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RehabMeasures Instrument

Differential Aptitude Test

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The Differential Aptitude Tests (DAT) is a multiple aptitude test battery designed to measure Grades 7-12 students' and some adults' ability to learn or to succeed in selected areas. 

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Instrument Details

Acronym DAT

Area of Assessment

Attention & Working Memory
Executive Functioning
Processing Speed
Reading Comprehension
Reasoning/Problem Solving

Assessment Type

Performance Measure

Administration Mode

Paper & Pencil


Not Free

Actual Cost


Cost Description

$20 per test for booklet, answer document, directions, and practice test materials;
$20 for norms booklet;
$20 for technical manual;
$1.50 for scoring each answer document.

Key Descriptions

  • The DAT was first published in 1947 and has multiple forms and levels.
  • The DAT contains eight scales:
    1) Verbal Reasoning (VR)
    2) Numerical Ability(NA)
    3) Abstract Reasoning (AR)
    4) Perceptual (Clerical) Speed and Accuracy (PSA)
    5) Mechanical Reasoning (MR)
    6) Space Relations (SR)
    7) Spelling (SP)
    8) Language Usage (LU)
  • Nine scores are provided, one for each scale and a composite score from VR and NR called the Scholastic Aptitude (SA) score.
  • All the tests except PSA are multiple-choice. In MR, problems are presented using drawings. Users may choose to score the tests by hand, by scanner, or to have them scored by The Psychological Corporation.
  • Maximum and minimum standard scores not established in research but reported in DAT manual.
  • The DAT is linked to the Career Interest Inventory to assist with vocational counseling and planning.

Number of Items


Equipment Required

  • Paper and pencil for Standard Administration
  • Computer, keyboard, and mouse for Computerized Administration

Time to Administer

Up to 3 hours

Subtest times vary from 12-25 minutes.
Up to 3 hours required to administer all parts.

Required Training

Training Course

Age Ranges


6 - 12



13 - 17


Instrument Reviewers

Initially reviewed by Timothy P. Janikowski, PhD and his University at Buffalo Rehabilitation Counseling Master’s students, Janel Anthony & Anthony Yammarino (4/10/2015).

ICF Domain


Measurement Domain



  • The DAT can be effective in a vocational assessment, determining what specialized areas one might focus in on for employment. (Layton & Swanson, 1958)

  • The DAT loses its ability to differentiate between individuals in higher education and advanced students.

  • The DAT is used in numerous international studies and has yielded results similar to the American version of the test.

  • Based upon the research articles, the DAT is a reliable and valid testing measure, however the time period of each article should be taken into consideration for future application of the DAT.

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Non-Specific Patient Population

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Standard Error of Measurement (SEM)

First Year Secondary School Students (Evers & Mur, 2000)

All SEM values were calculated used Standard Deviations & ICC from the above citation:

  • SP: SEM= +/- (4.57 - 4.13)

  • LU: SEM= +/- (3.53 - 3.19)

  • VR: SEM= +/- (3.53 - 3.19)

  • AR: SEM= +/- (3.34- 2.56)

  • SR: SEM= +/- (4.08 - 3.13)

  • MR: SEM= +/- (3.79- 2.91)

  • NA: SEM= +/- (2.72- 2.09)

  • PSA: SEM= +/- (3.09- 2.37)

Normative Data

The 5th edition of the DAT was normed on a sample of 84,000 students in grades 7 to 12.        

First Year Secondary School Students (Evers & Mur, 2000)

  • SP: Mean= 55.6 (8.8)

  • LU: Mean= 25.3 (6.8)

  • VR: Mean= 16.1 (6.8)

  • AR: Mean= 31.5 (8.1)

  • SR: Mean= 29.1 (9.9)

  • MR: Mean= 36.7 (9.2)

  • NA: Mean= 16.8 (6.6)

  • PSA: Mean= 28.2 (7.5)


Boys & Girls in UK Secondary Schools (Lynn, 1992)

Means are available for males and females for all DAT subsets.

13-14 year olds:

  • Males: Mean= 13.6 to 54.8

  • Female: Mean= 12.0 to 56.3

14-15 year olds:

  • Male: Mean= 15.8-57.9

  • Female: Mean= 14.4 to 62

15-16 year olds:

  • Male: Mean= 18.1 to 63.1
  • Female: Mean= 16.3 to 67.9

 16-17 year olds:

  • Male: Mean= 26.9 to 76.1
  • Female: Mean= 22.9 to 78.8

17-18 year olds:

  • Male: Mean= 30.2 to 81.4
  • Female: Mean= 25.8 to 86.0

The test reports results in percentile rankings based on age and gender and Stanines with a mean of 5 and SD of 2

Test/Retest Reliability

Unknown Population (French & Beaumont, 1991)

  • Language Usage Test: Excellent (ICC = 0.89)

  • Spelling Test: Excellent (ICC = 0.91)

Internal Consistency

Secondary school minority group students (Evers et al., 2000)

  • Excellent for AR, SR, MR, NA, PSA, subscales: Cronbach’s alphas= 0.83 - 0.90

  • Adequate for SP, LU, VR subscales: Cronbach’s alphas= 0.73 - 0.78


(Wang, 1993)

  • Excellent internal consistency coefficients computed with the K-R 20 formula (For all DAT subsets r varied from 0.82-0.95)


Kuwait Petroleum Corporation (KPC) refinery operator trainees (Alkhadher, et al. 1998)

  • Excellent internal consistency computed with the KR20 for both computerized and pencil forms (males – r varied for all DAT subsets from 0.85-0.94) and (females – r varied for all DAT subsets from 0.79-0.94)

  • Excellent split-half reliability coefficients for males and females, respectively (For all DAT subsets r varied from 0.89-0.95)

Criterion Validity (Predictive/Concurrent)

Predictive Validity

Minnesota high school students (Layton & Swanson, 1958)

  • Excellent predictive validity for VR and NA at 9th grade level when correlated to future performance on 11th grade American Council on Education Psychological Examination, Cooperative English Test, and high school rank (males – r= 0.63-0.69; females - r= 0.61-0.71)


N.C.O.  Academy soldiers with less than one year of military experience (Gray, 1965)

  • Excellent predictive validity for VR when correlated to Military Academic Class Standing (For all DAT subsets r= 0.69-0.78)

  • Excellent to Adequate predictive validity for AR when correlated to Military Academic Class Standing (For all DAT subsets r= 0.51 - 0.68)

  • Adequate predictive validity for LU (Sentences) when correlated to Military Academic Class Standing (For all DAT subsets r= 0.41 - 0.52)


Kuwait Petroleum Corporation (KPC) refinery operator trainees (Alkhadher, et al. 1998)

  • Adequate predictive validity for the NA test, in both computerized and pencil formats, when correlated with all the training courses and with the overall evaluation (For all DAT subsets r= 0.36-0.40)


Minnesota Institute of Technology engineering students (Berdie, 1951)

  • Adequate predictive validity for NA test when correlated to first year mathematics scores at the University of Minnesota Institute of Technology (r= 0.45)

  • Adequate predictive validity for SR test when correlated to first year drawing scores at University of Minnesota Institute of Technology (r= 0.38)

  • Poor predictive validity for the AR test when correlated with overall evaluation and with all training courses (For all DAT subsets r= 0.19-0.21)

  • Poor to Adequate predictive validity for the MR test when correlated with overall evaluation (For all DAT subsets r= 0.26-.031)


Concurrent Validity:

Eleventh grade students (Wolking, 1955)

  • Excellent concurrent validity on VR test when correlated to verbal scores on Test of Primary Mental Abilities (PMA) (r= 0.74)

  • Excellent concurrent validity on NA test when correlated to numerical scores on PMA (r= 0.63)

  • Adequate concurrent validity on SR test when correlated to spatial scores on PMA (r= 0.47)

Construct Validity

Convergent Validity:

Iowa (U.S.A) high school students (Doppelt & Wesman, 1952)

  • Excellent convergent validity for VR when correlated to Tests of Educational Development (TED) General Vocabulary scores (For all DAT subsets r= 0.69-0.88)

  • Excellent convergent validity for NA when correlated to TED Quantitative thinking scores (r= 0.80)

  • Adequate to Excellent convergent validity for LU (Sentences) when correlated to TED Correctness and Appropriateness of Expression scores (For all DAT subsets r= 0.57-0.89)

Content Validity

Not statistically assessed, however after thorough review and revising the test content was deemed to be appropriate considering the purposes of the DAT (Wang, 1993)

Face Validity

Not statistically assessed, but the items for each subtest appear to represent the corresponding aptitude being measured (Gray, 1965)


Alkhadher, O., Clarke, D. D., & Anderson, N. (1998). Equivalence and predictive validity of paper-and-pencil and computerized adaptive formats of the Differential Aptitude Tests. Journal Of Occupational And Organizational Psychology, 71(3), 205-217.

Berdie, R. F. (1951). The Differential Aptitude Tests as predictors in engineering training. Journal of Educational Psychology, 42(2), 114-123.

Doppelt, J. E., & Wesman, A. G. (1952). The Differential Aptitude Tests as predictors of achievement test scores. Journal Of Educational Psychology, 43(4), 210-217.

Nijenhuis, J. T., Evers, A., & Mur, J. P. (2000). Validity of the differential aptitude test for the assessment of immigrant children. Educational Psychology, 20(1), 99-115.

French, C. C., & Beaumont, J. G. (1991). The Differential Aptitude Test (Language Usage and Spelling): A clinical study of a computerized form. Current Psychology: A Journal for Diverse Perspectives on Diverse Psychological Issues, 10(1-2), 31-48.

Gray, B. (1965). The differential aptitude tests in a military academic setting. The Journal of Educational Research, 58(8), 352-354.

Layton, W. L., & Swanson, E. O. (1958). Relationship of ninth grade Differential Aptitude Test scores to eleventh grade test scores and high school rank. Journal Of Educational Psychology, 49(3), 153-155.

Lynn, R. (1992). Sex differences on the Differential Aptitude Test in British and American adolescents. Educational Psychology, 12(2), 101-106. Wang, L. (1993). The Differential Aptitude Test: A Review and Critique.

Wolking, W. D. (1955). Predicting academic achievement with the Differential Aptitude and the Primary Mental Ability Tests. Journal Of Applied Psychology, 39(2), 115-118.