Latent variable

This study examined agreement on aggressive and nonaggressive conduct disorder in a general population sample of 14to 17-year-old adolescents (n=1165) and their mothers. We collected diagnostic interview data and applied latent class analyses to estimate inter-informant agreement. The preferred model for aggressive conduct disorder for both males and females was a one-latent-variable/two-class model specifying no inter-informant disagreement beyond chance expectations. This model estimated the prevalence of aggressive conduct disorder to be 13% for males and 0.4% for females. For nonaggressive conduct disorder, a one-latent-variable/three-class model specifying asymmetric agreement was preferred for both males and females. This model estimated the prevalence of nonaggressive conduct disorder in adolescents to be 18% according to males and 13% according to mothers. Prevalence estimates were 12% according to females and 7% according to mothers. Symptom sensitivity estimates for all models were poor whereas specificity estimates were near perfect to perfect. Males had higher rates of aggressive and nonaggressive conduct disorder across informants. There was a high level of adolescent-mother agreement on both types of conduct disorder. However, there were some differences, suggesting that aggressive and nonaggressive are two valid subtypes of conduct disorder with different prevalence estimates and agreement levels.

Dimensionality reduction can be efficiently achieved by generative latent variable models such as probabilistic principal component analysis (PPCA) or independent component analysis (ICA), aiming to extract a reduced set of variables (latent variables) from the original ones. In most cases, the learning of these methods is achieved within the unsupervised framework where only unlabeled samples are used. In this paper we investigate the possibility of estimating independent factor analysis model (IFA) and thus projecting original data onto a lower dimensional space, when prior knowledge on the cluster membership of some training samples is incorporated. In the basic IFA model, latent variables are only recovered from their linear observed mixtures (original features). Both the mapping matrix (assumed to be linear) and the latent variable densities (that are assumed to be mutually independent and generated according to mixtures of Gaussians) are learned from observed data. We propose to learn this model within semisupervised framework where the likelihood of both labeled and unlabeled samples is maximized by a generalized expectationmaximization (GEM) algorithm. Experimental results on real data sets are provided to demonstrate the ability of our approach to find law dimensional manifold with good explanatory power.

The feasibility of utilizing infrared spectroscopy for the prediction of haze formation in white wines resulting from heat and colloidal stability tests was investigated. One-hundred eleven white wines, representing multiple regions and varieties from the 2008 California vintage, were collected and analyzed. The near and mid-infrared spectra were measured and heat and colloidal (ethanol addition) stability tests were performed on the same wines. Partial-least squares regression analysis was then used to construct models predictive of the resulting nepholometric turbidity to the acquired spectra. Preliminary models obtained following application of spectral pretreatments today considered as ''classical'' (e.g., derivatives, standard normal variate, vector normalization, constant offset elimination) lacked robustness; two alternative algorithms designed to remove spectral information unrelated to the turbidity were then employed (orthogonal signal correction; direct orthogonal signal correction). While OSC pretreatment did not result in more robust models, DOSC considerably enhanced the goodness of the PLS model constructed to predict the ethanol test turbidity. Predictive modeling of the short-NIR spectra, following DOSC preprocessing, allowed the prediction of colloidal stability on an unknown test set with an R 2 = 0.80 and a RMSEP = 10.12 using three latent variables. When the data set was restricted to Chardonnay wines alone, the predictive ability improved, with R 2 = 0.85 and RMSEP = 8.90.

Although socioeconomic status is a major contributing factor to health disparities, the mechanisms through which socioeconomic status influences health remain unclear. The purpose of the present study was to evaluate an a priori conceptual model of the pathways between socioeconomic status and modifiable health risk factors in a sample of 399 African Americans seeking smoking cessation treatment. A latent variable modeling approach was utilized to characterize the interrelationships among socioeconomic status, neighborhood disadvantage, social support, negative affect/perceived stress, and three specific modifiable risk factors (i.e., overweight/obesity, insufficient physical activity, at-risk drinking). Findings indicated that neighborhood disadvantage, social support, and negative affect/perceived stress function as pathways linking socioeconomic status and modifiable risk factors among African American smokers, and negative affect/perceived stress appears to play a key mediating role. Policy, community, and individual-level interventions may attenuate the impact of socioeconomic status on health by targeting intermediate psychosocial, environmental, and behavioral pathways.

In this paper, we empirically investigate the impact of exchange rate volatility on real international trade ows utilizing a 13 country dataset of monthly bilateral real exports for 1980 1998. We compute one month ahead exchange rate volatility from the intra monthly variations in the exchange rate to better quantify this latent variable. We nd that the effect of exchange rate volatility on trade ows is nonlin-1 We acknowledge the expert research assistance provided by Tairi Room, and thank John Barkoulas and Franc Klaassen for productive conversations on these issues. Consultations with Nicholas J. Cox on programming practices were very helpful. We received useful comments on an earlier

Robust calibration of option valuation models to quoted option prices is non-trivial but crucial for good performance. A framework based on the state-space formulation of the option valuation model is introduced. Non-linear (Kalman) filters are needed to do inference since the models have latent variables (e.g. volatility). The statistical framework is made adaptive by introducing stochastic dynamics for the parameters. This allows the parameters to change over time, while treating the measurement noise in a statistically consistent way and using all data efficiently. The performance and computational efficiency of standard and iterated extended Kalman filters (EKF and IEKF) are investigated. These methods are compared to common calibration such as weighted least squares (WLS) and penalized weighted least squares (PWLS). A simulation study, using the Bates model, shows that the adaptive framework is capable of tracking time varying parameters and latent processes such as stochastic ...

A structural equation model of the determinants of health and of the demand for health care, treating health status as a latent variable, was developed using cross-sectional sample survey data representing the adult noninstitutionalized population (n-10,712) of Finland in 1987. Attention was paid to evaluating the relative effects of different factors affecting health and demand for health care in order to make generalizations for health policy. The maximum likelihood estimates of MIMIC (multiple indicators and multiple causes) models are presented. The results indicate the important role of lifestyle variables as health production factors, and stress the importance of decreasing smoking and reducing overweight as targets for health policy. Under the assumptions of the model, smoking and overweight also have considerable effects on health care utilization. The socioeconomic variables-income, education and occupation-seem to have almost equal impacts on health, although their separate direct effects are somewhat smaller than the effects of the two lifestyle factors. The results of demand for doctor visits and prescribed medicines stress factors such as time cost and the supply of doctors as important determinants of utilization, and confirm the fact that a significant portion of health utilization depends on doctors decisions and is generated by patient-doctor contacts.

Although aggressive medical treatment protocols have led to 80% 5-year survival rates for most childhood cancers, many long-term survivors experience multiple troubling symptoms. Using data from 100 adult survivors of childhood cancers (ACC-survivors), we used latent variable mixture modeling to generate unique subgroups of survivors based on their experiences with a cluster of eight symptoms: lack of energy, worry, pain, difficulty sleeping, feeling irritable, feeling nervous, difficulty concentrating, and feeling sad (as measured by the Memorial Symptom Assessment Scale). We also examined factors that were likely to predict subgroup membership (chronic health conditions, health-promoting lifestyle, and demographic variables) and determined the extent to which satisfaction with quality of life (QoL) varied across the subgroups. The final mixture model included three subgroups of ACC-survivors: high symptoms (HS) (n=21), moderate symptoms (MS) (n=45), and low symptoms (LS) (n=34). ACC-survivors who reported at least one chronic health condition were six times as likely to be classified in the HS subgroup as compared to the LS subgroup. Mean health-promoting lifestyle scores were lowest in the HS subgroup and highest in the LS subgroup. Differences in QoL among the subgroups were statistically significant, thus validating that the subgroups were characterized uniquely for identifying those symptoms with highest life impact. To our knowledge, we are the first to identify distinct subgroups of ACC-survivors differentiated by symptom cluster experience profiles. The findings warrant additional research to confirm the subgroup-specific symptom cluster experience profiles in larger studies of ACC-survivors.

In this study, three approaches commonly used by communication scientists to specify structural relationships using full-information maximum likelihood structural equation modeling are investigated. Specifically, a simulation study using Monte Carlo techniques was conducted to compare the structural paths generated by each of the three structural equation model types. Two of the three approaches utilized forms of latent variable modeling, and the third approach employed observed variables only. Of the three, the observed variable approach produced the most conservative structural path coefficients, whereas the hybrid latent variable approach generated the least attenuated coefficients. The appropriateness of each technique in modeling structural relationships is discussed and an argument is made for greater use of latent variable structural equation modeling in the field of communication.

Le CIRANO est un organisme sans but lucratif constitué en vertu de la Loi des compagnies du Québec. Le financement de son infrastructure et de ses activités de recherche provient des cotisations de ses organisations-membres, d'une subvention d'infrastructure du ministère

The O2-PLS method is derived from the basic partial least squares projections to latent structures (PLS) prediction approach. The importance of the covariation matrix (Y T X) is pointed out in relation to both the prediction model and the structured noise in both X and Y. Structured noise in X (or Y) is defined as the systematic variation of X (or Y) not linearly correlated with Y (or X). Examples in spectroscopy include baseline, drift and scatter effects. If structured noise is present in X, the existing latent variable regression (LVR) methods, e.g. PLS, will have weakened score±loading correspondence beyond the first component. This negatively affects the interpretation of model parameters such as scores and loadings. The O2-PLS method models and predicts both X and Y and has an integral orthogonal signal correction (OSC) filter that separates the structured noise in X and Y from their joint X±Y covariation used in the prediction model. This leads to a minimal number of predictive components with full score±loading correspondence and also an opportunity to interpret the structured noise. In both a real and a simulated example, O2-PLS and PLS gave very similar predictions of Y. However, the interpretation of the prediction models was clearly improved with O2-PLS, because structured noise was present. In the NIR example, O2-PLS revealed a strong water peak and baseline offset in the structured noise components. In the simulated example the O2-PLS plot of observed versus predicted Y-scores (u vs u hat ) showed good predictions. The corresponding loading vectors provided good interpretation of the covarying analytes in X and Y.

In the literature about category effects in semantic memory, body parts and musical instruments are often considered atypical, because in cases with a disproportionate impairment of living categories body parts are relatively spared, while musical instruments are often severely defective. In this study the performance of 57 subjects affected by diseases generally associated with lexical-semantic impairment, for the most part

The feasibility of utilizing infrared spectroscopy for the prediction of haze formation in white wines resulting from heat and colloidal stability tests was investigated. One-hundred eleven white wines, representing multiple regions and varieties from the 2008 California vintage, were collected and analyzed. The near and mid-infrared spectra were measured and heat and colloidal (ethanol addition) stability tests were performed on the same wines. Partial-least squares regression analysis was then used to construct models predictive of the resulting nepholometric turbidity to the acquired spectra. Preliminary models obtained following application of spectral pretreatments today considered as ''classical'' (e.g., derivatives, standard normal variate, vector normalization, constant offset elimination) lacked robustness; two alternative algorithms designed to remove spectral information unrelated to the turbidity were then employed (orthogonal signal correction; direct orthogonal signal correction). While OSC pretreatment did not result in more robust models, DOSC considerably enhanced the goodness of the PLS model constructed to predict the ethanol test turbidity. Predictive modeling of the short-NIR spectra, following DOSC preprocessing, allowed the prediction of colloidal stability on an unknown test set with an R 2 = 0.80 and a RMSEP = 10.12 using three latent variables. When the data set was restricted to Chardonnay wines alone, the predictive ability improved, with R 2 = 0.85 and RMSEP = 8.90.

The present study utilized a latent variable modeling approach to examine the Simple View of Reading in a sample of students from 3rd, 7th, and 10th grades (N = 215, 188, and 180, respectively). Latent interaction modeling and other latent variable models were employed to investigate (a) the functional form of the relationship between decoding and linguistic comprehension in third grade, (b) the contribution of these predictors as children progress through school, and (c) the contribution of passage fluency, working memory, and performance IQ to a model of reading for all grades. Results did not support the Simple View of Reading for students in grades 3, 7 and 10. Additionally, it was found that passage fluency significantly predicted reading comprehension after controlling for decoding and linguistic comprehension for students in grades 7 and 10. Likewise performance IQ was found to be a significant predictor of reading comprehension for students in third grade.

This study examined the developmental trajectories of three frequently postulated executive function (EF) components, Working Memory, Shifting, and Inhibition of responses, and their relation to performance on standard, but complex, neuropsychological EF tasks, the Wisconsin Card Sorting Task (WCST), and the Tower of London (ToL). Participants in four age groups (7-, 11-, 15-, and 21-year olds) carried out nine basic experimental tasks (three tasks for each EF), the WCST, and the ToL. Analyses were done in two steps: (1) analyses of (co)variance to examine developmental trends in individual EF tasks while correcting for basic processing speed, (2) confirmatory factor analysis to extract latent variables from the nine basic EF tasks, and to explain variance in the performance on WCST and ToL, using these latent variables. Analyses of (co)variance revealed a continuation of EF development into adolescence. Confirmatory factor analysis yielded two common factors: Working Memory and Shifting. However, the variables assumed to tap Inhibition proved unrelated. At a latent level, again correcting for basic processing speed, the development of Shifting was seen to continue into adolescence, while Working Memory continued to develop into young-adulthood. Regression analyses revealed that Working Memory contributed most strongly to WCST performance in all age groups. These results suggest that EF component processes develop at different rates, and that it is important to recognize both the unity and diversity of EF component processes in studying the development of EF.

The aim of this research was to reveal the relations among fifth-grade Turkish students` silent reading fluency, oral reading fluency, retell fluency, and reading comprehension. With this aim, the study used correlational survey design and the study sample consisted of total 389 fifth-grade Turkish elementary students studying in elementary schools. The schools were located in middle socioeconomic status and the students` families had middle socioeconomic level. The informed consent letters obtained from all the students before the study began. For assessment process, the appropriate grade level texts were chosen and the students` fluency skills were measured through one-on-one sessions in the suitable place in the elementary schools, provided by the school principals. After this process, the reading comprehension tests related to the grade level texts read was administered to all the students. The data obtained from testing process were analyzed and the findings were presented in respond to research aims. According to these results, the conclusions were drawn and some recommendations fitted with the results and conclusions were made for future research

This study examined whether dysfunctional correlates of relational bullying parallel those that have been found for physical bullying. We used data from the population of 2,494 respondents in grades 7-12 from a school district in Colorado to compare latent variables of self-esteem, attitudes toward school, delinquent behavior, physical injury, physical bullying, and physical victimization among 291 relational bullies, 303 victims, 213 bully-victims, and 1,687 neutrals. We used gender as a control variable. Neutrals had the best school attitudes, least problem behavior, least injury, and were less likely to be physical bullies or victims. Bully-victims reported worse outcomes on the latent variables than either neutrals or victims, and they were more likely to be physically victimized than bullies. These outcomes parallel findings among physical bullies in a similar sample. Results show the need for improved intervention, heightened awareness, and particular attention to relational bullyvictims.

A taxometric analysis was conducted to test the hypothesis that the latent structure of melancholia in adolescents is categorical. Two taxometric procedures were used: Mean Above Minus Below a Cut (MAMBAC) and Maximum Covariance (MAXCOV) analyses. Participants were 378 adolescents presenting for a depression evaluation. Indicators of melancholia were constructed using items from the Schedule for Affective Disorders and Schizophrenia for School Aged Children (K-SADS) and the Beck Depression Inventory (BDI). The indicators of melancholia were consistent with a categorical latent variable. The findings suggest that the latent structure of melancholia in adolescents is similar to its previously identified categorical structure in adults. Implications for clinical research are discussed. #

What are creative people like? Openness to experience is important to creativity, but little is known about plasticity, the higher-order factor that subsumes openness. College students (n = 189) completed measures of the Big Five and measures of creative cognition (fluency and quality of divergent thinking), everyday creative behaviors, creative achievement, and self-rated creativity. Latent variable models found broad effects of openness to experience and few effects of the other four domains. At the higher-order level, plasticity predicted higher scores on nearly all of the facets of creativity, and stability had several significant effects. For some creativity measures, plasticity and stability had opposing effects. Tests of latent interactions found no significant effects: plasticity and stability predict creatively independently, not jointly.

Fourier transform Raman spectroscopy and chemometric tools have been used for exploratory analysis of pure corn and cassava starch samples and mixtures of both starches, as well as for the quantification of amylose content in corn and cassava starch samples. The exploratory analysis using principal component analysis shows that two natural groups of similar samples can be obtained, according to the amylose content, and consequently the botanical origins. The Raman band at 480 cm −1 , assigned to the ring vibration of starches, has the major contribution to the separation of the corn and cassava starch samples. This region was used as a marker to identify the presence of starch in different samples, as well as to characterize amylose and amylopectin. Two calibration models were developed based on partial least squares regression involving pure corn and cassava, and a third model with both starch samples was also built; the results were compared with the results of the standard colorimetric method. The samples were separated into two groups of calibration and validation by employing the Kennard-Stone algorithm and the optimum number of latent variables was chosen by the root mean square error of cross-validation obtained from the calibration set by internal validation (leave one out). The performance of each model was evaluated by the root mean square errors of calibration and prediction, and the results obtained indicate that Fourier transform Raman spectroscopy can be used for rapid determination of apparent amylose in starch samples with prediction errors similar to those of the standard method.

This paper presents a structural model of the determinants of harsh parenting among Mexican mothers. One hundred five mothers (46 from the community; 59 referred to agencies for child maltreatment) were recruited from Sonora (Northern) Mexico and interviewed. In this model the use of physical punishment was explained by (1) authoritarian parenting style (mothers' beliefs concerning the effective use of physical punishment and mothers' lack of disciplinary skills) and (2) family dysfunction (a latent variable constructed from reports of interspousal violence and the parents' use of alcohol and drugs). In addition, the indirect effects of demographic and historical variables on harsh parenting was included. The findings show that the most important factor influencing the use of physical punishment in these families was authoritarian parenting style, exerting a significant direct effect on the mothers' reports of their use of harsh punishment. Family dysfunction had an indirect effect through parenting style. Some sociodemographic variables also indirectly influenced the use of beliefs maternal punishment It is concluded that cultural beliefs play a major role in parenting within the framework of Mexican family relations.

The O2-PLS method is derived from the basic partial least squares projections to latent structures (PLS) prediction approach. The importance of the covariation matrix (Y T X) is pointed out in relation to both the prediction model and the structured noise in both X and Y. Structured noise in X (or Y) is defined as the systematic variation of X (or Y) not linearly correlated with Y (or X). Examples in spectroscopy include baseline, drift and scatter effects. If structured noise is present in X, the existing latent variable regression (LVR) methods, e.g. PLS, will have weakened score±loading correspondence beyond the first component. This negatively affects the interpretation of model parameters such as scores and loadings. The O2-PLS method models and predicts both X and Y and has an integral orthogonal signal correction (OSC) filter that separates the structured noise in X and Y from their joint X±Y covariation used in the prediction model. This leads to a minimal number of predictive components with full score±loading correspondence and also an opportunity to interpret the structured noise. In both a real and a simulated example, O2-PLS and PLS gave very similar predictions of Y. However, the interpretation of the prediction models was clearly improved with O2-PLS, because structured noise was present. In the NIR example, O2-PLS revealed a strong water peak and baseline offset in the structured noise components. In the simulated example the O2-PLS plot of observed versus predicted Y-scores (u vs u hat ) showed good predictions. The corresponding loading vectors provided good interpretation of the covarying analytes in X and Y.

The aim of this study was to test the reliability and validity of the COMFORT scale as a postoperative pain instrument for children aged 0± 3 years. Subjects were 158 neonates and toddlers after major abdominal or thoracic surgery. Trained nurses rated the children's pain at 3, 6 and 9 h postoperative on the Pediatric Surgical Intensive Care Unit using the COMFORT and a VAS for pain. Interrater reliability of the COMFORT items proved to be good (Kappa 0.63±0.93) for all items with the exception of the item`Respiratory response', which was moderate (Kappa 0.54). LISREL analyses showed that the structure of the COMFORT data was best represented by three latent variables: COMFORT`behaviour' with loadings from the behavioural items (Alertness, Calmness, Respiratory response/Crying, Physical movement, Muscle tone and Facial tension) and separate latent variables for`Heart rate baseline' (HR) and`Mean arterial blood pressure baseline' (MAP). Factor loadings of the items were invariant across time, indicating stability of the structure. The latent variables COMFORT behaviour' and VAS pain were highly interrelated indicating congruent validity. Stability of COMFORT`behaviour' and VAS pain was moderate which might be due to varying painful episodes in this sample. HR and MAP, although stable across time, were weakly related to VAS pain and COMFORT`behaviour'. These ®ndings support the use of the COMFORT`behaviour' scale to assess postoperative pain in neonates and infants. q 2000 International Association for the Study of Pain. Published by Elsevier Science B.V.

Objectives: The aims of this article are: (a) to test for the validity of the three constructs involved in the structural model; (b) to test for the effects of both coping strategies and resilient coping on well-being in a sample of elderly, by means of a structural model with latent variables; (c) to empirically study whether a brief scale of resilient coping could predict well-being over and above that predicted by the coping resources. Methods: The research is a survey design. The sample consisted of 225 non-institutionalized elderly people living in the city of Valencia (Spain). The three constructs measured were: well-being, resilient coping, and coping strategies. Results: The analyses consist of a series of alternative structural models with latent variables with resilience, problem-focused coping, and emotion-focused coping as the potential predictors of well-being as measured by Ryff's well-being scales. Due to parsimony reasons, the model retained is that with a single predictor of wellbeing: resilient coping. Conclusion: A latent variable measuring resilient coping is able to predict a significant and large part of the variance in well-being, without the need of including coping strategies. Results impact on well-being literature of the elderly is discussed.

The study investigated the influence of metacognition on critical thinking skills. It is hypothesized in the study that critical thinking occurs when individuals use their underlying metacognitive skills and strategies that increase the probability of a desirable outcome. The Metacognitive Assessment Inventory (MAI) by Schraw and Dennison (Contemporary Educational Psychology 19:460-475, 1994), which measures regulation of cognition and knowledge of cognition, and the Watson-Glaser Critical Thinking Appraisal (WGCTA) with the factors inference, recognition of assumptions, deduction, interpretations, and evaluation of arguments were administered to 240 college students from different universities in the National Capital Region in the Philippines. The Structural Equations Modeling (SEM) was used to determine the effect of metacognition on critical thinking as latent variables. Two models were tested: (1) In the first model, metacognition is composed of two factors while (2) in the second model, metacognition has eight factors as they affect critical thinking. The results indicated that in both models, metacognition has a significant path to critical thinking, p<.05. The analysis also showed that for both metacognition and critical thinking, all underlying factors are significant. The second model had a better goodness of fit as compared with the first as shown by the RMSEA value and other fit indices.

It was hypothesized that the eight Eriksonian psychosocial stages form a single latent variable or General Factor of Psychosocial Development (GFPD) and that this latent factor is associated with the General Factor of Personality (GFP) and Life History (LH) strategy to the extent that the variables form a higher order Super-K factor. Correlational analyses and confirmatory factor analyses each supported the hypothesized GFPD. Additionally, correlational and confirmatory factor analyses supported the hypothesis that the three variables form a single Super-K factor. The results of Study 3 suggest that the relationship between the psychosocial stages and between the GFPD, GFP, and LH strategy remained after controlling for social desirable response biases.

Abstract: The rapid growth of e-commerce is imposing profound impacts on modern society. On the supply side, the emergence of e-commerce is greatly changing the operation behavior of some retailers and is increasing product internationalization due to its geographically unlimited nature. On the demand side, the pervasiveness of e-commerce affects how, where, and when consumers shop, and indirectly influences the way in which we live our lives. However, the development of ecommerce is still in an early stage, and ...

Effective measurement of cultural value is often elusive because of its multidimensional nature. It is also influenced by sociodemographic characteristics (manifest variables) and attitudinal characteristics (latent variables) of populations. While the former is easily available to researchers, the latter has not been fully studied. This paper suggests the use of a cultural worldview scale that was developed to measure cultural attitudes of people, using factor and cluster analysis. Four factors comprise the scale: cultural linkages, recognition of cultural values, cultural loss, and preservation of traditions and customs. Some advantages of using this scale are demonstrated, and relationships with sociodemographic variables are investigated. Managerial and policy implications are discussed.

There are several studies showing that working memory and intelligence are strongly related. However, working memory tasks require simultaneous processing and storage, so the causes of their relationship with intelligence are currently a matter of discussion. The present study examined the simultaneous relationships among short-term memory (STM), working memory (WM), and general intelligence ( g). Two hundred and eight participants performed six verbal, quantitative, and spatial STM tasks, six verbal, quantitative, and spatial WM tasks, and eight tests measuring fluid, crystallized, spatial, and quantitative intelligence. Especial care is taken to avoid misrepresenting the relations among the constructs being studied because of specific task variance. Structural equation modelling (SEM) results revealed that (a) WM and g are (almost) isomorphic constructs, (b) the isomorphism vanishes when the storage component of WM is partialed out, and (c) STM and WM (with its storage component partialed out) predict g. D

The innovative performance of companies has been studied quite extensively and for a long period of time. However, the results of many studies have not yet led to a generally accepted indicator of innovative performance or a common set of indicators. So far the variety in ...

Six multifactor correlated traits models and a single-factor model of the WAIS-R's factor pattern were examined by confirmatory maximum likelihood factor analyses of a data matrix from the WAIS-R's Italian normative sample raw scores (N 02284). The main aim was to identify what model(s) best fit(s) the data for the different age groups considered. Analyses were designed to determine which of seven hypothesised factor solutions best explained the intellectual ability of four age bands (16 to 74 years of age) and of all samples. Results showed the same three-correlated-factors model (Verbal, Performance, and Freedom From Distractibility) for the younger (16 to 24 years of age) and for more aged (65 to 74 years of age), and two different three-factor models for the adult (band from 25 to 64 years of age) that had in common the presence of Digit Symbol loading on Freedom From Distractibility, that in the younger and aged band loaded on Perceptual Organisation. The individuals may use different strategies when performing a given task on the WAIS-R. Such differences in latent variability may represent real differences in neuropsychological functioning, and as such can account for some of the variability in results noted in the literature. Differences in latent variability across distinct demographic and clinical groups could provide important insight into brainÁbehaviour relationships.

The results from studies of the relationship between total quality management (TQM) practices and firm performance and from those on the effects of TQM practices on various types of performance measures are mixed. There is no extensive empirical evidence about the effect of TQM practices on employee performance and innovation performance, and few empirical studies have investigated the mediating effect (indirect relationship) of one type of performance measure on the relationship between TQM practices and another type of performance measure. The objective of this study was to investigate the relationships between TQM practices and multiple performance measures and to examine the mediating effects of employee performance and innovation performance on the relationship between TQM practices and firm performance. The study used a cross-sectional survey methodology-we sent questionnaires to 500 randomly selected ISO 9001:2000 certified firms in different industries in the Marmara region in Turkey in 2005 and 2006 and obtained 373 usable questionnaires. After confirming the validity and reliability of the latent variables with confirmatory and exploratory factor analyses, we tested the model and hypotheses using structural equation modeling. Results of the study support the proposed hypotheses that employee performance and innovation performance partially mediate the relationship between TQM practices and firm performance. The study also provides managerial and research implications, research limitations, and suggestions for future studies.

The service quality is considered a latent variable, eventually derived as the combination of some other independent latent variables (dimensions). The observed variables (attributes), that measure those dimensions, are generally expressed by an ordinal scale and are obtained by handing out questionnaires to the users of the service. Therefore, the questionnaires being a measuring instrument, it has to be calibrated. Statistical calibration is a procedure that achieves the best approximation of the real measure controlling measurement errors. The classical calibration models compare the measure given by the instrument with the true measure. Unfortunately in psychometric field the true measure is latent and not observable: In particular the effective quality of the service differs from the perceived quality. In the service quality analysis "the calibration of the questionnaire" would make it clear what influences the opinion of subjects about the satisfaction with each attribute. Two factors randomly influence the answer of a subject to each item: A specific attribute factor and a specific subject factor. The latter factor justifies the differences among subjects and in this particular case, it constitutes exactly the measurement error that has to be taken into account. In this paper, the Rasch model will be considered and applied to analyze the teaching quality of university course. Moreover, the Classification Tree Method will be proposed to obtain a segmentation of the student population, based on the satisfaction index level. † This paper is a revised version of the Departmental Working

The sum of squared intraday returns provides an unbiased and almost errorfree measure of ex-post volatility. In this paper we develop a nonlinear Autoregressive Fractionally Integrated Moving Average (ARFIMA) model for realized volatility, which accommodates level shifts, day-of-the-week effects, leverage effects and volatility level effects. Applying the model to realized volatilities of the S&P 500 stock index and three exchange rates produces forecasts that clearly improve upon the ones obtained from a linear ARFIMA model and from conventional time-series models based on daily returns, treating volatility as a latent variable.

Sex differences in the latent general and broad cognitive abilities underlying the Woodcock–Johnson Tests of Cognitive Abilities were investigated for children, youth, and adults ages 6 through 59. A developmental, multiple indicator–multiple cause, structural equation model was used to investigate sex differences in latent cognitive abilities as well as developmental changes in these differences across the 6 to 59 age span. Females showed a consistent advantage on the latent processing speed (Gs) factor, and males showed a small, consistent advantage on the latent comprehension–knowledge (Gc) factor. Males also showed an advantage on latent quantitative reasoning (RQ) and visual–spatial ability (Gv) factors at most ages, although the latter was statistically significant only for adults. No statistically significant sex differences were shown on latent auditory processing, short-term memory, long-term retrieval, or fluid reasoning factors. The higher-order, latent g factor showed inconsistent differences for children, small, non-significant differences favoring females for adolescents, and fairly consistent statistically significant differences favoring females in adulthood. Findings are inconsistent with developmental theory that suggests males should show an advantage on g in adulthood. Supplemental analyses suggested that methodological choices, including the use of latent variables versus composites and methods for dealing with missing data, can affect research findings.

This Festschrift honoring Ype Poortinga is focused on the developments in cross-cultural psychology over the past forty years and bears the stamp of his philosophy of mitigated universalism, the contention that human differences occur against a background of similarities.