Search Results for « Measurement Modeling »

Abstract The goal of reflective measurement model assessment is to ensure the reliability and validity of the construct measures and therefore provides support for the suitability of their inclusion in the path model. This chapter introduces the key criteria that are relevant in reflective measurement model assessment: indicator reliability, internal consistency reliability (Cronbach’s alpha, reliability coefficient rho A , and composite reliability rho C ), convergent validity, and discriminant validity. We illustrate their use by means of the SEMinR package and a well-known model on corporate reputation.

multidimensionality in a set of ordered categorical item responses. I begin by describing the Schmid-Leiman bifactor procedure (Schmid & Leiman, 1957), and highlight its relations with correlated-factors and second-order exploratory factor models. After describing limitations of the Schmid-Leiman, two newer methods of exploratory bifactor modeling are considered, namely, analytic bifactor (Jennrich & Bentler, 2011) and target bifactor rotations (Reise, Moore, & Maydeu-Olivares, 2011). In section two, I discuss limited and full-information estimation approaches to confirmatory bifactor models that have emerged from the item response theory and factor analysis traditions, respectively. Comparison of the confirmatory bifactor model to alternative nested confirmatory models and establishing parameter invariance for the general factor also are discussed. In the final section, important applications of bifactor models are reviewed. These applications demonstrate that bifactor modeling potentially provides a solid foundation for conceptualizing psychological constructs, constructing measures, and evaluating a measure's psychometric properties. However, some applications of the bifactor model may be limited due to its restrictive assumptions.

Abstract PLS-SEM is the preferred approach when formatively specified constructs are included in the PLS path model. In this chapter, we discuss the key steps for evaluating formative measurement models. These include the assessment of (1) convergent validity, (2) indicator collinearity, and (3) statistical significance and relevance of the indicator weights. We introduce key criteria and their thresholds and illustrate their use with an extended version of the corporate reputation model estimated with SEMinR.

Risk exposures are typically quantified in terms of a at (VaR) estimate. A VaR estimate corresponds to a specific critical value of a portfolio's potential one-day profit and loss distribution. Given their functions both as internal risk management tools and as potential regulatory measures of risk exposure, it is important to assess and quantify the accuracy of an institution's VaR estimates. This study considers the formal statistical procedures that could be used to assess the accuracy of VaR estimates. The analysis demonstrates that verification of the accuracy of tail probability value estimates becomes substantially more difficult as the cumulative probability estimate being verified becomes smaller. In the extreme, it becomes virtually impossible to verify with any accuracy the potential losses associated with extremely rare events. Moreover, the economic importance of not being able to reliably detect an inaccurate model or an under-reporting institution potentially becomes much more pronounced as the cumulative probability estimate being verified becomes smaller. It does not appear possible for a bank or its supervisor to reliably verify the accuracy of an institution's internal model loss exposure estimates using standard statistical techniques. The results have implications both for banks that wish to assess the accuracy of their internal risk measurement models as well as for supervisors who must verify the accuracy of an institution's risk exposure estimate reported under an internal models approach to model risk.

Firm performance is a relevant construct in strategic management research and frequently used as a dependent variable. Despite this relevance, there is hardly a consensus about its definition, dimensionality and measurement, what limits advances in research and understanding of the concept. This article proposes and tests a measurement model for firm performance, based on subjective indicators. The model is grounded in stakeholder theory and a review of empirical articles. Confirmatory Factor Analyses, using data from 116 Brazilian senior managers, were used to test its fit and psychometric properties. The final model had six first-order dimensions: profitability, growth, customer satisfaction, employee satisfaction, social performance, and environmental performance. A second-order financial performance construct, influencing growth and profitability, correlated with the first-order intercorrelated, non-financial dimensions. Results suggest dimensions cannot be used interchangeably, since they represent different aspects of firm performance, and corroborate the idea that stakeholders have different demands that need to be managed independently. Researchers and practitioners may use the model to fully treat performance in empirical studies and to understand the impact of strategies on multiple performance facets.

Confirmatory factor analyses (CFA) are often used in psychological research when developing measurement models for psychological constructs. Evaluating CFA model fit can be quite challenging, as tests for exact model fit may focus on negligible deviances, while fit indices cannot be interpreted absolutely without specifying thresholds or cutoffs. In this study, we review how model fit in CFA is evaluated in psychological research using fit indices and compare the reported values with established cutoff rules. For this, we collected data on all CFA models in Psychological Assessment from the years 2015 to 2020 [Formula: see text]. In addition, we reevaluate model fit with newly developed methods that derive fit index cutoffs that are tailored to the respective measurement model and the data characteristics at hand. The results of our review indicate that the model fit in many studies has to be seen critically, especially with regard to the usually imposed independent clusters constraints. In addition, many studies do not fully report all results that are necessary to re-evaluate model fit. We discuss these findings against new developments in model fit evaluation and methods for specification search.

The business environment of this new century has undertaken several changes, creating more and more complexity and uncertainty.In this changing environment, which characterizes the global economy today, firms face severe competitive pressure to do things better, faster, and low-priced.They need to cope with a growing number of challenges arising from their environment, and also increase their ability to adapt.Nowadays, continuous performance is the objective of any firm.This is because it is only through performance that companies are able to experience development and make progress.Consequently, assessing and measuring business performance is of significant importance, since companies are constantly seeking effective and efficient results.

Abstract Atmospheric aerosols are complex mixtures of different chemical species, and individual particles exist in many different shapes and morphologies. Together, these characteristics contribute to the aerosol mixing state. This review provides an overview of measurement techniques to probe aerosol mixing state, discusses how aerosol mixing state is represented in atmospheric models at different scales, and synthesizes our knowledge of aerosol mixing state's impact on climate‐relevant properties, such as cloud condensation and ice nucleating particle concentrations, and aerosol optical properties. We present these findings within a framework that defines aerosol mixing state along with appropriate mixing state metrics to quantify it. Future research directions are identified, with a focus on the need for integrating mixing state measurements and modeling.