Psychometric Assistant
Neuropsychological calculators for clinical practice — APA-formatted output
Score Converter
AACN = American Academy of Clinical Neuropsychology · Ranges shown as Standard Score (SS)
Clinical Outcomes Table
SD mode: * ≥1 SD below, ** ≥1.5 SD, *** ≥2 SD. SEE mode: * below 90% CI, ** below 95% CI, *** below 99% CI lower bound.
| # | Subtest | Raw | Score | CI | Percentile | Classification |
|---|
Input Type
Report Writer
A descriptive narrative is generated automatically from the scores you enter elsewhere on the site. Adjust the reference and descriptor system below; everything else updates live.
Add custom test / trial
Add measures first
Tab moves down columns (Score → Percentile → CI). Shift+Tab reverses.
| Test / trial | Score type | Score | Percentile | 95% CI | Descriptor |
|---|
Effect Size Tools
Convert between effect-size metrics or derive them from group data, with a visual against the standard normal.
Use these published effect sizes to anchor your results. The values below span small to huge magnitudes so you can compare your finding against familiar clinical and epidemiological benchmarks.
| Heavy smokers (30+/day) vs never smokers, lung cancer (Pesch et al., 2012) | 2.60 |
| UK male vs female adult height (UK Biobank; Lui et al., 2021) | 2.04 |
| Smokers (any) vs never smokers, lung cancer (Pesch et al., 2012) | 1.75 |
| Cognitive therapy vs control for PTSD (Watts et al., 2013) | 1.63 |
| Former smokers vs never smokers, lung cancer (Pesch et al., 2012) | 1.10 |
| Exposure therapy vs control for PTSD (Watts et al., 2013) | 1.08 |
| EMDR vs control for PTSD (Watts et al., 2013) | 1.00 |
| Clozapine vs placebo for schizophrenia (Huhn et al., 2019 Lancet) | 0.89 |
| CBT vs control for depression (Cuijpers et al., 2023 World Psychiatry) | 0.80 |
| Methylphenidate vs placebo for ADHD, children (Storebø et al., 2023 Cochrane) | 0.75 |
| CBT vs placebo for anxiety disorders (Hofmann & Smits, 2008) | 0.70 |
| CBT for depression, low-risk-of-bias subset (Cuijpers et al., 2023) | 0.60 |
| Interpersonal Therapy for depression (Cuijpers et al., 2011) | 0.50 |
| Antidepressants vs placebo (Cipriani et al., 2018 Lancet) | 0.30 |
| CBT vs treatment-as-usual for chronic pain (Williams et al., 2020 Cochrane) | 0.20 |
| CBT vs active control for chronic pain (Williams et al., 2020 Cochrane) | 0.10 |
| Sugar on children's hyperactivity (Wolraich et al., 1995 JAMA) | 0.00 |
| No or negligible effect | 0.00 |
Standard Deviation Index
Quantify abnormality of test-retest discrepancy in standard-deviation units. Useful when reliability data are unavailable or for descriptive comparison.
Score Type
Basic Reliable Change Index
Jacobson & Truax (1991). Computes whether observed change exceeds measurement error, using the test's reliability coefficient and standard deviation.
View formula
Test data & patient scores
| # | Subtest | SD | r | Date 1 | Date 2 | RCI (z) | p | Outcome |
|---|
Practice Effect-Adjusted Reliable Change Index
Iverson (2001). Adjusts the standard RCI to control for the average improvement (practice effect) observed between assessments in the normative sample.
View formula
Test data & patient scores
| # | Subtest | M₁ | SD₁ | M₂ | SD₂ | r | Date 1 | Date 2 | RCI (z) | p | Outcome |
|---|
McSweeney Regression-Based (SRB) Reliable Change Index
McSweeney et al. (1993). Predicts each patient's expected retest score from their baseline and the normative sample's regression parameters; the residual is standardised against the standard error of estimate.
View formula
Test data & patient scores
| # | Subtest | M₁ | SD₁ | M₂ | SD₂ | r | Date 1 | Date 2 | Ŷ₂ | RCI (z) | p | Outcome |
|---|
Crawford Regression-Based Reliable Change Index
Crawford & Garthwaite (2007). Extends the standardised regression-based approach to use a t-distributed test statistic that incorporates the normative sample size (N), correctly accounting for uncertainty in the regression parameters when N is modest. Returns a sample-size-adjusted standard error of prediction.
View formula
Test data & patient scores
| # | Subtest | M₁ | SD₁ | M₂ | SD₂ | r | N | Date 1 | Date 2 | Ŷ₂ | t(RB) | p | Outcome |
|---|
Premorbid Estimate
Inputs
Enter whichever predictors are available. Leave unavailable fields blank; the estimate table will update only for models with enough information.
Figure. Premorbid FSIQ estimates with 90% confidence intervals.
Enter the patient's actual WAIS-IV / WMS-IV index scores in the Achieved column to compute ToPF-predicted vs actual discrepancies. Base rates from the standardisation sample are shown only for negative discrepancies (achieved < predicted).
| Index | Predicted | Lower 90% | Upper 90% | Achieved | Difference | Base rate |
|---|---|---|---|---|---|---|
| WAIS-IV | ||||||
| Full Scale IQ | - | - | - | - | - | |
| Verbal Comprehension Index | - | - | - | - | - | |
| Perceptual Reasoning Index | - | - | - | - | - | |
| Working Memory Index | - | - | - | - | - | |
| Processing Speed Index | - | - | - | - | - | |
| WMS-IV | ||||||
| Immediate Memory Index | - | - | - | - | - | |
| Delayed Memory Index | - | - | - | - | - | |
| Visual Working Memory Index | - | - | - | - | - | |
Enter age plus Vocabulary and/or Matrix Reasoning raw scores in the Inputs panel above. Rows appear automatically for each model whose required inputs are present. Enter the patient's actual FSIQ / GAI in the Achieved column - the prorated index is calculated ACS manual procedures, excluding the subtest(s) used as predictors.
| Model | Predicted | Lower 90% | Upper 90% | Achieved | Difference | Base Rate |
|---|---|---|---|---|---|---|
| Enter age plus Vocabulary and/or Matrix Reasoning to populate the table. | ||||||
Custom Tests
Add your own normative data for any test you regularly use. Custom tests appear alongside built-in tests in the auto-fill database. Stored locally in this browser only.
Add a test family
Add subtests/trials to a family
| # | Subtest | M₁ | SD₁ | M₂ | SD₂ | R | N |
|---|
Database
Methods & References
A clinical psychometric calculation tool for neuropsychological report writing. All computation is local; no patient data is ever transmitted.
Methods & conventions
This tool supports score conversion/equating, effect-size conversion, SDI, four RCI approaches (basic, practice-adjusted, standardised regression-based, and Crawford/Garthwaite), and premorbid estimation workflows. All calculations run locally in-browser; no patient data is transmitted off-device.
Score conversions assume an approximately normal reference distribution. Wechsler descriptor bands follow WAIS-IV/WMS-IV manual conventions; AACN descriptor labels follow Guilmette et al. (2020). SDI and RCI p-values are two-tailed. Jacobson & Truax (1991), Iverson (2001), and McSweeney et al. (1993) implementations use the standard normal distribution; Crawford & Garthwaite (2007) uses the Student t distribution with N−2 degrees of freedom.
The auto-fill normative database includes published retest parameters for major batteries (including D-KEFS, WAIS-IV, WMS-IV, CVLT-3, RBANS, and WISC-V), with normative N where reported. N is required for Crawford/Garthwaite calculations and may need manual entry when unavailable. Clinicians should verify all imported parameters against the latest manual and local service standards before interpretation.
Premorbid estimates combine ToPF-based and demographic equations with OPIE-4 prorated models, and include predicted-versus-achieved discrepancy outputs with confidence intervals, base-rate lookups, and APA-formatted export tables. OPIE-4 regression terms are adapted for UK use by omitting US-specific education, region, and ethnicity terms; interpretation in UK settings should remain cautious because underlying normative regression data are US-derived.
Crawford, J. R., & Allan, K. M. (2001). Estimating premorbid WAIS–R IQ with demographic variables: Regression equations derived from a UK sample. The Clinical Neuropsychologist, 11(2), 192–197.
Crawford, J. R., & Garthwaite, P. H. (2007). Using regression equations built from summary data in the neuropsychological assessment of the individual case. Neuropsychology, 21(5), 611–620.
Crawford, J. R., Millar, J., & Milne, A. B. (2001). Estimating premorbid IQ from demographic variables: A comparison of a regression equation vs. clinical judgement. British Journal of Clinical Psychology, 40(1), 97–105.
Guilmette, T. J., Sweet, J. J., Hebben, N., Koltai, D., Mahone, E. M., Spiegler, B. J., Stucky, K., Westerveld, M., & Conference Participants. (2020). American Academy of Clinical Neuropsychology consensus conference statement on uniform labeling of performance test scores. The Clinical Neuropsychologist, 34(3), 437–453.
Holdnack, J. A., Drozdick, L., Weiss, L. G., & Iverson, G. L. (2013). WAIS-IV, WMS-IV, and ACS: Advanced clinical interpretation. Oxford: Academic Press.
Iverson, G. L. (2001). Interpreting change on the WAIS-III/WMS-III in clinical samples. Archives of Clinical Neuropsychology, 16(2), 183–191.
Jacobson, N. S., & Truax, P. (1991). Clinical significance: A statistical approach to defining meaningful change in psychotherapy research. Journal of Consulting and Clinical Psychology, 59(1), 12–19.
McSweeney, A. J., Naugle, R. I., Chelune, G. J., & Lüders, H. (1993). "T scores for change": An illustration of a regression approach to depicting change in clinical neuropsychology. The Clinical Neuropsychologist, 7(3), 300–312.
Wechsler, D. (2011). Test of Premorbid Functioning (TOPF-UK) manual. London: Pearson Assessment.
Alnasser, A., Alamuddin, R., Algahtani, H., Alenezi, W., Alanazi, A., Almegren, J., Alsoudi, A., Alhazmi, A., Alsawah, A., Alnasser, H., Alruwaili, T., Al-Qattan, R., Al-Turki, M., Aljazeeri, Y., Al-Khushail, I., Alrashed, S., & Aldahal, T. (2023). Paracetamol versus ibuprofen in treating episodic tension-type headache: a systematic review and network meta-analysis. JAMA Network Open, 6(11), e2343849.
Cuijpers, P., Cristea, I. A., Karyotaki, E., Reijnders, M., & Huibers, M. J. (2016). How effective are cognitive behavioural therapies for major depression and anxiety disorders? A meta-analytic update of the evidence. World Psychiatry, 15(3), 245–254.
Furukawa, T. A., Cipriani, A., Barbui, C., & Geddes, J. R. (2011). Phi index as a measure of clinical importance in meta-analysis: How to interpret a number needed to treat. The Journal of Clinical Psychiatry, 72(12), 1642–1648.
Health Survey for England (2021). Data tables. NHS Digital. Available at: https://digital.nhs.uk/data-and-information/publications/statistical/health-survey-for-england
Pesch, B., Kendzia, B., Gustavsson, P., Jöckel, K. H., Johnen, G., Pohlabeln, H., Olsson, A., Ahrens, W., Gross, I. M., Brüske, I., Wichmann, H. E., Musk, A. W., Vermeulen, R., Kromhout, H., Straif, K., & Brüning, T. (2011). Cigarette smoking and lung cancer – relative risk estimates for the major histological types from a pooled analysis of case-control studies. International Journal of Cancer, 131(12), 2912–2921.
Sawilowsky, S. S. (2009). New effect size rules of thumb. Journal of Modern Applied Statistical Methods, 8(2), 597–599.