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Minnesota Multiphasic… | 9 |
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Bartsch, Thomas W.; Hoffman, Joseph J. – Journal of Clinical Psychology, 1985
Millon Clinical Multiaxial Inventory (MCMI) and Minnesota Multiphasic Personality Inventory (MMPI) data were obtained from 125 male veteran alcoholic inpatients. Results showed five clusters exist among MCMI profiles and statistically significant relationships exist between MCMI profiles and MMPI scale scores. Findings support an alcoholism…
Descriptors: Adults, Alcoholism, Classification, Institutionalized Persons

Hart, Ronald R. – Journal of Clinical Psychology, 1984
Cluster analyzed the MMPI profiles of 70 male chronic pain patients into four homogeneous subgroups. Mean composites for each subgrouping were entered into a PDP 11 computer to generate an objective actuarial narrative description. Findings appeared to extend and replicate the work of earlier investigators. (Author)
Descriptors: Classification, Cluster Analysis, Males, Patients

Bayer, Betty M.; And Others – Journal of Clinical Psychology, 1985
Harris and Lingoes' six psychopathic deviate subscales were assessed empirically for their convergent validity and for their utility to discriminate among male offenders on the two outcome measures of successful completion of sentences at a correctional halfway house and reincarceration at a one-year follow-up. Results provided varying degrees of…
Descriptors: Classification, Males, Prediction, Prisoners

Sladen, Bernard J.; Mozdzierz, Gerald J. – Journal of Clinical Psychology, 1985
Developed Minnesota Multiphasic Personality Inventory scale to predict premature termination of patients from inpatient alcohol programing. A classification accuracy rate of 91.36 percent was achieved in the standardization sample comparing only valid profiles. (Author/BL)
Descriptors: Adults, Alcoholism, Classification, Institutionalized Persons

Megargee, Edwin I. – Psychological Assessment, 1994
Results of two studies with 1,635 male offenders on the impact of the Minnesota Multiphasic Personality Inventory-2 (MMPI-2) on the Megargee offender classification system indicate that the original Megargee rules should not be used to classify MMPI-2s. A new set of classification rules is proposed. (SLD)
Descriptors: Classification, Criminals, Males, Personality Measures

Berzins, Juris I.; And Others – Journal of Abnormal Psychology, 1974
The principal objective of the present investigation was to delineate homogeneous MMPI profile subgroups (types) through multivariate clustering procedures and to compare the derived (replicable) types on measures of the components of "sociopathy" as well as on other psychometric devices. (Author)
Descriptors: Alcoholism, Classification, Data Collection, Diagrams

Kurlychek, Robert T.; Jordan, L. – Journal of Clinical Psychology, 1980
Compared MMPI profiles and two-point code types of criminal defendants (N=50) pleading a defense of "not responsible due to mental disease or defect." A sign test was computed, treating the clinical scales as matched pairs, and a significant difference was found; the nonresponsible group profile was more elevated. (Author)
Descriptors: Classification, Comparative Analysis, Criminals, Individual Differences

Booth, Raydell J.; Howell, Robert J. – Criminal Justice and Behavior, 1980
Megargee's MMPI federal prisoner classification was used to determine validity with prisoners. Similarities and differences were examined and predictive validity was tested. It was concluded that the Megargee system can be used in grouping state prisoners. (Author/RC)
Descriptors: Adjustment (to Environment), Behavior Patterns, Classification, Institutional Research

Anderson, Wayne P.; Holcomb, William R. – Journal of Clinical Psychology, 1983
Identified five types of violent criminals (N=110) using cluster analysis of Minnesota Multiphasic Personality Inventory scores, and compared them on 24 sociological and behavioral variables. Results showed differences on 16 items including family history, drug and alcohol use, events preceding the crime, and relationship between offender and…
Descriptors: Background, Behavior Patterns, Classification, Cluster Analysis