Concurrent validity of the MacAndrew Alcoholism Scale: mixed-group validation.

Knowles, Elizabeth; Schroeder, David A.

doi:10.15288/jsa.1990.51.257

Cited by 10 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…MGV has been available for over 40 years. Nevertheless, MGV has rarely been incorporated into research designs (examples include Cobb, Hunt, & Harburg, 1969;Knowles & Schroeder, 1990) despite the availability of numerous source of prevalence data regarding psycholegal issues. Several potential objections may have contributed to this cool reception.…”

Section: Discussionmentioning

confidence: 99%

Mixed group validation: a method to address the limitations of criterion group validation in research on malingering detection*

Frederick¹

2000

Behavioral Sci & The Law

View full text Add to dashboard Cite

Mixed group validation (MGV) is offered as an alternative to criterion group validation (CGV) to estimate the true positive and false positive rates of tests and other diagnostic signs. CGV requires perfect confidence about each research participant's status with respect to the presence or absence of pathology. MGV determines diagnostic efficiencies based on group data; knowing an individual's status with respect to pathology is not required. MGV can use relatively weak indicators to validate better diagnostic signs, whereas CGV requires perfect diagnostic signs to avoid error in computing true positive and false positive rates. The process of MGV is explained, and a computer simulation demonstrates the soundness of the procedure. MGV of the Rey 15-Item Memory Test (Rey, 1958) for 723 pre-trial criminal defendants resulted in higher estimates of true positive rates and lower estimates of false positive rates as compared with prior research conducted with CGV. The author demonstrates how MGV addresses all the criticisms Rogers (1997b) outlined for differential prevalence designs in malingering detection research.

show abstract

Section: Discussionmentioning

confidence: 99%

Mixed group validation: a method to address the limitations of criterion group validation in research on malingering detection*

Frederick¹

2000

Behavioral Sci & The Law

View full text Add to dashboard Cite

show abstract

“…The MacAndrew Alcoholism Scale from the Minnesota Multiphasic Personality Inventory-2 (MMPI-Mac) was used as a validated (Knowles and Schroeder, 1990) corroborative measure of alcohol outcome. Its advantage as a self-reported measure of alcohol problems is that it taps into less obvious behavioral indicators of alcohol abuse, making falsification more difficult.…”

Section: Instrumentsmentioning

confidence: 99%

Brief Motivational Interviewing for DWI Recidivists Who Abuse Alcohol and Are Not Participating in DWI Intervention: A Randomized Controlled Trial

Brown

Dongier

Ouimet

et al. 2010

Alcoholism Clin & Exp Res

View full text Add to dashboard Cite

show abstract

“…For example, Mossman, Wygant, and Gervais (2012) applied latent class methods to multiple PVTs administered to a large cohort of examinees to make inferences about the accuracy of neurocognitive effort measures. Several recent articles (Crawford, Greene, Dupart, Bongar, & Childs, 2006; Frederick, 2000; Frederick & Bowden, 2009a, 2009b; Knowles & Schroeder, 1990; Tolin, Steenkamp, Marx, & Litz, 2010) have used mixed group validation (MGV), a method introduced into the psychology literature a half-century ago by Dawes and Meehl (1966), to offer estimates of accuracy parameters for several malingering measures without assigning individuals to criterion groups. As originally formulated in psychology, MGV shows that one can estimate the sensitivity and specificity of a test without having to create or establish two criterion groups if one can administer the test to two distinct groups with known but different base rates (Dawes & Meehl, 1966).…”

mentioning

confidence: 99%

A Bayesian approach to mixed group validation of performance validity tests.

Mossman¹,

Miller²,

Lee³

et al. 2015

Psychological Assessment

View full text Add to dashboard Cite

Mental health professionals often use structured assessment tools to help detect individuals who are feigning or exaggerating symptoms. Yet estimating the accuracy of these tools is problematic because no "gold standard" establishes whether someone is malingering or not. Several investigators have recommended using mixed group validation (MGV) to estimate the accuracy of malingering measures, but simulation studies show that typical implementations of MGV may yield vague, biased, or logically impossible results. In this article we describe a Bayesian approach to MGV that addresses and avoids these limitations. After explaining the concepts that underlie our approach, we use previously published data on the Test of Memory Malingering (TOMM; Tombaugh, 1996) to illustrate how our method works. Our findings concerning the TOMM's accuracy, which include insights about covariates such as study population and litigation status, are consistent with results that appear in previous publications. Unlike most investigations of the TOMM's accuracy, our findings neither rely on possibly flawed assumptions about subjects' intentions nor assume that experimental simulators can duplicate the behavior of real-world examinees. Our conceptual approach may prove helpful in evaluating the accuracy of many assessment tools used in clinical contexts and psycholegal determinations.

show abstract

Concurrent validity of the MacAndrew Alcoholism Scale: mixed-group validation.

Cited by 10 publications

References 0 publications

Mixed group validation: a method to address the limitations of criterion group validation in research on malingering detection*

Mixed group validation: a method to address the limitations of criterion group validation in research on malingering detection*

Brief Motivational Interviewing for DWI Recidivists Who Abuse Alcohol and Are Not Participating in DWI Intervention: A Randomized Controlled Trial

A Bayesian approach to mixed group validation of performance validity tests.

Contact Info

Product

Resources

About