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On Determination of Behavioural Characteristics Responsible for Psychopathic Personality Traits

Abnormal and irrational behaviour called psychopathic traits has eroded our society; it increases daily, thus leading to increased crime. The study focuses on determining behavioural characteristics that may be responsible for psychopathic traits in human beings that have purportedly led to increased crime in our society. A survey was conducted in five selected towns in Osun State, Nigeria, based on seventeen identified behavioural characteristics using a developed structured questionnaire to collect data on identified behavioural characteristics that could trigger psychopathy personality traits. Two personality traits considered in this study are aggressiveness and goal setting, while the considered behavioural characteristics are family upbringing, confidence in government, society values, industrialization, civilization, unemployment rate, technology advancement, socio-political class, impunity, environmental impact, embracing foreign culture based on materialism, religion believes, peer group influence, academic attainment, high wealth procession, family believes, and power intoxication. The data harvested from the questionnaire on behavioural characteristics from the five hundred and eight respondents were subjected to principal component analysis to determine the significant behavioural characteristics that are likely responsible for psychopathic traits in humans. Results from the principal component analysis reduced the seventeen characteristics to family upbringing, confidence in government, society values, industrialization, and civilization. Further analysis was carried out with binary logistic regression analysis using goal setting and aggressiveness as dependent variables. The results from the binary logistic regression reflected that technological advancement, foreign culture, religious beliefs, high wealth possession, and power intoxication have a significant effect on goal setting, while confidence in government, environmental impact, impunity, and power intoxication have a significant effect on aggressiveness personality trait. Based on the results obtained, it can be concluded that civilization and confidence in government are the main significant behavioural characteristics that trigger personality traits in the region covered in this study.


Introduction
Human conduct is part of a person's self-concept, which is how they see themselves as physical, social, spiritual, or moral. Aggressive, pessimistic, trusting, optimistic, and jealous behaviour are the most ubiquitous and perplexing among common aberrant human behaviours today. Physical or verbal aggression between spouses, siblings, or parents who use it as punishment for their children begins at home. Because of misunderstandings, neighbours frequently demonstrate hostility, pessimism, trust, and envious behaviours. Different gathering sites, including village meetings, are not immune to the display of these characteristics. Robbery, kidnapping, rape, and murder are among society's aggressive, pessimistic, and envious acts of hoodlums. Churches are also sometimes subjected to verbal abuse [1].
Psychological traits refer to the full range of physical and emotional behaviours that humans engage in, such as intellectual, social, and biological, to mention a few [2]. The psychological traits associated with the mental (antisocial) disorder, which manifests as a display of lack of love or hate, are associated with the antisocial disorder [3]. According to Gwen [4], "psychopathy is principally deficient in fear reactivity and is commonly associated with a reduced empathy response". Furthermore, Allen [5] concluded that children with psychopathic tendencies always show less concern for the negative emotions of others. Other studies in the literature have established that there are genetic influences (i.e., clinical factors) that account for all the psychopathy dimensions. The study by Sanz-Garcia et al. [6] categorized human behaviours as acceptable and non-acceptable based on social norms. A person's traits depict a person's characteristics in making a response and reaction to a particular situation in a specific way. Inge and Lawrence [7] opine that people with psychopathic personalities hardly adjust to society's standards, exhibit extreme selfish behaviours, and live their lives not minding the consequences of their actions. Much scientific literature has associated psychopathic personality traits in humans with clinical factors such as mental disorders and drug abuse [8][9][10]. Asrat et al. [8] asserted that psychopathic personality traits co-occur with toxic behaviours, aggressiveness, violence, criminal activities, and drug abuse, to mention but a few. Findings have shown that psychopathic personality traits are astonishingly common as mental disorders [11]. Damilare et al. [12] in his work, attributed psychopathic personality traits to neuroticism which is associated with emotional instability, irritability, and anxiety.
In Nigeria, many people behave according to their environmental status, religion, family issues, short temper, frustration, weather, climate conditions, poverty, and culture, to mention but a few. Yonas [13] asserted that 14.7% of the newly admitted prisoners in the Nigerian correctional institutions were psychopaths and that the risk of psychopathic personality traits accounted for (37-93) % of the effects of childhood maltreatment. Furthermore, the study claims that (58-97) % of Nigerians that committed sexual murder were discovered to be psychopaths based on the set criteria. The study further revealed that psychopathy was a vigorous predictor of general, violent, and sexual recidivism. Kagan, Marc, and Richard [14] associated psychopathic personality traits with social norms. Govertt et al. [15] associated psychopathic personality traits with human creativity, religion, and spirituality. A study by Rath [16] linked psychopathic personality traits to weather, climate, and human attitudes. More studies on psychopathy hypotheses based on personality and behaviour can be found in Lilienfeld [17], Pilkonis and Klein [18], and Blackburn [19].
In Nigeria, psychopathy dimensions do not appear to be more predominant in one racial or ethnic group than others [10]. However, Dawson [20] suggested that in Nigeria, the psychopathy pattern is more predominant in men than women and on social factors such as environmental factors, religion belief, family factors, interpersonal relationships, regional factors, and so on, which are believed to be behavioural characteristics that may influence psychopathy. Therefore, in this paper, a survey was conducted using a developed structured questionnaire to determine the behavioural characteristics that influenced significantly psychopathic personality traits. Because of paucity of funds, a state in the western region of Nigeria was used as a case study. It is our belief that results obtained in this study will be a reflection of the larger society which can be validated in future work.

Materials and Methods
Two statistical approaches were used in this study to determine the behavioural characteristics that significantly affect the personality traits of humans. The details of the methods are presented in sub-sections 2.1 to 2.4.

The Principal Component Analysis (PCA)
The Principal Component Analysis (PCA) method helps to reduce the dimension of a dataset in such a way as to maximize interpretability and minimize information loss. The PCA uses the eigenvalue to determine the variables to be retained for further analyses.
Mathematically, if A is an n by n matrix representing the dataset matrix, then the characteristic equation of the matrix A is (1) where is an identity matrix, and is eigenvalue of . The root of the characteristic equation in equation (1) is the eigenvalues of .
Suppose there exist a vector which is the eigenvector , then It should be noted that there is no unique solution for Therefore, there is a need for transformation. By transforming the samples into new subspace, we have (3) where is eigenvectors corresponding to maximum eigenvalues. Hence, the desired Principal Component. The mathematics of PCA is further highlighted as follows: Covariance, in the principal component analysis, measures how two variables change together. The following equations will be employed to determine the correlation and covariance matrix of any two variables: The covariance matrix is given as: The correlation is a normalized measure of how two variables change together, which is computed using equation (6) PCA loadings are the coefficients of the linear combination of the original variables from which the principal components (PCs) are constructed. The linear combination of the original variables X 1 , X 2 , X 3 , …,X p are The first principal component, PC 1 , represents the component that retains the maximum variance of the data, and corresponds to an eigenvector of the covariance matrix. Thus, the component loadings are determined with the expression: It should be noted that the elements of the eigenvector of the covariance is known as the loadings.

Logistic regression analysis
The two basic types of logistic regression are Binary and multinomial logistic regression. The Binary logistic regression is used when the dependent variable is dichotomous. To apply logistic regression, consider a linear probability model: The logistic regression uses the logistic function given as From (10) the odd ratio is obtained to be Taking the logarithm of equation (9), we have the logit regression equation given by The Wald test used to test the significance of the logistic regression coefficient is the statistic , where is the square root of the corresponding diagonal element of the covariance matrix, . It should be noted that when the sample size is large, .

Study setting and analytical method
The target population for the study is all human beings who possess the characteristics or know the phenomenon being studied in Osun state, Nigeria. The study took place in the last quarter of the year 2021. The five towns where the study was carried out are Osogbo, Ikirun, Ilesa, Ada-Ile, and Iree. The five towns were selected based on proximity factors and the funds available for the study. Five hundred and fifty questionnaires were distributed across the five towns according to the proportion of the size of each town under study. The study covered three months to administer the questionnaires across the five towns selected. A simple random sample approach was used in each town to distribute the questionnaire. At the end of the three months, five hundred and eight (508) respondents completed and returned the questionnaires, which amounted to a response rate of 92.4%. The questionnaire instrument captured seventeen behavioural characteristics of human beings. The seventeen factors are family upbringing -X1, confidence in government -X2, society value -X3, industrialization -X4, civilization -X5, unemployment rate -X6, environmental impact -X7, technology advancement -X8, socio-political class -X9, impunity -X10, embracing foreign culture -X11, religion believes -X12, peer group -X13, academic attainment -X14, wealth possession -X15, family believes -X16, and power intoxication -X1 7. The Data analysis was done with AMOS SPSS version 23.

Measures and procedures
A questionnaire was designed and used as an instrument for data collection. The questionnaire was deployed directly using face-to-face interviews with the respondents; closed-ended questions were used to collect data. The questionnaire was structured and designed so that the respondent could answer the research-based questions appropriately and with every sense of responsibility. The questionnaire was divided into three sections, namely: questions on the biodata of each respondent, questions that have to do with goal settings and signs of psychopathy, and questions on the factors responsible for behavioural characteristics. The questionnaire was structured on a five Likert scale which are: strongly agree, agree, neutral, disagree, and strongly disagree. Data were collected to detect the major factors responsible for behavioural characteristics in human beings. The data retrieved from the related questions on goal setting, aggressiveness, and questions about human actions were all obtained from the survey.
The reliability and internal consistency of the instrument used for data collection were carried out using Cronbach's Alpha and the standardized Cronbach's Alpha. The computed Cronbach's Alpha statistics was 0.805, while the standardized Cronbach's Alpha value equals 0.815. Thus, the instrument used is reliable and consistent for the study.

Test of sphericity
One of the assumptions related to Principal component analysis (PCA) is the sphericity test which must be satisfied before the analysis of data by PCA. In this study, Bartlett's test was used for the sphericity test, which produces a Chi-Square approximation value of 4642 with 136 degrees of freedom and a p-value of 0.000. Therefore, there is an indication of a relationship among the seventeen variables in the study responsible for behavioural characteristics and that the correlation matrix is not an identity matrix.

Sampling adequacy
To test for the factors to be retained among the seventeen variables ( to ), Kaizer-Meyer-Olkin (KMO) was used to check for the sampling adequacy and to determine the most significant factors that will account for about 70% of the behavioural personality traits under study. The obtained KMO results for the seventeen variables are presented in Table 1 in the Appendix.
From Table 1, the KMO overall value of 0.768 implies "moderate" according to the definition and condition of KMO. Since the computed KMO values from Table 1 are greater than 0.60, then, all the seventeen factors under consideration were retained for the computation of the principal components.

Computation of initial eigenvalue
The eigenvalues for the seventeen variables were computed to determine the most critical factors that can be used to explain the differences in the personality trait of human beings. Using equation (2) on the data set loaded in order to as appears in section 2.5, the eigenvalue, percentage of the total variance, and cumulative percentage of total variance were computed for each behavioural characteristic considered in this study. The obtained results are presented in Table 2 in the Appendix.
The most important factors that we considered to be strong enough to explain the variability in personality traits are the variables with an eigenvalue that is greater or equal to 1.
Based on the results in Table 2, the following BCs whose eigenvalues are greater than one, were, selected to be the determinant factors: family upbringing, confidence in government, society values, industrialization, and civilization. The five BCs represent 70.7% of the total explained variability in the dataset. Hence, based on the available data used in this study, the factors mentioned above contributed approximately 71% to the behavioural characteristics of human beings.

Component loading
Using equations (7) and (8), the computed correlation component loadings, which show the value of each factor considered to be responsible for the behavioural characteristics in human beings, are presented in Table 3 in the Appendix. Table 3 shows how each factor loads on the five components extracted by the PCA. For instance, for Component 1, confidence in government, society values, socio-political class, and impunity are relatively highly positively correlated with the values bolded in Table 3. Likewise, for Component 2, the following factors load with positive values: Technology advancement, foreign culture embracement, peer group, wealth possession, and power intoxication. Component 3 is loaded with a high positive correlation with industrialization, civilization, religious belief, academic attainment, and family beliefs. Component 4 loadings are with industrialization, unemployment rate, and environmental impact. The fifth component loaded with a high positive correlation with family upbringing.
It should be noted that positive loadings indicate that a behavioural characteristic and a principal component are positively correlated. This implies that an increase in one leads to increase in the other. However, a negative loading indicates a negative correlation, while large (either positive or negative) loadings suggest that a variable strongly affects the principal component.

Correlation and covariance of PCA components
To determine the correlation and covariance matrix for the five components specified from the PCA, equations (4) to (6) were used. The obtained results are presented in Table 4 in the Appendix. Table 4 implies a non-zero variance-covariance matrix of the five important variables determined earlier to contribute significantly to psychopathy personality traits.
To test the validity, reliability, and reproducibility of the results above, we reordered the seventeen variables in the order , , , and we re-ran the PCA on the reorder data. Based on the computed initial eigenvalue, the first six components have eigenvalues greater than 1. Therefore, the variable Society values( , Academic attainments ( , Religion believes ( , Peer group influence ( , Socio-political class ( , and Impunity ( ) were captured to have a significant contribution to the behavioural characteristics of human beings. The component loadings of the reordered data set are presented in Table 5 in the Appendix.
Based on the results in Table 5, the following components were extracted to be important: Society values, academic attainment, religion believes, peer group influence, socio-political class, and impunity. These factors are invariant with the factors extracted in Table 3 to be necessary. Thus, the results from the principal component analysis are not consistent in the determination of significant variables. To resolve the identified weakness of the PCA, we employed the binary logistic regression to determine the significant factor out of the seventeen variables under study and compared the result with the two PCA-based datasets.

Binary logistics regression
In this paper, the binary logistic regression was used to determine the relationship between the significant factors responsible for behavioural characteristics in human beings and (i) Goal setting; and (ii) Aggressiveness as signs of psychopathic personality traits in human beings.
Following the description in section 2.2, equation (10) was used to fit the regression model, while the computation of the Wald statistics was based on equation (13). Sub-section 3.4.1 considered the goal settings with the five variables determined to be highly significant to the psychopath behaviours, while sub-section 3.4.2 is on the aggressiveness factor.

Binary logistic regression: Goal settings versus family upbringing, citizen confidence in government, society values, industrialization, and civilization
Based on the data summary on goal setting and the five significant variables from Table (2), the binary logistic regression coefficients and the Wald test of significance are presented in Table 6 in the Appendix.
From the results in Table 6, only the component civilization significantly explains goal setting. Thus, for every unit increase in the number of civilized persons, there will be a decrease of 0.683 (68.3%) in the log-odds of goal setting.

Binary logistic regression: Aggressiveness versus family upbringing, citizen confidence in government, society values, industrialization, and civilization
Based on the data summary on aggressiveness and the five significant variables from Table (2), the binary logistic regression coefficients and the Wald test of significance are presented in Table 7 in the Appendix. Table 7 imply that the overall model significantly fits the aggressive data. However, at a 5% significance level, only citizen confidence in the government significantly explains aggressiveness. Thus, for every unit increase in the number of persons that has confidence in the government, there is a decrease in the log-odds of aggressiveness. This implies that there will be a decrease in the citizens' aggressiveness.

Binary logistic regression for ALL the BCs
Based on the inconsistent results from the PCA on the varying selected components, we decided to carry out a complete binary logistic regression for goal setting and aggressiveness using the seventeen behavioural characteristics considered in this study. Tables 8 and 9 in the Appendix contain information about the fitted binary logistic regression for the full model for goal setting and aggressiveness, respectively.
With the full model in Table 8, the following components: technological advancement, foreign culture, religion believes, high wealth possession, and power intoxication significantly influences goal setting at a 5% level of significance. We specified aggressiveness as the independent variable and the entire seventeen variables as dependent variables. The result of the binary logistic regression is presented in Table 9. The results in Table 9 reveal that citizens' confidence in government, environmental impact, impunity, and power intoxication are the significant components that affect aggressiveness at a 5% level of significance.

Summary of Results
This study used the principal component analysis to model psychopathic personality traits. On applying the PCA, the seventeen factors were reduced to five based on the first ordering, and six factors were extracted when re-ordered. The factors are considered strong enough to explain all other factors. The five factors based on the initial order were subjected to binary logistic regression to determine the factor that leads to goal setting and aggressiveness. In the first model, goal setting was used as a dependent variable, while the five factors retained were used as independent variables. In the second model, aggressiveness was used as the dependent variable, while the five variables retained were used as independent variables. In the first model, only 'civilization' significantly explains goal setting, while in the second model, only 'confidence in government' significantly explains aggressiveness. The results obtained in this study empirically support the study by Mahmood [21] and Xiao et al. [22]. We can conclude that confidence or lack of confidence in government determines how citizens perceive or handle corruption. If the citizens are not satisfied due to a lack of confidence in government, it may lead to aggressiveness. The results agree with Benish-Weisman et al. [23], who showed that the value system impacts aggression among youths [24][25][26].

Conclusion
In this work, principal component analyses and logistic regression have been used to identify the major factors responsible for behavioural characteristics in human beings. The binary logistics regression showed a relationship between factors responsible for personality traits as demonstrated by aggressiveness and goal setting. The result also revealed a relationship between the major factors responsible for behavioural characteristics and psychopathy personality traits in human beings. Based on the obtained results and conclusion, we recommend that government and health caregivers create more social awareness of the possible causes of identified behavioural characteristics identified in this study. Furthermore, citizens exhibiting abnormal behaviour should be encouraged and assisted in seeking expert help. Future work is to extend this work to cover more states in Nigeria to confirm or otherwise the behavioural characteristics that significantly affects personality traits in Nigeria. In future research, stepwise regression could be used to investigate the variables to include in the logistic regression analysis and results could be compared with the Principal Component extraction of significant factors. Environmental Impact 0.666 X 8 Technology advancements 0.772 X 9 Socio-politically 0.762 X 10 Impunity 0.874 X 11 Embracing foreign culture 0.745 X 12 Religion believes 0.779 X 13 Peer group influence 0.667 X 14 Academic attainments 0.814 X 15 High wealth possession 0.784 X 16 Family believes 0.784 X 17 Power Intoxication 0.705