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Date of Award


Document Type

Dissertation - Pacific Access Restricted

Degree Name

Doctor of Education (Ed.D.)



First Advisor

Stephen Trotter

First Committee Member

Robert R. Hopkins

Second Committee Member

LaVon Rupel

Third Committee Member

Gary N. Howells

Fourth Committee Member

Helmut H. Reimer


The purpose of this study was to investigate the abilities of students to fake good on the MMPI, to identify particular MMPI clinical scales most vulnerable to faking good and to determine the efficacy of the MMPI validity scales to detect that faking. A dual administration design was used. Subjects took the MMPI once under standard instructions and once under instructions to fake good. Faking good was measured by comparing the two profiles and calculating the change in $T$-score points in going from the standard to the fake-good condition across the clinical and validity scales. A counterbalanced design was used with 76 subjects in each condition to control for re-test effects. The quantification of faking-good behavior allowed for multiple regression equations to be calculated. The regression factors were found by correlating the clinical and validity scales with faking good performance measured to $T$-score changes. $T$-score reductions in going from standard to fake-good conditions were defined as successful faking good. The results showed that students were able to fake good to a significant degree overall on the MMPI. The average $T$-score decrease over all ten clinical scales was 21.5 $T$-score points scored with the $K$-correction factor and 30.8 $T$-score points scored without $K$-correction. Five of the ten clinical scales were significantly altered by the fake good condition. Those scales were D, Hs, Pd, Pt and Sc and the addition of the $K$-correction factor tended to mask measured fake-good behavior on these scales. Measured faking-good behavior was found to correlate at $r$ =.57 with regression equation (2). Equation (2) yielded Phi coefficients of.48 and.61 at faking-good levels of 25 and 50 $T$-score points, respectively. Equation (2) was able to correctly identify 82 percent of the fake-good profiles and 89 percent of the honest profiles using a fake-good criterion of 50 or more $T$-score points. Regression equation (2) is as follows: (2) Faking score without $K = 154.7 - 2.5F + .95L - 1.1K.$ The use of the dual administration design allowed for regression equations that yielded higher validity scale screening scores than those found in past research. This finding may have significance in testing situations with fake-good demand characteristics.



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