Categorical Indicators Research Articles

Ensemble Kalman Filter Predictor Bias Correction Method for Non-Gaussian Geological Facies Detection

The Ensemble Kalman Filter (EnKF) is a Monte-Carlo based technique for assisted history matching and real time updating of reservoir models. However, it often fails to detect facies boundaries and proportions as the facies distributions are non-Gaussian, while geologic data for reservoir modeling is usually insufficient. It is convenient to represent distinct facies with non-Gaussian categorical indicators; we implemented discrete cosine transform (DCT) to parameterize the facies indicators into coefficients of the retained cosine basis functions that are Gaussian. For highly complex and heterogeneous models, though observed data were matched, it failed to reproduce realistic facies distribution corresponding to reference variogram and facies proportion.In this paper we propose a new ensemble filtering method in-between of EnKF and PF, where EnKF as predictor combines the advantages of accurate large updates with small ensembles and corrector for non-Gaussian distributions followed by EnKF again for analysis step. Correction is performed by regenerating new realizations using a new pilot point method. The ensemble members that are more consistent with the early production history and the available geological information are considered as high weight particles and used for the applications.Combination of DCT-EnKF and regenerating new realizations using the new pilot point method demonstrates reasonable improvement and reduction of uncertainty in facies detection. Incorporating the new step in the procedure assists the filter to honor the reference distribution and experimental variogram during the history matching process and presents an important potential in improved characterization of complex reservoirs.

OT1-02-12: Early Detection of Cardiotoxicity by Advanced Cardiac Imaging and a Novel Biomarker in Breast Cancer Patients Undergoing Chemotherapy.

Abstract Background The survival rate of breast cancer patients has increased due to improvements in cancer treatment. However, many survivors develop irreversible or reversible cardiotoxicity associated with anthracycline or trastuzumab therapy, respectively. To detect cardiac damage, the currently accepted method is to measure left ventricular ejection fraction (LVEF) by echocardiography, which lacks the sensitivity to predict early cardiac dysfunction. Early identification of cardiotoxicity is essential to cancer survivors, as development of cardiomyopathy carries a worse outcome independent of cancer prognosis. Currently, there are no accepted guidelines for the early detection of myocardial injury. The use of cardiac biomarkers and more sensitive echocardiographic techniques have expanded options for monitoring, but have yet to reach a consensus. Hence, our study will evaluate the potential predictive value of novel cardiac biomarkers and advanced echocardiographic and cardiac magnetic resonance imaging (CMR) techniques to detect subclinical myocardial damage. Our findings may be applicable for monitoring new antineoplastic agents during food and drug administration (FDA) clinical trials. Trial Design Prospective cohort study with internal control of 20 patients newly diagnosed with breast cancer. The trial will assess endpoints at baseline, 2 weeks after initiation of therapy, and 2 weeks and 6 months after chemotherapy completion. 1. Primary Endpoint a. Decline in left ventricular ejection fraction assessed by CMR and 3D-echo not detected by conventional methods b. Presence of either myocardial fibrosis or edema detected by CMR c. Changes in myocardial deformation detected by echo or CMR strain d. Increase in cardiac biomarkers (Serum caspase-3 p17 peptide, Troponin I, B-type natriuretic peptide) and possible correlation with imaging parameters 2. Secondary Endpoint a. Development New York Heart Association class 1 to 4 symptoms b. Decrease in LVEF of ≥5% to ≤50% with or without symptoms Eligibility Criteria Inclusion Criteria: 1. Newly diagnosed stage I, II, or III breast cancer 2. Age between 18 and 75 years old. 3. Treatment with trastuzumab or anthracycline-based chemotherapy Exclusion Criteria: 1. History of cardiovascular disease 2. Pacemaker 3. History of mediastinal radiotherapy 4. Creatinine clearance <30 ml/min 5. Serum bilirubin >2.0 mg/dl, ALT and AST > 100 U/1) 6. Hypertension, uncontrolled >140/90 7. LVEF <55% per 2-D echocardiogram 8. Claustrophobia Specific Aims 1. Detect early myocardial injury. 2. Evaluate early predictors of left ventricular dysfunction. 3. Evaluate timing of monitoring during or post treatment Statistical Method This is a pilot study and 20 patients are required to reach statistical significance with 85% power. All values will be analyzed as mean±SD or n (%). Categorical indicators will be analyzed using nonparametric statistics such as Cochran's Q. Changes in imaging and biomarker parameters will be assessed using analysis of variance, while correlation between the two will be assessed using mixed models appropriate for binary outcome. Significance will be accepted at p ≤0.05 for all tests. Present accrual and target accrual Nine subjects are enrolled with a goal of 20. Contact for people interested in trial: 1. Dr. Erick Avelar, eavelar@uchc.edu 2. Dr. Susan Tannenbaum, stannenbaum@uchc.edu Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr OT1-02-12.

Handedness and schizotypy in non-psychotic relatives of patients with schizophrenia

Existing studies have found the relationship between handedness and schizotypy to be inconsistent, and had limited generalisability since only highly homogeneous groups have been investigated. This study aimed to examine the relation between handedness and the four schizotypal factors identified from a previous confirmatory factor analysis in a population of high familial loading for schizophrenia. Study participants consisted of non-psychotic first-degree relatives (850 parents and 334 siblings) of sib-pairs who were co-affected with schizophrenia. All participants were interviewed with the Diagnostic Interview for Genetic Studies, which contains a section of the modified Structured Interview for Schizotypy, and the Annett handedness questionnaire. Both categorical and continuous indicators for handedness were examined. Non-right-handed siblings of schizophrenia patients displayed more positive schizotypal features than their right-handed counterparts when the two-way Annett's handedness classification was adopted. No association was found when handedness was treated as continuous. The relationship between handedness and schizotypy was insignificant for parents probably due to the strong social pressure against left-handedness. We concluded that categorical non-right-handedness was associated with positive schizotypy in non-psychotic siblings of schizophrenia patients. The results indicate that an atypical cerebral lateralisation underlying non-right-handedness may be also a contributing factor to positive schizotypy.

Testing for Nonuniform Differential Item Functioning With Multiple Indicator Multiple Cause Models

In extant literature, multiple indicator multiple cause (MIMIC) models have been presented for identifying items that display uniform differential item functioning (DIF) only, not nonuniform DIF. This article addresses, for apparently the first time, the use of MIMIC models for testing both uniform and nonuniform DIF with categorical indicators. A latent variable interaction is added to the MIMIC model to test for nonuniform DIF. The approach is tested in simulations with small focal-group N and illustrated with an empirical example using a scale about agoraphobic cognitions. MIMIC-interaction models are compared with MIMIC models without the interaction as well as likelihood ratio DIF testing using item response theory (IRT-LR-DIF). The most important finding is that when the latent moderated structural equations approach is used to estimate the interaction, the Type I error in MIMIC-interaction DIF models is severely inflated.

Evaluation of Global Physical Activity Questionnaire (GPAQ) among Healthy and Obese Health Professionals in Central India

To assess physical activity using the Global Physical Activity Questionnaire (GPAQ) among healthy, overweight and obese Indian health professionals. The Global Physical Activity Questionnaire (GPAQ) was used to assess physical activ ity among 324 dental health care professionals (third-year students, final-year students, interns, and faculty). Metabolic Equivalents (MET) were used to express the intensity of physical activities, and are also used for the analysis of GPAQ data. The analysis of Variance (ANOVA) was used to compare the mean physical activity scores among dental health care professionals. The Chi Square test was used to compare categorical risk indicators and obesity (BMI). Kendall’s test was used to compute the correlation between physical activity categorical indicator (CI), obesity and sedentary behaviour. The logistic regression analysis was performed to determine the importance of the factors associated with obesity. Odds ratio was calculated for all variables with 95% confidence intervals. Total physical activity measured in mean MET minutes per week was 625.6, 786.3, 296.5, and 296.5 for third-year students, final-year students, interns, and faculty re spectively (p ≤ 0.05). Total energy expenditure of 0 MET minutes per week was calculated as 32.2%, 10.3%, 17.9% and 44.9% of third-year students, final-year students, interns, and faculty, respectively. Of the 211 health care professionals in high risk group 28.9% were in the third year, 19.9% in the final year, 20.4% were interns and 30.8% were faculty members. Obesity was calculated in 22.4% third-year students, 16.3% final-year students, 20.4% interns and 40.8% of faculty members. Overweight problems were seen in 19.7%, 24.7%, 24.7% and 30.8% of third-year students, final-year students, interns, and faculty members respectively (p ≤ 0.001). A sig nificant correlation was seen between physical activity categorical indicator and BMI. A significant negative correlation was noted between physical activity categorical indicator and sedentary behaviour. A significant correlation was also noted be tween BMI and sedentary behavior. Physical activity is a positive health behavior with so much potential to improve public health and so few risks that it deserves to be central to any future public health strategy.

Geoadditive Latent Variable Modeling of Count Data on Multiple Sexual Partnering in Nigeria

The 2005 National HIV/AIDS and Reproductive Health Survey (NARHS) in Nigeria provides evidence that multiple sexual partnering increases the risk of contracting HIV and other sexually transmitted diseases. Therefore, partner reduction is one of the prevention strategies to accomplish the Millenium Development Goal of halting and reversing the spread of HIV/AIDS. We consider the numbers of girlfriends, casual, and commercial partners of heterosexual men, reported in the NARHS study, as observed indicators of their latent attitude toward multiple partnering. To explore the influence of risk factors on this latent variable, we extend semiparametric methodology for latent variable models with continuous and categorical indicators to include count indicators. This allows us to simultaneously analyze linear and nonlinear effects of covariates, such as sociodemographic factors and knowledge about HIV/AIDS, on attitude toward multiple sexual partnering, which in turn influences the observable count indicators. The results provide insights for policy makers who are aiming to reduce the spread of HIV and AIDS among the Nigerian populace through partner reduction.

6. The Simultaneous Decision(s) about the Number of Lower- and Higher-Level Classes in Multilevel Latent Class Analysis

Recently, several types of extensions of the latent class (LC) model have been developed for the analysis of data sets having a multilevel structure. The most popular variant is the multilevel LC model with finite mixture distributions at multiple levels of a hierarchical structure; that is, with LCs for both lower-level units (e.g. individuals, citizens, or patients) and higher-level units (e.g. groups, regions, or hospitals). A problem in the application of this model is that determining the number of LCs is much more complicated than in standard (single-level) LC analysis because it involves multiple, nonindependent decisions. We propose a three-step model-fitting procedure for deciding about the number of higher- and lower-level classes. We also investigate the performance of information criteria (BIC, AIC, CAIC, and AIC3) in the context of multilevel LC analysis, with different types of response variables. A specific difficulty associated with using BIC and CAIC in any type of multilevel analysis is that these measures contain the sample size in their formulae, and we investigate whether this should be the number of groups, the number of individuals, or either the number of groups or individuals depending on whether one has to decide about model features concerning the higher or lower level. The three main conclusions of our simulations studies are that (1) the proposed three-step model-fitting strategy works rather well, (2) the number of higher-level units (K) is the preferred sample size for BIC and CAIC, both for decisions about higher- and lower-level classes, and (3) with categorical indicators, AIC3 and BIC based on the higher-level sample size are the preferred measures for deciding about the number of LCs at both the higher and lower level. With continuous indicators, BIC(K) performs better than AIC3. AIC performs best in very specific situations—namely, with poorly separated classes and categorical indicators.

Advanced Psychometric Methods for Developing and Evaluating Cut-Point-Based Indicators

In this study, we offer some advice on the use of empirical techniques to create categorical indicators from responses to multiple survey questions that ostensibly compose scales. We discuss the use of advanced psychometric methods, including confirmatory factor analysis for ordered categorical measures (CFA-OCM) and item response theory (IRT), to expand 12 previously published criteria for cut-point development and evaluation offered by Moore et al. (Indicators of Child Well-Being: The Promise for Positive Youth Development. Annals of the American Academy of Political and Social Science. Special Issue: Positive Development: Realizing the Potential of Youth. 591:125–145, 2004). We present an application of this advice by developing categorical cut-points for the social skills and behavior problems subscales of the U.S. National Survey of Children’s Health (NSCH) Social Competence Scale. We used data from the 2007 NSCH, a large cross-sectional, random-digit-dial telephone survey of a representative sample of the noninstitutionalized population of U.S. children 0–17 years of age. Parents of children aged 6 to 17 years (n = 63,364) responded to 4 questions about their children’s behavior problems and 4 questions about their children’s positive social skills. IRT analyses indicated that IRT model-based scores of +1.8 and −0.7 provided suitable high and low cut-points, respectively, for the behavior problems subscale. These cut-points had marginal reliabilities of 0.70 and 0.67 respectively, and corresponded to raw subscale scores of 13 and 8 IRT analyses indicated that an IRT model-based score of −1.8 provided a suitable cut-point for identifying children with low levels of positive social skills. This cut-point had a marginal reliability of 0.63 and corresponded to a raw subscale score of 13. For both subscales and cut-point sets, frequency distributions demonstrated that the cut-points identified sufficient proportions of children generally as well as within demographic subgroups. Our findings indicate that analysts can use raw subscale scores and cut-points of 13 and 8 to identify children with high, moderate, and low levels of behavior problems. Our results demonstrate that, for the positive social skills subscale, the subscale’s psychometric properties only support a single cut-point of 13 that identifies children with low levels of positive social skills. Our study highlights the iterative, empirically-based process that cut-point development should follow and shows the value of modern test theory-based psychometric methods to inform this process.

The personal vote and voter turnout

The level of electoral turnout is arguably the most widely monitored form of electoral participation. Consequently, electoral systems have often been cited as having a significant effect on turnout levels even though scholars do not agree on the effects of these complex institutions. Since most previous studies have relied on categorical or dichotomous electoral system indicators, this study utilizes Carey and Shugart’s personal vote index to gain theoretical leverage on other electoral system components. In short, I find that where electoral competition is predicated on party, rather than candidates’, reputations, turnout levels rise. The results of a time-series cross-sectional analysis reveal that the personal vote index significantly influences turnout levels even when controlling for a host of other factors.

Avaliação do desempenho ambiental de um operador de serviços logísticos por indicadores categóricos

This paper presents a case of environmental performance assessment in an anthropic operation by categorical judgement. The case assesses a logistic operator. The model for the assessment was constructed in focus group sessions with experts and was based on categorical indicators. The assessment was conducted by managers of the operator. They answered a closed questions questionnaire, using Likert scale, based on the model. The final result takes the format of an index waving from 0 to 100%. The assessed constructs were: atmospheric emissions, solid wastings, liquids effluents, usage of natural and energetic resources and attendance of law and certifications. The assessed index was about to 90%. Key words: environmental performance, Environmental indicators, environmental impact assessment.

The multidimensionality of schizotypy in nonpsychotic relatives of patients with schizophrenia and its applications in ordered subsets linkage analysis of schizophrenia

This study aimed to examine the multidimensionality of schizotypy and validate the structure using ordered subset linkage analyses on information from both relatives' schizotypy and probands' schizophrenia symptoms. A total of 203 and 1,310 nonpsychotic first-degree relatives from simplex and multiplex schizophrenia families, respectively, were interviewed with the Diagnostic Interview for Genetic Studies, which contains a modified Structured Interview for Schizotypy. Using Mplus program with categorical factor indicators, a four-factor model (Negative Schizotypy, Positive Schizotypy, Interpersonal Sensitivity, and Social Isolation/Introversion) was extracted by exploratory factor analysis from relatives of simplex families and was confirmed in relatives of multiplex families. The validity of each factor was supported by distinct linkage findings resulting from ordered subset analysis based on different combinations of schizophrenia-schizotypy factors. Six chromosomal regions with significant increase in nonparametric linkage z score (NPL-Z) were found as follows: 15q21.1 (NPL-Z = 3.60) for Negative Schizophrenia-Negative Schizotypy, 10q22.3 (NPL-Z = 3.83) and 15q21.3 (NPL-Z = 3.36) for Negative Schizophrenia-Social Isolation/Introversion, 5q14.2 (NPL-Z = 3.20) and 11q23.3 (NPL-Z = 3.31) for Positive Schizophrenia-Positive Schizotypy, and 4q32.1 (NPL-Z = 3.31) for Positive Schizophrenia-Interpersonal Sensitivity. The greatest NPL-Z of 3.83 on 10q22.3 in the subset was significantly higher than the greatest one of 2.88 in the whole sample (empirical P-value = 0.04). We concluded that a consistent four-factor model of schizotypy could be derived in nonpsychotic relatives across families of patients with different genetic loadings in schizophrenia. Their differential relations to linkage signals have etiological implications and provide further evidence for their validity.

Dynamic Model for Multivariate Markers of Fecundability

Dynamic latent class models provide a flexible framework for studying biologic processes that evolve over time. Motivated by studies of markers of the fertile days of the menstrual cycle, we propose a discrete-time dynamic latent class framework, allowing change points to depend on time, fixed predictors, and random effects. Observed data consist of multivariate categorical indicators, which change dynamically in a flexible manner according to latent class status. Given the flexibility of the framework, which incorporates semi-parametric components using mixtures of betas, identifiability constraints are needed to define the latent classes. Such constraints are most appropriately based on the known biology of the process. The Bayesian method is developed particularly for analyzing mucus symptom data from a study of women using natural family planning.

The Effects of Educational Diversity in a National Sample of Law Students: Fitting Multilevel Latent Variable Models in Data With Categorical Indicators

Controversy surrounding the use of race-conscious admissions can be partially resolved with improved empirical knowledge of the effects of racial diversity in educational settings. We use a national sample of law students nested in 64 law schools to test the complex and largely untested theory regarding the effects of educational diversity on student outcomes. Social scientists who study these outcomes frequently encounter both latent variables and nested data within a single analysis. Yet, until recently, an appropriate modeling technique has been computationally infeasible, and consequently few applied researchers have estimated appropriate models to test their theories, sometimes limiting the scope of their research question. Our results, based on disaggregated multilevel structural equation models, show that racial diversity is related to a reduction in prejudiced attitudes and increased perceived exposure to diverse ideas and that these effects are mediated by more frequent interpersonal contact with diverse peers. These findings provide support for the idea that administrative manipulation of educational diversity may lead to improved student outcomes. Admitting a racially/ethnically diverse student body provides an educational experience that encourages increased exposure to diverse ideas and belief systems.

Obesity and Type 2 Diabetes Risk in Midadult Life: The Role of Childhood Adversity

Child abuse has been associated with poorer physical health in adulthood, but less is known about childhood adversity more broadly, including neglect and family problems, or the pathways from adversity to adult disease. We have examined how different stressful emotional or neglectful childhood adversities are related to adiposity and glucose control in midadulthood, taking into account childhood factors, and whether the relationships are mediated by adult health behaviors and socioeconomic position. This was a prospective longitudinal study of 9310 members of the 1958 British birth cohort who participated in a biomedical interview at 45 years of age. Primary outcomes consisted of continuous measures of BMI, waist circumference, and glycosylated hemoglobin at 45 years and categorical indicators: total obesity (BMI > or = 30), central obesity (waist circumference: > or = 102 cm for men and > or = 88 cm for women), and glycosylated hemoglobin level of > or = 6. The risk of obesity increased by 20% to 50% for several adversities (physical abuse, verbal abuse, witnessed abuse, humiliation, neglect, strict upbringing, physical punishment, conflict or tension, low parental aspirations or interest in education, hardly takes outings with parents, and father hardly reads to child). Adversities with the strongest associations with adiposity (eg, physical abuse) tended to be associated with glycosylated hemoglobin levels of > or = 6, but in most cases associations were explained by adjustment for adulthood mediators such as adiposity. Effects of other adversities reflecting less severe emotional neglect and family environment were largely explained by childhood socioeconomic factors. Some childhood adversities increase the risk of obesity in adulthood and thereby increase the risk for type 2 diabetes. Research is needed to understand the interrelatedness of adversities, the social context of their occurrence, and trajectories from adversity to adult disease.

Testing the degree of cross-sectional and longitudinal dependence between two discrete dynamic processes.

Developmental research often involves studying change across 2 or more processes or constructs simultaneously. A natural question in this work is whether change in these 2 processes is related or independent. Associative latent transition analysis (ALTA) was designed to test hypotheses about the degree to which change in 2 discrete latent variables is related. The ALTA model is a type of latent class model, which is a categorical latent variable model based on categorical indicators. In the ALTA approach, level and change on 1 variable is predicted by level and change in another. Two types of hypotheses are discussed: (a) broad hypotheses of dependence between the 2 discrete latent variables and (b) targeted hypotheses comparing specific patterns of change between levels of the discrete variables. Both types of hypotheses are tested via nested model comparisons. Analyses of relations between psychological state and substance use illustrate the model. Recent psychological state and recent substance use were found to be associated cross-sectionally and longitudinally, implying that change in recent substance use was related to change in recent psychological state.

Classifying Patients by Antipsychotic Adherence Patterns Using Latent Class Analysis: Characteristics of Nonadherent Groups in the California Medicaid (Medi-Cal) Program

Objectives This study identifies latent classes defined by varying degrees of adherence to antipsychotic drug therapy and examines the sociodemographic, clinical, and resource utilization correlates associated with membership in each adherence class. Data and methods Patient-level data were drawn from the 1994 to 2003, 100%-sample California Medicaid fee-for-service paid claims data for patients with schizophrenia (N = 36,195). The date of the first antipsychotic medication filled after January 1, 1999 was then used to divide each patient's data into a 6-month preindex (baseline) and a 12-month postindex (follow-up) period. Three categorical adherence indicators—a dichotomous variable of medication possession ratio greater than 0.80, the number of antipsychotic treatment attempts, and time to a change in antipsychotic medications—and two covariates—a categorical variable of duration of therapy and a dichotomous variable of polypharmacy—were used in the latent class model. Results A three-class model returned the lowest values for all the information criteria and was therefore interpreted as follows: The prevalence rates of the latent classes were 1) 14.8% for the adherent; 2) 20.7% for the partially adherent; and 3) 64.5% for the nonadherent. Membership in the nonadherent class was associated with minority ethnicity, being female, eligibility due to welfare status, prior hospitalizations, and a higher number of prior treatment episodes. Membership in the partially adherent class was associated with higher use of outpatient care, higher rates of depot antipsychotic drug use, and polypharmacy. Conclusion Multiple indicators of adherence to antipsychotic medication can be used to define classes of adherence that are associated with patient characteristics and distinct patterns of prior health-care use.

Measuring legal systems

Factor-analytic models can substantially improve the measurement of comparative legal systems and thereby our understanding of how legal systems influence economic outcomes. These methods yield better estimates of latent constructs, allow us to evaluate whether institutional features are representative of a theoretical construct and whether allegedly distinct theoretical constructs can be separated empirically. We illustrate these points through a re-analysis of a 2003 study by Djankov, La Porta, Lopez-de-Silanes and Shleifer, using a factor-analytic method that combines continuous and categorical indicators. Our results strengthen these authors' findings with respect to how legal formalism relates to legal origin and the quality of the legal system. Yet, the results also show that many of the original index items are not significantly positively related to formalism. The results thus shed light on what institutional features should be prioritized for reform if we seek to make legal systems less formalistic. Moreover, we question the evidence that the formalism model better predicts the quality of the legal system than does the alternative “incentives” model. We argue, instead, that formalism and incentives may both relate to the tendency of a legal system to use bureaucratic rule-making. Our approach can readily be applied to the analysis of legal concepts other than formalism. Journal of Comparative Economics 35 (4) (2007) 711–728.

Measuring Legal Systems

Factor-analytic models can substantially improve the measurement of comparative legal systems and thereby our understanding of how legal systems influence economic outcomes. These methods yield better estimates of latent constructs, allow us to evaluate whether institutional features are representative of a theoretical construct and whether allegedly distinct theoretical constructs can be separated empirically. We illustrate these points through a re-analysis of a 2003 study by Djankov, La Porta, Lopez-De Silanes and Shleifer, using a factor-analytic method that combines continuous and categorical indicators. Our results strengthen these authors' findings with respect to how legal formalism relates to legal origin and the quality of the legal system. Yet, the results also show that many of the original index items are not significantly positively related to formalism. The results thus shed light on what institutional features should be prioritized for reform if we wished to make legal systems less formalistic. Moreover, we question the evidence that the formalism model better predicts the quality of the legal system that does the alternative model. We argue, instead, that formalism and incentives both relate to the tendency of a legal system to use bureaucratic rule-making. Our approach can readily be applied to the analysis of legal concepts other than formalism.

Confirmatory factor analysis for applied research

With its emphasis on practical and conceptual aspects, rather than mathematics or formulas, this accessible book has established itself as the go-to resource on confirmatory factor analysis (CFA). Detailed, worked-through examples drawn from psychology, management, and sociology studies illustrate the procedures, pitfalls, and extensions of CFA methodology. The text shows how to formulate, program, and interpret CFA models using popular latent variable software packages (LISREL, Mplus, EQS, SAS/CALIS); understand the similarities and differences between CFA and exploratory factor analysis (EFA); and report results from a CFA study. It is filled with useful advice and tables that outline the procedures. The companion website offers data and program syntax files for most of the research examples, as well as links to CFA-related resources. New to This Edition *Updated throughout to incorporate important developments in latent variable modeling. *Chapter on Bayesian CFA and multilevel measurement models. *Addresses new topics (with examples): exploratory structural equation modeling, bifactor analysis, measurement invariance evaluation with categorical indicators, and a new method for scaling latent variables. *Utilizes the latest versions of major latent variable software packages--

Avaliação comparativa do desempenho de três cadeias de suprimentos em manufatura

Este trabalho apresenta um caso de avaliação do desempenho de três cadeias de suprimentos em manufatura. Este tipo de avaliação pode realimentar ações de controle que visem a atingir objetivos estratégicos da cadeia e pode ser útil em benchmarking. A metodologia proposta parte do modelo SCOR de gestão de cadeias de suprimento. As idéias do modelo foram estruturadas e associadas a dimensões de desempenho, cujas importâncias relativas foram calculadas por especialistas, com o auxílio do AHP. As dimensões foram estruturadas em vinte e cinco indicadores categóricos, avaliados pelos pesquisadores e por gestores das cadeias. A metodologia foi aplicada em três cadeias de suprimentos com similaridades. Os resultados foram analisados e entende-se que possam servir como passo intermediário em direção a uma eventual futura teoria sobre medição de desempenho em cadeias de suprimentos.