Structural Equation Models in IS Theory and Measurement

Structural Equation Modeling (SEM) is taking on an increasingly important role in the examination of theory and measurement issues in information systems. Despite its popular use and the many software packages that exist for the researcher, SEM is a complex technique that provides may pitfalls for the unaware researcher. This workshop provides a gentle introduction to SEM from basic principles. It also covers some of the advanced issues that the researcher might encounter, not only from a statistical but also, and more importantly, from an ontological and epistemological perspective. The workshop is targeted at PhD students and IS researchers new to SEM and little to no statistical knowledge. Demonstrations and hands-on examples make use of the open source R system for statistical computing, which will be provided to all participants

Book Review of Rasch Models for Solving Measurement Problems: Invariant Measurement in the Social Sciences by Engelhard Jr. and Wang

As a paradigm shift from classical test theory, Rasch measurement theory introduces a framework of scales that is capable of producing invariant measurement, which plays an increasingly important role in solving practical measurement problems in the social sciences, psychometrics, and health sciences. Researchers have increasingly adopted Rasch measurement to develop and validate scales in a variety of educational and psychological contexts (e.g. Mendoza Yan, 2021; Yan Pastore, 2022). Rasch measurement is often used in the validation of scales in conjunction with structural equation models (SEM), which provide meaningful answers to a variety of measurement questions. However, it remains unfamiliar to many researchers, practitioners, and educators who have embraced classical test theory

Testing the Erdem and Swait Brand Equity Framework Using Latent Class Structural Equation Modelling

This paper tests the Erdem and Swait (1998) brand equity framework using latent class structural equation modelling. While there are a number of conceptual and measurement models of brand equity in the literature, we focus on the Erdem and Swait brand equity framework because it is based on formal theory in information economics. The Erdem and Swait framework was originally tested in a structural equation modelling framework without taking into account consumer preference heterogeneity. In this study, we extend the Erdem and Swait framework to incorporate preference heterogeneity via the use of latent class structural equation modelling. Data were collected from the financial services sector and results show two distinct segments of brand equity. The findings have implications for both academics and practitioners in brand management

Measuring Student Growth in K–12 Schools Using Item Response Theory Within Structural Equation Models

The use of test-based accountability has expanded beyond measurements of school effectiveness to include measurements of teacher effectiveness. However, whereas the use of test-based accountability has expanded, the understanding of the statistical methodologies used in accountability systems has not kept pace. Currently, Student Growth Percentiles and value-added modeling are the most prevalent methodologies for estimating annual student growth. Each of these methodologies is regression-based and relies on scale scores from standardized assessments. Given the prevalence of Item Response Theory in statewide assessment programs, these scale scores often result from Item Response Theory scaling practices. Grounded in earlier work of Brockman (2011), Chiu and Camilli (2013), and Lu, Thomas, and Zumbo (2005), concerning error related to Item Response Theory-based scale scores, this study considers using Item Response Theory as the measurement model in a structural equation model by including simulated item response patterns as indicators of ability. Data were simulated using parameters from the Mississippi Curriculum Test, Second Edition. Separate structural equation models for language arts and mathematics were considered. Upon examining the fit of each model, results indicated a good fit for the measurement model in language arts and in mathematics. Results also indicated a good fit for the overall structural equation model but none of the structural relationships were statistically significant. Additional results and implications of this study are discussed

Corporate codes of ethics in Australia, Canada and USA : measurement and structural properties of a cross-cultural model

The objective is to test the consistency of measurement and structural properties in a model of corporate codes of ethics (CCE) on an aggregated level and across multiple samples derived from three countries, namely Australia, Canada and the USA. The properties of four constructs of CCE are described and tested, these being: surveillance/training, internal communication, external communication, and guidance. The conclusion is that the measurement and structural models on an aggregated level have a satisfactory fit, validity and reliability. Furthermore, they are consistent when tested on each of the three samples (i.e. cross-validated). The cross-cultural model makes a contribution in addition to previous mostly descriptive studies and theory in the field using confirmatory factor analysis and structural equation modeling.<br /

Using Structural Equation and Item Response Models to Assess Relationship between Latent Traits

We deepen the two main approaches to the problem of measurement error in social sciences, the Structural Equation Models (SEM) and the Item Response Theory Models (IRM), comparing two different estimation procedures. The One-step procedure (related to SEM) requires that researcher specifies a complete model of both measurement aspects (single link between the latent variable and its indicators) and structural aspects (links between different latent variables), with the model parameters estimated simultaneously. In the Two-step procedure (related to IRM), we first estimate the measures (one for each construct), then we will assess, through a regression model, the relationships between these measures and the latent variables that they represent. Our aim is to define a Two-step method that, using information obtained in the first step about the measurement error, presents low levels of bias and loss of efficiency, as close as possible to that of One-step method

Bayesian Structural Equation Models for Cumulative Theory Building in Information Systems―A Brief Tutorial Using BUGS and R

Structural equation models (SEM) are frequently used in information systems (IS) to analyze and test theoretical propositions. As IS researchers frequently reuse measurement instruments and adapt or extend theories, they frequently re-estimate regression relationships in their SEM that have been examined in previous studies. We advocate the use of Bayesian estimation of structural equation models as an aid to cumulative theory building; Bayesian statistics offer a statistically sound way to incorporate prior knowledge into SEM estimation, allowing researchers to keep a “running tally” of the best estimates of model parameters. This tutorial on the application of Bayesian principles to SEM estimation discusses when and why the use of Bayesian estimation should be considered by IS researchers, presents an illustrative example using best practices, and makes recommendations to guide IS researchers in the application of Bayesian SEM

Measurement and structural properties of organizational codes of ethics in private and public Sweden

Purpose &ndash; The purpose of this paper is to test the measurement and structural properties in a model of organizational codes of ethics (OCE) in Sweden. Design/methodology/approach &ndash; The measurement and structural properties of four OCE constructs (i.e. surveillance/training, internal communication, external communication, and guidance) were described and tested in a dual sample based upon private and public sectors of Sweden. Findings &ndash; Results show that the measurement and structural models of OCE in part have a satisfactory fit, validity, and reliability. Research limitations/implications &ndash; The paper makes a contribution to theory as it outlines a set of OCE constructs and it presents an empirical test of and OCE model in respect to measurement and structural properties. A number of research limitations are provided. Practical implications &ndash; It provides a model to be considered in the implementation and monitoring of OCE. The present research provides opportunities for further research in refining, extending, and testing the proposed OCE model in other cultural and organizational settings. Originality/value &ndash; The OCE model extends previous studies that have been predominately descriptive, by using confirmatory factor analysis and structural equation modeling.<br /

Item response theory. A first approach

The Item Response Theory (IRT) has become one of the most popular scoring frameworks for measurement data, frequently used in computerized adaptive testing, cognitively diagnostic assessment and test equating. According to Andrade et al. (2000), IRT can be defined as a set of mathematical models (Item Response Models – IRM) constructed to represent the probability of an individual giving the right answer to an item of a particular test. The number of Item Responsible Models available to measurement analysis has increased considerably in the last fifteen years due to increasing computer power and due to a demand for accuracy and more meaningful inferences grounded in complex data. The developments in modeling with Item Response Theory were related with developments in estimation theory, most remarkably Bayesian estimation with Markov chain Monte Carlo algorithms (Patz & Junker, 1999). The popularity of Item Response Theory has also implied numerous overviews in books and journals, and many connections between IRT and other statistical estimation procedures, such as factor analysis and structural equation modeling, have been made repeatedly (Van der Lindem & Hambleton, 1997). As stated before the Item Response Theory covers a variety of measurement models, ranging from basic one-dimensional models for dichotomously and polytomously scored items and their multidimensional analogues to models that incorporate information about cognitive sub-processes which influence the overall item response process. The aim of this work is to introduce the main concepts associated with one-dimensional models of Item Response Theory, to specify the logistic models with one, two and three parameters, to discuss some properties of these models and to present the main estimation procedures.info:eu-repo/semantics/publishedVersio

Discovering Unobserved Heterogeneity in Structural Equation Models to Avert Validity Threats

A large proportion of information systems research is concerned with developing and testing models pertaining to complex cognition, behaviors, and outcomes of individuals, teams, organizations, and other social systems that are involved in the development, implementation, and utilization of information technology. Given the complexity of these social and behavioral phenomena, heterogeneity is likely to exist in the samples used in IS studies. While researchers now routinely address observed heterogeneity by introducing moderators, a priori groupings, and contextual factors in their research models, they have not examined how unobserved heterogeneity may affect their findings. We describe why unobserved heterogeneity threatens different types of validity and use simulations to demonstrate that unobserved heterogeneity biases parameter estimates, thereby leading to Type I and Type II errors. We also review different methods that can be used to uncover unobserved heterogeneity in structural equation models. While methods to uncover unobserved heterogeneity in covariance-based structural equation models (CB-SEM) are relatively advanced, the methods for partial least squares (PLS) path models are limited and have relied on an extension of mixture regression—finite mixture partial least squares (FIMIX-PLS) and distance measure-based methods—that have mismatches with some characteristics of PLS path modeling. We propose a new method—prediction-oriented segmentation (PLS-POS)—to overcome the limitations of FIMIX-PLS and other distance measure-based methods and conduct extensive simulations to evaluate the ability of PLS-POS and FIMIX-PLS to discover unobserved heterogeneity in both structural and measurement models. Our results show that both PLS-POS and FIMIX-PLS perform well in discovering unobserved heterogeneity in structural paths when the measures are reflective and that PLS-POS also performs well in discovering unobserved heterogeneity in formative measures. We propose an unobserved heterogeneity discovery (UHD) process that researchers can apply to (1) avert validity threats by uncovering unobserved heterogeneity and (2) elaborate on theory by turning unobserved heterogeneity into observed heterogeneity, thereby expanding theory through the integration of new moderator or contextual variables