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

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 ...

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. ...

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.

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

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.

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 ...

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 ...

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

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 ...

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

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

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

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

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 ...

Rizzo, Lucas B.; Swardfager, Walter; Maurya, Pawan Kumar; Graiff, Maiara Zeni; Pedrini, Mariana; Asevedo, Elson; Cassinelli, Ana Claudia; Bauer, Moises E.; Cordeiro, Quirino; Scott, Jan; Brietzke, Elisa; Cogo-Moreira, Hugo ...

Structural equation modeling may be the appropriate method. It tends to be most useful and valid when you have multiple links that you want to identify in a causal chain; when multivariate normality is present; when any missing data are missing completely at random; when N is fairly large; and (I think) when variables are measured without much error. Absent such conditions, exploratory factor analysis scores may be quite useful as regression predictors, assuming the EFA (as well as the regression) is done in a sound, thoughtful way. A lot of people make the mistake of treating EFA as a routinized procedure, as you can read about in the wonderful article Repairing Tom Swifts Electric Factor Analysis Machine. EFA involves many decision points and few iron-clad guidelines for them. 42.2% of all EFA solutions that I run across smack of what I believe to be significant errors in choice of extraction method, number of factors to extract, inclusion/exclusion of variables, or others.. ...

The path diagram in Output 37.5.3 also represents factor variances and error variances by double-headed links. However, each of these links points to an individual variable, rather than to a pair of variables as the double-headed links for correlations do. The path diagram also displays the numerical values of factor variances or error variances next to the associated links. The directed links from factors to variables in the path diagram represent the effects of factors on the variables. The path diagram displays the numerical values of these effects, which are the loading estimates that are shown in Output 37.5.2. However, to aid the interpretation of the factors, the path diagram does not show all factor loadings or their corresponding links. By default, the path diagram displays only the links that have loadings greater than 0.3 in magnitude. For example, instead of associating ...

The report describes a comparison of three methods of analyzing attribute data in a two-factor classification. Sets of proportions (where each proportion is based on a sample of ten) were developed within the constraints of various combinations of seven influencing factors. Each set of proportions was subjected to factorial analysis by the (0, 1) analysis of variance, factorial chi-square, and arcsine transformation of proportions methods. Using the percentage points of the F or chi-square distribution which coincide with each application of the three methods as response data, an analysis of factors influencing these percentage points was undertaken. The results of this analysis were used to conclude that the arcsine transformation of proportions method is the best of the three for use within the levels of the factors studied in this report. (Author)(*FACTOR ANALYSIS

Factor Analysis 1.A researcher is examining factors that predict language development among first grade students. The researcher believes that some of the variables may be correlated and would like to run factor analysis to.

This paper asesses the relative importance of the principal factors influencing the release of dissolved organic carbon (DOC) and dissolved forms of nitrogen (N) from a small upland headwater (the Birnie Burn at Glensaugh) during a sequence of autumn runoff events.

Understanding what factors cause players to be retained or churned by a game is one of todays key challenges for online game makers. HR & Motivational theories used by the Business world offers us some insight: Frederick Herzbergs Two-Factor Theory, first published 1968. Two-factor theory identifies the distinct factors that cause job satisfaction - intrinsic motivators - and the Hygiene factors that cause dissatisfaction if not present. This talk walks through Herzbergs theory as used in the work-place, and draws parallels with games concepts ...

In a quest for perfect security, the perfect is the enemy of the good. People are criticizing SMS-based two-factor authentication in the wake of the Reddit hack, but using SMS-based two factor is still much better than not using two-factor authentication at all.

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 ...

The 8 dimensions identified through the phase of factor analyses were submitted to reliability analysis, which led to further elimination of a few items. Table 2 exhibits the final number of items and Cronbach alpha values for each dimension; it also shows the mean values for each facet (recalculated to take values in 1-5 interval, 1 meaning total disagreement and 5 total agreement).. The last step of construct validation was a factor analysis which includes the 40 items (out of 82 included in the survey) which constitute these 8 dimensions. Results [The matrix of loadings have been skipped from the final version of the paper due to space constraints, but was included in the original paper submitted to the reviewers.] showed both internal consistency and discriminant validity for 7 out of the 8 dimensions. The items composing Icsd (price equity) did not load on the same factor; this result added to the low alpha value observed for this dimension led to its elimination for further analyses; the ...

FIG. 1. Path diagram showing the influence of age and variance factors A and E on glucose and HbA1c (Cholesky decomposition). The observed phenotypes are shown in squares, and latent factors are shown in circles. Factor loadings of observed variables on the different latent factors are represented by the arrows. The correlation between A variance factors is 1 and 0.5 for MZ and DZ pairs, respectively. For clarity, C and D latent factors are not included in the model, and arrows loading on the latent A factors are omitted. ...

The basic principles of analysis of variancewere developed by R.A. Fisher (later Sir Ronald Fisher), who is regarded by many as the greatest figure in the history of statistics. Fisher was possessed...

TITLE Factor immunization models * FactorImmunization.gms: Factor immunization models * Consiglio, Nielsen and Zenios. * PRACTICAL FINANCIAL OPTIMIZATION: A Library of GAMS Models, Section 4.5 * Last modified: Apr 2008. SET Time Time periods /2001 * 2011/; ALIAS (Time, t, t1, t2); SCALARS Now Current year Horizon End of the Horizon; Now = 2001; Horizon = CARD(t)-1; PARAMETER tau(t) Time in years; * Note: time starts from 0 tau(t) = ORD(t)-1; SET Bonds Bonds universe /DS-8-06, DS-8-03, DS-7-07, DS-7-04, DS-6-11, DS-6-09, DS-6-02, DS-5-05, DS-5-03, DS-4-02/; SET Factors Term structure factors /FF_1, FF_2, FF_3/; ALIAS(Factors, j); ALIAS(Bonds, i); PARAMETERS Coupon(i) Coupons Maturity(i) Maturities Liability(t) Stream of liabilities F(t,i) Cashflows; * Bond data. Prices, coupons and maturities from the Danish market $INCLUDE "BondData.inc" $INCLUDE "FactorData.inc" PARAMETER beta(j,t) Factor loadings; * Transpose factor loadings beta(j,t) = betaTrans(t,j); * Copy/transform data. Note division by ...

Jablonka and Lamb present a richer, more complex view of evolution than that offered by the gene-based Modern Synthesis, arguing that induced and acquired changes also play a role. Their lucid and accessible text is accompanied by artist-physician Anna Zeligowskis lively drawings, which humorously and effectively illustrate the authors points. Each chapter ends with a dialogue in which the authors refine their arguments against the vigorous skepticism of the fictional "I.M." (for Ipcha Mistabra - Aramaic for "the opposite conjecture"). The extensive new chapter, presented engagingly as a dialogue with I.M., updates the information on each of the four dimensions - with special attention to the epigenetic, where there has been an explosion of new research.. Praise for the first edition. "With courage and verve, and in a style accessible to general readers, Jablonka and Lamb lay out some of the exciting new pathways of Darwinian evolution that have been uncovered by contemporary research." - ...

Abstract:. This paper focuses on analyzing the properties of expected return estimators on individual assets implied by the linear factor models of asset pricing, i.e., the product beta and lambda. We provide the asymptotic properties of factor-model-based expected return estimators under the following set-ups: 1) when the underlying model is correct, 2) when some of the priced risk factors are omitted, and 3) when factors with small betas are present in the underlying model. Moreover, we analyze the role of traded, non-traded, and mimicking factors in the estimation of expected returns. We find that using factor-model-based risk premium estimates leads to precision gains of up to 31% when compared to the historical averages. In the presence of omitted factors, adding an alpha to the model captures mispricing only in case of traded factors, otherwise the bias caused by misspecification cannot be corrected. Finally, inference about expected returns, unlike inference on factor prices, does not ...

Linton, Oliver and Rodríguez-Poo, Juan M. (2001) Nonparametric factor analysis for residual time series. TEST, 10 (1). pp. 161-182. ISSN 1133-0686 ...