Interaction of psychiatric factors and comorbid psychopathology: does measurement method matter? (2023)

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Interaction of psychiatric factors and comorbid psychopathology: does measurement method matter? (1)

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The two dimensions of psychopathy, manipulated by different measurement tools, show different associations with psychopathology. However, there are statistical indicationsINTERACTIONThe combination of Factor 1 (F1) and Factor 2 (F2) may be important in understanding the association with psychopathology. Studies on the interaction of F1 and F2 have yielded mixed results as there are both enhancing and protective effects. In addition, methods of measuring F1 (eg, clinical interview vs. self-report) rely on different conceptions of F1, which may affect the interaction effect. The aim of the present study was 1) to clarify the effects of F1 and F2 on psychopathology using a variable and person-centered approach and 2) to determine whether the measure of F1 reflects the interactive effects of F1 and F2 by comparing the strength of F1 affects interaction The effect of the F1 measure on a sample of more than 1,500 criminals. In the analyses, there were only a few cases where F1 statistically influenced the association between F2 and psychopathology, such that F1 could not show a reinforcing or protective effect on F2. Furthermore, conceptualizing F1 on psychopathy measures did not affect the interaction of F1 and F2. These results suggest that F2 may moderate the relationship between psychosis and other forms of psychopathology and that F1 may not play as large a role in the interaction with F2 as previously thought.

Keywords:Psychopathy, Psychopathology, Interaction

Psychopathy is a personality disorder characterized by the coexistence of antisocial behavior, an impulsive and irresponsible lifestyle, a flamboyant interpersonal style, and a lack of emotional experience. Several studies have linked psychosis to criminal behavior, substance abuse, various types of comorbid psychopathology, intentional self-injury, and suicidal behavior.Poythress et al., 2010;).Classical recursiveIn the traditional theoretical framework, psychosis is conceptualized as consisting of two moderately correlated overall dimensions. Factor 1 (F1) includes interpersonal and affective traits (e.g., callousness, superficial emotionality, grandiosity), while Factor 2 (F2) includes impulsive-antisocial behavior (e.g., impulsivity, irresponsibility, early conduct problems). Although Hare has identified low-order aspects in these two major dimensions (Hase, 1991) the two-factor model of psychopathy remains the most widely studied measurement model in this context (seeaffairs, 2011).

Recent studies have shown disparate associations between these two factors and environmental stressors (eg, childhood trauma), psychopathology, and maladaptive outcomes. For example, F2 traits generally showed strong positive correlations with anxiety, depression, substance use symptoms, self-injury, suicidal behaviors, borderline personality traits, child abuse, and attachment, whereas F1 had little or no or negative correlations with these variables.;Skim et al., 2003;). Furthermore, compared to F1 traits, F2 showed stronger positive correlations with levels of neuroticism, affective psychopathology, goalless purposefulness, and impulsive aggression (Skim et al., 2003;Warren et al., 2003).

(Video) Chapter 3 Clinical Assessment, Diagnosis and Research in Psychopathology

Heterogeneity in psychiatric measures

There are many well-validated psychiatric measures, but the most widely used is the Psychiatric Checklist-Revised (PCL-R;Hase, 1991), assessment of F1 and F2 and four domains derived from factor analysis (interpersonal, affective, lifestyle, and antisocial) (Hare et al., 1990). The PCL-R involved a relatively time-consuming (ie, 1.5–2.5 hours) semistructured interview and a document review, which the researcher used to assess the presence or absence of 20 traits.

Psychopathic Personality Inventory (PPI;) was developed as a self-report measure of psychosis and as a more manageable and time-saving alternative to the PCL-R. However, PPIs are characterized by typical limitations of self-disclosure, such as: B. Reliance on respondents' insight and honesty, both of which can pose particular problems for psychopaths (Lillian Field, 1994). The most common factor analysis studies report that the BPI consists of a structure with three factors: F1 (fearless dominance), F2 (impulsive antisociality), and hardness, of which the latter has no significant effect on any of the factors and is therefore considered taken considered an independent factor. single factor (Benning et al., 2003;Patrick et al., 2006; but look, used to replace factor structures). Some characteristics measured by the PPI differ from those measured by the PCL-R. The most noticeable difference is in the F1's conceptualization and functionality. While the PCL-R F1 assesses interpersonal and affective traits such as grandiosity, lack of empathy, and callousness (Hase, 1991) the PPI-I captures relevant adaptive traits such as stress immunity, social efficacy, and fearlessness, which are related to traits such as risk-taking without fear of consequences, low anxiety, and social dominance.Benning et al., 2003;). In fact, the PPI and the PCL-R are designed to assess psychopathy differently because the PPI is designed to measure personality tendencies independent of antisocial behavior, whereas the PCL-R explicitly includes such behavior ().

Although the PCL-R and PPI scores showed good psychometric properties (Hase, 1991;) convergent validity results comparing F1 and F2 are mixed, especially for F1 given the different concepts described earlier. Specifically, the correlation between PCL-R F1 and PPI-I scores was low (Rs ranged from 0.15 to 0.24), whereas the range between PCL-R F2 and PPI-II scores was moderate to large across studies (Rs ranges from 0.39 to 0.58.Malterer et al., 2010;Poythress et al., 2010). The low (although positive) correlation of the F1 values ​​suggests that the PCL-R and PPI versions of this dimension do not measure the same construct. Furthermore, PCL-R F1 and PPI-I showed different associations with psychopathology comorbidity. Specifically, PCL-R F1 tended to show little or no relationship (Warren et al., 2003), while PPI-I tends to show a significant negative correlation (Benning et al., 2003).

Possible interaction of F1 and F2

This has been proven statistically by several studiesINTERACTIONChanges in F1 and F2 are important for understanding associations with negative precursors, psychopathology comorbidity, and maladaptive outcomes (Blonigen et al., 2010;;Sprague et al., 2012). This interaction may be consistent with the concept of at least some forms of psychopathology, particularly personality disorders, as involving interpersonal maladaptive configurations (random interactions) of two or more personality traits.). There are two possible interactions: enhancement and protection. The reinforcement effect suggests that high levels of F1 and F2 increase the risk of negative outcomes. In other words, a high level F1 would do itstrengthenAssociation between F2 and psychopathology and/or maladaptive behavior. There is evidence that the reinforcing effect may differ by gender. For example, in a sample of female inpatients at a maximum security hospitalCode (1993)A combination of F1 and F2 (indicated by the PCL-R) has been found to be associated with mood disorders, intense distress, self-injury, and property loss. Similar,Sprague et al. (2012)Interactions between F1 and F2 traits were found to significantly predict borderline personality disorder traits across all samples (students, incarcerated women) and measures (PCL-R and PPI). Specifically, the association between F2 and borderline traits was stronger when combined with high F1 scores, but only in females. last but not least,Verona, Sprague and Javdani (2012)showed that in women the association between F2 and suicidal ideation/self-injury was enhanced by high levels of F1, whereas in men F2 was associated with both suicidal ideation and self-injury independent of F1 levels. asSprague et al. (2012)The study used composite scores on the PPI and self-reported psychopathy scales for one sample and the PCL:SV for the other sample.

In contrast, the protective effects suggest that high levels of F1 and high levels of F2 reduce the risk of adverse outcomes. In this case a high level of F1 is requiredweaknessesAssociation between F2 and psychopathology and/or maladaptive behavior as protective factors against adverse outcomes. In support of this hypothesis, disinhibitory effects were found to be associated with depression and emotional distress in male inmates, such that the association between F2 and psychopathology became stronger after controlling for PCL-R F1 (Hicks et al., 2006). The PPI and PCL-R had similar effects on internalizing and externalizing disorder symptoms in male and female offenders (Blonigen et al., 2010). Although the statistical methods for analyzing inhibitory effects are not analogous to those used in studies investigating reinforcing effects (e.g.,Sprague et al., 2012;Verona et al., 2012), the fact that F1 attenuated the effect of F2 on psychopathology and that the relationship between F2 and psychopathology was stronger after accounting for F1 suggests that F1 may be protective. Furthermore, negative associations (or lack of association in some cases) between F1 and psychopathology and positive associations between F1 and adaptive traits (e.g., intelligence, positive emotions, academic success, self-efficacy) suggest protective effect of F1 versus F2. (Benning et al., 2003;).

These lines of studies report that F1—either enhancing or protective—moderates the relationship between F2 and psychopathology or other maladaptive outcomes. However, it is also important to consider the possibility that F1 is less related to such outcomes, as it may not act as either an enhancing or a protective factor. In fact, the interactive nature of the two factors of psychopathy in terms of external correlates has been questioned. For example, a recent meta-analysis (), who examined each of the PCL-R factors and their interactions in predicting violence, found that the interaction of these factors did not increase predictive validity. F2 alone predicted violence better than F1 or their interaction. These results were replicated by repeat offenders ().

Therefore, further studies on the interaction of F1 and F2 are needed as it is unclear whether F1 has a reinforcing or protective effect on F2 in the context of psychopathology. In addition, the above studies usually use PCL-R or PPI. Thus, there is a need to examine how psychosis is measured—and whether subsequent conceptualization of F1 influences the factors' relationship to psychopathology. In fact, the conceptualization of F1 may influence the degree to which it interacts with F2 in terms of psychopathology and other maladaptive behaviors. More importantly, investigating interactions could also shed light on current controversies about the utility of F1, particularly fearlessness, and whether psychopathy is best characterized by the interaction of F1 and F2 traits.

Methods for studying F1-F2 interactions

There are two general approaches to testing interactions between variables. The most common and easiest way to study the relationship between factors is based on regression (;Lanza et al., 2011), where the interaction term is used to predict other variables (eg, psychopathology) after controlling for main effects. This is referred to as a "variable-centric" approach. However, a major drawback of this approach is the increased potential for Type II errors (eg, low power minimizes the ability to detect interactions.;Lanza et al., 2011). Another approach is the “anthropocentric” approach (eg, latent class/profile analysis, cluster analysis, mixed models), which divides individuals into classes or subgroups (Lanza et al., 2011). A "person-centered" approach may be more useful for studying the "interactions" of factors, as it allows for a more complete understanding of variables and examines combinations of factors in actual individuals with a particular profile or compared to individuals with a different profile. profiles A limitation However, this approach suffers from the loss of statistical power associated with the formation of subgroups of people. Although each method has its advantages and disadvantages, it provides different but complementary information.

Six previous studies used person-centered approaches to study subtypes of psychopathy (eg,Altman et al., 1998;;Herve et al., 2000;;Skim et al., 2007;Vassileva et al., 2005) with inconsistent results in terms of number of classes (e.g. two, three, four or six classes) and topography (e.g. variation and determinants of F1 and F2 combinations). However, the studies seem to agree on one point. In general, the two populations generally resemble individuals with high F1 and high F2. The high F1 group was characterized by low anxiety and low externalizing symptoms (eg, substance use, aggression), whereas the high F2 group had higher scores on anxiety, substance abuse, and aggression, consistent with previous studies. actually bothSkim et al. (2007)AndHicks et al. (2004)A group with high F1 traits, low anxiety, low impulsivity and aggression and a second group with high F2 traits and high borderline traits, irritability and mood disorders were identified. These results suggest that F2 plays an important role in relation to higher levels of psychopathology and maladaptive behavior, whereas F1 appears to play a smaller role, suggesting that it is associated with psychopathology (eg, anxiety), impulsivity, and aggression. Correlation is low. The lack of class consistency in these studies may be due to differences in the populations studied (eg, inmates vs. drug users), different sex ratios, and differences in F1 measurements. Therefore, there is a need to extend previous work with large mixed-sex samples, including PPI and PCL-R measures of F1, and to use complementary regression-based and person-centered approaches.

Current research

The aim of the present study was to test whether F1 exerts a moderating or protective effect on the relationship between F2 and psychopathology and maladaptive behavior in a large mixed-sex sample (N > 1,500) of drug users and prisoners. Furthermore, given the different conceptualizations of F1 in PCL-R and PPI, we also investigated whether measures of F1 influence protective or enhancing effects by comparing the magnitude of F1 effects across different measures. In studying the interaction of F1 and F2, we used two complementary approaches to test the direction of the interaction effect—a classical regression-based approach and an empirically derived “anthropocentric” approach. This dual analysis approach allows for the investigation of the consistency of the interaction effects of two methods based on the same data but organizing the variables in a different way. Furthermore, the "person-centered" approach is broadly consistent with long-standing historical approaches to classifying subtypes of psychosis, such as Karpman's analysis and many other group analyzes of psychosis that focus on underlying subtypes (e.g.,Kapman, 1941;Hicks et al., 2004;Skim et al., 2007). Finally, we also tested whether this effect was sex-specific (in both directions), as previous work suggested that F1 enhancement may only be present in females.


Participants, Settings and Procedures

The sample was drawn from an anonymized dataset of 1,534 offenders convicted or serving sentences in community-based or inpatient drug treatment programs in Oregon, Utah, Nevada, and Florida1Participants were also recruited from a (prison) drug treatment program in Texas. There were 727 individuals from community drug rehabilitation programs (47.4% of the sample) and 807 individuals from prisons (53.6% of the sample). Overall, participants were predominantly male (83.3%) and Caucasian (65.8%). One-third (29.6%) did not complete high school, 43.2% completed high school or earned a GED, and 26.7% attended at least college. The procedure was as follows: participants were given a detailed explanation of the procedure, asked for written informed consent, and completed a series of self-report questionnaires and interviews.


demographic information

Information was collected on gender, ethnicity, education, and data collection facilities (eg, drug abuse facilities and prisons).


Participants were administered two measures of psychopathy: the PCL-R (Hase, 1991) and PPI (). In the analysis, both measures were divided into F1 and F2 scores.

The PCL-R total score ranges from 0 to 40, with 30 being the standard cutoff used to determine how well a person meets criteria for psychosis. PCL-R scores demonstrated high interrater reliability with intraclass correlations ranging from .87 to .95 (Hase, 1991;), as well as good internal consistency (alphas ranging from .83–.91) and convincing construct validity (Hase, 1990;). In the present study, reliability data were as follows: total score, α=0.82, F1, α=0.81, F2, α=0.68, with interrater reliability of 0.88.

The PPI is a self-report measure consisting of 187 items answered on a 4-point Likert scale. PPI scores showed high internal consistency (alpha ranged from 0.89 to 0.93) and high test–retest reliability (0.82 to 0.95)) and strong construct validity (Patrick et al., 2006). Here, reliability data are as follows: total score, alpha = .91, PPI-I alphas span the subscale range of .80–.86, and PPI-II alphas span the subscale range of .73–.89.2

Comorbid Psychopathology, Child Abuse, and Conception

scores on multiple scalespersonality rating scale(Club;Morey, 1991) was used to measure comorbid psychopathology and substance use. We used a scale that does not overlap and is not comorbid with F1 and F2 psychosis. The Borderline Trait Scale (BOR) was used to assess the presence of BPD features. The results of this scale showed good internal consistency (alpha = 0.84), test–retest reliability (0.73), and construct validity (Morey, 1991). In addition, the BOR scale has a high correlation (R= .58) Structured Clinical Interview with DSM-IV Axis II Disorders (The depression scale (DEP) was used as a measure of depression and the anxiety scale (ANX) as a measure of anxiety. Scores on these scales showed good internal consistency (alpha ≥ .93 for both scales) in the clinically standardized PAI sample. The Alcohol Checklist (ALC) and Drug Checklist (DRG) were used as benchmarks for drug abuse. Scores on these scales showed good internal consistency in a clinically standardized PAI sample (alpha ≥ .80 for both scales,Morey, 1991Results from the PAI Suicidality Inventory (SUI) were used to measure suicidal and self-injurious behavior and showed good internal consistency. The alpha range for the PAI scale in this sample is 0.79–0.94.

A history of child abuse is usedChild Abuse and Trauma Scale(Cat;), a 38-item self-report measure of physical abuse or punishment, sexual abuse, verbal or psychological abuse, neglect, and negative family environment. CATS results showed high internal consistency and test repeatability () alpha in the current study is 0.95. Finally, as a measure of criminal behavior, state and federal records were searched for arrest records of participants who were released into the community after completing the protocol. A dichotomous variable (yes/no) was created to indicate whether each participant was arrested for a crime of any kind within one year of enrolling in a drug treatment program or after being released into the community.


regression-based methods

A series of linear and logistic regressions of the PCL-R and PPI were conducted on F1, F2, and their interactions, respectively, to examine the independent and interactive effects of each factor and gender in predicting psychopathology, child maltreatment, and rearrest. Correlations between psychopathic factors and outcome variables are presented inTable 1.Covariates (Sex, Education, Race, Location), F1 and F2 in the first step, followed by the interaction F1xF2 in the second step, the three-way interaction of each factor with gender (F1xSex, F2xSex) and in the third step F1xF2xSex.

Table 1

Correlation of psychopathic factors and external factors

PCL-R F-1--0,50*0,25*0,18*−0,00−0,12*−0,06−0,12*−0,08*0,000,020,08
PCL-R F2--0,16*0,40*0,25*0,060,11*0,15*0,07*0,13*0,24**0,15*
Borderline Personality Disorder--0,74*0,72*0,52*0,31*0,54*0,44**0,00
I drink alcohol--0,43*0,19*0,13**−0,07

Observations. PCL-R = Psychiatric Checklist - Revised. PPI = Psychopathic Personality Inventory; BPD = Borderline Personality Disorder.

*p < .01

On the PCL-R, F1 and F2 had significant effects, with F1 negatively related to psychopathology and F2 positively related to psychopathology (seeTable 2). However, it is noteworthy that the PCL-R F1 showed significant correlations only with anxiety, depression, drug and alcohol abuse, and childhood abuse. The gender interactions between F1xF2 and F1xF2x were not significant, suggesting that F1 has no impact and there are no gender differences. For PPIs, F1 and F2 had main effects, with F1 negatively related to psychopathology and childhood maltreatment and F2 positively related (seeTable 2). F1xF2 interaction is significant for depression and suicidality (Second= −.09 andSecond= −.07 orPis < .01). Further investigation of the interaction revealed that F1 was protective. Furthermore, the three-way interaction of F1xF2xgender is significant for suicidality (Second= −.17, p <.01), so the interaction is significant for men but not for women.3Finally, the unnormalized beta weights and standard errors of the PCL-R and PPI interaction terms were converted to z-scores and compared using a cut-off table to determine if there were differences between measures. +/- 2.58 cutoff forPi< .01, a conservative level of significance controlling for Type I errors. Significant differences were found for depression (z = 4.29) and suicidality (z = 2.70), but not for BPD (z = −.72 ), anxiety (z = 2.29), drug use (z = .84), and alcohol use (z = .36), child abuse (z = −1.79), and rearrest (z = −.37). Although the PPI showed a stronger association with psychopathology, there was little evidence for significant differences between measures.

Table 2

Regression analysis of PCL-R and PPI factors and gender to predict psychopathological and maladaptive outcomes

ModelBorderline Personality DisorderWorrymelancholicmedicineAlcoholSuicideabusearrest
Step 1
Genus−0,17*−0,16*−0,13*−0,12*0,05−0,08*−0,19*0,21 (0,19)
raise−0,04−0,07−0,030,040,07*0,040,06−0,20 (0,09)
race0,060,040,060,26*0,060,07*0,10*−0,62 (0,15)*
Ort0,23*0,18*0,12*0,41*0,25*0,10*0,02−0,11 (0,15)
F1−0,07−0,11*−0,08*−0,10*−0,11*−0,04−0,09*−0,01 (0,02)
F20,33*0,15*0,17*0,28*0,16*0,18*0,33*0,06 (0,02)*
step 2
F1×F2−0,28−0,23−0,19−0,13−0,11−0,17−0,18−0,01 (0,00)
step 3
F1×F2×Geschlecht0,310,420,580,130,250,470,250,01 (0,01)
producer price index
Step 1
Genus−0,11*−0,11*−0,07*−0,09*0,07*−0,04−0,15*0,23 (0,18)
raise0,00−0,010,030,030,060,07*0,04−0,27 (0,09)*
race0,020,030,040,24*0,07*0,050,08*−0,61 (0,15)*
Ort0,10*0,09*0,010,36*0,23*0,03−0,06−0,12 (0,14)
F1−0,21*−0,42*−0,39*−0,05−0,13*−0,18*−0,07*0,17 (0,10)
F20,63*0,42*0,48*0,30*0,15*0,36*0,33*−0,03 (0,09)
step 2
F1×F20,01−0,05−0,09*−0,02−0,01−0,07*0,040,04 (0,12)
step 3
F1×F2×Geschlecht0,05−0,06−0,02−0,07−0,08−0,17*−0,080,14 (0,37)

Observations.Cell scores for psychopathology variables and abuse represent standardized linear regression weights, and cell scores for cessation represent unstandardized logistic regression weights and standard errors. PCL-R = Psychiatric Checklist-Revised. PPI = Psychopathic Personality Inventory; BPD = Borderline Personality Disorder. F1 = factor 1, F2 = factor 2.

*p <.01.

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possible profiling4

One- to five-class solutions were obtained by fitting two sets of latent profile models to the PCL-R and PPI F1 and F2 scores using Mplus version 6.1 (Muthen & Muthen, 2006). Latent profile analysis was preferred over latent class analysis as it was more appropriate due to the continuity of the variables. Estimates for each model were performed using 1,500 random seed sets and 500 final-stage optimizations to ensure that the resulting estimates were based on global rather than local maximum likelihoods. Model selection was based on the interpretability of parameter estimates and the fit of the comparative data models in terms of information criteria and hypothesis testing. For each model, the Akaike information criterion (AIC), Bayesian information criterion with sample size adjustment (SS–BIC), andLilac, 1987), relative entropy and the Lo Mendell Rubin (LMR) test. are the preferred criteria for choosing the number of classes to commit to latent data structure profile models (Hansen et al., 2007;). AIC and SS-BIC are based on the log-likelihood of the model, with the penalty term accounting for model complexity. Relative entropy measures the uncertainty in classifying a subject into a latent class. Values ​​range from 0 to 1, with values ​​close to 1 indicating a reasonable level of classification accuracy between observed and predicted class participation. The LMR test compares the considered model (K) with a classless model (K-1). A non-significant value indicates that the number of classes in the model under study has no better fit than a model with one less class (). Therefore, the best-fitting model will have smaller AIC and SS-BIC values, higher entropy values, and a significant Lo Mendell Ruby test. The fit index is used in conjunction with theory as a guide to model selection, as the most appropriate model must not only be statistically significant, but also interpretable and provide a clear and plausible explanation for the data.

Tisch 3Contains general adjustment statistics for latent profile analysis. Four categories of models were identified as the best fitting models for PCL-R and PPI. In the PCL-R, four model categories stand out as the best fit on most fit indices. Although the AIC value for the five-level model is lower, all other metrics for the five-level model worsen, suggesting that the four-level model fits the data better. Furthermore, the four-level model provided the best category explanation based on psychiatric factors. The results for PPI are slightly different as the three-level model has a lower SS-BIC value while the five-level model has a lower AIC value and slightly higher entropy compared to the other class solutions. However, the LMR statistics for the three- and five-category models were not significant and uninterpretable. The other fit indices indicated that the four-category model was the best fit.

Tisch 3

Matching PCL-R and PPI latent signature analysis results

1 LessonLevel 2level 3Level 4Level 5
Lidar--419,84 (p = ,00)123,44 (p = ,00)38,14 (p = ,00)10,26 (p = 0,21)
producer price index
Lidar--8,65 (p = 0,04)10,39 (p = 0,26)9,46 (p = 0,02)8,72 (p = 0,09)

Observations.PCL-R = Psychiatric Checklist-Revised. PPI = Psychopathic Personality Inventory; AIC = Akaike Information Criterion; SS-BIC = Stichprobenumfang aggenständer BIC; LMR = Lo-Mendell Rubin likelihood ratio test.

Four levels vary depending on the combination of F1 and F2 values: level 1 (low F1-low F2), level 2 (low F1-high F2), level 3 (high F1-low F2) and level (high F1-high Level ). F2). PCL-R and PPI class members are thereTable 2.When using PCL-R most fall into the low F1 - high F2 category, while when using PPI most fall into the low F1 - low F2 category. However, while the distribution of individuals between classes is different, the PCL-R and PPI form similar classes based on F1 and F2 scores. The categories of different genders are also consistent.5In addition, mean conditional probabilities (all >.70) indicated good agreement between each category and the category in which the individual had been placed (Table 4).

Table 4

Classroom membership for PCL-R and PPI

Nitrogen(%)m (standard deviation)CP
Low F1-Low F2
F1F2total score
PCL-R283 (19,5)3,22 (.12)6,93 (0,14)11,49 (.21)0,86
producer price index1038 (70,3)−.05 (.02).21 (.01).21 (.71)0,73
Low F1-High F2
F1F2total score
PCL-R618 (42,5)6,54 (0,08)12,94 (0,10)21,56 (.14)0,81
producer price index25 (1,7)−1,33 (0,09)1,33 (0,06).42 (.62)0,79
High F1-Low F2
F1F2total score
PCL-R46 (3,2)11,61 (0,29)6,78 (0,27)20,24 (0,50)0,72
producer price index356 (24,1).15 (.04)−.99 (.02)−1,02 (0,75)0,82
High F1-High F2
PCL-R506 (34,8)12,56 (0,08)15,14 (.11)30,14 (.16)0,88
producer price index58 (3,9).57 (.08)1,56 (0,04)2,28 (0,53)0,81
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Observations.PCL-R = Psychiatric Checklist - Revised. PPI = Psychopathic Personality Inventory; F1 = factor 1; F2 = factor 2; CP = conditional probability.

Next, a four-category PCL-R and best-fit PPI model was used to test whether F1 has a protective or enhancing effect on F2 in the context of psychopathology, child maltreatment, and arrest. A series of analyzes of covariance (ANCOVA) were performed on continuous variables (eg, BPD, anxiety, depression, drug use, alcoholism, suicide, and child abuse) and chi-square analyzes were used to examine between-class differences in categorical variables (eg ., eg gender, race, education and conception). A significance level of 0.01 was used for each ANCOVA and Chi-Square analysis to test for Type I error rates.

class difference


as the picture showsTable 5Participants of the four classes were compared on several demographic characteristics. Chi-square analysis revealed a significant association between class and gender.X2(3) = 82,32,Pi<.001; Rasse χ2(3) = 40,86,Pi<.001; education x2(6) = 39,56,Pi<.001; and location x2(3) = 52,09,Pi<.001. Because all demographic variables showed significant differences, they were included as covariates in subsequent analyses. A series of univariate ANCOVAs were conducted to determine whether the four categories differed on psychopathology, child maltreatment, or re-incarceration. Teaching has a significant impact on BPD [eat(3) = 29,62,Pi< .001, Cohen'sHey= .50], anxiety[eat(3) = 6,90,Pi< .01,Hey= .24] Depression [eat(3) = 5,64,Pi< .01,Hey= 0.22], drugs [eat(3) = 21,14,Pi<.001,Hey= .42], drink [eat(3) = 3,76,Pi< .05,Hey= .18], suicide [eat(3) = 6,73,Pi<.001,Hey= .24] and child abuse [eat(3) = 16,47,Pi<.001,Hey= .38]. as the picture showsFigure 1.1-1.7, both high F2 classes showed higher scores on BPD, anxiety, depression, drug and alcohol abuse, suicidality, and child abuse than the two low F2 classes. Chi-square analysis revealed significant differences between class and rear seat [x2(3) = 14,02,Pi< .01,Hey= .24] and two individuals with a high F2 score were more likely to be rearrested than two individuals with a lower F2 score.

Interaction of psychiatric factors and comorbid psychopathology: does measurement method matter? (2)
Interaction of psychiatric factors and comorbid psychopathology: does measurement method matter? (3)

Psychiatric Checklist-Revised (PCL-R) and Psychiatric Personality Inventory (PPI) including mean BPS scores, anxiety, depression, substance use, alcoholism, suicide, child abuse, and arrest rate one year after exit. CoensHeyIndividual contrasts between classes appear with a high coefficient of 2. The essentialz– Also shown are the measured contrast values ​​between classes with a high factor of 2. Abbreviations are as follows: LL, low F1-low F2; HL, high F1-low F2; left hand, low F1-high F2; HH, high F1-high F2;

Table 5

Demographic variables and group differences between PCL-R and PPI categories

statistical data
Gender (% of men)67,884,691,392.1H2(3) = 82,32, p <.001
Race (% Caucasian),8H2(3) = 40,86, p <.001
raiseH2(6) = 39,56, p < 0,001
less than HS19.427.213.025.9
HS or equivalent44.248,926.143,7
some universities and
more than
OrtH2(3) = 52,09, p < 0,001
drug abuse60.152,832.636.8
producer price index
Gender (% of men)84.16084,289,7H2(3) = 12,09, p <,01
Race (% Caucasian)66,57560,883,9H2(3) = 13,14, p < .01
raiseH2(6) = 39,91, p < 0,001
less than HS26.32420.236.2
HS or equivalent47,63637,948.3
some universities and
more than
OrtH2(3) = 36,77, p < 0,001
drug abuse51.35634.362.1

Observations.PCL-R = Psychiatric Checklist - Revised. PPI = Psychopathic Personality Inventory; F1 = factor 1; F2 = factor 2.

To determine whether F1 has an enhancing or protective effect, a significant comparison between the high F2 class and between high F1 and low F1 is needed to isolate the effect of F1 on high F2. Post hoc comparison (Figure 1.1-1.7) showed that the Low F1-High F2 and High F1-High F2 scales differed significantly on the magnitude of anxiety but not on other types of psychopathology. Anxiety levels were higher in the Low F1-High F2 scale than in the High F1-High F2 scale, suggesting a possible protective effect of F1.6A second set of ANCOVAs was performed to determine the presence of gender differences. Overall, gender had no effect on the association of all four categories with externalizing variables: BPD [eat(3) = .18,Pi= .913,Hey= 0], anxiety[eat(3) = .77,Pi= .510,Hey= 0.09], depression [eat(3) = 1,2,Pi= .320,Hey= 0.09], drugs [eat(3) = 0,17,Pi= .920,Hey= 0], alcohol consumption [eat(3) = 0,25,Pi= .247,Hey= 0.06], suicide[eat(3) = 1,41,Pi= .238,Hey= .11], child abuse[eat(3) = 1,58, p = 0,193,Hey= .11] and recaptured [Wald'sX2(1) = 3.42, p = 0.064, Exp(B) = 1.37 (0.98-1.9)]. Furthermore, a comparison of the "low F1-high F2" and "high F1-high F2" categories showed no significant effect of gender in: BPD [eat(1) = 0,02,Pi= .877,Hey= 0], anxiety[eat(1) = 0,31,Pi= .575,Hey= 0], Depressioneat(1) = .10,Pi= .758,Hey= 0], drugs [eat(1) = 0,22,Pi= .641,Hey= 0], alcohol consumption [eat(1) = 0,14,Pi=.708,Hey= 0], suicide[eat(1) = .79,Pi= .373,Hey= .06], child abuse[eat(1) = 0,43,Pi= .511,Hey= 0] and recapture [Wald'sX2(1) = 2,43,Pi= 0.119, End (B) = 1.47 (.91-2.39)].

producer price index

as the picture showsTable 5Participants of the four classes were compared on several demographic characteristics. Chi-square analysis revealed significant differences between categories and gender, e.g2(3) = 12,09,Pi< .01; Rasse χ2(3) = 13,14,Pi< .01; education x2(6) = 39,91,Pi<.001; and location x2(3) = 36,77,Pi<.001. All demographic variables were included as covariates in subsequent analyzes because they differed significantly between categories7. A series of univariate ANCOVAs were conducted to determine whether the four categories differed on psychopathology, child maltreatment, or re-incarceration. Teaching has a significant impact on BPD [eat(3) = 187,66,Pi<.001,Hey= 1.25], anxiety[eat(3) = 89,74,Pi<.001,Hey= .86], depression [eat(3) = 105,22,Pi<.001,Hey= .93], drugs [eat(3) = 44,29,Pi<.001,Hey= .61], drink [eat(3) = 10,16,Pi<.001,Hey= .29], suicide[eat(3) = 44,61,Pi<.001,Hey= .61] and child abuse [eat(3) = 34,26,Pi<.001,Hey= .54]. as the picture showsFigure 1.1-1.7, the two high F2 scores had higher levels of BPD, anxiety, depression, drug use, alcohol abuse, suicide, and child abuse than the two low F2 scores. Chi-square analysis showed no significant difference between recapture categories [eg2(3) = .127,Pi=.99,Hey=.02].

Compare the categories 'low F1 - high F2' and 'high F1 - high F2' to determine the enhancing or protective effect of F1, as these categories allow isolation of the effect of F1 at high levels of F2. Post hoc comparison (Figure 1.1-1.7) showed that the low F1–high F2 and high F1–high F2 categories differed significantly for levels of anxiety and depression, but not for other types of psychopathology. Low F1-high F2 scales showed higher levels of anxiety and depression than high F1-high F2 scales, suggesting a possible protective effect of F1.1A second set of ANCOVAs was performed to determine the presence of gender differences. Overall, gender had a significant effect on suicidality across the four categories [eat(3) = 4,49,Pi< .01,Hey= .19], but not for BPD [eat(3) = 1,35,Pi= .257,Hey= .11], anxiety[eat(3) = 0,32,Pi= .813,Hey= 0.06], depression [eat(3) = .81,Pi=.490,Hey= 0.09], drugs [eat(3) = .89,Pi= .448,Hey= 0.09], drink [eat(3) = 2,57,Pi= 0,053,Hey= .14], child abuse [eat(3) = 1,58,Pi= .193,Hey= .11] and recaptured [Wald'sX2(1) = 1,55,Pi= .213, Exp(B) = 1.29 (0.87-1.9)]. A comparison of the two high F2 scores found no significant effect of gender in: BPD [eat(1) = 0,17,Pi= .684,Hey= .09], anxiety[eat(1) = 0,33,Pi= .567,Hey= .14], depression[eat(1) = 0,30,Pi= .587,Hey= 0.13], drugs [eat(1) = 1,12,Pi= .293, d = .25], alcohol consumption [eat(1) = 1,33,Pi= .254,Hey= 0.27], suicide [eat(1) = 5,43,Pi= 0,023,Hey= 0.54], child abuse [eat(1) = 2,99,Pi= .088, d = .41] and recaptured [Wald'sX2(1) = 0,07,Pi= .797, End (B) = 1.22 (0.26-5.7)].2

difference between measurements

To determine whether measures of F1 influence the association between F2 and psychopathology, a formal comparison of regression weights was used. In this approach, the low F1-high F2 and high F1-high F2 categories are selected from the PCL-R and localized to psychopathology. The same procedure was repeated for the PPI class. Compare the unnormalized beta weights and standard errors of PCL-R regression and PPI regression using a cutoff table. +/- 2.58 critical z value forPi< .01. Results showed a significant difference (z=3.35) in depression, indicating a greater protective effect of PPI-I than PCL-R F1. However, there were no significant differences in BPD (z=0.89), anxiety (z=2.39), drug use (z=0.90), alcohol use (z=0.99), suicidality (z=1.07 ) and in child abuse (z = −.19) or rearrest (z = .02).8Thus, PCL-R F1 and PPI-I had the same effect at F2 on child maltreatment, psychopathology, and readmission. However, PPI-I showed a stronger effect on these outcomes.


The purpose of this study was to examine (1) whether F1 psychopathy interacts statistically with F2 to influence its relationship with psychopathology and other maladaptive behaviors (i.e., a protective or enhancing effect), (2) whether there is a protective or enhancing effect. specific gender. Specifically, (3) whether the measure of F1 affects the nature of the effect and (4) the consistency of the interaction through the use of regression-based and anthropocentric methods. Results showed that a four-level solution was the most appropriate model for PCL-R and PPI, with categories including low F1-low F2, low F1-high F2, high F1-low F2, and high F1-high F2. High F2 PCL-R and PPI classes (low F1-high F2 and high F1-high F2) performed better on BPD, anxiety, depression, drug use, alcohol abuse, suicide, child abuse, and rearrest F2 class scores than those with a low F2 value. F2 also showed a strong positive correlation with each outcome, while F1 showed a negative correlation (in the case of the PPI) or no relationship (in the case of the PCL-R).

improvement v. PROTECTION

In the analyses, there were few instances where F1 scores influenced the associations of F2 with psychopathology, childhood maltreatment, and re-encapsulation in either a protective or an ameliorative direction. There are a few exceptions: F1 was found to be protective against stress on the PCL-R and the PPI for anxiety, depression, and suicidality, meaning that individuals who scored high by 2 on each reading experienced these maladaptive outcomes increases “Low " is also rated with factor 1 as "high". However, we are not sure how much confidence there is in these results, as F1 neither protected nor enhanced in all other cases. these effects contrast with results showing a reinforcing effect (Code et al., 1993;Sprague et al., 2012;Verona et al., 2012). Our comparison results may be due to the control of covariates in the regression analysis, the use of latent profile analysis, and the small number of individuals in the main category comparisons. Furthermore, we used only the PPI, whereas previous studies examining reinforcement effects used a composite score of the PPI with another psychiatric measure and used a screening version of the PCL-R instead of the original measure. Despite these differences, F2 is likely to determine the relationship between measures of psychosis and psychopathology and other maladaptive outcomes, with little or no impact on F1, consistent with other studies finding that F2 alone has stronger associations and has yes ratios to F1 or its association with psychopathology and other external correlates (eg, recidivism, violence;Keneally et al., 2010;).


Several notable findings emerged from the various measurements. First, the PCL-R and PPI still appear to function in similar ways related to psychopathology and other maladaptive behaviors. Formal comparisons further supported this conclusion and showed that in most cases there were no significant differences between the interventions. This study shows for the first time that the PCL-R and the PPI differ slightly in their external correlates, despite previous concerns about their different perceptions of F1.

Although there was little difference in the correlation between the two measures, the LPA models differed in sample size composition, and the PPI showed a stronger relationship with child maltreatment, psychopathology, and rearrest. For example, depression and suicidality were the only outcomes that differed and had a protective effect on PPI. Although methodological differences cannot be ruled out, these results are consistent with the nature of the PCL-R F1 and PPI-I (Benning et al., 2003;Skim et al., 2007;Warren et al., 2003) and opposed the parallelism of the two measures, at least in their conceptualization for F1. That is, PPI-I, representing fearless mastery, is largely an adaptive trait that has been consistently shown to be inversely related to psychopathological stress disorder (Benning et al., 2003;Patrick et al., 2006).

Therefore, in this case, F1 may act as a protective factor against some forms of mental impairment. This result is consistent with the fact that only 3% of samples had a high F1 value and a low F2 value in PPI. In general, we want people in this group to be less likely to be incarcerated than others. Further support comes from the fact that individuals with a high F1 score in this sample are more likely to have a higher level of education. In contrast, PCL-R F1, which mainly includes lack of empathy, callousness, and arrogance, is rather maladaptive and generally unrelated or weakly related to psychopathology.Warren et al., 2003). The potential protective effect of PPI-I may have important implications for therapeutic approaches, particularly in populations that respond poorly to treatment due to characteristics traditionally associated with F1 (eg, numbness, lack of empathy) (seeSalekin et al., 2010for review). An important difference, therefore, may be the adaptive value of PPI-I-related traits and their relationship to risk for psychopathology and other F2-related maladaptive behaviors.

From a broader perspective, the results of the current study raise questions about the utility of F1 in the conceptualization of psychosis, given growing evidence of its limited clinical relevance. In fact, a combination of F1 and F2 features is often used to identify psychosis, as shown by the total score of the psychosis measure. However, in the case of the PCL-R, F1 consistently lacked correlation with external correlates and failed to add additional value to F2, which was well captured by the ASPD (Keneally et al., 2010;). This raises the question of whether F1 is psychopathological in nature or should be considered part of a psychosis. Furthermore, the PPI-I tended to show stronger correlations with correlates considered orthogonal to psychopathy (e.g., psychological distress) than with those considered central to psychopathy (e.g., aggression, empathy, antisocial behavior), which contrasts with factors detected by the PPI-I PCL-R and other measures of psychopathy (Blonigen et al., 2010;;Differential correlations also hold for personality dimensions, as the PPI-I tends to be negatively related to neuroticism, positively related to extraversion, and unpleasantness, while the PCL-R F1 shows no correlation with negative and positive affect or weakly and negatively correlated with reconciliation (). Also, the degree of overlap between F1 and F2 varies considerably depending on the conceptualization and measurement used. Specifically, the PCL-R factors were generally correlated, but the PPI factors were not, obscuring the similarities between these two factors and highlighting the ongoing controversy regarding the utility of F1 in the conceptualization of psychosis. Furthermore, PPI-I subscales often overlap with PPI-II subscales, raising questions about optimal factor solutions (Newman et al., 2008).

Strengths, limitations and future directions

The current study has several strengths. The first is a comparison of two analytical methods for studying interaction effects. Although both methods have been used in psychosis research, they have not been used together, nor have latent profile analyzes been used to examine the statistical influence of F1 on F2 correlations. Furthermore, this is one of the first studies to specifically examine F1 and its impact on F2 across different measurement methods, with particular emphasis on examining measurement differences between PCL-R and PPI. Third, the sample included both men and women to examine potential gender differences in interaction effects.

Despite these advantages, there are still some limitations. The sample is fairly uniform in that it consists primarily of white male offenders and should be replicated in a more diverse sample. Second, the proportion of women in the sample compared to men is relatively small - although very high (>250). Third, several groups were small, which may have affected our ability to detect interactions. Fourth, given that both the PAI and PPI are self-reported measures, the slightly stronger effect on PPI (and the apparent protective effect) could be due to methodological differences. However, the potential implications of methodological differences are in stark contrast to the fact that the results overall are consistent with psychopathic theory and the results of previous studies. Fourth, despite empirical support for the two-factor structure of psychopathy, there is still debate about the factor structure of psychopathy. Instead of three faces () and the four-facet model of psychosis (NeumannWait.2013) is suggested. In further work with larger samples, it may be useful to examine statistical interactions between these facets in exploratory analyses, although in this sample (eg, comparing all pairwise combinations or facets or all together) this increased significantly the risk of a bergen type I' It is wrong given the large number of analyses.

In fact, analyzes using different factor structures/conceptualizations can lead to different results. For example, the three-way model analyzed traits related to F1, and validation studies showed that lack of emotional factors (factor 2 in the three-way model) was associated with criminal behavior and social withdrawal, while interpersonal traits (arrogant-deceptive interpersonal behavior) style factor) were associated with indicators of adaptive functioning (eg, positive mood, low stress reactivity) that were more strongly associated with potential protective effects (;). Thus, the lack of association in the current F1 study could be due to the two-factor model combining all F1 traits, negating the effect of the finer-grained analysis. Therefore, choosing a two-factor model of psychosis in this case could lead to a type II error. However, given the limited clinical relevance of this model and the two-factor model, the utility of the deficient affective component of F1 (and the three-factor model) in conceptualizing psychosis may also be questionable.

Future directions may include examining other components of F1 that may statistically influence F2. For example, it might be worth considering the 'passive' element of the PPI (which expresses a lack of emotionality, a lack of response to the suffering of others, a lack of guilt and empathy) or the related concepts of 'meanness', which include arrogance and aggression. include competition and aggressive aggression recently proposed as part of psychosis () have a protective or enhancing effect against psychopathology. Furthermore, examination of other models of psychopathy, such as the three- and four-facet models, may help further analyze the structure of psychosis and clarify the role of F1 traits in associations with external correlates. Next, examination of other well-validated measures of self-reported psychopathy could provide further insight into the reinforcing or protective effects of different psychopathy concepts. Finally, F1 and F2 appear to be heterogeneous combinations of traits derived from existing models of normal personality, such as the affective five-factor model. In particular, the PCL-R F1 mainly reflected low agreeableness, while the PPI-I reflected low neuroticism/negative affectivity. For example in a literature search:Lilienfeld, Smith, Watts, Berg, and Latzman (in press)The PPI-I, reflecting fearless dominance or boldness, was found to be strongly negatively correlated with Big Five personality model neuroticism and Big Three personality model negative emotionality. In contrast, the F2 of these two measures largely reflected low agreeableness and low conscientiousness, with little contribution from high neuroticism/negative affect (). Statistical interactions can emerge at the level of these seemingly more homogeneous dimensions. The answers to these and other unexplored questions can serve as a basis for prevention and intervention efforts for particularly difficult and burdensome populations.

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supplementary material


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Data for this project were collected by the Drug Abuse Comprehensive Coordinating Office (DACCO) in Tampa, Florida. Florida Department of Corrections? Gateway Foundation, Huntsville, TX; Nevada Department of Corrections? Odyssey, Salt Lake City, UT Home; Operation PAR, Pinellas Park, FL; Oregon Department of Corrections? Texas Department of Criminal Justice, Institutional Division. Utah Department of Corrections? Volunteers of America, Portland, OR; WestCare, Las Vegas, NV Garth, Nevada. This research was supported by Grant R01 MH63783-01A1 from the National Institutes of Mental Health. All authors had full access to all study data and are responsible for the integrity of the data and the accuracy of the data analyses.


Win Show:Dr. Lilienfeld is a co-developer of the Psychopathic Personality Inventory-Revised and receives royalties from the sale of this inventory published by Psychological Assessment Resources.

1The study used data obtained as part of a grant from the National Institutes of Mental Health to examine personality traits in social deviance. Previous work has described the data collection process, other uses of the data, and other variables recorded in the dataset (;).

2It should be noted that the Cold Heart factor of the PPI was not used because it would not allow for a parallel comparison of two measures of psychopathy or to examine the nature of the interaction of psychopathy factors among externally correlated factors.

3Due to space limitations, the full regression model is not included and can be accessed assupplementary material.

4Categories were also derived from the mean split of the F1 and F2 scores of the PCL-R and the PPI and a subsequent analysis was performed. Despite the more even distribution of individuals across classes, the results for interaction effects, gender differences, and measurement differences speak to the current results.

5Due to the focus of this document on evaluation and space limitations, this information is not included in the table, but is available assupplementary material.

6Individual post hoc comparisons, which for reasons of space do not include all possible class comparisons, can be taken assupplementary material.

7To ensure that the inclusion of education as a covariate did not control for PPI-I-related traits (e.g., fearless dominance), the analysis was rerun without education as a covariate and showed no change in the observed results. In most cases, however, the PCL-R effect is weak and the PPI effect is strong.

8Due to space limitations, z-scores for each possible combination of class comparisons are not included and can be determined as followssupplementary material.

There are no conflicts of interest for any of the authors.

(Video) This is How Genes Can Affect Mental Health


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What measurement is used to evaluate psychological disorders? ›

GHQ is often used in research to generate a value on a continuous scale for the severity of psychological distress of a person or population. Validated cut off scores may be applied to group data by clinical severity and identify likely common mental health conditions.

Does comorbidity in psychopathology mean that a psychological disorder can occur? ›

Psychiatric comorbidity means that there is the coexistence of multiple psychiatric disorders. For example, a person with schizophrenia may also suffer from any of these disorders: panic, PTSD, OCD, generalized anxiety, social anxiety.

Why is the study of psychopathology important in understanding mental illness? ›

Psychopathology helps in diagnosis in psychiatry where many conditions are syndromes underpinned by abnormal subjective experiences of the patient. Psychopathology functions as a bridge between the human and clinical sciences, providing the basic tools to make sense of mental suffering.

What are the four criteria used to determine if someone is experiencing psychopathology? ›

There are several ways to characterise the presence of psychopathology in an individual as a whole. One strategy is to assess a person along four dimensions: deviance, distress, dysfunction, and danger, known collectively as the four Ds.

What are the measurement methods in psychological research? ›

The use of multiple operational definitions, or converging operations, is a common strategy in psychological research. Variables can be measured at four different levels—nominal, ordinal, interval, and ratio—that communicate increasing amounts of quantitative information.

What is an example of psychological measurement? ›

These include sex, age, height, weight, and birth order. You can often tell whether someone is male or female just by looking. You can ask people how old they are and be reasonably sure that they know and will tell you.

What are psychopathology comorbidities? ›

Comorbid psychopathology is defined as the occurrence of two or more forms of psychopathology in the same person (Matson and Nebel-Schwalm 2007).

What is the problem of psychiatric comorbidity? ›

It occurs frequently in psychiatry: as many as 45 % of patients satisfy the criteria for more than one disorder in the course of a year. Disorders that co-occur often are mood and anxiety disorders, such as major depressive disorder (MDD) and generalized anxiety disorder (GAD) [5].

What does it mean if one disorder has comorbidity with another disorder? ›

Panel 1: Comorbidity: when a person has two or more disorders at the same time or one after the other. This occurs frequently with substance use and mental disorders. Comorbidity also means that interactions between these two disorders can worsen the course of both.

What are the methods of psychopathology? ›

Within the section on etiological research, activities are subdivided into four general methods: case studies, correlational studies, quasi experiments, and experiments.

What is the relationship between mental health and psychopathology? ›

Mental Health and Psychopathology Defined

Mental health is a positive mental status, with an individual capable of coping with normal life stressors as well as the ability to work productively. Psychopathology is a study of mental and social disorders and also a synonym for mental illness.

How is psychopathology assessed and diagnosed? ›

Patients are assessed through observation, psychological tests, neurological tests, and the clinical interview, all with their own strengths and limitations.

What tests measure psychopathology? ›

Two of the most commonly used measures are the Minnesota Multiphasic Personality Inventory (MMPI) and the Millon Clinical Multiaxial Inventory (MCMI).

How is psychopathology measured? ›

Instruments Used in Assessing Psychopathology. Many tests have been used to gather information about clients' psychological and mental health other than direct interviews. Tests specific to addressing the presence and severity of psychopathology include both projective methods and objective self-report inventories.

What factors influence psychopathology? ›

  • Biological factors, including genes and brain chemistry.
  • Chronic medical conditions.
  • Family members with mental illness.
  • Feelings of isolation.
  • Lack of social support.
  • Substance or alcohol use.
  • Traumatic or stressful experiences.
Nov 29, 2022

Why is psychological measurement important? ›

Psychological assessment can help diagnose conditions such as depression, anxiety, bipolar disorder, and attention deficit hyperactivity disorder (ADHD), among others. It can also be used to assess an individual's cognitive abilities, such as memory, problem-solving skills, and intellectual functioning.

What is the purpose of mental measurement? ›

What do psychological tests measure? A mental health assessment includes information about a person's medical history, their family history, and the current status of their mental health. The assessment helps identify if there are any mental health issues present, and determine a diagnosis and treatment accordingly.

What are the two main types of measurement that psychologists use? ›

In addition to self-report and behavioral measures, researchers in psychology use physiological measures. An electroencephalograph (EEG) records electrical activity from the brain. For any given variable or construct, there will be multiple operational definitions.

What is psychological measurement in psychology? ›

What are psychological tests? Psychological tests (also known as mental measurements, psychological instruments, psychometric tests, inventories, rating scales) are standardized measures of a particular psychological variable such as personality, intelligence, or emotional functioning.

What are the three types of measurement in psychology? ›

In psychology, there are different ways that variables can be measured and psychologists typically group measurements into one of four scales: nominal, ordinal, interval or ratio.

How common is comorbidity in psychopathology? ›

Comorbidity in Childhood Psychopathology

Significant comorbidity appears as early as the preschool years—odds ratios of 1.81 to 18.44 in one study (2) and 4.4 to 26 in another (3), with few exceptions of nonsignificant comorbidity. Comorbidity is associated with greater impairment and less optimal course and prognosis.

What are the most common comorbid psychological disorders? ›

A large meta-analysis showed that four mental disorders, namely, anxiety, depression, bipolar disorders, and schizophrenia, are linked to as much as two out of three chronic physical disorders.

What is an example of comorbid disorders? ›

Examples of Comorbidity
  • Heart disease.
  • High blood pressure.
  • Respiratory disease.
  • Mental health issues like dementia.
  • Cerebrovascular disease.
  • Joint disease.
  • Diabetes.
  • Sensory impairment.
Nov 17, 2021

What is comorbidity and why is it important? ›

Comorbidity means you have more than one illness at once. It has many causes. Some conditions have common underlying causes or risk factors, while some comorbidities are directly caused by another condition, its symptoms, or its treatments. Sometimes, conditions are comorbid by coincidence.

Why do people have comorbid disorders? ›

It may be a chance occurrence or be due to the conjunction of independent risk factors; or it may develop because two disorders have shared or overlapping risk factors, or because one disorder causes the other; or the comorbid condition may be a multiform expression of one of the pure disorders, or a third independent ...

What are some possible reasons for comorbidity of disorders? ›

What are the causes of comorbidity?
  • chance occurrence between two conditions.
  • overlapping risk factors.
  • one condition results from complications of the other.
  • a third condition causes both conditions.
Apr 4, 2022

What is an example of a comorbidity in mental health? ›

Comorbidities of Depression, Anxiety Disorders, Insomnia/Hypersomnia, and Drug and Alcohol Abuse.

Is comorbidity the same as dual diagnosis? ›

Dual diagnosis is sometime referred to as co-occurring disorders or comorbidity. The term “comorbidity” describes two or more disorders occurring in the same person. They can occur at the same time or one after the other. Comorbidity also implies interactions between the illnesses that can worsen the course of both.

What is it called when you are diagnosed with more than one disorder? ›

Having more than one medical illness is known as a comorbid condition. Unfortunately, comorbid mental illnesses are more common than most people think.

What are the three approaches to psychopathology? ›

The contemporary views of psychopathology include biological approaches, psychological approaches, and behaviorism.

What are the different approaches to psychopathology and abnormal behavior? ›

In general, there are seven approaches to the study of abnormal psychology: biological, psychodynamic, behavioral, cognitive, humanistic, sociocultural and diathesis-stress.

What are the five dimensions of psychopathology? ›

Van Kampen's five dimensions are Neuroticism, Extraversion, Insensitivity, Absorption, and Orderliness.

What is the difference between psychopathology and mental disorder? ›

A psychological disorder is a condition characterized by abnormal thoughts, feelings, and behaviors. Psychopathology is the study of psychological disorders, including their symptoms, etiology (i.e., their causes), and treatment.

What is the difference between psychopathology and psychiatry? ›

Psychopathology attempts to define what is abnormal (rather than taking for granted common-sense views) and to grasp which elements of mental life remain normal in the context of illness. (e) Psychiatry is about caring for troubled human existence, rather than judging, marginalising, punishing or stigmatising it.

What are the 4 models of psychopathology? ›

The four main models to explain psychological abnormality are the biological, behavioural, cognitive, and psychodynamic models.

How do you measure psychological disorders? ›

Diagnostic interview:

The gold standard, diagnostic, definitive assessment of a person's mental health status comes from rigorous psychiatric interview by trained clinicians, in most countries, a psychiatrist or clinical psychologist.

What are the 3 main methods used in clinical assessment? ›

Let's look closer at three common types of clinical assessments: clinical interviews, neurological and biological testing and intelligence testing.

What is the best definition of psychopathology? ›

: the study of psychological and behavioral dysfunction occurring in mental illness or in social disorganization.

What are the methods of measuring reliability of a psychological test? ›

Reliability is generally assessed in four ways:
  • Test-retest: Consistency of test scores over time (stability, temporal consistency);
  • Inter-rater: Consistency of test scores among independent judges;
  • Parallel or alternate forms: Consistency of scores across different forms of the test (stability and equivalence); and.

What test is used to measure serious mental problems? ›

The MMPI is most commonly used by mental health professionals to assess and diagnose mental illness, but it has also been utilized in other fields outside of clinical psychology. The MMPI-2 is often used in legal cases, including criminal defense and custody disputes.

What characteristic of psychological test measures what it intends to measure? ›

Validity: It refers to extent to which the test measures what it intends to measure. For example, when an intelligent test is developed to assess the level of intelligence, it should assess the intelligence of the person, not other factors.

What are the criteria to identify psychopathology in someone? ›

Making a Diagnosis

These themes are known as "The Four D's." They are deviance, distress, dysfunction and anger. These four criteria define the concept of abnormality. Deviance refers to those behaviors, thoughts and emotions that are seen as unacceptable in a society or group.

What kind of factors do most psychological disorders result from? ›

Risk Factors for Mental Illnesses

Genetic, environmental, and social factors interact to influence whether someone becomes mentally ill. Environmental factors such as head injury, poor nutrition, and exposure to toxins (including lead and tobacco smoke) can increase the likelihood of developing a mental illness.

Which scale of measurement is used in psychology? ›

In psychology and many disciplines that draw on psychology, data is classified as having one of four measurement scale types: nominal, ordinal, interval, and ratio.

How are psychological disorders evaluated? ›

A psychological evaluation may consist of a series of formal or structured psychological or neuropsychological tests as well as clinical interviews designed to identify and describe emotional, behavioral, or learning problems. Psychological assessments may be written or given orally, or administered via a computer.

How do you assess psychological disorders? ›

How a mental health assessment works
  1. Interview with your doctor (GP) While your doctor is asking about your mental illness symptoms, they will be paying attention to how you look, the way you speak and your mood to see if this gives any clues to explain your symptoms. ...
  2. Physical examination. ...
  3. Other medical tests.

How do you test for psychological disorders? ›

To determine a diagnosis and check for related complications, you may have:
  1. A physical exam. Your doctor will try to rule out physical problems that could cause your symptoms.
  2. Lab tests. These may include, for example, a check of your thyroid function or a screening for alcohol and drugs.
  3. A psychological evaluation.
Dec 13, 2022

Why is measurement important in psychology? ›

Developing adequate measures is essential for the advancement of psychology as a science. Without the ability to adequately measure intended constructs, it is difficult for scientists to conduct experiments, form theories, or improve interventions.

How is psychopathology assessed? ›

The evaluation and success of our efforts to prevent, detect, and treat mental illness depend on the assessment of psychopathology. Almost all psychiatric assessments consist of asking questions, through questionnaires or interviews, about behaviors and experiences.

How do psychologists determine if a mental illness is present? ›

A physical examination, lab tests, and psychological questionnaires may be included, often to rule out other illnesses. As all of this information is obtained and integrated, the professional will begin to determine if the person's symptoms match up with one or more official diagnoses.

What is the most commonly used psychological assessment procedures? ›

Many assessment tests have been developed to complement the clinicians clinical observation and other assessment activities. Some of these include the SCID-5, the most widely used. Clinical observation—Clinical psychologists are also trained to gather data by observing behavior.

What not to say during a psych eval? ›

Here are 13 things you should never say to a therapist:
  • Telling Lies & Half-Truths. ...
  • Leaving Out Important Details. ...
  • Testing Your Therapist. ...
  • Apologizing for Feelings You Express in Therapy. ...
  • “I Didn't Do My Homework” ...
  • Detailing Every Minute Detail of Your Day. ...
  • Just Stating the Facts. ...
  • Asking Them What You Should Do.
Sep 15, 2022

What is most commonly used to diagnose psychological disorders? ›

The Diagnostic and Statistical Manual of Mental Disorders (DSM) is the handbook used by health care professionals in the United States and much of the world as the authoritative guide to the diagnosis of mental disorders.


1. Classification of Psychopathology and Unified Theory
(Warren Mansell)
2. Extending Adaptive Explanations of Personality to the Evolution of Psychopathology by Adam Hunt
(College of Psychiatrists of Ireland)
3. Form follows function: An evolutionary model of the structure of psychopathology
4. Psychopathology - Revision
(RED Psychology)
5. PROFESSIONALS— CBT for Childhood OCD: An Integrated Child & Family Approach (PART 2)
(Effective Child Therapy)
6. The Structure of Psychopathology
(UC Davis Social Sciences)


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