Assessment of cumulative incidences of mung bean crop production in lowlands of south Ethiopia through multiple factor analysis.

Ethiopian farmers face a combination of risks in agricultural production during a single crop season. However, cumulative incidence assessment of the risks in the agriculture was not researched. Thus, this study aimed at assessing the cumulative incidence components in the production of the mung bean crop in the lowlands of South Ethiopia. A cross-sectional study was used with 384 mung bean farmers selected from lowlands of Gamo, Gofa and Wolaita zones. Thirteen original risk types were used to be categorized into a few factors through multiple factor analysis. Factor analysis identified latent variables with an Eigenvalues greater than one. The economic risk factor, the climate-related risk factor, and the systematic risk factor were the first three factors that explain 92.87 % of the inertia cumulatively. The rotated factor loading matrix indicated that under factor one, the encountered risks are an increase in the price of inputs, an increase in the price of food, and financial incidence, which accounted 50 % of the total variance. Under factor two extended dry spells, crop diseases, and wild animals' damage and livestock diseases and deaths variables that accounted for 23.24 % of the total variance. The risk types categorized under systematic risks were social risks and political risks that accounted for 19.61 % of the total variance of risks encountered by mung bean farmers in the study area. Therefore, disentangling systematic risks is important by providing much-needed information to mung bean farmers and policymakers regarding systematic risk management priorities.

Burnout, work engagement and workaholism in a group of Dutch judges: distinctiveness and two-year structural stability.

The aim of this two-wave study is to investigate whether burnout, work engagement and workaholism can be empirically distinguished in one model and whether this model shows structural stability over a period of 2 years (i.e. whether the distinguishability between the constructs holds across time). The study was conducted among 118 judges in the Netherlands who completed questionnaires measuring burnout, work engagement and workaholism. The results showed that these are relatively distinguishable constructs, despite a considerable overlap of professional efficacy loading on work engagement (instead of burnout; as hypothesized), absorption loading on workaholism (in addition to work engagement; as hypothesized) and exhaustion loading on workaholism (in addition to burnout), which represents a new finding. These extra loadings led to model modifications, which were found at both time points. As hypothesized, this model appeared to be stable over time. Nevertheless, further clarification and conceptualization of these constructs are undoubtedly needed.

Prior Sensitivity in Bayesian Structural Equation Modeling for Sparse Factor Loading Structures.

Bayesian structural equation modeling (BSEM) is a flexible tool for the exploration and estimation of sparse factor loading structures; that is, most cross-loading entries are zero and only a few important cross-loadings are nonzero. The current investigation was focused on the BSEM with small-variance normal distribution priors (BSEM-N) for both variable selection and model estimation. The prior sensitivity in BSEM-N was explored in factor analysis models with sparse loading structures through a simulation study (Study 1) and an empirical example (Study 2). Study 1 examined the prior sensitivity in BSEM-N based on the model fit, population model recovery, true and false positive rates, and parameter estimation. Seven shrinkage priors on cross-loadings and five noninformative/vague priors on other model parameters were examined. Study 2 provided a real data example to illustrate the impact of various priors on model fit and parameter selection and estimation. Results indicated that when the 95% credible intervals of shrinkage priors barely covered the population cross-loading values, it resulted in the best balance between true and false positives. If the goal is to perform variable selection, a sparse cross-loading structure is required, preferably with a minimal number of nontrivial cross-loadings and relatively high primary loading values. To improve parameter estimates, a relatively large prior variance is preferred. When cross-loadings are relatively large, BSEM-N with zero-mean priors is not recommended for the estimation of cross-loadings and factor correlations.

Basic Gene Expression Characteristics of Glioma Stem Cells and Human Glioblastoma.

BACKGROUND: Glioma stem cells (GSCs) play important roles in the tumorigenesis of glioblastoma multiforme (GBM). Using a novel cellular bioinformatics pipeline, we aimed to characterize the differences in gene-expression profiles among GSCs, U251 (glioma cell line), and a human GBM tissue sample. MATERIALS AND METHODS: Total RNA was extracted from GSCs, U251 and GBM and microarray analysis was performed; the data were then applied to the bioinformatics pipeline consisting of a principal component analysis (PCA) with factor loadings, an intracellular pathway analysis, and an immunopathway analysis. RESULTS: The PCA clearly distinguished the three groups. The factor loadings of the PCA suggested that v-myc avian myelocytomatosis viral oncogene neuroblastoma derived homolog (MYCN), dipeptidyl-peptidase 4 (DPP4), and macrophage migration-inhibitory factor (MIF) contribute to the stemness of GSCs. The intracellular pathway and immunopathway analyses provided relevant information about the functions of representative genes in GSCs. CONCLUSION: The newly-developed cellular bioinformatics pipeline was a useful method to clarify the similarities and differences among samples.

Rape Myths and the Cross-Cultural Adaptation of the Illinois Rape Myth Acceptance Scale in China.

The study examines the similarities and differences between China and the United States with regard to rape myths. We assessed the individual level of rape myth acceptance among Chinese university students by adapting and translating a widely used measure of rape myth endorsement in the United States, the Illinois Rape Myth Acceptance (IRMA) scale. We assessed whether the IRMA scale would be an appropriate assessment of attitudes toward rape among young adults in China. The sample consisted of 975 Chinese university students enrolled in seven Chinese universities. We used explorative factor analysis to examine the factor structure of the Chinese translation of the IRMA scale. Results suggest that the IRMA scale requires some modification to be employed with young adults in China. Our analyses indicate that 20 items should be deleted, and a five-factor model is generated. We discuss relevant similarities and differences in the factor structure and item loadings between the Chinese Rape Myth Acceptance (CRMA) and the IRMA scales. A revised version of the IRMA, the CRMA, can be used as a resource in rape prevention services and rape victim support services. Future research in China that employs CRMA will allow researchers to examine whether individual's response to rape myth acceptance can predict rape potential and judgments of victim blaming and community members' acceptance of marital rape.

Item-Score Reliability in Empirical-Data Sets and Its Relationship With Other Item Indices.

Reliability is usually estimated for a total score, but it can also be estimated for item scores. Item-score reliability can be useful to assess the repeatability of an individual item score in a group. Three methods to estimate item-score reliability are discussed, known as method MS, method λ 6 , and method CA. The item-score reliability methods are compared with four well-known and widely accepted item indices, which are the item-rest correlation, the item-factor loading, the item scalability, and the item discrimination. Realistic values for item-score reliability in empirical-data sets are monitored to obtain an impression of the values to be expected in other empirical-data sets. The relation between the three item-score reliability methods and the four well-known item indices are investigated. Tentatively, a minimum value for the item-score reliability methods to be used in item analysis is recommended.

An Entropy-Based Approach for Measuring Factor Contributions in Factor Analysis Models.

In factor analysis, factor contributions of latent variables are assessed conventionally by the sums of the squared factor loadings related to the variables. First, the present paper considers issues in the conventional method. Second, an alternative entropy-based approach for measuring factor contributions is proposed. The method measures the contribution of the common factor vector to the manifest variable vector and decomposes it into contributions of factors. A numerical example is also provided to demonstrate the present approach.

Analysis of the Changes of Burden Factor Loading for Chronic Disease Inpatients before and after the Im-plementation of Zero Price Policy by Establishing Structure Equation Model / 中国药房

OBJECTIVE:To construct the structure equation model for the burden of chronic disease inpatients in primary hos-pital before and after the implementation of zero price policy,and analyze the changes of burden factor loading. METHODS:6 pri-mary hospitals were randomly selected in Danyang,Jiangsu province. The data of hospitalization expenses for chronic diseases(hy-pertension,diabetes,bronchitis)were collected before and after the implementation of zero price policy. Using drug cost,nursing fees,inspection fees and treatment fees as independent variables,hospitalization burden as latent variable,SPSS and AMOS 24.0 software were adopted to establish the model. RESULTS:The burden factor loading of inpatients changed greatly before and after the implementation of zero price policy. χ2=24.586,χ2/df=1.446, RMSEA=0.019,GFI=0.995,AGFI=0.989,CFI=0.988,NFI=0.963 manifested good model fitting. Factor loading of drug cost increased greatly,indicating the burden of inpatients was reduced after the implementation of zero price policy. Factor loading of inspection fees and treatment fees increased significantly,the charac-terization effects of them to the burden of inpatients were enhanced. Factor loading of nursing fees was the lowest,and it had the weakest effects on the burden of inpatients,of which the government should enhance the regulation. CONCLUSIONS:Established model has good reliability and validity. It can reflect the change of burden factor loading of inpatients before and after the implemen-tation of zero price policy.

Using a latent variable model with non-constant factor loadings to examine PM2.5 constituents related to secondary inorganic aerosols.

Factor analysis is a commonly used method of modelling correlated multivariate exposure data. Typically, the measurement model is assumed to have constant factor loadings. However, from our preliminary analyses of the Environmental Protection Agency's (EPA's) PM2.5 fine speciation data, we have observed that the factor loadings for four constituents change considerably in stratified analyses. Since invariance of factor loadings is a prerequisite for valid comparison of the underlying latent variables, we propose a factor model that includes non-constant factor loadings that change over time and space using P-spline penalized with the generalized cross-validation (GCV) criterion. The model is implemented using the Expectation-Maximization (EM) algorithm and we select the multiple spline smoothing parameters by minimizing the GCV criterion with Newton's method during each iteration of the EM algorithm. The algorithm is applied to a one-factor model that includes four constituents. Through bootstrap confidence bands, we find that the factor loading for total nitrate changes across seasons and geographic regions.

[A guide on the use of factor analysis in the assessment of construct validity].

PURPOSE: The purpose of this study is to provide researchers with a simplified approach to undertaking exploratory factor analysis for the assessment of construct validity. METHODS: All articles published in 2010, 2011, and 2012 in Journal of Korean Academy of Nursing were reviewed and other relevant books and articles were chosen for the review. RESULTS: In this paper, the following were discussed: preliminary analysis process of exploratory factor analysis to examine the sample size, distribution of measured variables, correlation coefficient, and results of KMO measure and Bartlett's test of sphericity. In addition, other areas to be considered in using factor analysis are discussed, including determination of the number of factors, the choice of rotation method or extraction method of the factor structure, and the interpretation of the factor loadings and explained variance. CONCLUSION: Content validity is the degree to which elements of an assessment instrument are relevant to and representative of the targeted construct for a particular assessment purpose. This measurement is difficult and challenging and takes a lot of time. Factor analysis is considered one of the strongest approaches to establishing construct validity and is the most commonly used method for establishing construct validity measured by an instrument.

A Guide on the Use of Factor Analysis in the Assessment of Construct Validity

PURPOSE: The purpose of this study is to provide researchers with a simplified approach to undertaking exploratory factor analysis for the assessment of construct validity. METHODS: All articles published in 2010, 2011, and 2012 in Journal of Korean Academy of Nursing were reviewed and other relevant books and articles were chosen for the review. RESULTS: In this paper, the following were discussed: preliminary analysis process of exploratory factor analysis to examine the sample size, distribution of measured variables, correlation coefficient, and results of KMO measure and Bartlett's test of sphericity. In addition, other areas to be considered in using factor analysis are discussed, including determination of the number of factors, the choice of rotation method or extraction method of the factor structure, and the interpretation of the factor loadings and explained variance. CONCLUSION: Content validity is the degree to which elements of an assessment instrument are relevant to and representative of the targeted construct for a particular assessment purpose. This measurement is difficult and challenging and takes a lot of time. Factor analysis is considered one of the strongest approaches to establishing construct validity and is the most commonly used method for establishing construct validity measured by an instrument.

Application of corresponding analysis in study on trace elements of Chinese materia medica / 中草药

The principle of corresponding analysis was introduced.The relationship between the content of trace elements and the efficacy of eleven kinds of Chinese materia medica(CMM) was analyzed by the corresponding analysis.The result showed that the corresponding analysis could reveal the relationship between the efficacy of eleven kinds of CMM and the trace elements content that they contain.According to the analysis,the efficacy of CMM was classified.The result shows that the corresponding analysis is a good method for the efficacy analysis of CMM.