Multilevel Factor Research Articles

A multilevel perspective on the role of job demands, job resources, and need satisfaction for employees' outcomes

This study investigates the mediator role of psychological need satisfaction for the effects of job demands and resources on turnover intentions, psychological distress, and work-to-family conflict, simultaneously at the employee and work unit levels. In doing so, we consider how need satisfaction, when considered at the work unit level, creates a context likely to play an additional role in the prediction of these outcomes. These questions were investigated using a combination of doubly latent multilevel confirmatory factor analyses and structural equation models applied to responses provided by a large sample (N = 5716 employees nested within 50 work units) of Canadian Armed Forces/Department of National Defence personnel. The results supported the idea that work environment effects on the outcomes considered in this study were mediated by psychological need satisfaction at the individual and work unit levels and demonstrated that these associations were driven by global work environment perceptions and global need satisfaction. Furthermore, need satisfaction was found to create a context, at the work unit level, leading employees working in units including more highly satisfied co-workers to present higher levels of turnover intentions but lower levels of work-to-family conflict than would be expected based on their individual levels of need satisfaction.

Developmental changes in mothers’ perception of knowledge, solicitation, and child disclosure during middle childhood

AbstractParents can actively seek knowledge (solicitation) or receive information provided willingly by the child (disclosure). In adolescence, disclosure is the main source of parental knowledge, but its importance may take root earlier in the course of development. We examined: 1) the factor structure of an instrument adapted for middle childhood measuring maternal perception of knowledge, solicitation, and children's self‐disclosure; 2) changes in these dimensions over middle childhood; and 3) the respective contribution of solicitation and disclosure to parental knowledge. The mothers of 793 elementary school students (61.5% boys, 80.2% Canadian‐born) completed a questionnaire annually from Grades 1 to 4. Multilevel confirmatory factor analysis confirmed the instrument's structure at all time points. Growth curve analyses showed that mothers’ perception of knowledge slightly declined from Grades 1 to 4. With respect to mothers’ perceived parental solicitation and child disclosure, gender interactions emerged. Solicitation declined for girls but remained stable for boys, while disclosure declined for girls but increased for boys over time. In addition, mothers' perception ofdisclosure and solicitation are both main sources of maternal knowledge regardless of age and gender in middle childhood.

INFLUENCE OF ATMOSPHERIC PRESSURE ON THE COVID-19 EPIDEMIC PROCESS

Introduction. Identification of the main determinants of temporal changes in the epidemic process of COVID-19 is important for the development of effective strategies for the prevention and control of this infection. Attempts to determine the relationship between the cyclical changes in atmospheric pressure and the course of the epidemic process of COVID-19 were made by scientists repeatedly, but scientific data on the influence of atmospheric pressure on the epidemic process of COVID‑19 are still controversial. The objective of the research is to investigate the influence of atmospheric pressure on the epidemic process of COVID-19 using the example of Sumy city (Ukraine). Materials and methods. In this research, we used data on the daily number of new cases of COVID-19, which were obtained from the daily reports of the Sumy Regional Center for Disease Control and Prevention of the Ministry of Health of Ukraine, as well as the results of daily monitoring of atmospheric pressure indicators of the Sumy Regional Hydrometeorology Center. The period of observation was from 05/1/2020 to 12/1/2022. The dynamics of changes in meteorological indicators and the daily number of new cases of COVID-19 (hereinafter, the incidence of COVID-19) in Sumy were studied using simple moving averages. The smoothing period for morbidity indicators was equal to 7 days, for atmospheric pressure indicators was 19 days, and the lag between a series of indicators was 7 days. The total number of paired observations of the variables is n = 945. To find out whether the incidence of COVID‑19 (the response variable) varies depending on the level of atmospheric pressure (the independent variable), a non-parametric Kruskal–Wallis’s analysis of variance was used. For this, the numerical series of atmospheric pressure values was converted into a categorical series, and the quartile of the series was used as a grouping feature. A posteriori analysis (post hoc test) was performed using the Mann–Whitney test. The quantitative assessment of the differences between groups in the Mann–Whitney test was evaluated by Cohen's test. Results. Kruskal–Wallis’s analysis of variance. The statistically significant difference in the incidence of COVID-19 was established in the four comparison groups (χ2 = 119.462, 3 df, p-value = 0.0001). The median of incidence of COVID-19 and the interquartile range in the 1st comparison group was 25 (6.4; 85.3) cases, in the 2nd group – 10.6 (5.0; 40.6) cases, in the 3rd group – 60.4 (14.3; 149.9) cases, in the 4th group – 99.1 (13.6; 202.5) cases. Mann–Whitney test. The incidence of COVID-19 is lowest within the 2nd quartile of atmospheric pressure (743.63–745.0 mm Hg); an increase in atmospheric pressure to the level of the 3rd (745.01–748.11 mm Hg) and 4th (748.12–755.1 mm Hg) quartiles, as well as its decrease to the level of the 1st quartile (738.6–743.62 mm Hg), is associated with a statistically significant increase in the number of COVID-19 cases (p-value = 0.0000–0.0012). We estimated the magnitude of the effect as small (r = 0.15) in the case of a decrease in atmospheric pressure and medium in the case of an increase in atmospheric pressure. Conclusions. 1. The results of the Kruskal–Wallis’s test showed that the multilevel factor, which is atmospheric pressure (explanatory variable), affects the level of the incidence of COVID-19 (response variable) and, therefore, the activity of its mechanism of transmission (χ2 = 119.462, 3 df, p-value = 0.0001). The dependence of the daily cases of COVID-19 on atmospheric pressure is a non-linear function. This confirms the expediency of using the Kruskal–Wallis’s test in this study, and also indicates the irrationality of using Spearman and Pearson correlation analyses to study the correlation between variables. The incidence of COVID-19 was minimal at average atmospheric pressure values of 743.6–745.0 mm Hg. Any changes in atmospheric pressure that went beyond this interval in one direction or another led to a statistically significant increase in morbidity. We estimate the magnitude of the effect as small in the case of a decrease in atmospheric pressure and medium in the case of an increase in atmospheric pressure.

Model Selection for General Multi-Level Group Factor Models with Global, Regional and Local Factors

Model Selection for General Multi-Level Group Factor Models with Global, Regional and Local Factors

A single-phase level-shifted duty controlled multilevel rectifier-based EV charger for wide output voltage range

Nowadays, electric vehicles (EVs) are gaining popularity with customers and as an environmentally-friendly option owing to the rise in petroleum prices and pollution, respectively. The battery is the prime source of power for EVs. This paper presents, a level-shifted duty controlled multilevel power factor correction (PFC) AC-DC converter, followed by a phase-shifted full-bridge converter (PSFBC) as an efficient charging solution for EVs. The adopted topology uses fewer semiconductor switching devices than traditional multilevel converters, which minimises switching losses, making it a good choice for a compact EV charger. Complete system design is carried out for a single-phase, 3.3 kW, EV charger, with 220 vrms AC input and 48-72 V DC output voltage for batteries. This EV charger has improved the system reliability and efficiency, along with the near-unity power factor (UPF) in compliance with the international IEC-61000-3-2 PQ standard. MATLAB/Simulink and experimental results are presented to validate the aforementioned functionalities of this EV charger.

Analysis and Application of Eco-environmental Value of Saihanba Forest Farm Based on Fuzzy Analytic Hierarchy Process and K-Means Clustering

In this paper, we aim to establish an evaluation model for the impact of Saihanba on the ecological environment to quantitatively evaluate its impact due to its significant role on maintaining ecological balance and stability. We apply the fuzzy analytic hierarchy process method on building an evaluation model of Saihanba with a multilevel evaluation factor system. In addition, we promote the model with the help of the K-means clustering analysis method after optimizing to determine which geographical locations in China and Pakistan need to establish ecological reservation.

MEASURING PERSONALITY IN DAILY LIFE: EVIDENCE FROM AN AGE-HETEROGENEOUS ADULT SAMPLE

Abstract Research on personality has theorized that repeated short-term experiences can lead to changes in personality traits across years or decades. Whereas much research on these short-term experiences relevant to personality has been done in samples of college students, this study intended to measure personality-relevant short-term experiences among an age-heterogeneous sample of adults. As part the Effects of Stress on Cognitive Aging, Physiology, and Emotions study, 260 participants (Mage=46.49 years, range=25-65 years) completed a measure of Big Five personality traits before completing a 14-day ecological momentary assessment (EMA) period during which participants reported their momentary negative and positive thoughts, emotions, and social interactions up to six times per day on study-provided smartphones. We hypothesized that these EMA reports could be used as daily markers of trait extraversion and trait neuroticism such that these daily experiences could be interpreted as manifestations of personality traits in daily life. Results of parallel multilevel confirmatory factor analyses showed good model fit (extraversion: CFI=0.96; TLI=0.95; RMSEA=0.03; neuroticism: CFI=0.95; TLI=0.94; RMSEA=0.04). For both extraversion and neuroticism, the latent trait factor and the latent daily-marker factor were positively correlated (extraversion: r=0.36; SE=0.07; p<.001; neuroticism: r=0.45; SE=0.07; p<.001). Results suggest that among an age-heterogeneous adult sample, momentary thoughts, feelings, and behaviors across a two-week period represented expressions of extraversion and neuroticism in daily life. Measuring these short-term experiences is meaningful for understanding how personality changes across adulthood, and future work can use longitudinal data to test if daily markers of personality are sensitive to fluctuations and changes in personality.

Disentangling motivation and engagement: Exploring the role of effort in promoting greater conceptual and methodological clarity.

Conflation over motivation and engagement has historically impeded research and practice. One reason for this is because definition and measurement have often been too general or diffuse-especially in the case of engagement. Recently conceptual advances aimed at disentangling facets of engagement and motivation have highlighted a need for better psychometric precision-particularly in the case of engagement. To the extent that engagement is inadequately assessed, motivation research involving engagement continues to be hampered. The present study investigates multidimensional effort (a specific facet of engagement) and how it relates to motivation. In particular, we examine the associations between specific positive and negative motivation factors and dimensions of effort, thereby shedding further insight into how different types of motivation interplay with different types of engagement. Drawing on data from a sample of 946 Australian high school students in 59 mathematics classrooms at five schools, this study hypothesized a tripartite model of academic effort in terms of operative, cognitive, and social-emotional dimensions. A novel nine-item self-report Effort Scale measuring each of the three factors was developed and tested for internal and external validity-including its relationship with multidimensional motivation. Multilevel confirmatory factor analyses were conducted to test the factor structure and validity of multidimensional effort. Additionally, doubly-latent multilevel structural equation models were conducted to explore the hypothesized motivation → engagement (effort) process, and the role of student- and classroom-level background attributes as predictors of both motivation and effort. Results supported the hypothesized model of tripartite effort and its distinctiveness from motivation, and showed that key dimensions of motivation predicted effort at student- and classroom-levels. This study provides implications and suggestions for future motivation research and theorizing by (1) establishing evidence for the validity of a novel engagement framework (multidimensional effort), and (2) supporting future measurement and practice in academic engagement juxtaposed with multidimensional motivation-critical for better understanding engagement, and motivation itself.

Supplemental Material for Creating a Brief Form of the Supervisory Working Alliance Inventory in the Chinese Cultural Context Using Multilevel Factor Analyses

Supplemental Material for Creating a Brief Form of the Supervisory Working Alliance Inventory in the Chinese Cultural Context Using Multilevel Factor Analyses

Examining the within- and between-person structure of a short form of the positive and negative affect schedule: A multilevel and dynamic approach.

Increasing research uses intensive longitudinal designs to examine antecedents and consequences associated with dynamic affective processes. These studies often rely on the Positive and Negative Affect Schedule (PANAS) to measure affect. Studies assessing the structure of the PANAS are largely cross-sectional in nature and cannot always disentangle within-person variability from between-person differences in affect. A paucity of studies examines structural similarities and differences in affect at the between- and within-person levels, and few have done so with short-form versions of the PANAS. This study investigates the multilevel factor structure of the 10-item PANAS-short-form in a sample of young adults (n = 272) measured daily consecutively over 1 month. Additionally, dynamic relations between positive and negative affect, depressive symptoms, stress, and physical symptoms were examined. Results support a three factors within and two factors between multilevel structural model. Distinct dynamic relations were observed among positive affect, negative affect, stress, and physical symptoms at the within level. Positive and negative affect were correlated with depressive symptoms, stress, and physical symptoms at the between level. Findings indicate the need to disentangle structural components of positive and negative affect when conducting intensive longitudinal studies to examine correlates linked to dynamic affective processes. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Workplace Social Capital: Redefining and Measuring the Construct

Although workplace social capital (WSC) is a relevant construct that contributes positively to employee and organizational wellness, there is lack of consensus regarding its definition and measurement. The aim of this paper was to synthesize a valid definition of WSC and develop an instrument measuring the construct. Based on a literature review of existing definitions (Phase 1), we conceptualized WSC as a workplace resource that concerns employees’ perceptions regarding trust, reciprocity (cognitive WSC), and network interactions (structural WSC) that exist among peers (bonding WSC) and among individuals across hierarchical levels and organizations (bridging WSC). Next, we developed the WSC Inventory (WoSCi; Phase 2) and, we tested the psychometric properties of the new scale (Phase 3). The initial structure of the scale was explored in a sample of university employees (N = 376). Multilevel confirmatory factor analysis in a heterogeneous sample of 733 employees nested in 158 work groups supported the hypothesized factor structure of the WoSCi. Results also supported the internal consistency, as well as the convergent, criterion-related and incremental validity of the WoSCi in explaining work engagement, burnout, job performance over and above similar constructs, such as individual social capital and psychological capital. These results highlight the relevance of WSC as defined and measured with the WoSCi and underline its value for explaining work-related well-being and organizational behavior.

Investigating the performance of level-specific fit indices in multilevel confirmatory factor analysis with dichotomous indicators: A Monte Carlo study.

We conducted a Monte Carlo study to examine the performance of level-specific χ2 test statistics and fit regarding their capacity to determine model fit at specific levels in multilevel confirmatory factor analysis with dichotomous indicators. Five design factors-numbers of groups (NG), group size (GS), intra-class correlation (ICC), thresholds of dichotomous indicators (THR), and factor loadings (FL)-were considered in this study. According to our simulation results, we recommend that practitioners should be aware that the performance of between-level-specific (b-l-s) χ2 and fit indices was mainly influenced by ICC and FL, followed by NG. At the same time, THR could slightly weigh in the performance of b-l-s fit indices in some conditions. Both b-l-s χ2 and fit indices were more promising indicators to correctly indicate model fit when ICC or FL increased. A small to medium NG (50-100) might be sufficient for b-l-s χ2 and fit indices only if both ICC and factor loadings were high, while in remaining conditions, an NG of 200 was needed. Moreover, practitioners could use within-level-specific (w-l-s) χ2 and fit indices (except for RMSEAW) along with traditional cut-off values to evaluate within-level models comprising dichotomous indicators. W-l-s χ2 and fit indices were more promising to determine model fit when FL increased. THR had a slight impact and could weigh in the performance of [Formula: see text], RMSEAW, CFIW, and TLIW. Unfortunately, RMSEAW was heavily affected by FL and THR and could determine model fit only when FL was high and THR was symmetric.

Development of a brief therapist presence inventory in China using multilevel factor analysis and item response theory

Objectives The aims of this study were to investigate the multilevel factor structure of the Therapist Presence Inventory-Therapist version (TPI-T) in a sample of Chinese beginning trainees, and to develop a 6-item brief version (TPI-T-Brief) using multilevel item response theory (M-IRT). Methods: Participants included 131 therapists from a master’s level counseling training program in China. After every session, therapists were sent the TPI-T and measures assessing their ratings of working alliance and session quality. Results: Multilevel factor analysis indicated that though conceptually TPI-T was hypothesized as having a unidimensional structure, the positively and negative worded items emerged as two statistical artifact factors. Using M-IRT, we shortened the 21-item TPI-T into the 6-item TPI-T-Brief, with three positive and three negative items. Conclusions: Evidence was found for the multilevel reliability, convergent validity, and concurrent validity of TPI-T-Brief given its significant associations with therapist- and client-rated working alliance and session quality. Further, TPI-T-Brief had significant predictive effect of session quality above and beyond working alliance at the session level. We discussed limitations (including using only one dataset) and recommended that the TPI-T-Brief be used in routine clinical practice to track therapy process.

Research on Fuzzy Comprehensive Evaluation Method in Wireless Sensor Network Course Evaluation

Wireless Sensor network (WSN) is a Capstone course in engineering education certification, because the implementation of this course should reflect implementation, complexity and teamwork. It is difficult for teachers to use traditional and linear assessment criteria to evaluate students' learning effects. Aiming at the above problems, an evaluation method of wireless sensor network course based on fuzzy theory and multi-level evaluation index factor set is proposed in this paper. The method imitates the evaluation experience and intelligent behavior of education experts, and obtains an optimal course evaluation model. Through the fuzzy comprehensive evaluation of multi-level index system, students' learning effect can be evaluated accurately, objectively, fairly and scientifically.

Assessing Collective Efficacy in School: A Norwegian Validation Study of Goddard’s 12-item Scale

The psychometric properties of the short Collective Teacher Efficacy Scale (CTE; Goddard, 2002) were examined in a sample of 1,524 teachers from 65 Norwegian primary schools. Associations between items were investigated using network analysis, and multilevel confirmatory factor analysis was applied to determine the optimal factor structure across the twelve items. Criterion validity was examined by inspecting correlations between CTE, academic achievement, and school problem behavior. The results supported the expected two-factor structure, group competence, and task analysis. Correlations in the expected directions support the instrument’s concurrent and predictive validity. Implications of the results are discussed.

Individualized Short-Term Electric Load Forecasting Using Data-Driven Meta-Heuristic Method Based on LSTM Network.

Short-term load forecasting is viewed as one promising technology for demand prediction under the most critical inputs for the promising arrangement of power plant units. Thus, it is imperative to present new incentive methods to motivate such power system operations for electricity management. This paper proposes an approach for short-term electric load forecasting using long short-term memory networks and an improved sine cosine algorithm called MetaREC. First, using long short-term memory networks for a special kind of recurrent neural network, the dispatching commands have the characteristics of storing and transmitting both long-term and short-term memories. Next, four important parameters are determined using the sine cosine algorithm base on a logistic chaos operator and multilevel modulation factor to overcome the inaccuracy of long short-term memory networks prediction, in terms of the manual selection of parameter values. Moreover, the performance of the MetaREC method outperforms others with regard to convergence accuracy and convergence speed on a variety of test functions. Finally, our analysis is extended to the scenario of the MetaREC_long short-term memory with back propagation neural network, long short-term memory networks with default parameters, long short-term memory networks with the conventional sine-cosine algorithm, and long short-term memory networks with whale optimization for power load forecasting on a real electric load dataset. Simulation results demonstrate that the multiple forecasts with MetaREC_long short-term memory can effectively incentivize the high accuracy and stability for short-term power load forecasting.

Incorporating Measurement Error in the Dynamic Structural Equation Modeling Using a Single Indicator or Multiple Indicators

Dynamic Structural Equation Modeling (DSEM) was recently introduced as an advanced multilevel time series method to analyze Intensive Longitudinal Data (ILD). Although measurement error is likely to be embedded in ILD, many applications of intensive longitudinal studies often rely on composite scores without modeling measurement error. However, measurement error can be captured in the DSEM framework using a single indicator or multiple indicators. In this paper, we discussed and compared three multilevel DSEM models that analyze within-person dynamic process of a construct with different approaches to addressing measurement error: (1) single-indicator multilevel AR(1) model without measurement error; (2) single-indicator multilevel AR(1) model with measurement error; (3) multiple-indicator multilevel dynamic factor AR(1) model. The difference between (1) and (2) and the similarity between (2) and (3) were highlighted. An experience sampling data set was analyzed to show the difference and similarity of the numerical results from the three approaches.

Developing and validating a comprehensive measure of coordination in patient aligned care teams

Background/ObjectiveDespite numerous extant measures assessing context-specific elements of care coordination, we are unaware of any comprehensive, team-based instrument that measures the requisite mechanisms and conditions required to coordinate successfully. In this study we develop and validate the psychometric properties of the Coordination Practices Survey, a context-agnostic measure of coordination for primary care teams.MethodsCoordination items were developed based on a systematic literature review; items from previously developed scales were adapted and new items were created as needed; all items were refined after subject matter expert review and feedback. We collected data from Primary Care teams drawn from 1200 Veterans Health Administration (VHA) medical centers and outpatient clinics nationwide. 1645 primary care team members from 512 patient aligned care teams in the Veterans Health Administration completed the survey from 2015 to 2016. Psychometric properties were assessed after data collection using Cronbach’s alpha, intraclass correlations and multilevel confirmatory factor analysis to assess the factor structure.ResultsOur findings confirmed the psychometric properties of two distinguishable subscales of coordination: (a) Accountability and (b) Common Understanding. The within- and between-team latent structure of each subscale exhibited adequate fit to the data, as well as appropriately high Cronbach’s alpha and intraclass correlations. There was insufficient variability in responses to the predictability subscale to properly assess its psychometric properties.ConclusionWith context-specific validation, our subscales of accountability and common understanding may be used to assess coordination processes in other contexts for both research and operational applications.

Variability in intensively assessed mood: Systematic sources and factor structure in outpatients with opioid use disorder.

In intensive longitudinal studies using ecological momentary assessment, mood is typically assessed by repeatedly obtaining ratings for a large set of adjectives. Summarizing and analyzing these mood data can be problematic because the reliability and factor structure of such measures have rarely been evaluated in this context, which-unlike cross-sectional studies-captures between- and within-person processes. Our study examined how mood ratings (obtained thrice daily for 8 weeks; n = 306, person moments = 39,321) systematically vary and covary in outpatients receiving medication for opioid use disorder (MOUD). We used generalizability theory to quantify several aspects of reliability, and multilevel confirmatory factor analysis (MCFA) to detect factor structures within and across people. Generalizability analyses showed that the largest proportion of systematic variance across mood items was at the person level, followed by the person-by-day interaction and the (comparatively small) person-by-moment interaction for items reflecting low arousal. The best-fitting MCFA model had a three-factor structure both at the between- and within-person levels: positive mood, negative mood, and low-arousal states (with low arousal considered as either a separate factor or a subfactor of negative mood). We conclude that (a) mood varied more between days than between moments and (b) low arousal may be worth scoring and reporting separately from positive and negative mood states, at least in a MOUD population. Our three-factor structure differs from prior analyses of mood; more work is needed to understand the extent to which it generalizes to other populations. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Emotional Intelligence as a Personality State

Abstract: Contemporary research has begun to explore the notion that emotional intelligence (EI) has an important state component in addition to the trait component, as represented in the whole trait theory. This implies that state EI (or enacted EI) has similar cognitive, affective, and motivational contents as its corresponding trait. The question, however, of whether a trait EI construct means the same across the individual (trait) and state levels of analysis has not been empirically investigated. To address this gap, the present study examines the assessment of enacted EI, using the full version of the Wong and Law Emotional Intelligence Scale (WLEIS) on both between-person and within-person levels of analysis. Participants were 493 Greek employees who completed the WLEIS for 5 consecutive workdays. Multilevel confirmatory factor analyses confirmed that the original four-factor multilevel model appeared to best fit the data. Multilevel measurement invariance analysis supported the equivalence of the measure across different levels of analysis. In conclusion, the WLEIS is a configural cluster construct, believed to be a valuable and reliable tool for assessing enacted EI within the workplace. Implications for future research on enacted EI are discussed.