Is there a social desirability scale in the MMPI-2-RF?

Jiménez-Gómez, Fernando; Sánchez-Crespo, Guadalupe; Ampudia-Rueda, Amada

doi:10.1016/s1130-5274(13)70017-3

Cited by 2 publications

(3 citation statements)

References 30 publications

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“…The validity scales of the Minnesota Multiphasic Personality Inventory (MMPI) (20, 21), also in its restructured form (MMPI-2-RF) (22), are the most suitable prototypes for this purpose. These scales allow researchers to measure response consistency, the presence of overexaggerating symptoms (23), and symptom minimization (underreporting) patterns (24). Specifically, the logic behind the underreporting Lie scale (L-r) and the Correction-Defensiveness scale (K-r) is that only people who want to provide a socially virtuous and well-adapted image of themselves will not answer genuinely those items that refer to common behaviors or small infractions that the majority of individuals are keen to admit to (e.g., “Sometimes I get angry”).…”

Section: Introductionmentioning

confidence: 99%

Introducing Machine Learning to Detect Personality Faking-Good in a Male Sample: A New Model Based on Minnesota Multiphasic Personality Inventory-2 Restructured Form Scales and Reaction Times

et al. 2019

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Background and Purpose. The use of machine learning (ML) models in the detection of malingering has yielded encouraging results, showing promising accuracy levels. We investigated the possible application of this methodology when trained on behavioral features, such as response time (RT) and time pressure, to identify faking behavior in self-report personality questionnaires. To do so, we reintroduced the article of Roma et al. (2018), which highlighted that RTs and time pressure are useful variables in the detection of faking; we then extended the number of participants and applied an ML analysis. Materials and Methods. The sample was composed of 175 subjects, of whom all were graduates (having completed at least 17 years of instruction), male, and Caucasian. Subjects were randomly assigned to four groups: honest speeded, faking-good speeded, honest unspeeded, and faking-good unspeeded. A software version of the Minnesota Multiphasic Personality Inventory-2 Restructured Form (MMPI-2-RF) was administered. Results. Results indicated that ML algorithms reached very high accuracies (around 95%) in detecting malingerers when subjects are instructed to respond under time pressure. The classifiers’ performance was lower when the subjects responded with no time restriction to the MMPI-2-RF items, with accuracies ranging from 75% to 85%. Further analysis demonstrated that T -scores of validity scales are ineffective to detect fakers when participants were not under temporal pressure (accuracies 55–65%), whereas temporal features resulted to be more useful (accuracies 70–75%). By contrast, temporal features and T -scores of validity scales are equally effective in detecting fakers when subjects are under time pressure (accuracies higher than 90%). Discussion. To conclude, results demonstrated that ML techniques are extremely valuable and reach high performance in detecting fakers in self-report personality questionnaires over more the traditional psychometric techniques. Validity scales MMPI-2-RF manual criteria are very poor in identifying under-reported profiles. Moreover, temporal measures are useful tools in distinguishing honest from dishonest responders, especially in a no time pressure condition. Indeed, time pressure brings out malingerers in clearer way than does no time pressure condition.

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Section: Introductionmentioning

confidence: 99%

Introducing Machine Learning to Detect Personality Faking-Good in a Male Sample: A New Model Based on Minnesota Multiphasic Personality Inventory-2 Restructured Form Scales and Reaction Times

et al. 2019

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“…Only four Spanish-language studies examined symptom minimization, one of which utilized the MMPI-2-RF (Jiménez-Gómez et al, 2013). The most frequently analyzed scale in these studies was the L/L-r scale, with three studies generating four effect sizes.…”

Section: Resultsmentioning

confidence: 99%

“…The most frequently analyzed scale in these studies was the L/L-r scale, with three studies generating four effect sizes. Only one study (Jiménez-Gómez et al, 2013) generated a very large effect size for the L-r scale ( d = 2.03), when comparing community participants instructed to answer in a socially desirable manner to those instructed to respond honestly. These authors also observed a large effect for the K-r scale ( d = 1.47).…”

Section: Resultsmentioning

confidence: 99%

Detecting symptom exaggeration and minimization using translated versions of the MMPI-2 and MMPI-2-RF: A systematic review and preliminary meta-analysis.

Bopp¹,

Aparcero²,

Rosenfeld³

2022

Law and Human Behavior

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Objective: This systematic review and preliminary meta-analysis examined the effectiveness of translated versions of the Minnesota Multiphasic Personality Inventory-2 (MMPI-2) and the MMPI-2 Restructured Form (MMPI-2-RF) in detecting response distortion (i.e., symptom exaggeration and minimization), a central concern in forensic assessment. Hypotheses: We hypothesized that translated versions of the MMPI-2 and MMPI-2-RF would generate significantly weaker effect sizes in detecting response distortion than those observed with English-language studies. Method: We identified 26 studies, representing seven language translations of the MMPI-2 (k = 20) and two of the MMPI-2-RF (k = 6). We calculated effect sizes (Cohen’s ds) based on the mean score difference between honest and nongenuine responders for each study on each symptom exaggeration (e.g., F/F-r, Fp/Fp-r) and minimization (e.g., L/L-r, K/K-r) scale examined, along with mean effect size estimates (Hedges’ g) for the Spanish and Italian translations (no other translation had more than two published studies). Results: Spanish-language studies generally produced large (d ≥ 1.25) to very large (d ≥ 1.75) effect sizes when detecting both symptom exaggeration and minimization. Italian-language studies generally produced small to moderate effect sizes when detecting symptom exaggeration and predominately moderate (d ≤ 1.25) effect sizes when detecting minimization. Significant variability within and across scales was observed in both Spanish-language and Italian-language studies. Most studies utilizing a translated version of the MMPI-2 other than Spanish or Italian produced very large (d ≥ 1.75) effect sizes when detecting symptom exaggeration and weaker (d ≤ 1.00) effect sizes when detecting minimization. Conclusions: This systematic review and preliminary meta-analysis demonstrated effect sizes that overlapped with those observed in English-language studies. Although clearly preliminary, given the limited published research to date, these data suggest that the MMPI instruments retain some utility in detecting response distortion when translated.

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Is there a social desirability scale in the MMPI-2-RF?

Cited by 2 publications

References 30 publications

Introducing Machine Learning to Detect Personality Faking-Good in a Male Sample: A New Model Based on Minnesota Multiphasic Personality Inventory-2 Restructured Form Scales and Reaction Times

Introducing Machine Learning to Detect Personality Faking-Good in a Male Sample: A New Model Based on Minnesota Multiphasic Personality Inventory-2 Restructured Form Scales and Reaction Times

Detecting symptom exaggeration and minimization using translated versions of the MMPI-2 and MMPI-2-RF: A systematic review and preliminary meta-analysis.

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