged):

Possible interpretation. The sum of these six values is −12. The strongest positive association is "sustainable" (+38) and the strongest negative is "highly processed" (−52). The dominance gap is 52 − 38 = +14: at the top, the negative side weighs more. The value close to zero for "taste" may mean little is said about it or that strong positive and negative judgements cancel out; only the distribution shows which.

Next step. The producer does not conclude that flexitarians reject plant-based meat. It first investigates, for example in interviews, what "highly processed" means to this group and whether it influences the choice in the shop.

• Reading net valence as a validated score: there are no norms for "high" or "low".
• Mixing measurement designs: different questions, scales or sets of associations make figures incomparable.
• Reading zero as neutral: a zero can also arise when strong judgements cancel out.
• Using the sum as an overall verdict: it depends on which associations you include.
• Overrating the dominance gap: one rarely mentioned association can shift it.
• Coding without a zero point in the middle: with 1 to 5, the products lose their meaning.

Net valence and dominance gap describe a group at one moment within one measurement design.

• You can: rank associations within the same measurement, flag negative outliers and track shifts with the same design.
• You can't: predict what an individual will choose; group means are not individual predictions.
• You can't: conclude that an association causes a choice.
• You can't: compare results from different studies or methods without further ado.

Always state which associations were asked about, how strength was measured and who took part.

Net valence turns valence and strength into one comparable number; the dominance gap shows whether the strongest negative association outweighs the strongest positive one. Attitude models and research on negativity bias provide context, but the calculation itself is a working convention. Use it within one design, report spread and choices, and treat the result as the start of a question.