Beogradski inventar za procenu ličnosti adolescenata kao most između procene ličnosti dece i odraslih

Personality inventories for assessment and study temperament and character of adolescents, an age critical for the finalisation of both normal and pathological mental development, have not been specifically developed. In this paper, we introduce a new personality inventory based on Cloninger's Psychobiological model of personality to assess adolescents from 14 to 18 years of age (the Belgrade Adolescent Personality Inventory - BAPI), which bridges the gap between personality assessment in children and adults. The BAPI is a 46-item Likert scale inventory derived from previous studies using the TCI and JTCI on the Serbian sample. The psychometric properties of the BAPI were tested on the sample of 535 secondary school students in Serbia, aged 15 to 16. In order to assess the fit of the model, the Confirmatory factor analysis (CFA) was performed. The differences between sexes were analysed by MANOVA, while the Latent class analysis (LCA) was applied in order to identify different adaptation profiles among adolescents. The BAPI scales had a satisfactory internal consistency (.66 - .80.) considering the small number of items (5-6) in each scale. The confirmatory factor analysis (CFA) generally supported the main dimensions of temperament and character, as postulated by Cloninger. The only exception was Novelty Seeking, which best fitted a two-factor solution, Explorative curiosity and Impulsivity, separating the 'good' (curious, inquisitive, creative) and 'bad' (impulsive, impatient, disorderly) facets of this trait. Such polarity of Novelty Seeking could be specific for personality development and behaviour disorders in adolescence. The LCA differentiated the personality profiles of well-adapted adolescents from those at risk for maladaptation that manifested low character traits, lack of Persistence and high Novelty seeking Impulsivity subscale. The results support the construct validity of the BAPI and thus provide the basis for its practical application in personality assessment of adolescents and contribute to the theoretical understanding of personality structure and the risk of psychopathology in adolescence.S obzirom da postoji nedostatak namenski konstruisanih inventara za procenu i proučavanje ličnosti adolescenata koji su u uzrastu ključnom za završetak kako normalnog, tako i patološkog mentalnog razvoja, u radu je predstavljen novi inventar ličnosti zasnovan na Klonindžerovom Psihobiološkom modelu ličnosti. Beogradski Inventar Ličnosti Adolescenata (BILA) koristi se za procenu adolescenata uzrasta od 14 do 18 godina i trebalo bi da premosti jaz između procene ličnosti kod dece i odraslih. Upitnik obuhvata 46 stavki Likertovog tipa i nastao je na osnovu prethodnih istraživanja u kojima su korišćeni TCI i JTCI na uzorku naših ispitanika. Psihometrijske karakteristike upitnika ispitane su na uzorku od 535 srednjoškolaca iz Srbije, starosti od 15 do 16 godina. Kako bi se utvrdila adekvatnost modela, urađena je konfirmatorna faktorska analiza. U cilju analize polnih razlika korišćena je MANOVA, dok je analiza latentnih klasa primenjena kako bi se utvrdilo da li se mogu izdvojiti specifični profil ličnosti adolescenata iz uzorka iz opšte populacije. Skale upitnika pokazale su zadovoljavajuću internu konzistenciju (.66 - .80), posebno imajući u vidu mali broj stavki (5-6) u svakoj skali. Konfirmatorna faktorska analiza je načelno potvrdila glavne dimenzije temperamenta i karaktera onako kako ih je definisao Klonindžer. Jedini izuzetak je bio u slučaju Potrage za novinama, za koju je podesnije dvofaktorsko rešenje. Potragu za novinama čine Eksplorativna radoznalost i Impulsivnost koie razlikuju 'dobre' (znatiželjan, radoznao, kreativan) i 'loše' (impulsivan, nestrpljiv, haotičan) odlike ove dimenzije. Ovaj polaritet Potrage za novinama mogao bi da bude specifičan za razvoj ličnosti i poremećaja u ponašanju tokom adolescencije. Analizom latentnih klasa utvrđene su razlike između profila ličnosti dobro adaptiranih adolescenata i onih kod kojih postoji rizik za razvoj problema u ponašanju, a koji imaju nizak skor na crtama karaktera, niži stepen Perzistencije i povišenu Impulsivnost. Dobijeni rezultati potvrđuju konstrukt validnost BILA upitnika i što omogućava njegovu praktičnu primenu u proceni ličnosti adolescenata, pružajući doprinos teorijskom razumevanju strukture ličnosti i rizika od psihopatologije u adolescenciji

Mixed normal conditional heteroskedasticity

Both unconditional mixed-normal distributions and GARCH models with fat-tailed conditional distributions have been employed for modeling financial return data. We consider a mixed-normal distribution coupled with a GARCH-type structure which allows for conditional variance in each of the components as well as dynamic feedback between the components. Special cases and relationships with previously proposed specifications are discussed and stationarity conditions are derived. An empirical application to NASDAQ-index data indicates the appropriateness of the model class and illustrates that the approach can generate a plausible disaggregation of the conditional variance process, in which the components' volatility dynamics have a clearly distinct behavior that is, for example, compatible with the well-known leverage effect. Klassifikation: C22, C51, G1

A dynamic copula approach to recovering the index implied volatility skew

Equity index implied volatility functions are known to be excessively skewed in comparison with implied volatility at the single stock level. We study this stylized fact for the case of a major German stock index, the DAX, by recovering index implied volatility from simulating the 30 dimensional return system of all DAX constituents. Option prices are computed after risk neutralization of the multivariate process which is estimated under the physical probability measure. The multivariate models belong to the class of copula asymmetric dynamic conditional correlation models. We show that moderate tail-dependence coupled with asymmetric correlation response to negative news is essential to explain the index implied volatility skew. Standard dynamic correlation models with zero tail-dependence fail to generate a sufficiently steep implied volatility skew.Copula Dynamic Conditional Correlation, Basket Options, Multivariate GARCH Models, Change of Measure, Esscher Transform

The Value of Mentoring in Living Out Your Calling

The COVID-19 pandemic has altered the way people think about the role that their job plays in their life. There is a greater desire for purposeful work and engaging in a role that positively impacts society, or more simply, to perceive and live a calling. One perceives a calling when they know the occupation that they were destined for or that fits with their values, where their strengths and passions are leveraged, and the job is prosocial in nature. However, perceiving this calling is only a piece of it, as one needs to work in a role where they actively live their calling. Those who solely perceive, but do not live their calling are vulnerable to detrimental psychological and physical consequences. Study 1 of this dissertation used latent profile analysis to determine distinct profiles of participants with varying levels of perceiving a calling and living a calling. With a sample of 498 adults recruited through Prolific, a four-profile solution emerged with the following profiles: “Enacted Calling” (high perceiving a calling, high living a calling), “Average Calling” (average perceiving a calling, average living a calling), “Unanswered Calling” (high perceiving a calling, low living a calling), and “Absent Calling” (low perceiving a calling, low living a calling). Two resources were examined as predictors of group membership: calling motivation to proactively seek a state of lived calling and work volition in terms of perceived agency over occupational choices despite potential barriers. The results revealed that the greater the extent to which participants had calling motivation and work volition, the more likely they were to be classified into the “Enacted Calling” group. Results also revealed that participants with lower work volition were more likely to be classified into the “Unanswered Calling” group. These results suggest that individuals who have both high calling motivation and high work volition are more likely to find themselves in a state of living out their calling, which is aligned with many positive outcomes. Results also suggest that individuals who suffer from not living out their calling (i.e., having an unanswered calling) perceive low agency over their occupational choices. Study 2 of this dissertation used path analysis to test the impact of two types of mentoring support (psychosocial and instrumental) on the calling experience, with a sample of 292 participants from the same sample as Study 1 who responded to all four surveys of the study over eight weeks. Results revealed that psychosocial support positively impacted living a calling and instrumental support positively impacted work volition, which in turn predicted living a calling. Calling motivation was also found to be a predictor of living a calling. These results further demonstrate the key impact that calling motivation and work volition have on living a calling, and adds the dimension that mentors can be leveraged as a relational tool to further protégés on their pathway to reaching a state of enacted calling. Study 3 of the dissertation involved an exploratory analysis of the mentoring relationships involved in Study 2. The 292 protégés reported the details of their mentoring relationship and how similar they were to their mentor on several characteristics. Results revealed that there was an impact of formality, ethnicity similarity, deep-level similarity (e.g., values, beliefs), and industry similarity on protégés who received psychosocial and instrumental support. These results inform practical considerations a protégé may take in seeking a mentor to help them live out their calling

Statistically validated coeherence and intensity in temporal networks of information flows

We propose a method for characterizing the local structure of weighted multivariate time series networks. We draw intensity and coherence of network motifs, i.e. statistically recurrent subgraphs, to characterize the system behavior via higher-order structures derived upon effective transfer entropy networks. The latter consists of a model-free methodology enabling to correct for small sample biases affecting Shannon transfer entropy, other than conducting inference on the estimated directional time series information flows. We demonstrate the usefulness of our proposed method with an application to a set of global commodity prices. Our main result shows that, despite simple triadic structures are the most intense, coherent and statistically recurrent over time, their intensity suddenly decreases after the Global Financial Crisis, in favor of most complex triadic structures, while all types of subgraphs tend to become more coherent thereafter

AN INVESTIGATION OF GROWTH MIXTURE MODELS WHEN DATA ARE COLLECTED WITH UNEQUAL SELECTION PROBABILITIES: A MONTE CARLO STUDY

As researchers begin to use Growth Mixture Models (GMM) with data from nationally representative samples, it becomes increasingly critical for researchers to understand the difficulties associated with modeling data that come from complex sample designs. If researchers naively apply GMM to nationally representative data sets without adjusting for the way in which the sample was selected, the resulting parameter estimates, standard errors and tests of significant may not be trustworthy. Therefore, the objective of the current study was to quantify the accuracy of parameter estimates and class assignment when subjects are sampled with unequal probabilities of selection. To this end, a series of Monte Carlo simulations empirically investigated the ability of GMM to recover known growth parameters of distinct populations when various adjustments are applied to the statistical model. Specifically, the current research compared the performance of GMM that 1) ignores the sample design; 2) accounts for the sample design via weighting; 3) accounts for the sample design via explicitly modeling the stratification variable; and 4) accounts for the sample design by using weights and modeling the stratification variable. Results suggested that a model-based approach does not improve the accuracy of parameter estimates when individuals are sampled with disproportionate sampling probabilities. Not only does this method often fail to converge, when it did converge the parameter estimates exhibited an unacceptable amount of bias. The weighted model performed the best out of all of the models tested, but still resulted in parameter estimates with unacceptably high percentages of bias. It is possible that the distributions of the manifest variables overlap too much, and the aggregate distribution may be unimodal, making it potentially difficult to distinguish among the latent classes and thus affecting the accuracy of parameter estimates. In sum, the current research indicates that GMM should not be used when data are sampled with disproportionate probabilities. Researchers should therefore attend to the study design and data collection strategies when considering the use of a Growth Mixture Model in the analysis phase

A SYSTEMATIC INVESTIGATION OF WITHIN-SUBJECT AND BETWEEN-SUBJECT COVARIANCE STRUCTURES IN GROWTH MIXTURE MODELS

The current study investigated how between-subject and within-subject variance-covariance structures affected the detection of a finite mixture of unobserved subpopulations and parameter recovery of growth mixture models in the context of linear mixed-effects models. A simulation study was conducted to evaluate the impact of variance-covariance structure difference, mean separation, mixture proportion and sample size on parameter estimates from growth mixture models. Data were generated based on 2-class growth mixture model framework and estimated by 1-, 2-, and 3-class growth mixture models using Mplus. Bias, precision and efficiency of parameter estimates were assessed as well as the model enumeration accuracy and classification quality. Results suggested that sample size and data overlap were key factors influencing the convergence rates and possibilities of local maxima in the estimation of GMM models. BIC outperformed ABIC and LMR in identifying the correct number of latent classes. Model enumeration using BIC could be improved by increasing sample size and/or decreasing overall data overlap, and the latter had more impact. Relative bias of parameters was smaller when subpopulation data were more separated. Both the magnitude of mean and variance-covariance separation and variance-covariance differences impacted parameter recovery. Across all conditions, parameter recovery was better for intercept and slope estimates than variance and covariances estimates. Entropy values were as high as the acceptable standards suggested by previous studies for any of the conditions even when data were very well-separated. Class membership assignment was more accurate when mean growth trajectories were more different among subpopulations and mixing proportions were more balanced