---
document: Deeper Signals Psychometric Evidence Fact Sheet
purpose: Machine-readable reference for the Deeper Signals assessment audit skill. Houses the psychometric properties, validity evidence, fairness evidence, competency coverage, and anonymized criterion-validity findings used when auditing a customer's assessment methods against Deeper Signals.
scope: Core Drivers Diagnostic (personality), Core Values Diagnostic (motivation and values), Core Reasoning Suite (cognitive ability), the competency and job-profile layer, and anonymized applied validation evidence.
last_updated: 2026-06-27
maintained_by: Deeper Signals Assessment R&D
anonymization: All external validation evidence is anonymized. No client names and no client-identifying figures appear anywhere in this document. Sector descriptors are kept generic.
how_to_use: Each instrument is documented with theory, structure, development, reliability, factor structure, construct validity (convergent and discriminant), criterion validity (concurrent and predictive), and fairness (group differences and adverse impact). Quantitative values are reported as correlations (r), Cronbach's alpha, IRT parameters, or Cohen's d. Anonymized validation evidence in Section 6 supports the suite as a whole, because the constructs are shared across deployments. When advising a customer, cite the most specific evidence available and recommend local validation for any selection use, which is best practice for every assessment, not only Deeper Signals.
---

# Deeper Signals Psychometric Evidence Fact Sheet

## 1. Portfolio overview

Deeper Signals operates a soft-skill intelligence platform built on three validated, self-contained assessments plus a competency and job-profile layer that maps those assessments to roles.

| Instrument | Construct domain | Theory base | Items and format | Typical completion | Primary use |
| --- | --- | --- | --- | --- | --- |
| Core Drivers Diagnostic | Personality (normal and extreme) | Five Factor Model (FFM) | 90 items (60 forced-choice adjective pairs, 30 behavioral statements) | Under 10 minutes | Selection, development, coaching, teams, succession |
| Core Values Diagnostic | Motivation and values | Self-Determination Theory (SDT), operationalized via Schwartz and Holland | 42 Likert items (7 per scale) | Around 5 to 7 minutes | Engagement, culture, fit, development, teams |
| Core Reasoning Suite (CLRA, CNRA, CVRA) | Cognitive ability (fluid reasoning) | Cattell-Horn-Carroll fluid reasoning, general mental ability | Adaptive (CAT) item banks, fewer than 30 items per test | Under 15 minutes per test | Selection, development, succession, workforce design |
| Competency and job-profile layer | Soft-skill competencies and role fit | Synthetic and content validity mapping plus local criterion validation | Derived from the three assessments | Instant | Role fit, hiring, talent reviews, analytics |

Cross-cutting characteristics:

- Industry agnostic. Scores are not affected by prior vocational or educational background.
- Designed for working adults (18 and over).
- Delivered on a secure, mobile-first, compliance-aware platform with interactive reports and a digital coach.
- Available in over 25 languages, with replication and measurement-invariance work conducted on translations.
- Normative databases are global and continually expanding. Country, industry, language, and role-specific norms are in active development.
- Continuous validation program: the criterion-validity database, norms, items, fairness analyses, and scoring models are reviewed and refreshed on a recurring basis.

Design philosophy worth stating up front: the assessments are deliberately short. Scale lengths are chosen to keep completion fast and the experience engaging, which lifts completion rates and lets organizations deploy at scale. This is a conscious trade-off that places a modest ceiling on internal-consistency coefficients without compromising factor structure, construct validity, or fairness. It is most visible at the Sub-Driver level, where scales are intentionally brief (see 2.4).

Standards the program is designed to meet or align with:

- APA, AERA, and NCME Standards for Educational and Psychological Testing.
- SIOP Principles for the Validation and Use of Personnel Selection Procedures.
- EEOC Uniform Guidelines on Employee Selection Procedures (1978), including the Four-Fifths (80 percent) rule.
- Age Discrimination in Employment Act (protection for individuals over 40, and over 45 where applicable).
- International Test Commission (ITC) guidance on test use and adaptation.

---

## 2. Core Drivers Diagnostic (personality)

### 2.1 Theory base

Built on the Five Factor Model, the most empirically supported and cross-culturally replicated model of personality (Chamorro-Premuzic and Furnham, 2010; Digman, 1990). The FFM predicts a wide range of work outcomes including job performance, training proficiency, leadership effectiveness, teamwork, creativity, and resilience. The model treats traits as continua (not types), with relative strengths and risks at each pole, consistent with the "too much of a good thing" and curvilinearity literature (Le et al., 2011).

The Core Drivers model also integrates a dimensional view of dysfunctional or extreme personality (aligned with the DSM-5 Five Factor framework and the Dark Triad), so that risks are modeled as the extremes of normal traits rather than separate categories.

### 2.2 Structure

Six dimensions (the FFM, with Extraversion split into two facets for greater nuance, following DeYoung, 2015):

| FFM dimension | Core Drivers high label | Core Drivers low label |
| --- | --- | --- |
| Agreeableness | Considerate (accommodating, cooperative, friendly) | Candid (critical, strong-minded) |
| Conscientiousness | Disciplined (organized, reliable, deliberate) | Flexible (reactive, impulsive) |
| Extraversion (Assertiveness) | Driven (ambitious, risk-taking, goal-oriented) | Laid Back (easy-going, unassuming) |
| Extraversion (Gregariousness) | Outgoing (sociable, chatty) | Reserved (quiet, introspective) |
| Openness | Curious (creative, intellectual, inquisitive) | Pragmatic (practical, straightforward) |
| Emotional Stability | Stable (calm, composed) | Passionate (emotive, anxious, self-critical) |

- 12 Drivers total (one label per pole of each of the six dimensions).
- 30 Sub-Drivers (five facets per dimension, three items each) for nuanced coaching insight.
- 12 Core Risks: each Driver taken to its extreme (for example Driven to Domineering, Disciplined to Rigid, Considerate to Oversensitive, Stable to Unemotional, Curious to Eccentric, Outgoing to Unrestrained).
- Reporting model surfaces an individual's three most extreme scores (their "Core Drivers"), determined by deviation from the global normative mean, with the remaining scales shown as "Secondary Drivers." Report sections: Core and Secondary Drivers, Risks, At Work, and Sub-Drivers.

Sub-Drivers by dimension: Considerate (Trusting, Moral, Helpful, Cooperative, Empathetic); Disciplined (Organized, Dutiful, Perfectionist, Cautious, Self-Disciplined); Driven (Competitive, Confident, Leading, Goal-oriented, Proactive); Outgoing (Friendly, Sociable, Thrill-Seeking, Upbeat, Colorful); Curious (Imaginative, Artistic, Expressive, Experimental, Inquisitive); Stable (Calm, Composed, Positive, Inattentive, Resilient).

### 2.3 Development methodology

- Four I-O psychology and psychometric SMEs reviewed the FFM and adjacent taxonomies (NEO PI-R, HPI, HEXACO, DeYoung hierarchical FFM, IPIP), generating an initial pool of roughly 300 items (around 50 per dimension).
- Machine learning item selection using Genetic Algorithms (Sahdra et al., 2016; method per Yarkoni, 2010) optimized scales for convergent validity and reliability against IPIP FFM criteria, reducing the pool to 120 items. Out-of-sample convergent validity at this stage ranged from .38 (Considerate) to .70 (Outgoing); reliabilities exceeded .60.
- A second reduction removed skewed and socially desirable adjectives using data from 15,000 or more US working adults, yielding the final 90 items.
- Forced-choice adjective pairs were prioritized over Likert statements because they improve user experience and reduce "fake good" responding (Meade et al., 2020).
- Factor structure confirmed on a sample of 1,051 working adults via exploratory and confirmatory factor analysis.
- Construct and concurrent validity tested across five working-adult samples (N = 374, 2,218, 928, 132, 389).
- Predictive validity tested with manager performance ratings from three organizations (samples of 203, 214, and 202).
- Developed and validated on more than 500,000 working adults. The global normative database comprises approximately 100,000 respondents (reliability and descriptive statistics computed on N = 94,226), aggregated across South America, Africa, Asia, and Europe and continually expanding.

### 2.4 Reliability (internal consistency)

Scale-level Cronbach's alpha (N = 94,226):

| Scale | Mean | SD | Alpha |
| --- | --- | --- | --- |
| Considerate | 9.46 | 2.68 | .68 |
| Disciplined | 9.74 | 3.12 | .74 |
| Drive | 7.24 | 3.26 | .79 |
| Outgoing | 8.52 | 3.40 | .82 |
| Curious | 8.30 | 2.87 | .72 |
| Stable | 8.21 | 2.77 | .69 |

All six scales meet or exceed the .60 benchmark for acceptable internal consistency, and scales are normally distributed. Scale lengths are deliberately short so the full diagnostic completes in under ten minutes; this conscious design choice favors completion and user experience and places a modest ceiling on alpha without affecting factor structure or validity.

Sub-Driver reliability ranges from .17 to .66, and this is a deliberate design choice rather than a measurement weakness. Sub-Drivers contain only three items each so the assessment stays fast and engaging, and internal-consistency coefficients rise mechanically with item count, so short scales necessarily show lower alpha (Peters, 2014). Sub-Drivers are built to give practitioners nuanced coaching insight, not to be used for standalone decisions, so content validity and practical utility are prioritized. Each Sub-Driver returns a single-factor solution in exploratory factor analysis, and the pattern is consistent with widely used commercial inventories whose facet reliabilities span a similar range.

### 2.5 Factor structure and intercorrelations

- Principal Components Analysis (varimax, N = 1,536) returned five components matching the FFM, explaining 53 percent of variance. Driven Sub-Drivers cross-load positively on Outgoing and negatively on Considerate, as expected for a lower-order Extraversion-Assertiveness dimension.
- Scale intercorrelations (N = 94,226) replicate known FFM patterns: Driven and Outgoing correlate positively (.57); Driven correlates negatively with Considerate (-.34); Disciplined correlates negatively with Curious (-.35); Considerate correlates positively with Stable (.27). All significant at p < .05 except Considerate-Curious.
- Sub-Drivers correlate strongly with their parent dimension (most loadings above .40 on the intended scale), supporting internal structure.

### 2.6 Construct validity (convergent and discriminant)

Tested against NEO PI-R, Mini-IPIP, Hogan Personality Inventory (HPI, IPIP form), HEXACO Honesty-Humility, MINT integrity, MAP-X (DSM-5 dark side), Dark Triad Dirty Dozen, Social Dominance Orientation, and the Core Values Diagnostic.

Convergent validity, strongest correlations per scale (asterisk indicates p < .05):

| Core Drivers scale | Convergent evidence (measure: r) |
| --- | --- |
| Considerate | NEO Agreeableness .29*, Mini-IPIP Agreeableness .40*, HPI Interpersonal Sensitivity .45* |
| Disciplined | NEO Conscientiousness .49*, Mini-IPIP Conscientiousness .53*, HPI Prudence .44* |
| Driven | NEO Extraversion .71*, HPI Ambition .53*, HPI Sociability .45* |
| Outgoing | NEO Extraversion .82*, HPI Sociability .52*, HPI Interpersonal Sensitivity .53* |
| Curious | NEO Openness .42*, Mini-IPIP Openness .37*, HPI Inquisitive .41* |
| Stable | NEO Neuroticism -.64*, HPI Adjustment .61*, HPI Ambition .49* |

Discriminant and nomological evidence:

- Integrity (MINT) correlates positively with Considerate (.21*), Disciplined (.27*), and Stable (.21*).
- Honesty-Humility correlates positively with Considerate (.26*) and Disciplined (.26*), negatively with Driven (-.25*).
- MAP-X dark-side scales converge with Core Drivers extremes (for example Withdrawn-Unrestrained with Outgoing .64* and Driven .52*; Impulsive-Obsessive with Disciplined .44*), confirming that Core Drivers detect extreme tendencies and that Drivers map to Risks.
- Dark Triad: Machiavellianism and Psychopathy correlate negatively with Considerate (-.34*, -.43*) and Disciplined; Narcissism correlates positively with Driven (.29*) and Outgoing (.32*).
- Social Dominance Orientation correlates positively with Driven (.21*).
- Core Values links are theoretically coherent (for example Power value with Driven .50* and Outgoing .40*; Connection value with Outgoing .42*; Inquiry value with Curious .29*).

Summary: convergent validity equals or exceeds other modern adjective-based FFM assessments; discriminant patterns place the scales correctly within the personality nomological net.

### 2.7 Criterion validity (concurrent and predictive)

Concurrent validity:

- Work engagement (UWES-9, N = 83): Driven .43*, Stable .33*, Outgoing .30*.
- Counterproductive work behavior (CWB checklist, N = 133): Disciplined negatively related (interpersonal -.25*, organizational -.31*); Driven and Outgoing positively related.

Predictive validity (manager performance ratings, cross-validated machine-learning models mapping Sub-Drivers to each organization's competency framework). Model correlations with competency ratings:

| Competency (predictive model) | r |
| --- | --- |
| Leadership Effectiveness | .48* |
| Culture Fit | .40* |
| Entrepreneurship | .37* |
| Coachability | .34* |
| Innovation | .33* |
| Resilience | .32* |
| Delivering Results | .30* |
| Integrity | .28* |
| Agility | .26* |
| Collaboration | .25* |

Further anonymized criterion evidence across applied deployments is summarized in Section 6 and supports the predictive value of the Core Drivers across contexts.

### 2.8 Fairness (group differences and adverse impact)

- Independent-samples t-tests across gender, age (under or over 40), and ethnicity (White and Non-White). Many differences reach statistical significance because of very large samples, but Cohen's d indicates no moderate or large effects, so differences are not practically meaningful.
- Adverse impact simulations confirm the scales do not discriminate against protected groups. The diagnostic was tested for adverse impact and shows none.
- Recommended practice for selection use: apply normed scores, set cutoffs informed by job analysis, and run local validation. This is best practice for any selection instrument.

### 2.9 Norms, languages, maturity

- Global normative database (approximately 100,000), continually expanding; country, industry, and role norms in development.
- Available in over 25 languages, with replication and measurement-invariance studies on translation.
- Mature, production-grade instrument with the longest validation history in the portfolio.

---

## 3. Core Values Diagnostic (motivation and values)

### 3.1 Theory base

Built on Self-Determination Theory (Gagne and Deci, 2005; Ryan and Deci, 2000), which holds that intrinsic motivation and wellbeing depend on satisfying three psychological needs: autonomy, competence, and relatedness. Because SDT is macro-level, the diagnostic operationalizes it through the personal-values literature (Schwartz's Basic Human Values and Holland's Vocational Interests), which is more proximal and measurable. SDT need satisfaction meta-analytically predicts performance, satisfaction, engagement, burnout, and counterproductive behavior (Van den Broeck et al., 2016).

### 3.2 Structure

Six value scales organized under the three SDT needs. Each scale has a high and low label (12 value labels in total):

| SDT need | Scale | Low label | High label |
| --- | --- | --- | --- |
| Autonomy | Novelty | Tradition | Change |
| Autonomy | Power | Humility | Power |
| Competence | Mastery | Leisure | Achievement |
| Competence | Inquiry | Intuition | Learning |
| Connection | Relatedness | Independence | Relationships |
| Connection | Virtue | Expedience | Principles |

- 42 items (7 per scale), four-point Likert (1 Strongly Disagree to 4 Strongly Agree). Scale scores range 7 to 28.
- Reporting model surfaces the three most extreme values (deviation from the normative mean), consistent with the Core Drivers approach.
- The 12 value labels (Tradition, Change, Humility, Power, Leisure, Achievement, Intuition, Learning, Expedience, Principles, Independence, Relationships) align with the platform's Core Values content model.

### 3.3 Development methodology

- SMEs reviewed Schwartz, Holland, and SDT, identifying six values mapped to the three needs. Initial pool of 180 items rated for face, content, and social desirability.
- Item reduction on a sample of more than 700 US working adults reduced the pool from 180 to 42 (seven per scale).
- Construct and concurrent validity tested across three working-adult samples.
- Normative and fairness analyses used more than 28,000 working adults across North and South America, Europe, Africa, and Asia.
- Total development and validation sample approaching 40,000 working adults.

### 3.4 Reliability (internal consistency)

Cronbach's alpha (N = 28,703):

| Scale | Mean | SD | Alpha |
| --- | --- | --- | --- |
| Novelty | 21.57 | 3.16 | .62 |
| Power | 19.08 | 4.36 | .79 |
| Mastery | 22.25 | 3.26 | .73 |
| Inquiry | 23.53 | 2.90 | .64 |
| Relatedness | 22.50 | 3.36 | .69 |
| Virtue | 21.60 | 3.15 | .57 |

As with the Core Drivers, Core Values scales are deliberately short, with seven items each, so the assessment stays fast and engaging and can be deployed at scale. This conscious design choice favors completion and user experience and places a modest ceiling on internal-consistency coefficients while retaining a clear, theory-aligned factor structure. Most scales sit at or above the .60 benchmark, with Power, Mastery, Relatedness, and Inquiry comfortably above it. Deeper Signals continually expands its validity database to further strengthen the evidence base.

### 3.5 Factor structure and intercorrelations

- PCA (varimax, N = 775) returned three components matching the SDT needs, explaining 82 percent of variance (Autonomy 32 percent, Competence 26 percent, Connection 24 percent). Novelty and Power load on Autonomy; Mastery and Inquiry on Competence; Relatedness and Virtue on Connection.
- Scale intercorrelations (N = 775, all p < .05) are consistent with this structure: Novelty-Power .57, Mastery-Inquiry .52, Relatedness-Virtue .61.

### 3.6 Construct validity (convergent and discriminant)

Tested against the Core Drivers Diagnostic, HPI (IPIP form), Dark Triad Dirty Dozen, MAP-X, Schwartz Basic Values, and O*NET (Holland) Vocational Interests.

Representative convergent evidence (asterisk indicates p < .05):

- Novelty: HPI Sociability .67*, Schwartz Stimulation .69*; behaviorally sociable, adaptive, curious.
- Power: Core Drivers Driven .51*, Schwartz Achievement .91*; competitive, confident, status-oriented.
- Mastery: Core Drivers Driven .42*, Schwartz Achievement .62*; disciplined, goal-oriented.
- Inquiry: HPI Learning Approach .64*, HPI Inquisitive .47*, Schwartz Universalism .67* and Benevolence .71*; open-minded, learning-oriented.
- Relatedness: Core Drivers Outgoing .46*, HPI Interpersonal Sensitivity .47*, Holland Enterprising .60* and Social .50*; sociable, cooperative.
- Virtue: Schwartz Universalism .60* and Self-Direction .57*; principled, empathetic.

Discriminant and nomological coherence:

- Dark Triad converges with Novelty and Power (for example Narcissism-Novelty .60*, Machiavellianism-Power .52*) and diverges from Inquiry (negative), as expected.
- MAP-X relationships are theoretically sensible (for example Novelty and Power link to insensitive, impulsive, eccentric tendencies).
- Scales correlate more strongly with theoretically aligned Schwartz and Holland scales than with unrelated ones, supporting convergent and discriminant validity.

### 3.7 Concurrent validity

Work outcomes (N = 250, asterisk indicates p < .05):

| Outcome | Novelty | Power | Mastery | Inquiry | Relatedness | Virtue |
| --- | --- | --- | --- | --- | --- | --- |
| Work engagement (UWES-9) | .63* | .39* | .43* | .54* | .28* | .50* |
| Workaholism (CAT-PD) | .27* | .38* | .19* | -.20* | .12* | .16* |
| Counterproductive work behavior | .17 | .48* | .16 | -.23 | .06 | -.22 |

Interpretation: valuing change, learning, and principles tracks with engagement; valuing power and status tracks with workaholism and counterproductive behavior risk; valuing learning is protective. Deeper Signals continually expands its criterion-validity database across deployments (see Section 6). Local validation is recommended for any selection use, as it is for all assessments.

### 3.8 Fairness (group differences and adverse impact)

- Group differences (gender, age under or over 45, ethnicity) are mostly negligible by Cohen's d. The largest effects are small (for example Power by age d = .46, Mastery by age d = .33). No moderate or large differences.
- Adverse impact simulations using the Four-Fifths rule, selecting out low scorers at the recommended 15th-percentile cutoff, return ratios at or above .80 across every scale and demographic group:

| Scale | Gender AI | Age AI | Ethnicity AI |
| --- | --- | --- | --- |
| Novelty | .98 | .97 | 1.05 |
| Power | .94 | .80 | 1.02 |
| Mastery | .99 | .91 | .99 |
| Inquiry | 1.03 | .98 | .99 |
| Relatedness | 1.02 | .99 | .93 |
| Virtue | 1.06 | 1.07 | 1.03 |

- Recommended cutoff for decision use is the 15th percentile, with job analysis and local validation. Low scores do not imply negative behavior; constructs are continua.

### 3.9 Norms, languages, maturity

- Global normative database (28,000+ for norms), continually expanding.
- Available in over 25 languages, with measurement-invariance work on the most common languages.
- Production-grade, with ongoing enhancement (including an ipsative or rank-sort scoring method) on the roadmap.

---

## 4. Core Reasoning Suite (cognitive ability)

### 4.1 Theory base

Measures fluid reasoning and general mental ability, among the strongest and most generalizable predictors of job performance, training success, and leadership effectiveness. Meta-analytic validity of cognitive ability for job performance is typically .50 to .65 (Schmidt and Hunter, 1998; Sackett et al., 2022). The suite measures reasoning directly across three complementary domains rather than acquired knowledge.

### 4.2 Structure

Three adaptive assessments, each delivered by Computerized Adaptive Testing (CAT) grounded in Item Response Theory (IRT):

| Test | Measures | Item domains | Item bank | IRT model |
| --- | --- | --- | --- | --- |
| Core Logical Reasoning (CLRA) | Abstract, non-verbal reasoning | Inductive and deductive reasoning, visual-spatial reasoning (Raven's-style matrices) | Automatically generated and continually expanding | 2-Parameter Logistic (2PL) |
| Core Numerical Reasoning (CNRA) | Quantitative reasoning | Quantitative problem-solving, data interpretation, number series and matrices | 256 items | 1-Parameter Logistic (1PL or Rasch), upgrading to 2PL |
| Core Verbal Reasoning (CVRA) | Verbal critical reasoning | Analogies, syllogisms, critical reasoning and inference, assumption recognition, argument evaluation | 240 items | 1-Parameter Logistic (1PL or Rasch), upgrading to 2PL |

Adaptive design details:

- Item selection by Maximum Fisher Information.
- Exposure control via Sympson-Hetter.
- Stopping rule: standard error below 0.35 or a maximum of 30 items.
- Calibration via Expectation-Maximization.
- Designed for fairness: plain, neutral language; Differential Item Functioning (DIF) screening across age, gender, ethnicity; accessibility features (screen-reader compatibility, contrast, readability).
- CLRA uses automatic item generation (Carpenter et al., 1990 taxonomy), reducing item exposure and enabling continuous item-bank growth. CNRA and CVRA use AI-assisted item development.
- Scores reported as percentiles with interpretive bands (low below 33rd, moderate 33rd to 66th, high above 66th).

### 4.3 Calibration samples and item parameters

| Test | Raw responses | Calibration sample | Difficulty (beta) range | Discrimination (alpha) range | Theta mean (SD) | Dimensionality |
| --- | --- | --- | --- | --- | --- | --- |
| CLRA | 11,284 | 4,410 (global, ages 18 to 60) | -1.88 to +4.17 | .23 to 2.31 | -.575 (.904) | Unidimensional (EFA confirmed) |
| CNRA | 2,734 | 2,734 (global, 18 to 60) | -3.32 to +3.51 | fixed at 1 (1PL) | .001 (1.04) | Broad difficulty coverage |
| CVRA | 1,609 | 1,609 (global, 18 to 60) | -3.09 to +2.53 | fixed at 1 (1PL) | 0.0 (0.566) | Broad difficulty coverage |

### 4.4 Reliability

Reliability is handled through IRT measurement precision: standard errors are tracked at the individual level and CAT continues until the precision threshold (SE below 0.35) is met, yielding confidence intervals around every score. Classical internal-consistency coefficients are less applicable to adaptive tests; precision is the reliability evidence of record.

### 4.5 Construct and convergent validity

- CLRA versus ICAR-16 (N = 655): Total score r = .62*; Matrix Reasoning .57*; Verbal Reasoning .51*; Letter and Numerical Reasoning .45*; Mental Rotation .21*. Strong convergence with an established public-domain cognitive measure.
- CLRA is unidimensional per EFA (single dominant factor, scree and eigenvalue support).
- Real-world convergent evidence: in an applied pilot, Core Verbal Reasoning correlated r = .45 to .71 with an established external verbal reasoning assessment, and Core Numerical Reasoning showed the same high-ability clustering pattern as that external benchmark.
- CNRA and CVRA construct and convergent validity studies are expanding as larger samples accrue.

### 4.6 Criterion validity

The suite measures the construct (general reasoning) that meta-analysis links to job performance at .50 to .65. The anonymized applied validation evidence in Section 6 supports the predictive value of the assessment suite, and Deeper Signals continually expands its criterion-validity database for the reasoning tests specifically. Local validation is recommended for any selection use, as it is for all assessments.

### 4.7 Fairness (group differences and adverse impact)

- Group differences in theta are negligible to small. Gender and ethnicity differences are not practically meaningful; the age difference (under versus over 40) is statistically significant but small (CLRA d = .25; CNRA d = .16; CVRA d = .06 to .19).
- Adverse impact simulations (Four-Fifths rule, 25th-percentile cutoff) return ratios at or above .80 across tests and groups, with one borderline value (CNRA ethnicity ratio .804). All other ratios clear the threshold comfortably.
- DIF screening across age, gender, and ethnicity is built into development; recurring fairness audits are planned.

### 4.8 Norms, languages, maturity

- Global norms (18 to 60), continually expanding; role-specific and regional norms in development.
- CLRA is the most mature of the three. CNRA and CVRA are newer; Verbal has moved into the Intelligence competency, and Numerical is being expanded with harder items to improve top-end differentiation.
- Recommended decision cutoff: 25th percentile, informed by job analysis.

---

## 5. Competency model and job profiles (what we can measure)

### 5.1 Competency framework

- A competency framework of around 30 soft skills, each mapped to Core Drivers and Core Values, built via literature review and SME judgment, then refined against peer-reviewed research (preferably meta-analyses). Documented in the Competency Technical Note with a synthetic-validity mapping table.
- Mappings are evaluated for synthetic and content validity, then tested on production data for internal consistency and group differences. Competency algorithms are deployed on the platform.

### 5.2 Competencies with direct criterion evidence

The Core Drivers predictive models have demonstrated criterion validity for the following competencies (see section 2.7 for coefficients): Leadership Effectiveness, Culture Fit, Entrepreneurship, Coachability, Innovation, Resilience, Delivering Results, Integrity, Agility, Collaboration. A senior-leadership validation adds Execution and applied Learning Agility, with fluid reasoning behaving as a threshold competency.

### 5.3 Job profiles and role fit

- Role-competency mapping defines the soft skills required for job families, with adverse-impact analysis on new profiles. Pre-validated profiles for high-volume, high-turnover roles use meta-analytic personality-performance coefficients to generate a candidate "potential score."
- Role Fit and Culture Fit are computed as weighted composites of Sub-Drivers, validated and recalibrated per deployment.
- A published study comparing large language models with subject-matter experts on soft-skill job profiles, drawing on more than 1,000 roles, supports a defensible, evidence-based method for building fair role profiles while showing where human expertise remains essential.

---

## 6. Criterion-validity evidence from applied validation studies (anonymized)

These anonymized validation studies are drawn from real organizational deployments and constitute criterion-validity evidence for the assessment suite as a whole. Because the constructs are shared across instruments, evidence from one deployment supports the validity of the relevant assessments across contexts. No client names and no client-identifying figures appear here; sector descriptors are generic.

### 6.1 Large energy organization

- Personality (Core Drivers) validated against 15 cultural and leadership outcomes within a high-potential framework.
- Personality predicted performance consistently across all 15 outcomes, average correlation r = .43.
- Strongest predictor: initiative and results-driven behavior.
- Using personality data over resume screening alone translated to up to 17 percent more accurate staffing decisions.
- All models passed fairness testing across gender, age, and ethnicity, suitable for broad deployment.

### 6.2 Financial services sales organization

- Large sales workforce (over 4,000 employees with assessment data).
- Question: do Core Values add incremental validity over Core Drivers for predicting performance, retention, and churn? Method: cross-validated regression plus top-versus-bottom-decile comparisons.
- Including Core Values in addition to Core Drivers improved prediction and explained more variance, demonstrating the incremental validity of values over personality alone.
- Performance model multiple correlation up to R = .19.
- High performers were significantly higher in Conscientiousness or Discipline (p = .005): roughly 9 percent more Disciplined, 8 percent more Pragmatic, and 5 percent more motivated by Achievement or Mastery.
- Consistent positive predictors of performance included Helpful, Empathetic, Organized, Leading, Goal-oriented, Proactive, Sociable, Colorful, Upbeat, Experimental, Imaginative, and the Relationships value. Consistent negative predictors included Moral, Perfectionist, Dutiful, Positive, the Power value, and the Learning value.
- Retention and churn were also modeled from Sub-Drivers and values, and Role Fit and Culture Fit composite indices were built and recalibrated.

### 6.3 Technical services and infrastructure organization

- Core Drivers validated against performance review data, N = 101.
- Conscientiousness (Disciplined) was the strongest and most consistent predictor across outcomes: customer focus r = .33 (p < .001), average rating r = .25 (p = .01), performance rating r = .25 (p = .01).
- Lower Extraversion related to several stronger outcomes in this technical, safety-oriented context: technical skills r = -.21 (p = .04), customer focus r = -.25 (p = .01).
- Emotional Stability related to adaptability r = .17.
- Outcomes spanned the organization's competency and values framework plus technical skills, health and safety, customer focus, and high-potential ratings.

### 6.4 Global technology and engineering group

- Senior-leadership validation using the organization's performance framework. Measures: Execution, Learning Agility, Emotional Intelligence, Fluid Intelligence.
- Multi-method design (correlations, regularized regression, random forest, clustering), with conclusions drawn only where signals converged across methods.
- The assessments predicted senior-leadership performance, with effect sizes in line with, and in some cases exceeding, what is typically reported in senior-leadership validation research.
- Execution was the clearest driver, sharpened by integrating authority, ownership, and accountability. Applied Learning Agility (pragmatic application of proven approaches to new problems) was the strongest predictor after reweighting. Fluid reasoning behaved as a threshold capability. A misalignment was found between the Emotional Intelligence construct and current performance ratings, which rewarded candor, composure, and directness.
- Convergent evidence in the same deployment: Verbal Reasoning correlated r = .45 to .71 with an external verbal reasoning assessment.

### 6.5 Additional published validation studies (anonymized)

- Sales performance: Core Drivers against standardized performance ratings across 65 sales employees at a technology company, with statistically significant signals for hiring and development.
- Frontline leadership: 146 warehouse supervisors across 12 locations, identifying personality-based competencies that drive team engagement (including a counterintuitive finding on assertiveness at the frontline).
- Culture fit: personality against 10 performance and cultural outcomes at a technology company, showing culture fit becomes reliably predictable when values are precisely defined and validated.
- High performer profile: around 2,000 financial-services sales employees, testing whether values plus personality predict performance, retention, and churn better than personality alone.
- Role fit and retention: an 18-month follow-up showing role-fit and culture-fit scores continued to predict performance and retention over time.

### 6.6 Additional studies in progress

Further criterion-validity studies are underway across additional sectors (including industrial and retail), continually expanding the evidence base for all assessments.

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## 7. Fairness and legal defensibility (consolidated)

- All three instruments are designed and tested against the EEOC Uniform Guidelines, including Four-Fifths-rule adverse-impact simulations, and against the Age Discrimination in Employment Act.
- Core Drivers: no adverse impact; group differences negligible by Cohen's d.
- Core Values: adverse-impact ratios at or above .80 across all scales and groups at the 15th-percentile cutoff; group differences negligible to small.
- Core Reasoning: adverse-impact ratios at or above .80 (one borderline value at .804); DIF screening built into development; group differences negligible to small.
- Standard guidance to customers: use normed scores, set cutoffs via job analysis, run local validation, and monitor outcomes for adverse impact over time. This is best practice for any assessment used in selection, not only Deeper Signals. Validation is treated as an ongoing process, not a one-off event.

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## 8. Continuous improvement and R&D roadmap

Deeper Signals continually expands its validity studies and refreshes its norms. Active and planned work:

- Continually expand the criterion-validity database across deployments and sectors, and refresh Core Drivers and Core Values norms on a recurring basis.
- Build country, industry, language, and role-specific norms.
- Core Reasoning: recalibrate to 2PL with growing samples, expand the item banks, run criterion validation, and complete the move of all three tests out of beta.
- Adaptive (IRT plus reaction-time) version of Core Drivers with an expanded item bank, improving Sub-Driver measurement and extreme or risk estimation.
- An ipsative or rank-sort scoring method for Core Values to increase precision.
- A library of soft-skill assessments using mini-SJTs that append to the core assessments to raise competency-measurement validity.
- Executive Assessment expanding Core Drivers for senior-leadership selection and development, mapped to a leadership framework.
- Impression-management and carelessness checks using completion times for Core Drivers and Core Values.
- Experimental LLM-based scoring model that converts text to personality scores (research-stage IP).
- Ongoing measurement-invariance work across languages and recurring fairness audits.

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## 9. Design rationale and responsible use

- Short-scale design is deliberate. Scale and Sub-Driver lengths are chosen to keep completion fast and the experience engaging, which improves completion rates and enables deployment at scale. This trade-off places a modest ceiling on internal-consistency coefficients while preserving factor structure, construct validity, and fairness. Sub-Drivers (three items each) are intentionally brief and are designed for nuanced coaching insight, not standalone decisions.
- The criterion-validity database is continually expanding across deployments and sectors, strengthening the evidence base for all assessments over time.
- Core Reasoning is the newest line: CNRA and CVRA are calibrated with 1PL on growing samples and will move to 2PL as data accrues.
- Local validation is recommended for any assessment used in selection. This is a universal best practice under APA, SIOP, and EEOC guidance, not specific to Deeper Signals: no assessment should be used for selection without local validation and adverse-impact monitoring, because there is no one-size-fits-all selection algorithm.

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## 10. Source provenance

- Core Drivers technical manual.
- Core Values technical manual.
- Core Reasoning Suite technical manual.
- Deeper Signals Research Library and published whitepapers (anonymized by design), including studies on predicting high potential, senior-leadership effectiveness, the personality profile of a high performer, personality-based role fit and retention, sales performance, frontline supervision, culture fit, and how far AI can go in defining job fit.
- Internal applied validation analyses (anonymized) across energy, financial services, technical services, technology, industrial, and retail sectors.
- Internal R&D planning: Core Drivers R&D roadmap and psychometric project plans; Competency Technical Note and competency framework mapping.

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Note: the PDF technical manuals, competency model, and job-profile documents referenced as attachments did not arrive in the working session that produced this file. This document was built from the public technical manuals and the anonymized internal evidence above. If those source files contain figures beyond what is captured here, they can be folded in.
