Factor analysis models with continuous and ordinal responses are a useful tool for assessing relations between the latent variables and mixed observed responses. These models have been successfully applied to many different fields, including behavioral, educational, and social-psychological sciences. However, within the Bayesian analysis framework, most developments are constrained within parametric families, of which the particular distributions are specified for the parameters of interest. This leads to difficulty in dealing with outliers and/or distribution deviations. In this paper, we propose a Bayesian semiparametric modeling for factor analysis model with continuous and ordinal variables. A truncated stick-breaking prior is used to model the distributions of the intercept and/or covariance structural parameters. Bayesian posterior analysis is carried out through the simulation-based method. Blocked Gibbs sampler is implemented to draw observations from the complicated posterior. For model
Within structural equation modeling, the most prevalent model to investigate measurement bias is the multigroup model. Equal factor loadings and intercepts across groups in a multigroup model represent strong factorial invariance (absence of measurement bias) across groups. Although this approach is possible in principle, it is hardly practical when the number of groups is large or when the group size is relatively small. Jak, Oort and Dolan (2013) showed how strong factorial invariance across large numbers of groups can be tested in a multilevel structural equation modeling framework, by treating group as a random instead of a fixed variable. In the present study, this model is extended for use with three-level data. The proposed method is illustrated with an investigation of strong factorial invariance across 156 school classes and 50 schools in a Dutch dyscalculia test, using three-level structural equation modeling.
2018, Springer Science+Business Media, LLC, part of Springer Nature. The construct of dysphoria has been described inconsistently across a broad range of psychopathology. The term has been used to refer to an irritable state of discontent, but is also thought to incorporate anger, resentment and nonspecific symptoms associated with anxiety and depression, such as tension and unhappiness. The Nepean Dysphoria Scale has been developed to allow assessment of dysphoria, but its factor structure has not yet been investigated in clinical samples. We aimed to determine the latent structure of dysphoria as reflected by the Nepean Dysphoria Scale, using a clinical sample. Adults (N = 206) seeking treatment at a range of mental health services were administered the Nepean Dysphoria Scale. Four putative factor structures were investigated using confirmatory factor analysis: a single-factor model, a hierarchical model, a bifactor model and a four-factor model as identified in previous studies. No model fit ...
This study examined the psychometric properties of the Revised Illness Perception Questionnaire adapted for a clinical sample of low-income Latinos suffering from depression. Participants (N = 339) were recruited from public primary care centers. Their average age was 49.73 years and the majority was foreign born females of either Mexican or Central American descent. Confirmatory factor analysis was used to test the factor structure of this measure. Construct and discriminant validity and internal consistency were evaluated. After the elimination of three items because of low factor loadings (| .40) and the specification of seven error covariances, a revised model composed of 24 items had adequate goodness-of-fit indices and factor loadings, supporting construct validity. Each of the subscales reported satisfactory internal consistency. Intercorrelations between the 5 illness perception factors provided initial support for the discriminant validity of these factors in the context of depression. The
TY - JOUR. T1 - Ignoring clustering in confirmatory factor analysis: Some consequences for model fit and standardized parameter estimates. AU - Pornprasertmanit, S. AU - Lee, Jae Hoon. AU - Preacher, K J. PY - 2014. Y1 - 2014. N2 - In many situations, researchers collect multilevel (clustered or nested) data yet analyze the data either Ignoring the clustering (disaggregation) or averaging the micro-level units within each cluster and analyzing the aggregated data at the macro level (aggregation). In this study we investigate the effects of Ignoring the nested nature of data in confirmatory factor analysis (CFA). The bias incurred by Ignoring clustering is examined in terms of model fit and standardized parameter estimates, which are usually of interest to researchers who use CFA. We find that the disaggregation approach increases model misfit, especially when the intraclass correlation (ICC) is high, whereas the aggregation approach results in accurate detection of model misfit in the macro ...
Evaluating the psychometric properties of a newly developed instrument is critical to understanding how well an instrument measures what it intends to measure, and ensuring proposed use and interpretation of questionnaire scores are valid. The current study uses Structural Equation Modeling (SEM) techniques to examine the factorial structure and invariance properties of a newly developed construct called Superwoman Schema (SWS). The SWS instrument describes the characteristics of a superwoman (strong woman) which consists of 35 items representing five subscales: obligation to present an image of strength, obligation to suppress emotions, resistance to being vulnerable, intense motivation to succeed, and obligation to help others. Multigroup confirmatory factor analysis (CFA) and a multiple indicators multiple causes (MIMIC) model were the SEM approaches used to examine measurement invariance in the SWS instrument. Specifically in the multigroup CFA analyses, configural invariance,
Conventional factor models assume that factor loadings are fixed over a long horizon of time, which appears overly restrictive and unrealistic in applications. In this paper, we introduce a time-varying factor model where factor loadings are allowed to change smoothly over time. We propose a local version of the principal component method to estimate the latent factors and time-varying factor loadings simultaneously. We establish the limiting distributions of the estimated factors and factor loadings in the standard large N and large T framework. We also propose a BIC-type information criterion to determine the number of factors, which can be used in models with either time-varying or time-invariant factor models. Based on the comparison between the estimates of the common components under the null hypothesis of no structural changes and those under the alternative, we propose a consistent test for structural changes in factor loadings. We establish the null distribution, the asymptotic local power
RESULTS: Mean age of participants was 38.13 years (SD = 11.45) and all men were married. Cronbach α of the MGSIS-I was 0.89 and interclass correlation coefficients ranged from 0.70 to 0.94. Significant correlations were found between the MGSIS-I and the International Index of Erectile Function (P , .01), whereas correlation of the scale with non-similar scales was lower than with similar scale (confirming convergent and divergent validity). The scale could differentiate between subgroups in age, smoking status, and income (known-group validity). A single-factor solution that explained 70% variance of the scale was explored using exploratory factor analysis (confirming uni-dimensionality); confirmatory factor analysis indicated better fitness for the five-item version than the seven-item version of the MGSIS-I (root mean square error of approximation = 0.05, comparative fit index , 1.00 vs root mean square error of approximation = 0.10, comparative fit index , 0.97, respectively ...
Background: Depression and anxiety in patients with coronary heart disease (CHD) is associated with a poorer prognosis. Therefore, the screening for psychological distress is strongly recommended in cardiac care and rehabilitation. The Hospital Anxiety and Depression Scale (HADS) is a widely used screening tool that has demonstrated good sensitivity and specificity for mental disorders. The factor structure of the HADS was investigated in CHD populations across three countries (Germany, Hong Kong, United Kingdom). Methods: In total, HADS data from 1793 patients with CHD were explored using confirmatory factor analysis to establish the underlying factor structure of the instrument. Results: Three-factor models were found to offer a superior fit to the data compared with two-factor (anxiety and depression) models in all countries. The anxiety items can be separated in a factor labelled autonomic anxiety and negative affectivity. Conclusions: The HADS offers good possibilities to detect distressed ...
This study investigated the item parameter recovery of two methods of factor analysis. The methods researched were a traditional factor analysis of tetrachoric correlation coefficients and an IRT approach to factor analysis which utilizes marginal maximum likelihood estimation using an EM algorithm (MMLE-EM). Dichotomous item response data was generated under the 2-parameter normal ogive model (2PNOM) using PARDSIM software. Examinee abilities were sampled from both the standard normal and uniform distributions. True item discrimination, a, was normal with a mean of .75 and a standard deviation of .10. True b, item difficulty, was specified as uniform [-2, 2]. The two distributions of abilities were completely crossed with three test lengths (n= 30, 60, and 100) and three sample sizes (N = 50, 500, and 1000). Each of the 18 conditions was replicated 5 times, resulting in 90 datasets. PRELIS software was used to conduct a traditional factor analysis on the tetrachoric correlations. The IRT approach to
Factor analysis and cluster analysis differ in how they are applied to real data. Because factor analysis has the ability to reduce a unwieldy set of variables to a much smaller set of factors, it is suitable for simplifying complex models. Factor analysis also has a confirmatory use, in which the researcher can develop a set of hypotheses regarding how variables in the data are related. The researcher can then run factor analysis on the data set to confirm or deny these hypotheses. Cluster analysis, on the other hand, is suitable for classifying objects according to certain criteria. For example, a researcher can measure certain aspects of a group of newly-discovered plants and place these plants into species categories by employing cluster analysis.. ...
Published on 11 March 2014. PURPOSE: The role of various foods and nutrients, and their combinations, on prostate cancer risk remains largely undefined.. We addressed therefore the issue of complex dietary patterns.. METHODS: We analyzed data from an Italian case-control study, including 1,294 men with prostate cancer and 1,451 hospital controls. We carried out an exploratory principal component factor analysis on 28 selected nutrients in order to identify dietary patterns. We estimated odds ratios (ORs) and corresponding confidence intervals (CIs) using logistic regression models on quintiles of factor scores, adjusting for major confounding variables.. RESULTS: We identified five dietary patterns, labeled Animal Products, Vitamins and Fiber, Starch-rich, Vegetable Unsaturated Fatty Acids (VUFA), and Animal Unsaturated Fatty Acids (AUFA). We found positive associations between prostate cancer and Animal Products (OR for the highest vs. the lowest score quintile: 1.51, 95 % CI ...
Provided that the factor analysis is itself valid, then you can replace your 20 predictors with the 5 factor score variables. That is, there is no special relationship between multinomial logistic regression and factor analysis that makes the application of factor analysis as a way of pre-processing the data invalid. In terms of the validity of the factor analysis, you may want to use dimension reduction technique that are designed for categorical data. From memory, SPSS has one or two (HOMALS and Categorical Principal Components Analysis). The other practical challenge is that it is possible that the best predictor variable may end up with relatively little weight in the factor analysis, so it may be useful to only do the factor analysis with variables that are known to have some predictive relationship with your dependent variable. ...
The study purpose was to validate the psychometric properties of a Spanish-language version of the weight pressures in sport scale for male athletes. The weight pressures in sport scale for male athletes assesses risk factors associated with sport-specific weight pressures from coaches, peers, and team uniform. The scale was back translated and administered to 407 Spanish male college athletes. The sample was randomly split to perform the exploratory and confirmatory analysis. After item analysis, three items were removed. The exploratory analysis identified two latent constructs (referring to coaches and teammates pressures, and pressures due to uniform), and the confirmatory analysis produced a two-factor model (comparative fit indexSB = .946, Tucker-Lewis indexSB = .925, root mean square of approximationSB = .071, standardized root mean square residualSB = .068). The overall scale showed adequate internal consistency (α = .82) and demonstrated adequate convergent validity with the other ...
This package implements a Bayesian sparse factor model for the joint analysis of paired datasets, one is the gene expression dataset and the other is the drug sensitivity profiles, measured across the same panel of samples, e.g., cell lines. Prior knowledge about gene-pathway associations can be easily incorporated in the model to aid the inference of drug-pathway associations.
The aim of this study is to develop a self-efficacy measuring tool that can predict the computational thinking skill that is seen as one of the 21st centurys skills. According to literature review, an item pool was established and expert opinion was consulted for the created item pool. The study group of this study consists of 319 students educated at the level of secondary school. As a result of the exploratory factor analysis, the scale consisted of 18 items under four factors. The factors are Reasoning, Abstraction, Decomposition and Generalization. As a result of applied reliability analysis, the Cronbach Alpha reliability coefficient can be seen to be calculated as .884 for the whole self-efficacy scale consisting of 18 items. Confirmative factor analysis results and fit indexes were checked, and fit indexes of the scale were seen to have good and acceptable fits. Based on these findings, the Computational Thinking Self-efficacy Scale is a valid and reliable tool that may be used in ...
Development, Administration and Confirmatory Factor Analysis of a Secondary School Test Based on the Theory of Successful Intelligence
Confirmatory factor analysis, standardized estimates. TE = Treatment Effectiveness, GS = General Satisfaction, ID = Impact on Activities of Daily Living, CU = C
A theoretical and computational introduction to factor analysis as a method for understanding complex multivariate data in applied social/health science research. Principal components analysis (PCA), exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and applications of structural equation modeling (SEM) will be examined. Additional topics may include scale development, multi-group analysis, and methods/concerns about measurement invariance ...
ANOVA Determining Which Means Differ in Single Factor Models. Single Factor Models Review of Assumptions. Recall that the problem solved by ANOVA is to determine if at least one of the true mean values of several different treatments differs from the others. For ANOVA we assumed: Slideshow 6806389 by baxter-kemp
A New Decision Making Model based on Factor Analysis (FA), F-ANP, and F-ARAS for Selecting and Ranking Maintenance Strategies: 10.4018/IJBAN.2016100103: Today, companies have admired that maintenance is a profitable commercial element. Therefore, its role in modern manufacturing systems has become more
Chinas Food Security Evaluation Based on Factor Analysis. . Biblioteca virtual para leer y descargar libros, documentos, trabajos y tesis universitarias en PDF. Material universiario, documentación y tareas realizadas por universitarios en nuestra biblioteca. Para descargar gratis y para leer online.
DOI: 10.14689/ejer.2020.86.8. ABSTRACT Purpose: This study aims to investigate the dimensionality of the Academic Motivation Scale items by depending on the graded response model, the generalized graded unfolding model, the bifactor model and the DIMTEST.. Research Methods: The Academic Motivation Scale was implemented on 1858 students who were studying at Ankara University. The fit of models was examined based on the general, person and item level model data fit statistics that were produced by the models.. Findings: It was found out that the bifactor model provided the most consistent results with the theoretical foundation underlying the items. The findings revealed that the generalized graded unfolding model and the bifactor model enabled better results than the graded response model concerning to the general model data fit. About item fit statistics, the models that provided the best fit were the bifactor model, the generalized graded unfolding model and the graded response model, ...
OBJECTIVE. Having access to high-quality, rigorously developed, valid visual-motor integration assessment tools is the first step in the process of providing effective clinical services to children presenting with visual-motor integration problems. The aim of this study was to examine the factor structure of four visual-motor integration instruments through factor analysis.. METHOD. The participants included 400 children ages 5 to 12, recruited from six schools in the Melbourne metropolitan area, Victoria, Australia. Children completed the Developmental Test of Visual-Motor Integration (VMI), Test of Visual-Motor Integration (TVMI), Test of Visual-Motor Skills-Revised (TVMS-R), and Slosson Visual-Motor Performance Test (SVMPT). The factor analysis was completed using SPSS.. RESULTS. Results indicated that the VMI displayed six factors; TVMI, three factors; TVMS-R, four factors; and SVMPT, three factors.. CONCLUSION. The VMI, TVMI, TVMS-R, and SVMPT exhibited multidimensionality, and it is ...
Alessi, L., Barigozzi, M. and Capassoc, M. (2010). Improved penalization for determining the number of factors in approximate factor models. Statistics and Probability Letters, 80, 1806-1813.. Bai, J. (2003). Inferential theory for factor models of large dimensions. Econometrica. 71 135-171.. Bai, J. and Li, K. (2012). Statistical analysis of factor models of high dimension. Ann. Statist. 40, 436-465.. Bai, J. and Ng, S.(2002). Determining the number of factors in approximate factor models. Econometrica. 70 191-221.. Bickel, P. and Levina, E. (2008a). Covariance regularization by thresholding. Ann. Statist. 36 2577-2604.. Bickel, P. and Levina, E. (2008b). Regularized estimation of large covariance matrices. Ann. Statist. 36 199-227.. Bien, J. and Tibshirani, R. (2011). Sparse estimation of a covariance matrix. Biometrika. 98, 807-820.. Breitung, J. and Tenhofen, J. (2011). GLS estimation of dynamic factor models. J. Amer. Statist. Assoc. 106, 1150-1166.. Cai, T. and Liu, W. (2011). Adaptive ...
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Existing empirical literature on the risk-return relation uses a relatively small amount of conditioning information to model the conditional mean and conditional volatility of excess stock market returns. We use dynamic factor analysis for large datasets to summarize a large amount of economic information by few estimated factors, and find that three new factors- termed
Recovery-oriented services are a goal for policy and practice in the Australian mental health service system. Evidence-based reform requires an instrument to measure knowledge of recovery concepts. The Recovery Knowledge Inventory (RKI) was designed for this purpose, however, its suitability and validity for student health professionals has not been evaluated. The purpose of the current article is to report the psychometric features of the RKI for measuring nursing students views on recovery. The RKI, a self-report measure, consists of four scales: (I) Roles and Responsibilities, (II) Non-Linearity of the Recovery Process, (III) Roles of Self-Definition and Peers, and (IV) Expectations Regarding Recovery. Confirmatory and exploratory factor analyses of the baseline data (n = 167) were applied to assess validity and reliability. Exploratory factor analyses generally replicated the item structure suggested by the three main scales, however more stringent analyses (confirmatory factor analysis) did ...
Table 5 shows that 87.1 per cent of the participants would prefer to find a job within a month. Further, 75.6 per cent expected to find work within a month. 42.3 per cent of the participants asked for a job every day, with 34.6 per cent asking once, twice or three times, while 6.3 per cent were not looking for a job at that time. Only 4.2 per cent of the participants never enquired about the availability of work, while 9.2 per cent never presented themselves for work.. Factor analysis. Exploratory factor analysis was used to explore the factor structure of the EUQ. A simple principal component analysis was carried out on the 26 items of the EUQ. An analysis of the eigenvalues (, 1.00; Tabachnick & Fidell, 2007) indicated that four factors explained 45.65 per cent of the variance. The scree plot confirmed that four factors could be extracted. A principal factor analysis with a direct Oblimin rotation was then performed. The results of the principal factor analysis with loadings of variables on ...
The Three-Factor-Eating-Questionnaire (TFEQ) is an established instrument to assess eating behaviour. Analysis of the TFEQ-factor structure was based on selected, convenient and clinical samples so far. Aims of this study were (I) to analyse the factor structure of the German version of the TFEQ and (II)--based on the refined factor structure--to examine the association between eating behaviour and the body mass index (BMI) in a general population sample of 3,144 middle-aged and older participants (40-79 years) of the ongoing population based cohort study of the Leipzig Research Center for Civilization Diseases (LIFE Health Study). The factor structure was examined in a split-half analysis with both explorative and confirmatory factor analysis. Associations between TFEQ-scores and BMI values were tested with multiple regression analyses controlled for age, gender, and education. We found a three factor solution for the TFEQ with an uncontrolled eating, a cognitive restraint and an emotional ...
Attitudes is a key help-seeking construct that influences treatment seeking behavior via intention to seek help, per the theory of planned behavior (TPB). This article presents the development and psychometric evaluation of the Mental Help Seeking Attitudes Scale (MHSAS), designed to measure respondents overall evaluation (unfavorable vs. favorable) of their seeking help from a mental health professional. In Study 1 (N = 857 United States adults), exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and item response theory (IRT) analysis were used to identify an optimal set of 9 items that demonstrated initial evidence of internal consistency, unidimensionality, and strong measurement equivalence/invariance (ME/I) across gender, past help-seeking experience, and psychological distress ...
RNA-seq is a powerful tool for measuring transcriptomes, especially for identifying differentially expressed genes or transcripts (DEGs) between sample groups. A number of methods have been developed for this task, and several evaluation studies have also been reported. However, those evaluations so far have been restricted to two-group comparisons. Accumulations of comparative studies for multi-group data are also desired. We compare 12 pipelines available in nine R packages for detecting differential expressions (DE) from multi-group RNA-seq count data, focusing on three-group data with or without replicates. We evaluate those pipelines on the basis of both simulation data and real count data. As a result, the pipelines in the TCC package performed comparably to or better than other pipelines under various simulation scenarios. TCC implements a multi-step normalization strategy (called DEGES) that internally uses functions provided by other representative packages (edgeR, DESeq2, and so on). We found
Abstract:. PURPOSE: to develop a tool for measuring the difficulty of intravenous line insertion in cancer patients (DIVA-CP) receiving peripheral chemotherapy.. METHODS: a pilot-validation study divided into two phases was performed in a north-eastern Italian outpatient chemotherapy centre. In the first phase, a review of the literature and brainstorming sessions/direct discussions among expert oncology nurses were used to develop items on the DIVA-CP tool, and a panel of expert oncology nurses assessed the tool face and content validity. In the second phase, 260 adult patients undergoing single chemotherapy cycles were consecutively enrolled. Data was analysed for construct validity (explorative factor analysis) and inter-rater reliability (Cohens Kappa).. RESULTS: a 10-item tool was developed with four factors that were identified through factor analysis, explaining a total variance of 61.578%: accessibility to first choice veins (23.057%), venous fragility (15.197%), probable difficulties ...
TY - JOUR. T1 - Associations between respiratory diseases and dietary patterns derived by factor analysis and reduced rank regression. AU - Lin, Yong Pei. AU - Kao, Ya Chun. AU - Pan, Wen Harn. AU - Yang, Yao Hsu. AU - Chen, Yang Ching. AU - Lee, Yungling Leo. PY - 2016/7/1. Y1 - 2016/7/1. N2 - Background/Aims: The study aims to identify childrens dietary patterns and explore the relationship between dietary patterns and respiratory diseases. Methods: Subjects were 2,397 fourth graders in 14 Taiwanese communities who participated in the Taiwan Children Health Study. This study is based on an evaluation of dietary patterns, performed from April until June 2011. Information pertaining to respiratory disease was obtained by The International Study of Asthma and Allergies in Childhood questionnaire, and dietary intake data obtained by food frequency questionnaire. Factor analysis and reduced rank regression (RRR) were both used to analyze dietary patterns. Results: Using factor analysis, it was ...
Urban MICE competitiveness research consists of two clusters, one that is public-statistics-based and another that is questionnaire-based. Supply-side research on urban MICE competitiveness is rare. Based on the findings of Chen (2014) and other scholars, the purpose of this paper is to design counterpart statistical indicators to empirically analyze CMCA member cities.,After calculating the standardized Z value of the original statistical data for 17 CMCA member cities, the authors conducted confirmatory factor analysis for the first-level principal components, based on which hierarchical clustering was performed; then, regression analysis was conducted with the MICE profit factor as the dependent variable and the cost factor, tight support factor and facilitating factor as the independent variables to support publishing articles.,The confirmatory factor analysis showed that the urban MICE competitiveness indicators from the supply-side perspective include the profit factor, cost factor, tight support
Current reviews outside of sport indicate that the Life Orientation Test-Revised (LOT-R) items load on two separate factors (optimism and pessimism) and, therefore, should be treated as independent constructs. However, researchers in the sport scienc
Within an embodied cognition framework, it is argued that presence in a virtual environment (VE) develops from the construction of a spatial-functional mental model of the VE. Two cognitive processes lead to this model: the representation of bodily actions as possible actions in the VE, and the suppression of incompatible sensory input. It is hypothesized that the conscious sense of presence reflects these two components as spatial presence and involvement. This prediction was confirmed in two studies (N = 246 and N = 296) assessing self-reports of presence and immersion experiences. Additionally, judgments of realness were observed as a third presence component. A second-order factor analysis showed a distinction between presence, immersion, and interaction factors. Building on these results, a thirteen-item presence scale consisting of three independent components was developed and verified using confirmatory factor analyses across the two studies. ...
Spector and Fleishman (1998) used exploratory and confirmatory factor analyses as well as Item Response Theory to analyze data of the combined ADL/IADL scale, which included 16 items and was collected through the NLTCS project. This study used only data from individuals who reported disabled in at least one of the disability indicators. Results of exploratory and confirmatory factor analyses provided supportive evidence for the unidimensionality of the combined scale. In addition, the authors made a comparison between the goodness-of-fit of the one-parameter and that of the two-parameter IRT model. It was found that the one-parameter model yielded a sufficient good fit, which is used as the supporting evidence for the feasibility of using a composite score to summarize the ADL/IADL data ...
Comparing groups with respect to hypothetical constructs requires that the measurement models are equal across groups. Otherwise conclusions drawn from the observed indicators regarding differences at the latent level (mean differences, differences in the structural relations) might be severly distorted. This article provides a state of the art on how to apply multi-group confirmatory factor analysis to assess measurement invariance. The required steps in the analysis of the observed indicator means and variances/covariances are described, placing special emphasis on how to identify noninvariant indicators. The procedure is demonstrated considering the construct brand strength (Brand Potential Index, BPI®) introduced by GfK Market Research as an example ...
Read this Business Essay and over 29,000 other research documents. Difference Between 5 Factor Model and Q Factor Model. Introduction This essay would compare two journals written by Fama and French (2015) and Hou, Xue, and Zhang (2015) who introduced two new asset pricing models. The main similarities and differences between five-factor asset pricing model and q-factor model would be investigated in part one and two respectively. Performance of...
Experienced runners completed a Thoughts During Running Scale (TORS) immediately after a typical training run to assess the prevalence of certain thoughts during running. The Profile of Mood States (POMS) was also completed before and after the run. Confirmatory factor analyses revealed that a five-factor model provided better fit than simpler models. Items concerning the demands of the running activity and the monitoring of body responses loaded on one associative factor. The four nonassociative factors in this model were labeled Daily Events, Interpersonal Relationships, External Surroundings, and Spiritual Reflection. Correlational analyses indicated small but significant relationships between the TDRS dimensions and changes in mood. Increases in vigor were correlated with the tendency to engage in nonassociative thought, and decreases in tension and anxiety were found among those who thought about interpersonal relationships during the run. These results supplement findings on the ...
TY - JOUR. T1 - A multi-population evaluation of the Poisson common factor model for projecting mortality jointly for both sexes. AU - Li, Jackie. AU - Tickle, Leonie. AU - Parr, Nick. PY - 2016/12/1. Y1 - 2016/12/1. N2 - Mortality forecasts are critically important inputs to the consideration of a range of demographically-related policy challenges facing governments in more developed countries. While methods for jointly forecasting mortality for sub-populations offer the advantage of avoiding undesirable divergence in the forecasts of related populations, little is known about whether they improve forecast accuracy. Using mortality data from ten populations, we evaluate the data fitting and forecast performance of the Poisson common factor model (PCFM) for projecting both sexes mortality jointly against the Poisson Lee-Carter model applied separately to each sex. We find that overall the PCFM generates the more desirable results. Firstly, the PCFM ensures that the projected male-to-female ...
Downloadable! We use a heterogeneous panel VAR model identified through factor analysis to study the dynamic response of exports, imports, and per capita GDP growth to a
This study describes the development and validation of an individual-level well-being assessment and scoring method (IWBS) adapted from the population-based Gallup-Healthways Well-being Index across six domains (physical health, emotional health, healthy behaviors, work environment, basic access and overall life-evaluation). Exploratory analyses were conducted on half the sample (n = 2036) using principal component analyses (PCA) with varimax rotation and confirmatory factor analysis was conducted on the second half of the sample (n = 2100) using structural equation modeling to validate the measurement model found by the PCA. Optimal results in the exploratory sample were achieved for a seven-factor solution, accounting for 52.0% of the variance. All domains displayed adequate reliability, ranging from .42 to .79. The IWBS met each of the criteria that were established for measurement development. Findings indicated that there was initial support for using the IWBS to assess well-being at the individual
Author Summary The transcriptional responses of human hosts towards influenza viral pathogens are important for understanding virus-mediated immunopathology. Despite great advances gained through studies using model organisms, the complete temporal host transcriptional responses in a natural human system are poorly understood. In a human challenge study using live influenza (H3N2/Wisconsin) viruses, we conducted a clinically uninformed (unsupervised) factor analysis on gene expression profiles and established an ab initio molecular signature that strongly correlates to symptomatic clinical disease. This is followed by the identification of 42 biomarkers whose expression patterns best differentiate early from late phases of infection. In parallel, a clinically informed (supervised) analysis revealed over-stimulation of multiple viral sensing pathways in symptomatic hosts and linked their temporal trajectory with development of diverse clinical signs and symptoms. The resultant inflammatory cytokine
In 1972, Peter Mazur, Stanley Leibo, and Ernest Chu published, A Two-Factor Hypothesis of Freezing Injury: Evidence from Chinese Hamster Tissue-culture Cells, hereafter, A Two-Factor Hypothesis of Freezing Injury, in the journal, Experimental Cell Research. In the article, the authors uncover that exposure to high salt concentrations and the formation of ice crystals within cells are two factors that can harm cells during cryopreservation. Cryopreservation is the freezing of cells to preserve them for storage, study, or later use. Mazur originally suggested the two factors in a 1970 paper, but that article was based on evidence from simple yeast cells. By using hamster cells in 1972, Mazur, Leibo, and Chu confirmed that Mazurs two-factor hypothesis applied to more complex mammalian cells. The article dispelled the widely accepted notion that rapid cooling rates were safest for all cells, and instead showed that each kind of cell had a different optimal cooling rate depending on the solution ...
The following answer describes four methods of finding the greatest common factor, with examples, and several tricks or shortcuts that can make it easier. Method: Guess and Refine Sometimes, you can look at two numbers and make a good guess that you can refine. Example 1: Find the greatest common factor of 45 and 50. Because both numbers end in either a 5 or 0, you know that they are both divisible by 5. If you divide both numbers by 5 and the results have no common factors (except 1), 5 is the greatest common factor. 45 ÷ 5 = 9 50 ÷ 5 = 10 Since 9 and 10 are consecutive numbers, they have no common factors. Therefore, the greatest common factor is 5. Example 2: Find the greatest common factor of 150 and 750. Both numbers end in 50, so they are both divisible by 50. If you divide both numbers by 50 and the results have another common factor, you continue identifying common factors until you have a pair without common factors. 150 ÷ 50 = 3 750 ÷ 50 = 15 Since 15 is divisible by 3, and 3 is
The literature has offered an interesting debate about whether the performance of Fama-Frenchs three-factor benchmark model is inadequate because it fails to pass some model specification tests and its R2 is not convincingly high in cross-sectional estimations. Previous studies have been quite limited, since they only focused on the time-series procedure with many models. We extend their work by providing a more robust investigation of the performance of several well-regarded pricing models in pooled portfolios and other portfolios sorted by new and important anomalies, using cross-sectional GMM tests for robustness. Finally, we find that, in addition to Fama and Frenchs five-factor model proposed in 1993, Fama-Frenchs three-factor model augmented by other factors usually outperforms Fama-Frenchs three-factor model across a significant proportion of different portfolios. In particular, Frazzini, Kabiller, and Pedersens model shows the best overall performance and consistency across ...
Downloadable! Diffusion functions in term-structure models are measures of uncertainty about future price movements and are directly related to the risk associated with holding financial securities. Correct specification of diffusion functions is crucial in pricing options and other derivative securities. In contrast to the standard parametric two-factor models, we propose a non-parametric two-factor term-structure model that imposes no restrictions on the functional forms of the diffusion functions. Hence, this model allows for maximum flexibility when fitting diffusion functions into data. A non-parametric procedure is developed for estimating the diffusion functions, based on the discretely sampled observations. The convergence properties and the asymptotic distributions of the proposed non-parametric estimators of the diffusion functions with multivariate dimensions are also obtained. Based on U.S. data, the non-parametric prices of the bonds and bond options are computed and compared with those
TY - JOUR. T1 - Testing the factor structure of the Family Quality of Life Survey - 2006. AU - Isaacs, B. AU - Wang, M. AU - Samuel, P. AU - Ajuwon, P. AU - Baum, N. AU - Edwards, M. AU - Rillotta, Fiona. PY - 2012/1. Y1 - 2012/1. N2 - Background Although the Family Quality of Life Survey - 2006 (FQOLS-2006) is being used in research, there is little evidence to support its hypothesised domain structure. The purpose of this study was to test the domain structure of the survey using confirmatory factor analysis. Method Samples from Australia, Canada, Nigeria and the USA were analysed using structural equation modelling. The data from Australia, Canada and the USA were combined on the assumption that these countries are similar, at least to some degree, in economic development, language and culture. The Nigerian data were analysed on its own. The analysis was undertaken in two phases. First, the hypothesis that each of nine domains of the FQOLS-2006 is a unidimensional construct that can reliably ...
This study examined the effect of model size on the chi-square test statistics obtained from ordinal factor analysis models. The performance of six robust ...