Machine Learning–Driven Adaptive Testing: An Application for the MMPI Assessment

Minnesota multiphasic personality inventory 2 (MMPI-2) scores of recruited oocyte donors based on donation outcome

Background and AimsAssessing pain deception is challenging due to its subjective nature. This study explores using Minnesota Multiphasic Personality Inventory-2 (MMPI-2) analysis with machine learning (ML) to detect malingering. We...

Background and Objectives: Assessing pain deception is challenging due to its subjective nature. The main goal of this study was to evaluate the diagnostic value of pain deception using machine learning (ML) analysis with the Minnesota Multiphasic Personality Inventory-2 (MMPI-2) scales, considering accuracy, precision, recall, and f1-score as diagnostic parameters. Materials and Methods: This study was a single-blinded, randomized controlled trial. Subjects were randomly allocated into a non-deception (ND) group and a deception (D) group. Pain deception, as a form of psychological intervention, was taught to subjects in the D group to deceive the physician. MMPI-2, Waddell's sign, and salivary alpha-amylase (SAA) were also measured. For analyzing the MMPI-2, the XGBoost ML algorithm was applied. Results: Of a total of 96 participants, 50 and 46 were assigned to the ND group and the D group, respectively. In the logistic regression analysis, pain and MMPI-2 did not show diagnostic value. However, in the ML analysis, values of the selected MMPI-2 (sMMPI-2) scales related to pain deception showed an accuracy of 0.724, a precision of 0.692, a recall of 0.692, and an f1-score of 0.692. Conclusions: Using MMPI-2 test results, ML can diagnose pain deception better than the conventional logistic regression analysis method by considering different scales and patterns together.

Minnesota Multiphasic Personality Inventory-2 (MMPI-2) is a widely used tool for early detection of psychological maladjustment and assessing the level of adaptation for a large group in clinical settings, schools, and corporations. This study aims to evaluate the utility of MMPI-2 in assessing suicidal risk using the results of MMPI-2 and suicidal risk evaluation. A total of 7,824 datasets collected from college students were analyzed. The MMPI-2-Resturcutred Clinical Scales (MMPI-2-RF) and the response results for each question of the Mini International Neuropsychiatric Interview (MINI) suicidality module were used. For statistical analysis, random forest and K-Nearest Neighbors (KNN) techniques were used with suicidal ideation and suicide attempt as dependent variables and 50 MMPI-2 scale scores as predictors. On applying the random forest method to suicidal ideation and suicidal attempts, the accuracy was 92.9% and 95%, respectively, and the Area Under the Curves (AUCs) were 0.844 and 0.851, respectively. When the KNN method was applied, the accuracy was 91.6% and 94.7%, respectively, and the AUCs were 0.722 and 0.639, respectively. The study confirmed that machine learning using MMPI-2 for a large group provides reliable accuracy in classifying and predicting the subject's suicidal ideation and past suicidal attempts.

This study developed a Minnesota Multiphasic Personality Inventory-2 (MMPI-2) portrait of narcissism using the Narcissistic Personality Inventory and 5 narcissism scales derived from the MMPI-2 with a nonclinical sample of 283 subjects. Correlational analyses revealed a divergent pattern of relationships among the 16 narcissism measures and MMPI-2 scales, with 1 set of narcissism scales correlating positively with MMPI-2 mania (Ma) and a second set correlating positively with MMPI-2 depression (D), psychasthenia (Pt), feelings of inferiority (Sc), social introversion (Si), and other measures of distress. A principal-components analysis of the 6 narcissism scales produced 2 orthogonal factors, 1 suggesting Grandiosity and the other Depletion. High scorers on the Grandiosity factor were equally well characterized by a 98/89 or 96/69 MMPI-2 profile with an average F, whereas high scorers on the Depletion factor were best represented by an 87/78 profile with an elevated F. Profile analyses of high scorers on the narcissism scales indicated that a 98/89 MMPI-2 profile with an elevated F score is the best overall representation of the narcissistic personality in nonclinical samples. Results supported 3 alternative interpretations, including a narcissistic continuum, narcissism as a pathological defense against depression and rage, and 2 forms of narcissism, 1 grandiose and overt and the other depleted and covert.

A computerized adaptive version and the standard version of the Minnesota Multiphasic Personality Inventory-2 (MMPI-2) were administered 1 week apart to a sample of 155 college students to assess the comparability of the two versions. The countdown method was used to adaptively administer Scales L, F, the I0 clinical scales, and the 15 new content scales. Profiles across administration modalities show a high degree of similarity, providing evidence for the comparability of computerized adaptive and conventional testing with the MMPI-2. Substantial item savings were found with the adaptive version. Future directions in the study of adaptive testing with the MMPI-2 are discussed.

This study developed a Minnesota Multiphase Personality Inventory (MMPI) description of the narcissistic personality in a nonclinical population. The Narcissistic Personality Inventory (NPI) and the MMPI were administered to two samples of 57 and 173 subjects. A correlational analysis produced a cross-validated positive relationship between narcissism and MMPI mania (Ma) and cross-validated negative relationships between narcissism and MMPI depression (D), psychasthenia (Pt), social introversion (Si), anxiety (A), repression (R), and ego control (Ec). A correlational analysis of the 7-factor components of the NPI (Authority, Exhibitionism, Superiority, Vanity, Exploitativeness, Entitlement, and Self-Sufficiency) and the MMPI validity, clinical, commonly scored, and content scales suggests that the seven NPI components reflect different levels of psychological maladjustment. Narcissistic Entitlement and Exploitativeness reflect the most maladjustment, whereas narcissistic Authority reflects the least maladjustment. In addition, a profile analysis of the high NPI scorers suggest that a 98/89 MMPI profile with an elevated F score is most representative of the narcissistic personality in nonclinical samples.

(1) Background: Adult attention-deficit/hyperactivity disorder (ADHD) symptoms cause various social difficulties due to attention deficit and impulsivity. In addition, in contrast to ADHD in childhood, ADHD in adulthood is difficult to diagnose due to mixed psychopathologies. This study aimed to determine whether it is possible to predict ADHD symptoms in adults using the Minnesota Multiphasic Personality Inventory-2 (MMPI-2) with machine learning (ML) techniques; (2) Methods: Data collected from 5726 college students were analyzed. The MMPI-2-Restructured Form (MMPI-2-RF) was used, and ADHD symptoms in adults were evaluated using the Attention-Deficit/Hyperactivity Disorder Self-Report Scale (ASRS). For statistical analysis, three ML algorithms were used, i.e., K-nearest neighbors (KNN), linear discriminant analysis (LDA), and random forest, with the ASRS evaluation result as the dependent variable and the 50 MMPI-2-RF scales as predictors; (3) Results: When the KNN, LDA, and random forest techniques were applied, the accuracy was 93.1%, 91.2%, and 93.6%, respectively, and the area under the curve (AUC) was 0.722, 0.806, and 0.790, respectively. The AUC of the LDA method was the largest, with an excellent level of diagnostic accuracy; (4) Conclusions: ML using the MMPI-2 in a large group could provide reliable accuracy in screening for adult ADHD.

In this study, first, we improved an existing variant of the Nearest Centroid algorithm. In this new version, the predictive power of features and within-class variances are used as weights in distance calculation. This version is called the Enhanced Nearest Centroid (ENC). Second, we proposed a new model tree algorithm for binary classification. It is named as the Enhanced Nearest Centroid Model Tree (ENCMT). The model tree is built using ENC at each leaf node of the decision tree. To evaluate the performance of the new model tree, we used an independent test platform and ran the algorithm on 30 binary datasets available therein. Results showed that ENCMT improves the performance of the decision tree algorithm. We also compared ENCMT with the Logistic Model Tree (LMT) algorithm and showed that it outperforms LMT as well. We also designed a bagging algorithm where ENCMT is used to build a random forest. Our comparison results show that its performance is significantly better than the Random Forest (RF) algorithm.

Computerized adaptive testing in personality assessment can improve efficiency by significantly reducing the number of items administered to answer an assessment question. Two approaches have been explored for adaptive testing in computerized personality assessment: item response theory and the countdown method. In this article, the authors review the literature on each and report the results of an investigation designed to explore the utility, in terms of item and time savings, and validity, in terms of correlations with external criterion measures, of an expanded countdown method-based research version of the Minnesota Multiphasic Personality Inventory-2 (MMPI-2), the MMPI-2 Computerized Adaptive Version (MMPI-2-CA). Participants were 433 undergraduate college students (170 men and 263 women). Results indicated considerable item savings and corresponding time savings for the adaptive testing modalities compared with a conventional computerized MMPI-2 administration. Furthermore, computerized adaptive administration yielded comparable results to computerized conventional administration of the MMPI-2 in terms of both test scores and their validity. Future directions for computerized adaptive personality testing are discussed.

This study examined the relationship between personality functioning as assessed by the Minnesota Multiphasic Personality Inventory (MMPI) and individuals' reports of marital distress on the Marital Satisfaction Inventory (MSI). Both the MMPI and MSI were administered to a total sample of 90 couples with 30 couples from each of three sources: couples in marital therapy, couples in which one partner was psychiatrically hospitalized or in individual outpatient psychotherapy, and nonclinic couples from the general population. Replicated scale-by-scale correlations between the two instruments generally indicated a positive but modest association between individuals' scores on the MMPI and self-reported marital distress. Both bivariate and multiple regression analyses employing either individual scales or conjoint configural analyses of couples' MMPI profiles consistently identified scale 4, Psychopathic deviance (Pd), as the best predictor of relationship distress across a broad spectrum of marital functioning. However, both individual MMPI scales and complex configural approaches misclassified a large percentage of couples across all three samples. Clinical implications of results are discussed.

[Correction Notice: An erratum for this article was reported in Vol 23(4) of Psychological Assessment (see record 2011-12640-001). The article contains an error under the Participants and Procedure heading. This is addressed in the correction.] In the current study, we investigated Minnesota Multiphasic Personality Inventory-2 (MMPI-2) characteristics in an Old Order Amish nonclinical sample (N = 84), comparing these data with both the United States normative sample (N = 2,600) and a sample of Old Order Amish outpatients (N = 136). Consistent with our hypothesis, the Old Order Amish nonclinical group scored similarly to the United States normative sample and lower than the Old Order Amish outpatients on most scales. Thus, overall, the MMPI-2 appears to be sensitive to psychopathology, especially depression and psychosis, among Old Order Amish test takers. Still, several Validity, Clinical, Supplementary, Content, and Personality Psychopathology Five (PSY-5) scale score differences materialized between the Old Order Amish nonclinical group and the United States group, suggesting that certain MMPI-2 scales may need to be interpreted differently for Old Order Amish test takers. Further MMPI-2 research is needed with the Old Order Amish to replicate and generalize our findings. (PsycINFO Database Record (c) 2011 APA, all rights reserved).

Wave run-up prediction for composite bucket foundation due to regular wave and current

Background: It is widely recognized that software effort estimation is a regression problem. Model Tree (MT) is one of the Machine Learning based regression techniques that is useful for software effort estimation, but as other machine learning algorithms, the MT has a large space of configurations and requires to carefully setting its parameters. The choice of such parameters is a dataset dependent so no general guideline can govern this process which forms the motivation of this work. Aims: This study investigates the effect of using the most recent optimization algorithm called Bees algorithm to specify the optimal choice of MT parameters that fit a specific dataset and therefore improve prediction accuracy. Method: We used MT with optimal parameters identified by the Bees algorithm to construct software effort estimation model. The model has been validated over eight datasets come from two main sources: PROMISE and ISBSG. Also we used 3-Fold cross validation to empirically assess the prediction accuracies of different estimation models. As benchmark, results are also compared to those obtained with Stepwise Regression, Case-Based Reasoning and Multi-Layer Perceptron. Results: The results obtained from combination of MT and Bees algorithm are encouraging and outperforms other well-known estimation methods applied on employed datasets. They are also interesting enough to suggest the effectiveness of MT among the techniques that are suitable for effort estimation. Conclusions: The use of the Bees algorithm enabled us to automatically find optimal MT parameters that are required to construct accurate effort estimation model for each individual dataset.

The comparability and validity of a computerized adaptive (CA) Minnesota Multiphasic Personality Inventory-2 (MMPI-2) were assessed in a sample of 571 undergraduate college students. The CA MMPI-2 administered adaptively Scales L, E the 10 clinical scales, and the 15 content scales, utilizing the countdown method (Butcher, Keller, & Bacon, 1985). All subjects completed the MMPI-2 twice, with three experimental conditions: booklet test-retest, booklet-CA, and conventional computerized (CC)-CA. Profiles across administration modalities show a high degree of similarity, providing evidence for the comparability of the three forms. Correlations between MMPI-2 scales and other psychometric measures (Beck Depression Inventory; Symptom Checklist-Revised; State-Trait Anxiety and Anger Scales; and the Anger Expression Scale) support the validity of the CA MMPI-2. Substantial item savings may be realized with the implementation of the countdown procedure.