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48
Cyclodextrins—a review
 Organics; 1988 (NewcastleuponTyne NE3 3TT
"... As a steroid hormone that regulates mineral homeostasis and bone metabolism, 1α, 25dihydroxycholecalciferol (calcitriol) also has broad spectrum antitumor activities as supported by numerous epidemiological and experimental studies. Calcitriol potentiates the antitumor activities of multiple chem ..."
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Cited by 126 (2 self)
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As a steroid hormone that regulates mineral homeostasis and bone metabolism, 1α, 25dihydroxycholecalciferol (calcitriol) also has broad spectrum antitumor activities as supported by numerous epidemiological and experimental studies. Calcitriol potentiates the antitumor activities of multiple chemotherapeutics agents including DNAdamaging agents cisplatin, carboplatin and doxorubicin; antimetabolites 5fluorouracil, cytarabine, hydroxyurea, cytarabine and gemcitabine; and microtubuledisturbing agents paclitaxel and docetaxel. Calcitriol elicits antitumor effects mainly through the induction of cancer cell apoptosis, cell cycle arrest, differentiation, angiogenesis and the inhibition of cell invasiveness by a num b e r mechanisms. Calcitriol enhances the cytotoxic effects of gamma irradiation and certain antioxidants and naturally derived compounds. Inhibition of calcitriol metabolism by 24hydroxylase promotes growth inhibition effect of calcitriol. Calcitriol has been used in a number of clinical trials and it is important to note that sufficient dose and exposure to calcitriol is critical to achieve antitumor effect. Several trials have demonstrated that safe and feasible to administer high doses of calcitriol through intermittent regimen. Further well designed clinical trials should be conducted to better understand the role of calcitriol in cancer therapy. o f Key words: vitamin D, calcitriol, cancer, chemotherapy
A new look at the Big Five factor structure through exploratory structural equation modeling. Psychological Assessment
 Substance and artifact in the higherorder factors of the Big Five. Journal of Personality and Social Psychology, 95, 442–455. doi:10.1037/00223514.95.2.442 McGrew, K. (2009). CHC theory and the human cognitive abilities project: Standing on the shoulder
, 2010
"... NEO instruments are widely used to assess Big Five personality factors, but confirmatory factor analyses (CFAs) conducted at the item level do not support their a priori structure due, in part, to the overly restrictive CFA assumptions. We demonstrate that exploratory structural equation modeling (E ..."
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Cited by 18 (0 self)
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NEO instruments are widely used to assess Big Five personality factors, but confirmatory factor analyses (CFAs) conducted at the item level do not support their a priori structure due, in part, to the overly restrictive CFA assumptions. We demonstrate that exploratory structural equation modeling (ESEM), an integration of CFA and exploratory factor analysis (EFA), overcomes these problems with responses (N � 3,390) to the 60item NEO–FiveFactor Inventory: (a) ESEM fits the data better and results in substantially more differentiated (less correlated) factors than does CFA; (b) tests of gender invariance with the 13model ESEM taxonomy of full measurement invariance of factor loadings, factor variances– covariances, item uniquenesses, correlated uniquenesses, item intercepts, differential item functioning, and latent means show that women score higher on all NEO Big Five factors; (c) longitudinal analyses support measurement invariance over time and the maturity principle (decreases in Neuroticism and increases in Agreeableness, Openness, and Conscientiousness). Using ESEM, we addressed substantively important questions with broad applicability to personality research that could not be appropriately addressed with the traditional approaches of either EFA or CFA.
Bayesian SEM: A more flexible representation of substantive theory. Submitted for publication. Retrieved from http://www.statmodel.com/download/ BSEMv4.pdf
, 2010
"... This paper proposes a new approach to factor analysis and structural equation modeling using Bayesian analysis. The new approach replaces parameter specifications of exact zeros with approximate zeros based on informative, smallvariance priors. It is argued that this produces an analysis that bette ..."
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Cited by 16 (6 self)
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This paper proposes a new approach to factor analysis and structural equation modeling using Bayesian analysis. The new approach replaces parameter specifications of exact zeros with approximate zeros based on informative, smallvariance priors. It is argued that this produces an analysis that better reflects substantive theories. The proposed Bayesian approach is particularly beneficial in applications where parameters are added to a conventional model such that a nonidentified model is obtained if maximumlikelihood estimation is applied. This approach is useful for measurement aspects of latent variable modeling such as with CFA and the measurement part of SEM. Two application areas are studied, crossloadings and residual correlations in CFA. The approach encompasses three elements: Model testing, model estimation, and model modification. Monte Carlo simulations and real data are analyzed using Mplus. 2 1
Current methodological considerations in exploratory and confirmatory factor analysis
 Journal of Psychoeducational Assessment
, 2011
"... Researchers must make numerous choices when conducting factor analyses, each of which can have significant ramifications on the model results. They must decide on an appropriate sample size to achieve accurate parameter estimates and adequate power, a factor model and estimation method, a method fo ..."
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Researchers must make numerous choices when conducting factor analyses, each of which can have significant ramifications on the model results. They must decide on an appropriate sample size to achieve accurate parameter estimates and adequate power, a factor model and estimation method, a method for determining the number of factors and evaluating model fit, and a rotation criterion. Unfortunately, researchers continue to use outdated methods in each of these areas. The present article provides a current overview of these areas in an effort to provide researchers with uptodate methods and considerations in both exploratory and confirmatory factor analysis. A demonstration was provided to illustrate current approaches. Choosing between confirmatory and exploratory methods is also discussed, as researchers often make incorrect assumptions about the application of each. Keywords factor analysis, exploratory factor analysis, confirmatory factor analysis, structural equation modeling Using factor analysis (FA) procedures such as exploratory factor analysis (EFA) and confirma
How should the internal structure of personality inventories be evaluated? Personality and Social Psychology Review
 Child Development
, 2010
"... Personality trait inventories often perform poorly when their structure is evaluated with confirmatory factor analysis (CFA). The authors demonstrate poor CFA fit for several widely used personality measures with documented evidence of criterionrelated validity but also show that some measures perf ..."
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Cited by 16 (0 self)
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Personality trait inventories often perform poorly when their structure is evaluated with confirmatory factor analysis (CFA). The authors demonstrate poor CFA fit for several widely used personality measures with documented evidence of criterionrelated validity but also show that some measures perform well from an exploratory factor analytic perspective. In light of these results, the authors suggest that the failure of these measures to fit CFA models is because of the inherent complexity of personality, issues related to its measurement, and issues related to the application and interpretation of CFA models. This leads to three recommendations for researchers interested in the structure and assessment of personality traits: (a) utilize and report on a range of factor analytic methods, (b) avoid global evaluations regarding the internal validity of multiscale personality measures based on model fit according to conventional CFA cutoffs, and (c) consider the substantive and practical implications of model modifications designed to improve fit. Keywords personality assessment, factor analysis, construct validity Every scientist in the back of his mind takes it for granted that even the best theory is likely to be an approximation to the true state of affairs.
How to perform multiblock component analysis in practice
 Behavior Research Methods
, 2012
"... Abstract To explore structural differences and similarities in multivariate multiblock data (e.g., a number of variables have been measured for different groups of subjects, where the data for each group constitute a different data block), researchers have a variety of multiblock component analysis ..."
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Cited by 11 (6 self)
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Abstract To explore structural differences and similarities in multivariate multiblock data (e.g., a number of variables have been measured for different groups of subjects, where the data for each group constitute a different data block), researchers have a variety of multiblock component analysis and factor analysis strategies at their disposal. In this article, we focus on three types of multiblock component methods—namely, principal component analysis on each data block separately, simultaneous component analysis, and the recently proposed clusterwise simultaneous component analysis, which is a generic and flexible approach that has no counterpart in the factor analysis tradition. We describe the steps to take when applying those methods in practice. Whereas plenty of software is available for fitting factor analysis solutions, up to now no easytouse software has existed for fitting these multiblock component analysis methods. Therefore, this article presents the MultiBlock Component Analysis program, which also includes procedures for missing data imputation and model selection.
Rotation criteria and hypothesis testing for exploratory factor analysis: implications for factor pattern loadings and interfactor correlations
 Educational and Psychological Measurement
, 2011
"... Exploratory factor analysis (EFA) has long been used in the social sciences to depict the relationships between variables/items and latent traits. Researchers face many choices when using EFA, including the choice of rotation criterion, which can be difficult given that few research articles have d ..."
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Cited by 7 (2 self)
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Exploratory factor analysis (EFA) has long been used in the social sciences to depict the relationships between variables/items and latent traits. Researchers face many choices when using EFA, including the choice of rotation criterion, which can be difficult given that few research articles have discussed and/or demonstrated their differences. The goal of the current study is to help fill this gap by reviewing and demonstrating the utility of several rotation criteria. Furthermore, this article discusses and demonstrates the importance of using factor pattern loading standard errors for hypothesis testing. The choice of a rotation criterion and the use of standard errors in evaluating factor loadings are essential so researchers can make informed decisions concerning the factor structure. This study demonstrates that depending on the rotation criterion selected, and the complexity of the factor pattern matrix, the interfactor correlations and factor pattern loadings can vary substantially. It is also illustrated that the magnitude of the factor loading standard errors can result in different factor structures. Implications and future directions are discussed.
General Random Effect Latent Variable Modeling: Random Subjects, Items, Contexts, and Parameters
, 2012
"... Bayesian methodology is wellsuited for estimating latent variable models where subjects are not the only random mode, but also items and contexts. A general crossclassified structural equation model is presented where observations are nested within two independent clustering variables. The model i ..."
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Cited by 6 (3 self)
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Bayesian methodology is wellsuited for estimating latent variable models where subjects are not the only random mode, but also items and contexts. A general crossclassified structural equation model is presented where observations are nested within two independent clustering variables. The model includes continuous and categorical dependent variables as well as continuous latent variable. Random effects, intercepts and slopes, are used to model the clustering effects for both nesting structures. We describe the Bayesian methodology implemented in Mplus version 7 used to estimate such models. Bayesian methodology can also be used to estimate cluster specific structural equation models in twolevel data where all measurement and structural coefficients, including factor loadings and regression coefficients between factors can be estimated as cluster level random effects rather than fixed parameters. The maximumlikelihood estimation for such models is generally prohibitive due to the large dimension of numerical integration. We also discuss the effect of priors on the Bayesian estimation. In particular we show how a small variance prior can be used to easily identify more random effects than traditional ML methodology can, which can yield flexible structural models with many cluster specific coefficients. Applications are discussed such as multiple group analysis with large number of groups and measurement noninvariance, crosscultural research and Gtheory. 1 1