A Bayesian approach to model selection for structural equation models is outlined. This enables us to compare individual models, nested or non-nested, and also to search through the (perhaps vast) set of possible models for the best ones. The approach selects several models rather than just one, when appropriate, and so enables us to take account, both informally and formally, of uncertainty about model structure when making inferences about quantities of interest. The approach tends to select simpler models than strategies based on multiple P-value-based tests. It may thus help to overcome the criticism of structural

This is a working paper. elab.vanderbilt.edu Measuring the Flow Construct in Online Environments: A Structural Modeling Approach Though marketers have made great strides in understanding the Internet, they still understand little about what makes for a compelling consumer experience online. Recently, the flow construct has been proposed as important for understanding consumer behavior on the World Wide Web. Although widely studied over the past twenty years, quantitative modeling efforts of the flow construct have been neither systematic nor comprehensive. In large parts, these efforts have been hampered by considerable confusion regarding the exact conceptual definition of flow. Lacking precise definition, it has been difficult to measure flow empirically, let alone apply the concept in practice. Following the conceptual model of flow proposed by Hoffman and Novak (1996), we conceptualize flow as a complex multidimensional construct characterized by directed relationships among a set of unidimensional constructs, most of which have previously been incorporated in various definitions of flow. In a quantitative modeling framework, we use data collected from a large-sample Web-based consumer survey to measure this set of constructs, and fit a series of structural equation models that test Hoffman and Novak’s (1996) theory. The conceptual model is largely supported and the improved fit offered by the revised model provides additional insights into the antecedents and consequences of flow. A key insight from the paper is that the degree to which the online experience is compelling can be defined and measured. As such, our flow model may be useful both theoretically and in practice as marketers strive to decipher the secrets of commercial success in interactive online environments. 1

Addresses issues related to partial measurement in variance using a tutorial approach based on the LISREL confirmatory factor analytic model. Specifically, we demonstrate procedures for (a) using "sensitivity analyses " to establish stable and substantively well-fitting baseline models, (b) determining partially invariant measurement parameters, and (c) testing for the invariance of factor covariance and mean structures, given partial measurement invariance. We also show, explicitly, the transformation of parameters from an all-^fto an all-y model specification, for purposes of testing mean structures. These procedures are illustrated with multidimensional self-concept data from low ( « = 248) and high (n = 582) academically tracked high school adolescents. An important assumption in testing for mean differences is that the measurement (Drasgow & Kanfer, 1985; Labouvie,

An approach to sample size planning for multiple regression is presented that emphasizes accuracy in parameter estimation (AIPE). The AIPE approach yields precise estimates of population parameters by providing necessary sample sizes in order for the likely widths of confidence intervals to be sufficiently narrow. One AIPE method yields a sample size such that the expected width of the confidence interval around the standardized population regression coefficient is equal to the width specified. An enhanced formulation ensures, with some stipulated probability, that the width of the confidence interval will be no larger than the width specified. Issues involving standardized regression coefficients and random predictors are discussed, as are the philosophical differences between AIPE and the power analytic approaches to sample size planning. Sample size estimation from a power analytic perspective is often performed by mindful researchers in order to have a reasonable probability of obtaining parameter estimates that are statistically significant. In general, the social sciences have slowly become more aware of the problems associated with underpowered studies and their corresponding Type II errors, which can yield misleading results in a given

This article examines the theoretical status of latent variables as used in modern test theory models. First, it is argued that a consistent interpretation of such models requires a realist ontology for latent variables. Second, the relation between latent variables and their indicators is discussed. It is maintained that this relation can be interpreted as a causal one but that in measurement models for interindividual differences the relation does not apply to the level of the individual person. To substantiate intraindividual causal conclusions, one must explicitly represent individual level processes in the measurement model. Several research strategies that may be useful in this respect are discussed, and a typology of constructs is proposed on the basis of this analysis. The need to link individual processes to latent variable models for interindividual differences is emphasized. Consider the following sentence: “Einstein would not have been able to come up with his e � mc 2 had he not possessed such an extraordinary intelligence. ” What does this sentence express? It relates observable behavior (Einstein’s writing e � mc 2)toan unobservable attribute (his extraordinary intelligence), and it does so by assigning to the unobservable attribute a causal role in

According to Kenny and McCoach (2003), chi-square tests of structural equation models produce inflated Type I error rates when the degrees of freedom increase. So far, the amount of this bias in large models has not been quantified. In a Monte Carlo study of confirmatory factor models with a range of 48 to 960 degrees of freedom it was found that the traditional maximum likelihood ratio statistic, TML, overestimates nominal Type I error rates up to 70 % under conditions of multivariate normality. Some alternative statistics for the correction of model-size effects were also investigated: the scaled Satorra–Bentler statistic, TSC; the adjusted Satorra–

In this paper we extend the standard approach of correlation structure analysis in order to reduce the dimension of highdimensional statistical data. The classical assumption of a linear model for the distribution of a random vector is replaced by the weaker assumption of a model for the copula. For elliptical copulae a ’correlation-like ’ structure remains but different margins and non-existence of moments are possible. Moreover, elliptical copulae allow also for a ’copula structure analysis ’ of dependence in extremes. After introducing the new concepts and deriving some theoretical results we observe in a simulation study the performance of the estimators: the theoretical asymptotic behavior of the statistics can be observed even for a sample of only 100 observations. Finally, we test our method on real financial data and explain differences between our copula based approach and the classical approach. Our new method yields a considerable dimension reduction also in non-linear models.

This study examined how three item response models performed when they were applied to data collected from a conventionally developed Likert-type personality scale. Each model examined is based on a different response assumption: the multiple linear factor analysis model (continuous responses), the TOBIT factor analysis model (censored responses), and the multidimensional graded response model. The item and examinee parameters of the models were estimated using different discrepancy functions and current software implementations. Comparisons were made in terms of the goodness-of-fit of the model, parameter estimates, and criterion-related validity. Results showed that the models ’ response assumptions were reasonably tenable and that the

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When integrated with key organizational characteristics, institutional theory can yield new insights to understand differences between firms ’ strategies. We propose that a company’s functional organization and internal power structure influence its facility managers ’ sensitivity to and interpretation of institutional pressures. Combining over 500 responses from an original survey with existing data sources, we show how two corporate departments affect how facility managers perceive and respond to various institutional pressures to adopt environmental management practices.