Factor Model Research Articles

Ukrainian adaptation of the Clance Impostor Phenomenon Scale (CIPS)

Expanding the methodological toolkit of research, particularly through adapting foreign psychometric tools, is crucial for Ukrainian psychology. This allows for conducting new research and comparing its results. One promising direction is studying the impostor phenomenon, manifested in an individual’s doubts about their achievements. The Clance Impostor Phenomenon Scale, developed by foreign researchers, is used for this purpose. This study aimed to adapt the Clance Impostor Phenomenon Scale into Ukrainian. The Clance Impostor Phenomenon Scale was translated into Ukrainian by two psychologists using the back-translation method. The research sample consisted of 297 students aged 18 to 22 (M = 20.4, SD = 1.49), including 82 males and 215 females. Descriptive statistics, reliability analysis of scales, correlation analysis (Spearman’s coefficient), and confirmatory factor analysis were used. The results of the study indicate a considerable degree of consistency between the obtained data and the foundational theoretical model. The assessment of content validity revealed a significant number of statistically significant correlations. A high level of test-retest reliability was identified for the adapted methodology. The resulting factor model is conceptually similar to the original; however, it is not identical. Other studies have also noted the emergence of new factor models, suggesting the potential for refinement of this methodology or reflecting age, temporal, and cross-cultural differences among the subjects studied. The evaluation of the psychometric characteristics of the Ukrainian version of the Clance Impostor Phenomenon Scale demonstrated high internal validity and reliability for the adapted tool. The Ukrainian version of the Clance Impostor Phenomenon Scale may be valuable for the psychodiagnosis of impostor syndrome manifestations and further standardisation and application in comparative research. The findings could contribute to optimising psychological support by taking into account the specific characteristics of impostor syndrome in the course of psychological work

Using Data-Driven Algorithms with Large-Scale Plasma Proteomic Data to Discover Novel Biomarkers for Diagnosing Depression.

Given recent technological advances in proteomics, it is now possible to quantify plasma proteomes in large cohorts of patients to screen for biomarkers and to guide the early diagnosis and treatment of depression. Here we used CatBoost machine learning to model and discover biomarkers of depression in UK Biobank data sets (depression n = 4,479, healthy control n = 19,821). CatBoost was employed for model construction, with Shapley Additive Explanations (SHAP) being utilized to interpret the resulting model. Model performance was corroborated through 5-fold cross-validation, and its diagnostic efficacy was evaluated based on the area under the receiver operating characteristic (AUC) curve. A total of 45 depression-related proteins were screened based on the top 20 important features output by the CatBoost model in six data sets. Of the nine diagnostic models for depression, the performance of the traditional risk factor model was improved after the addition of proteomic data, with the best model having an average AUC of 0.764 in the test sets. KEGG pathway analysis of 45 screened proteins showed that the most significant pathway involved was the cytokine-cytokine receptor interaction. It is feasible to explore diagnostic biomarkers of depression using data-driven machine learning methods and large-scale data sets, although the results require validation.

Self-concealment scale (SCS) in middle-aged people: psychometric features and cross-age equivalence test

BackgroundIndividuals’ concealment of negative information and privacy may lead to impaired social interactions and threatened health conditions. This study aimed to investigate the effectiveness of the Self-Concealment Scale (SCS) in the middle-aged Chinese population and to examine the equivalence of the SCS among different age groups.MethodsThe current research adopted the SCS, Distress Disclosure Index (DDI), Revised Cheek and Buss Shyness Scale (RCBS), Social Interaction Anxiety Scale (SIAS), Social Phobia Scale (SPS), UCLA Loneliness Scale (ULS-8), and Kessler Psychological Distress Scale (K10) to survey 1124 middle-aged people. To explore the factor structure of the SCS, the study employed exploratory factor analysis and confirmatory factor analysis. The reliability of the SCS was measured based on Cronbach’s α coefficients, McDonald’s Omega coefficients, and split-half reliability. Correlation analysis was applied to examine the relationship between SCS and RCBS, SIAS, SPS, ULS-8, and K10. Moreover, this study recruited 1458 emerging adults and 1104 older adults to identify the cross-age invariance of the SCS.ResultsExploratory factor analysis of the middle aged adults’ data supported a single factor model. The factor loadings of SCS items ranged from 0.62 to 0.73, the commonality ranged from 0.39 to 0.53, and the single-factor model fitted well. The scale showed a Cronbach’s α coefficient value of 0.895, McDonald’s Omega coefficient of 0.893, and a split-half reliability coefficient value of 0.861. In addition, the SCS demonstrated invariance in emerging adults, middle-aged adults, and older adults. Further analysis showed that the scores of the SCS (F = 3.55, p = 0.029) among emerging adults (M = 26.43, SD = 7.96) were significantly higher than among middle-aged (M = 25.77, SD = 7.79), and older cohort (M = 25.69, SD = 7.91).ConclusionThe SCS revealed favorable psychometric characteristics among the middle-aged Chinese population. The degree of self-concealment among emerging adults was higher than that of middle-aged and older people.

Factor Modelling of Russian Parents’ Opinions on the Impact of the Parental Leave System on the Birth Rate

In the context of the negative demographic trend in Russia, new mechanisms for stimulating birth rates are of particular relevance. One such instrument is parental leave policy. The aim of our study is to model the impact of potential transformations in parental leave on the reproductive intentions of Russian parents using factor analysis. In 2022, we conducted a survey of Russian parents with children under the age of 18 months. The sample consisted of 1,000 individuals, and the survey covered nearly all regions of Russia. We identified two main components: 1. Opinions on the flexibility of the parental leave system as a factor for improving birth rates; 2. Opinions on the amount of parental leave benefits as a factor for improving birth rates. We found significant correlations between the values of these components and several socio-demographic characteristics of the respondents, including gender, age, satisfaction with the current division of parental responsibilities within the family, the desire to legally mandate fathers to take a portion of parental leave in Russia, and the prospective evaluation of the financial status change if mandatory paternal leave were to be introduced. Our results contribute to the ongoing debate on the necessity and feasibility of improving the existing parental leave system in Russia and its potential impact on birth rates.

Long-Term Manuring Enhanced Compositional Stability of Glomalin-Related Soil Proteins through Arbuscular Mycorrhizal Fungi Regulation

Glomalin-related soil proteins (GRSP) play a crucial role in strengthening soil structure and increasing carbon (C) storage. However, the chemical stability of GRSP and related arbuscular mycorrhizal fungi (AMF) community response to fertilization remains unclear. This study investigated C and nitrogen (N) contents, three-dimensional fluorescence characteristics in GRSP, and AMF properties based on a field experiment that was subjected to 29 years of various fertilizations. The experiment included treatments with no fertilizer (CK), chemical fertilizer (NPK), manure (M), and manure combined with NPK (NPKM) treatments. Results showed that GRSP contained 37–49% C and 6–9% N, respectively. Compared with CK and NPK, the C and N proportions in GRSP significantly increased under M and NPKM. Using the parallel factor model, four fluorescent components of GRSP were identified: one fulvic acid-like component (C2), one tyrosine-like component (C4), and two humic acid-like components (C1, C3). Under M and NPKM, the fluorescent intensity of C2 and C4 decreased, while the humification index (HIX) increased relative to CK and NPK, indicating that organic fertilization could enhance the stability of GRSP. The C and N proportion in GRSP positively associated with soil organic C (SOC), total N (TN), available phosphorus (AP), AMF biomass, and diversity, while C2 and C4 showed negative associations. Structural equation modeling further revealed that manure-induced changes in pH, SOC, TN, and AP increased AMF biomass and diversity, thereby altering GRSP composition and stability. This study provides valuable insights into the compositional traits of GRSP, contributing to sustainable soil management and C sequestration in agroecosystems.

Ab initio uncertainty quantification in scattering analysis of microscopy.

Estimating parameters from data is a fundamental problem in physics, customarily done by minimizing a loss function between a model and observed statistics. In scattering-based analysis, it is common to work in the reciprocal space. Researchers often employ their domain expertise to select a specific range of wave vectors for analysis, a choice that can vary depending on the specific case. We introduce another paradigm that defines a probabilistic generative model from the beginning of data processing and propagates the uncertainty for parameter estimation, termed the ab initio uncertainty quantification (AIUQ). As an illustrative example, we demonstrate this approach with differential dynamic microscopy (DDM) that extracts dynamical information through minimizing a loss function for the squared differences of the Fourier-transformed intensities, at a selected range of wave vectors. We first show that the conventional way of estimation in DDM is equivalent to fitting a temporal variogram in the reciprocal space using a latent factor model as the generative model. Then we derive the maximum marginal likelihood estimator, which optimally weighs the information at all wave vectors, therefore eliminating the need to select the range of wave vectors. Furthermore, we substantially reduce the computational cost of computing the likelihood function without approximation, by utilizing the generalized Schur algorithm for Toeplitz covariances. Simulated studies of a wide range of dynamical systems validate that the AIUQ method improves estimation accuracy and enables model selection with automated analysis. The utility of AIUQ is also demonstrated by three distinct sets of experiments: first in an isotropic Newtonian fluid, pushing limits of optically dense systems compared to multiple particle tracking; next in a system undergoing a sol-gel transition, automating the determination of gelling points and critical exponent; and lastly, in discerning anisotropic diffusive behavior of colloids in a liquid crystal. These studies demonstrate that the new approach does not require manually selecting the wave vector range and enables automated analysis.

End-to-end, decision-based, cardinality-constrained portfolio optimization

Portfolios employing a (factor) risk model are usually constructed using a two step process: first, the risk model parameters are estimated, then the portfolio is constructed. Recent works have shown that this decoupled approach may be improved using an integrated framework that takes the downstream portfolio optimization into account during parameter estimation. In this work we implement an integrated, end-to-end, predict-&-optimize framework to the cardinality-constrained portfolio optimization problem. To the best of our knowledge, we are the first to implement the framework to a nonlinear mixed integer programming problem. Since the feasible region of the problem is discontinuous, we are unable to directly differentiate through it. Thus, we compare three different continuous relaxations of increasing tightness to the problem which are placed as an implicit layers in a neural network. The parameters of the factor model governing the problem’s covariance matrix structure are learned using a loss function that directly corresponds to the decision quality made based on the factor model’s predictions. Using real world financial data, our proposed end-to-end, decision based model is compared to two decoupled alternatives. Results show significant improvements over the traditional decoupled approaches across all cardinality sizes and model variations while highlighting the need of additional research into the interplay between experimental design, problem size and structure, and relaxation tightness in a combinatorial setting.

Multivariate Stochastic Volatility with Co-Heteroscedasticity

Abstract A new methodology that decomposes shocks into homoscedastic and heteroscedastic components is developed. This specification implies there exist linear combinations of heteroscedastic variables that eliminate heteroscedasticity; a property known as co-heteroscedasticity. The heteroscedastic part of the model uses a multivariate stochastic volatility inverse Wishart process. The resulting model is invariant to the ordering of the variables, which is shown to be important for volatility estimation. By incorporating testable co-heteroscedasticity restrictions, the specification allows estimation in moderately high-dimensions. The computational strategy uses a novel particle filter algorithm, a reparameterization that substantially improves algorithmic convergence and an alternating-order particle Gibbs that reduces the amount of particles needed for accurate estimation. An empirical application to a large Vector Autoregression (VAR) is provided, finding strong evidence for co-heteroscedasticity and that the new method outperforms some previously proposed methods in terms of forecasting at all horizons. It is also found that the structural monetary shock is 98.8 % homoscedastic, and that investment and the SP 500 index are nearly 100 % determined by fat tail heteroscedastic shocks. A Monte Carlo experiment illustrates that the new method estimates well the characteristics of approximate factor models with heteroscedastic errors.

Is the Fama French model robust in the Chinese stock market?

Many previous studies have analyzed the different stock return models in various segments of the Chinese stock market. This essay analyzes the Fama-French five factors and three factors model in the main board, the second board, and STAR market. It finds that the three-factor models are efficient in all three markets expect for the STAR market, while the five-factor model is inefficient in testing the second board market alone and overall efficient in testing the mixed market and the STAR market. Furthermore, it is also found that the MKT factors are the most significant contributors to the portfolio excess return in all six situations. The HML and SML factors also exhibit constant relations with the portfolio return. Besides, it is found that HML and SMB factors are not significantly correlated with the stock’s performance on the STAR market. Overall, the relative efficiency of Fama French's three and five-factor model in explaining these markets is proved. Investors can use these two models to analyze the stock return and earn profit in the next period, and policymakers can also employ this model to understand the market and decide suitable fiscal and monetary policies.

Recollections of parental psychological control: Associations with dysfunctional individuation, depressive symptoms, and shame and guilt.

Excessive parental psychological control may disrupt individuals' psychosocial development, thus influencing later psychological problems. The present study tests a retrospectively worded Parental Control and Disrespect Scale (PCDS) to examine factor structure and model fit as well as associations with individuation-related difficulties and negative affect. Study measures were completed by 482 adults at a single time-point. Confirmatory factor analysis suggested acceptable model fit of the retrospectively worded PCDS. PCDS scores were significantly associated with dysfunctional individuation, depressive symptoms, and generalized shame and guilt, remaining significant after controlling for age, gender, and neuroticism. Moderation analysis indicated certain associations as stronger among female participants, such that dysfunctional individuation was lower at decreased levels of parental control among women, while generalized guilt was higher at increased parental control among women. Findings support the retrospective use of the PCDS, indicating links between parental psychological control, individuation-related difficulties, and negative affect problems.

Regime-Switching Factor Models and Nowcasting with Big Data

Regime-Switching Factor Models and Nowcasting with Big Data

Personality and leadership: Meta-analytic review of cross-cultural moderation, behavioral mediation, and honesty-humility.

We advance the trait approach to leadership by leveraging a large multinational database on leader emergence (k = 120 samples, N = 32,579) and leader effectiveness (k = 116, N = 42,487) to extend Judge et al.'s (2002) classic meta-analysis of Big Five personality and leadership. By testing novel hypotheses rooted in culturally endorsed implicit leadership theory and socioanalytic theory, we offer three unique insights. First, in collectivist societies (cultures that value interdependence with one's group), the five factor model traits-and leader Extraversion and Agreeableness in particular-are stronger predictors of leader effectiveness, consistent with the theorized need for enhanced social coordination in such cultures. Second, a theoretical model is proposed to specify that leader Big Five trait effects are mediated by leader behavior (confirming that Consideration mediates Extraversion and Agreeableness, whereas Initiating Structure mediates Conscientiousness, Extraversion, and Openness). Third, trait Honesty-Humility robustly predicts leader effectiveness beyond the Big Five traits, expanding the trait approach. New implications for understanding when and why personality traits predict leadership are discussed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Psychometric Properties and Factor Structure of the Disgust Scale-Revised (DS-R) for the Romanian population

Research on measuring the intensity of disgust has required the development of instruments to measure this construct. Given that, a more culturally appropriate scale is needed to ensure a valid and reliable measurement of disgust. The concept of “disgust” does not have an appropriate literature in Romanian pathology, and Disgust Scale-Revised (DS-R) is quite rarely used in the public health context. Therefore, this study focuses on the psychometric properties of the Romanian adaptation of the DS-R, one of the preferred instruments for measuring disgust. More specifically, the research investigated different forms of reliability and validity of the DS-R, using both Classical Test Theory (CTT) and Item Response Theory (IRT) analysis approaches. In general, the results showed good fidelity, fit of the single- factor model of the DS-R, good convergent and divergent validity with respect to the relationships of this scale with other variables. The study also highlights the important role of disgust and anxiety in predicting obsessive- compulsive symptoms. In conclusion, further refinement of the scale could be useful for a more comprehensive development of this instrument in the clinical setting.

Oscillation mechanism and predictive model of explosion load for natural gas in confined tube

Gas explosion in confined space often leads to significant pressure oscillation. It is widely recognized that structural damage can be severe when the oscillation frequency of the load resonates with the natural vibration frequency of the structure. To reveal the oscillation mechanism of gas explosion load, the experiment of gas explosion was conducted in a large-scale confined tube with the length of 30 m, and the explosion process was numerically analyzed using FLACS. The results show that the essential cause of oscillation effect is the reflection of the pressure wave. In addition, due to the difference in the propagation path of the pressure wave, the load oscillation frequency at the middle position of the tunnel is twice that at the end position. The average sound velocity can be used to calculate the oscillation frequency of overpressure accurately, and the error is less than 15%. The instability of the flame surface and the increase of flame turbulence caused by the interaction between the pressure wave and the flame surface are the main contributors to the increase in overpressure and amplitude. The overpressure peaks calculated by the existing flame instability model and turbulence disturbance model are 31.7% and 34.7% lower than the numerical results, respectively. The turbulence factor model established in this work can describe the turbulence enhancement effect caused by flame instability and oscillatory load, and the difference between the theoretical and numerical results is only 4.6%. In the theoretical derivation of the overpressure model, an improved model of dynamic turbulence factor is established, which can describe the enhancement effect of turbulence factor caused by flame instability and self-turbulence. Based on the one-dimensional propagation theory of pressure wave, the oscillatory effect of the load is derived to calculate the frequency and amplitude of pressure oscillation. The average error of amplitude and frequency is less than 20%.

The society human immunodeficiency virus health literacy scale: the development and psychometric assessment

Society human immunodeficiency virus (HIV) related health literacy is an essential behavioural skill that contributes to understanding the disease and responding to people living with HIV. Measuring HIV health literacy in the community requires reliable tools to produce an objective health literacy index. This study aimed to design and examined the society HIV health literacy scale's (SHIVAL) psychometric properties. A cross sectional study was conducted on 381 people without HIV. Sample recruitment used convenience sampling. Instrument development and psychometric analysis include item pool construction and content validity examination, consistency reliability test, and exploratory and confirmatory factor analysis. A standardized four factor model fits the HIV health literacy measure well. This scale has 15 items with good Cronbach's Alpha reliability index and content validity index. The final SHIVAL Scale has been determined reliable and appropriate for measuring health literacy related to HIV. Nurses or healthcare professionals can use this scale to predict an individual's HIV health literacy, thereby influencing the social intervention of HIV disease and enabling effective community health literacy strategies.

Metacognitive Abilities and Socio-Psychological Adaptation of People of Mature Age: Features of Relationships

The analysis is aimed at determining characteristics of relationships between metacognitive abilities and socio-psychological adaptation in homogeneous cluster groups represented by people of mature age. The main research methods were: cluster analysis, which allows us to identify relatively homogeneous groups; “bootstrap” method to check the normality of the distribution. The study also used factor analysis to determine the relationship between the characteristics of metacognitive abilities and socio-psychological adaptation. The total following variance in the factor models (86.24%, 99.77%, and 100%) as well as the levels of predictive consistency (p≤0.01 to p≤0.05) reflect the reliability and significance of the results obtained. Data processing and interpretation were carried out using qualitative and quantitative statistical methods: Fisher’s angular transformation criterion (φ*) for comparing two samples according to the occurrence frequency of the effect which is interesting to the researcher; Kruskal-Wallis rank sum test (IBM SPSS Statistics).The results of the study allow us to talk about ambivalence in the manifestation of metacognitive abilities formation in people of mature age: on the one hand, a general tendency towards a decrease in the development of metacognitive abilities was discovered, on the other hand, an increase in the degree of structural organization of the entire metacognitive system was revealed in the process of growing up, which generally compensates for the natural decline of potential resource capabilities of adults. This ambiguity in the manifestation of metacognitive abilities is reflected in their relationship with indicators of socio-psychological adaptation of adults. The identified features of the relationship in terms of harmony/inconsistency, integrity, and the content of crystallizing factors generally indicate a multidimensional structure of the relationship between metacognitive abilities and socio-psychological adaptation, when only in some cases can we talk about a direct correlation of these variables: a high level of development of metacognitive abilities determines high social-psychological adaptation.

Bootstrap inference on a factor model based average treatment effects estimator

We propose a novel bootstrap procedure for conducting inference for factor model-based average treatment effects estimators. Our method overcomes bias inherent to existing bootstrap procedures and substantially improves upon existing large sample normal inference theory in small sample settings. The finite sample improvements arising from the use of our proposed procedure are illustrated via a set of Monte Carlo simulations, and formal justification for the procedure is outlined.

Stress intensity factor models using mechanics-guided decomposition and symbolic regression

The finite element method can be used to compute accurate stress intensity factors (SIFs) for cracks with complex geometries and boundary conditions. In contrast, handbook solutions act as surrogate SIF models that provide significantly faster evaluation times. However, the development of conventional surrogate SIF models relies on manual development based on low-order parameterizations. This limits surrogate model accuracy and generalizability. In this paper, we develop a framework for the automated development of mechanics-guided handbook SIF solutions by using interpretable machine learning via genetic programming for symbolic regression (GPSR). Formalizing the mechanics-based approach of Raju and Newman, SIF training data is decomposed into multiple subsets. This decomposition enables parallel GPSR model development of subfunctions, each of which accounts for specific geometrical corrections with respect to a known analytical model. Using this mechanics-based approach with GPSR allows for equations to be learned with improved accuracy and reduced complexity relative to the Raju Newman equations while maintaining the inherent interpretability of mathematical expressions. In this paper, we present equations that match the complexity of the Raju Newman equations while having reduced error, as well as equations with similar errors and reduced complexity.

Mental health profiles of 15-year-old adolescents in the Nordic Countries from 2002 to 2022: person-oriented analyses

BackgroundStudies of time trends in Nordic adolescents' mental health have often relied on analyses of adolescents’ psychosomatic symptoms. In this study, we examine adolescents' self-reports on mental health in the context of the dual factor model, which encompasses both overt manifestations of mental health symptoms and subjective perception of one's health status.MethodThe objective of this study was to employ a person-oriented approach utilizing cluster analysis to discern time trends in mental health profiles of Nordic adolescents, using their psychosomatic complaints and their perception of their overall health as cluster variables. The resulting health profiles were then subjected to a comparative analysis with regard to different measures of psychological and social adjustment. The mental health profiles were based on data from the Health Behaviour in School-aged Children (HBSC) survey, which was conducted among almost 50000 15-year-olds in five Nordic countries (Denmark, Finland, Iceland, Norway, and Sweden) between 2002 and 2022.ResultsMental health profiles exhibiting comparable content were observed in all Nordic countries, including profiles indicating adequate health, perceived good health, perceived poor health, high psychosomatic complaints, and dual health problems (defined as both high psychosomatic complaints and perceived poor health). These health profiles showed similar trends over time in the Nordic countries. Significant gender differences were observed. In 2002, adequate health was the dominant profile for both sexes. After 20 years, however, the high psychosomatic profile became the most common profile among girls. Among the three risk profiles, namely perceived poor health, high psychosomatic complaints and dual health problems, adolescents in the dual problems profile had the most psychological and social adjustment problems.ConclusionsThe comparatively lower incidence of adjustment problems among adolescents in the high psychosomatic profile relative to the dual mental health group challenges the prevailing view that there has been a sharp increase in mental health problems among Nordic adolescents. This view was largely based on the observed rise in psychosomatic symptoms. Indeed, there was a doubling in the proportion of adolescents in the high psychosomatic complaints profile between 2002 and 2022. This increase was considerably more pronounced than that observed for the dual health problems profile which exhibited most problems.

An optimized intelligent open-source MLaaS framework for user-friendly clustering and anomaly detection

As data grow exponentially, the demand for advanced intelligent solutions has become increasingly urgent. Unfortunately, not all businesses have the expertise to utilize machine learning algorithms effectively. To bridge this gap, the present paper introduces a cost-effective, user-friendly, dependable, adaptable, and scalable solution for visualizing, analyzing, processing, and extracting valuable insights from data. The proposed solution is an optimized open-source unsupervised machine learning as a service (MLaaS) framework that caters to both experts and non-experts in machine learning. The framework aims to assist companies and organizations in solving problems related to clustering and anomaly detection, even without prior experience or internal infrastructure. With a focus on several clustering and anomaly detection techniques, the proposed framework automates data processing while allowing user intervention. The proposed framework includes default algorithms for clustering and outlier detection. In the clustering category, it features three algorithms: k-means, hierarchical clustering, and DBScan clustering. For outlier detection, it includes local outlier factor, K-nearest neighbors, and Gaussian mixture model. Furthermore, the proposed solution is expandable; it may include additional algorithms. It is versatile and capable of handling diverse datasets by generating separate rapid artificial intelligence models for each dataset and facilitating their comparison rapidly. The proposed framework provides a solution through a representational state transfer application programming interface, enabling seamless integration with various systems. Real-world testing of the proposed framework on customer segmentation and fraud detection data demonstrates that it is reliable, efficient, cost-effective, and time-saving. With the innovative MLaaS framework, companies may harness the full potential of business analysis.