Appropriate Number Of Factors Research Articles

Home care aides' perspectives of occupational tobacco smoke exposure: A Q methodology study.

The increased growth of older adults has generated demand for home care aides (HCAs). Occupational tobacco smoke exposure (OTSE) may risk their health that should be paid attention to. This study explored the HCAs' perspectives of OTSE to inform health promotion programs catering to individual needs. A two-stage Q methodology was employed for data collection and analysis. 39 Q statements were extracted in the first stage and then 51 HCAs with OTSE were recruited to complete Q sorting in the second stage. PQ Method software was used for data analysis. Principal component analysis was performed to determine the most appropriate number of factors. The five factors identified from HCAs' perspective of OTSE explained 51% of the variance. The HCAs agreed that OTSE could increase cancer risk. The HCAs with Factor I did not care about OTSE, tending to complete their work. The HCAs with Factor II agreed the health hazards of OTSE, but did not know how to help their clients stop smoking. The HCAs with Factor III cared about OTSE but were afraid of disrupting the client-provider relationship. The HCAs with Factor IV regarded OTSE as a priority for occupational interventions whereas the HCAs with Factor V did not think OTSE was an issue and believed that they could balance work and OTSE health hazards. Our findings will inform the design of home care pre-service and on-the-job training courses. Long-term care policies should be developed to promote smoke-free workplaces. There are five types of home care aides' perspective on occupational tobacco smoke exposure (OTSE). The tailor interventions can be developed to help them avoid the OTSE (e.g., opening windows for ventilation or using air purification equipment) and have an OTSE-free spaces.

Conceptualising the Scholarship of Teaching and Learning within the Context of the Research--Teaching Nexus in Higher Education

Conceptualising the research-teaching nexus within the context of the scholarship of teaching and learning is really intriguing. This is attributed to the different notions held by several scholars and stakeholders within the higher educational landscape. Though, previous studies have attempted to espouse how several stakeholders have thought about the link between research and teaching, however, these attempts have not materialised. Therefore, the purpose of this paper is to espouse how lecturers conceptualise the research-teaching nexus, especially, within the context of the scholarship of teaching and learning and to further establish whether their conceptualisation differ across their ranks. Using the descriptive cross-sectional survey design, and through a questionnaire, 732 lecturers were selected using the stratified proportionate technique to respond to the survey. In order to ensure the construct validity of the self-developed questionnaire, a Principal Component Analysis (PCA) was conducted. An oblique, specifically, promax rotation was used, where the eigenvalue-greater-than-one rule was used to determine an appropriate number of factors to retain. In terms of data analysis, after a thorough check, the closed-ended questionnaire items were analysed statistically using descriptive statistics (i.e. frequency counts, percentages, means, and standard deviations) and inferential statistics (MANOVA) was also used to examine the statistical effects and differences between lecturers’ rank and their conceptualisation. It emanated from the study that lecturers conceptualise the research-teaching nexus as knowledge currency, as well as, scholarship and curriculum orientations. And that a statistically significant difference existed between the levels of lecturers’ rank and their conceptualisation of the link between research and teaching. It was therefore, concluded that several stakeholders and scholars have different connotations and representations of the research-teaching nexus, especially, within the context of the research-teaching nexus. It is therefore, recommended that University authorities should encourage their faculty to embrace and apply research-based teaching in their teaching and learning expedition. Lecturers must also ensure that pedagogical practices must be thoroughly prepared, constantly reviewed, and explicitly linked to the topic they teach by way of promoting scholarship at the highest level. Keywords: Research-teaching nexus, scholarship of teaching and learning, knowledge currency conceptualisation, scholarship-oriented conceptualisation, curriculum-oriented conceptualisation DOI: 10.7176/RHSS/12-6-02 Publication date: March 31 st 2022

Perseverative Thinking about Suicide Questionnaire (PTSQ): Validation of a new measure to assess suicide-specific rumination

BackgroundSuicide-specific rumination has been shown to be associated with lifetime suicide attempts as well as suicide intent and might be an important risk factor for the transition from suicidal ideation to suicidal behavior. Based on this background, the wording of the items of an often-used self-report measure assessing core characteristics of rumination was adapted to assess specifically suicide-specific rumination. MethodsThe entire study sample comprised N = 1689 participants from the German healthcare sector. A total of 721 participants with a history of suicidal ideation (68% female; Mage = 30.63, SDage = 8.41, range: 18–81 years) who had completed five measures assessing suicide-specific rumination, suicidal ideation, depression, hopelessness, and resilience were included for the present examination. Factorial validity (Exploratory [EFA] to determine the appropriate number of factors and confirmatory factor analyses [CFA] after randomly splitting of the sample to validate the EFA solution), construct validity, and reliability were evaluated. ResultsThe EFA revealed a one-factor solution consisting of four items, which could be confirmed within a CFA (RMSR = 0.006; RMSEA = 0.039; CFI = 0.999; TLI = 0.998). Internal consistency was excellent with Cronbachs α = 0.93. Construct validity was given with moderate to high positive correlations with suicidal ideation (0.76), depression (0.55), and hopelessness (0.38) and negative correlation with resilience (−0.31). Participants with recent suicidal ideation and/or lifetime suicide attempt reported significantly more suicide-specific rumination than those with only lifetime suicidal ideation. ConclusionA new 4-item measure for suicide-specific rumination was developed and could be shown to be a reliable and valid instrument in a large German sample. Results emphasize the potential importance of suicide-specific rumination for the understanding of trajectories of suicidal ideation and suicide risk assessments.

Bayesian Information Criterion and Selection of the Number of Factors in Factor Analysis Models

In maximum likelihood exploratory factor analysis, the estimates of unique variances can often turn out to be zero or negative, which makes no sense from a statistical point of view. In order to overcome this difficulty, we employ a Bayesian approach by specifying a prior distribution for the variances of unique factors. The factor analysis model is estimated by EM algorithm, for which we provide the expectation and maximization steps within a general framework of EM algorithms. Crucial issues in Bayesian factor analysis model are the choice of adjusted parameters including the number of factors and also the hyper-parameters for the prior distribution. The choice of these parameters can be viewed as a model selection and evaluation problem. We derive a model selection criterion for evaluating a Bayesian factor analysis model. Monte Carlo simulations are conducted to investigate the effectiveness of the proposed procedure. A real data example is also given to illustrate our procedure. We observe that our modeling procedure prevents the occurrence of improper solutions and also chooses the appropriate number of factors objectively.

DESCRIPTOR POTENTIAL OF THE UKRAINIAN LANGUAGE FROM THE PERSPECTIVE OF NEW METHODS OF LEXICAL ANALYSIS OF PERSONALITY STRUCTURE

Introduction. The relevance of the study is caused by the existing need to develop psycholexical research, which will allow the universalization and operationalization of theoretical psychological constructs and models. The specifics of psycholexical research are revealed. The main advantages and limitations of the psycholexical method are analyzed. Methodology. Prospects for the analysis of the Ukrainian language with the help of a new method of separating the factor structure of low and high dimensions are presented. The initial provisions and procedure of psycholexical research on the basis of the Ukrainian lexicon of personality are described. Results. Based on the determination of the appropriate number of factors and the comparison of different methods of data processing, the choice of personality descriptors and the rotation of the factors, structures of different dimensions are singled out on the basis of the same data. Discussion. The authors emphasized on the need to analyze the resulting structures, taking into account the theory of the psycholexic industry, as well as giving theoretical meaning to open dimensions, checking their descriptive and explanatory meaning on the way to creating a theoretical model.

Development and Validation of the Smartphone Addiction Scale for Children- Parent Version (SASC-P)

Introduction: The use of smartphone among children and adolescents has been increasing steadily over the past decade and is becoming a cause of concern for parents and healthcare professionals alike. Excessive use of smartphone could make a child vulnerable to develop addictive behaviour leading to decrease in academic performance and impairments in social and personal environment. Early identification is key to addressing this issue and although there are scales to measure smartphone addiction in adults, there are no scales to measure smartphone use in children objectively. Aim: To construct a smartphone addiction scale for children that can be administered to parents. Materials and Methods: A set of statements were created to assess smartphone addiction in children. Initially, 43 statements were selected after identifying its content validity and face validity and the scale was administered to parents of children in the age group of 3-17 years after obtaining informed consent from the parents. The construct validity was examined by the exploratory factor analysis. The screen plot of ordered eigen values of a correlation matrix was used to decide the appropriate number of factors extracted. A factor loading of >0.30 was used to determine the items for each factor. Intra-class correlations were calculated for the test-retest reliability, and Cronbach's alpha was calculated for the internal consistency. The final questionnaire contained 24 statements across six subdomains of smartphone addiction and it was administered to a small sample group of 65 parents of children aged 3-17 years and the data was used to test for reliability and validity of the scale. Results: Alpha correlation for the Smartphone Addiction Scale for Children-Parent (SASC-P) ranged from 0.670 to 0.823. The intrinsic validity for the domains was calculated using Cronbach’s alpha and it ranged from 0.819 to 0.907 for the domains and was 0.972 for the whole questionnaire. Thus the scale was found to be reliable and valid for use in children and adolescents. Conclusion: The SASC-P has good reliability and validity and can be used to measure smartphone use in children and adolescent.

High-dimensionality personality structure in the natural language: Further analyses of classic sets of English-language trait-adjectives.

Contemporary structural models of personality, like the Big Five, are rooted in natural-language lexicons in which socially important individual-differences concepts are sedimented. But key studies of these lexicons have been narrow in scope and mainly sought confirmatory evidence for one model of interest, rather than the maximum number of meaningful dimensions obtainable from the lexicon. Here, building on established methods for determining the appropriate number of factors, and comparing various methods of data-treatment and factor-rotation, analyses allowed higher-dimensionality structures to emerge from the same data. Factor-number-determination methods always recommended well more than 5 or 6 factors. In data using the largest 1,710-term set, we compared 18 candidate-structures derived from different method-combinations, identifying 15-, 21-, and 28-factor structures as most robust and promising. Among these, in a larger sample using a smaller 540-term set, the factors related to the 21-dimensional structure were most advantageously robust. Robustness-comparisons in an even larger sample with a 449-term subset of the original 1,710 converged on a similar number of factors. Though such a high-dimensionality model is slightly less robust than the Big Five across method-variations, we were able to confirm its clearly superior predictive capacity. And as comprehensiveness would imply, one can readily identify a low-dimensionality structure like the Big Five from within this higher-dimensionality structure, but one cannot generate this structure from the Big Five. A high-dimensionality structure can function as an improved scientific framework for cataloging trait variables and dimensions, including those that fall outside popular classifications involving only 5 or 6 factors. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Automated learning of mixtures of factor analysis models with missing information

The mixture of factor analyzers (MFA) model has emerged as a useful tool to perform dimensionality reduction and model-based clustering for heterogeneous data. In seeking the most appropriate number of factors (q) of a MFA model with the number of components (g) fixed a priori, a two-stage procedure is commonly implemented by firstly carrying out parameter estimation over a set of prespecified numbers of factors, and then selecting the best q according to certain penalized likelihood criteria. When the dimensionality of data grows higher, such a procedure can be computationally prohibitive. To overcome this obstacle, we develop an automated learning scheme, called the automated MFA (AMFA) algorithm, to effectively merge parameter estimation and selection of q into a one-stage algorithm. The proposed AMFA procedure that allows for much lower computational cost is also extended to accommodate missing values. Moreover, we explicitly derive the score vector and the empirical information matrix for calculating standard errors associated with the estimated parameters. The potential and applicability of the proposed method are demonstrated through a number of real datasets with genuine and synthetic missing values.

FactorLib: A MATLAB Library for Static Factor Analysis

We present a library of MATLAB functions designed for the estimation and analysis of static factor models, both by the method of the principal component and by maximum likelihood. The library includes functions for the treatment of unbalanced panels, the evaluation through transversal resampling of the uncertainty associated with the estimation of the factors as well as exploratory procedures for determining the appropriate number of factors. The various functions are applied to analyze the latent structure of the yield curve of the Spanish Treasury.

P158 Development and Validation of a Self-Efficacy in Garden Education Survey

Background Head Start centers across the nation are encouraged to incorporate outdoor teaching into playground landscapes and preschool gardens; however, no validated measurement tool exists to assess teachers’ self-efficacy for garden education. Objective Researchers collected data and developed an online 18 statement self-efficacy in garden education survey utilizing a multiphase process to achieve both reliability and validity. Study Design, Settings, Participants This scale validation process included two online expert reviews and telephone cognitive interviews before it was administered to 357 preschool teacher participants who completed the survey. Measurable Outcome/Analysis Exploratory factor analysis was completed on the six-point forced response likert style scale using principle axis factoring. An iterative factor analysis process to confirm the most appropriate number of factors and the correlated statements within each factor by analyzing the loading tables. Cronbach's alpha was calculated for each of the identified factors. Results Exploratory factor analysis revealed a three-factor structure with an overall correlation of .915 and acceptable to good reliability within each factor (Cronbach's alpha Factor 1 = .866, Factor 2 = .822, and Factor 3 = .788). Factor 1 related to planning and preparing, Factor 2 related to comfort in teaching in the alternative outdoor setting, and Factor 3 related to nature orientation and gardening. Conclusion Having a valid and reliable tool could reveal teachers’ areas of greatest training need and better utilize program training funds into purposeful in-service sessions, continuing education seminars, and one-on-one or team barrier troubleshooting. Funding None.

Determining the number of factors for non-negative matrix and its application in source apportionment of air pollution in Singapore

The non-negative matrix factorization has been used in many disciplines of research, where the number of factors plays a crucial role. However, a fully data-driven method for determining the number is yet not available in the literature. Based on the fact that the most appropriate number of factors should generate the best prediction, in this paper we propose a selection method using a two-step delete-one-out approach, called twice cross-validation. This method is easy to implement and is fully data-driven. It also works when constraints are imposed on the factorization including the sparsity. Intensive simulations and real data analyses suggest that the proposed method performs well in most cases and can select the number of factors correctly when the number of factors is much less than the dimension of variables and the sample size is reasonably large. As an important application, the proposed method is used for source apportionment of air pollution in Singapore, and provides physically reasonable source profiles.

Development of a Household Sustainable Consumption Index and Its Application to EU‐28

AbstractThe purpose of this paper is to present the construction of the Household Sustainable Consumption Index (HSCI). The index is based on existing data from reputable institutions, which allows us to form a broad perspective on all three aspects of sustainability for 28 EU countries for the years 2005 and 2010. HSCI is a composite index developed in line with statistical principles using parallel analysis to select the appropriate number of factors, and factor analysis for calculation of weights. In both years, HSCI rankings have placed Scandinavian and Benelux countries at the top of the table, while Baltic countries, Romania and Bulgaria have been placed at the bottom. The explanatory power of HSCI is confirmed by linking the index to GDP per capita. The analysis brings a novel and first statistically developed tool to empirically simultaneously follow the changes in household sustainable consumption. Copyright © 2017 John Wiley & Sons, Ltd and ERP Environment

Simultaneous Quantification of Paracetamol and Caffeine in Powder Blends for Tableting by NIR-Chemometry

Near-infrared spectroscopy (NIRS) is a technique widely used for rapid and nondestructive analysis of solid samples. A method for simultaneous analysis of the two components of paracetamol and caffeine from powder blends has been developed by using chemometry with near-infrared spectroscopy (NIRS). The method development was performed on samples containing 80, 90, 100, 110, and 120% active pharmaceutical ingredients, and near-infrared spectroscopy (NIRS) spectra of prepared powder blends were recorded and analyzed in order to develop models for the prediction of drug content. Many calibration models were applied in order to perform quantitative determination of drug content in powder, and choosing the appropriate number of factors (principal components) proved to be of highly importance for a PLS chemometric calibration. Once the methods were developed, they were validated in terms of trueness, precision, and accuracy. The results obtained by NIRS methods were compared with those obtained by HPLC reference method, and no significant differences were found. Therefore, the NIR chemometry methods were proved to be a suitable tool for predicting chemical properties of powder blends and for simultaneous determination of active pharmaceutical ingredients.

Likert scale and change in range of response categories vs. the factors extraction in EFA model

Abstract: The objective article is the comparative analysis of Likert rating scale based on the following range of response categories, i.e. 5, 7, 9 and 11 in context of the appropriate process of factors extraction in exploratory factor analysis (EFA). The problem which is being addressed in article relates primarily to the methodological aspects, both in selection of the optimal number of response categories of the measured items (constituting the Likert scale) and identification of possible changes, differences or similarities associated (as a result of the impact of four types of scales) with extraction and determination the appropriate number of factors in EFA model.Keywords: Exploratory factor analysis, Likert scale, experiment research, marketing

Automated resolution of overlapping peaks in chromatographic data

Parallel factor analysis 2 (PARAFAC2) has been shown to be a powerful tool for resolution of complex overlapping peaks in chromatographic analyses. It is particularly useful because of its ability to handle shifts in the elution time mode and peak shape changes. Like all curve resolution techniques, PARAFAC2 will only find chemically meaningful parameters (elution time profiles and mass spectra) if the correct number of factors are determined. So far, the primary way to determine an appropriate number of factors, when using PARAFAC2, is to calculate models with different number of factors and then inspect the models manually. This approach is time consuming, and the result may be biased because of the manual assessment of the model quality, making PARAFAC2 inaccessible for analytical chemists in general. Here, we develop a method that can determine an appropriate number of factors in an automated way. The automation is based on a number of model diagnostics (quality criteria) collected from models with different numbers of factors. Combining these diagnostics, it is possible to assess what the appropriate number of components is. In this work, only gas chromatography–mass spectrometry data are considered. However, it will most likely be fairly straightforward to expand the work to also cover liquid chromatography data (with a multivariate detector). Automating the model quality evaluation of the PARAFAC2 model enables both the inexperienced and trained user to perform comprehensive and advanced analysis of chromatographic data with a minimum of manual work. © 2013 The Authors. Journal of Chemometrics Published by John Wiley & Sons Ltd.

Factor Structure of the Children’s Depression Inventory: Evidences from Asian Children and Adolescents

The main goal of this study was to investigate the factorial validity of the CDI utilizing both exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) for children and adolescents in Taiwan. In this study, the CDI was applied to 633 children (aged 9–12; 52 % male; 48 % female) and 774 adolescents (aged 13–15; 52.5 % male; 47.5 % female). The samples were randomly split into two subsamples. Sample 1 (313 children: 53.7 % boys and 46.3 % girls; 384 adolescents: 53.4 % boys and 46.6 % girls) was applied to principal axis analysis with promax oblique rotation. Additionally, the parallel analysis technique was used to determine the appropriate number of factors for extraction. In CFA, sample 2 (320 children: 50.3 % boys and 49.7 % girls; 390 adolescents: 51.5 % boys and 48.5 % girls) was applied to evaluate the fit indices for the established models which were derived from EFA as well as the previous studies. Results showed that the three-factor models demonstrated good fit indices for both child and adolescent samples. Also, one single second-order factor was found in both child and adolescent samples, suggesting that scoring mechanism of the CDI was justified. Finally, the similarities and differences on the factor structure of the CDI between children and adolescents were discussed.

An alternative quadrilinear decomposition algorithm for four-way calibration with application to analysis of four-way fluorescence excitation–emission–pH data array

A novel quadrilinear decomposition algorithm for four-way calibration (third-order tensor calibration), which was called as regularized self-weighted alternating quadrilinear decomposition (RSWAQLD), has been developed in this work. It originates from the alternating trilinear decomposition (ATLD) algorithm, inherits the philosophy behind self-weighting operation from the self-weighted alternating trilinear decomposition (SWATLD) algorithm. The RSWAQLD algorithm is based on a nearby least-squares scheme, in which two extra terms are added to each loss function, making it more stable and flexible. Experiment shows that RSWAQLD has the features of fast convergence and being insensitive to the excess estimated factors in the model. Owing to its unique optimizing approach, RSWAQLD is much more efficient than four-way PARAFAC. Moreover, the performance of RSWAQLD is quit stable as the number of factors used in calculation varies (as long as it is no less than the true number of factors). Such a feature will simplify the analysis of four-way data arrays, since it is unnecessary to spend a lot of time and effort on accurately determining the appropriate number of factors in the matrix. In addition, the result of four-way fluorescence excitation–emission–pH data, as well as that of simulated data, illustrated that RSWAQLD can not only remain the “higher-order advantage” but also provide a satisfying result even in high collinear systems.

Model Selection Criteria for Factor‐Augmented Regressions*

AbstractExisting dynamic factor selection criteria determine the appropriate number of factors in a large‐dimensional panel of explanatory variables, but not all of these have to be relevant for modeling a specific dependent variable within a factor‐augmented regression. We develop theoretical conditions that selection criteria have to meet in order to get consistent estimates of the relevant factor dimension for such a regression. These incorporate factor estimation error and do not depend on specific factor estimation methodologies. Using this framework, we modify standard model selection criteria, and simulation and empirical applications indicate that these are useful in determining appropriate factor‐augmented regressions.

Model Selection Criteria for Factor-Augmented Regressions

In a factor-augmented regression, the forecast of a variable depends on a few factors estimated from a large number of predictors. But how does one determine the appropriate number of factors relevant for such a regression? Existing work has focused on criteria that can consistently estimate the appropriate number of factors in a large-dimensional panel of explanatory variables. However, not all of these factors are necessarily relevant for modeling a specific dependent variable within a factor-augmented regression. This paper develops a number of theoretical conditions that selection criteria must fulfill in order to provide a consistent estimate of the factor dimension relevant for a factor-augmented regression. Our framework takes into account factor estimation error and does not depend on a specific factor estimation methodology. It also provides, as a by-product, a template for developing selection criteria for regressions that include standard generated regressors. The conditions make it clear that standard model selection criteria do not provide a consistent estimate of the factor dimension in a factor-augmented regression. We propose alternative criteria that do fulfill our conditions. These criteria essentially modify standard information criteria so that the corresponding penalty function for dimensionality also penalizes factor estimation error. We show through Monte Carlo and empirical applications that these modified information criteria are useful in determining the appropriate dimensions of factor-augmented regressions.

Adaptation of Runco Ideational Behavior Scale in Latvia

ABSTRACT Runco Ideational Behavior Scale (RIBS) was developed as an alternative instrument to measure creativity in contrast with divergent thinking tests which have only moderate predictive validity. Measure of RIBS is based on the assumption that ideas are the products of original, divergent, and creative thinking behaviour which one can observe and report about his/herself. The aim of the study was to adapt RIBS in Latvia. For the estimation of psychometric properties and constructing validity with confirmatory factor analysis (CFA) two samples were involved - initial sample (N=107) and comparison sample (N=130). After evaluating the Latvian version of RIBS, two issues were identified: the first pertains to reversed items which have no sufficient shared variance with the total scale while the second issue points to serious problems of model fit challenging one factor solution of ideational behaviour. The possible reasons of these problems are discussed and further steps for RIBS development are proposed, including a change of response scale in order to test the effect of reversed items, establishing the validity with other instruments and investigating the appropriate number of factors to reveal more valid structure of ideational behaviour. Similar to the original RIBS the adapted version also has low factorial validity and thus is temporarily recommended only for studies with purpose to develop this instrument.