On the evaluation of structural equation models

Bagozzi, Richard P.; Yi, Youjae

doi:10.1007/bf02723327

Cited by 17,827 publications

(10,431 citation statements)

References 33 publications

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“…Our analysis reproduced the estimates of model fit reported in Bagozzi and Yi (1988) with χ 2 (11) = 6.801,F = 0.0273, RMSEA = 0, and CFI = TLI = 1. Parameter estimates of all parameters are given in the supplementary material.…”

Section: Empirical Data Illustrationssupporting

confidence: 76%

“…Trying and Subjective Norm are measured variables themselves. The model has a total of 17 parameters, and the correlation matrix of all measured variables based on N = 250 is reported in Table 5 of Bagozzi and Yi (1988).Our analysis reproduced the estimates of model fit reported in Bagozzi and Yi (1988) with χ 2 (11) = 6.801,F = 0.0273, RMSEA = 0, and CFI = TLI = 1. Parameter estimates of all parameters are given in the supplementary material.…”

supporting

confidence: 73%

Section: Example 1: Conversation With a Strangermentioning

confidence: 99%

“…Example 1: Conversation with a Stranger Bagozzi and Yi (1988) presented research by Bagozzi and Warshaw (1988) which investigated what motivates people to initiate a conversation with an attractive stranger. Attitude towards trying to initiate such a conversation is hypothesized as a predictor of actual, subsequent Trying to initiate a conversation in the succeeding week, and Intention to try is included as a mediator.…”

Section: Empirical Data Illustrationsmentioning

confidence: 99%

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Parameter uncertainty in structural equation models: Confidence sets and fungible estimates.

Pek

2018

Psychological Methods

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Current concerns regarding the dependability of psychological findings call for methodological developments to provide additional evidence in support of scientific conclusions. This paper highlights the value and importance of two distinct kinds of parameter uncertainty which are quantified by confidence sets (CSs) and fungible parameter estimates (FPEs; T. Lee, MacCallum, & Browne, in press); both provide essential information regarding the defensibility of scientific findings. Using the structural equation model, we introduce a general perturbation framework based on the likelihood function that unifies CSs and FPEs and sheds new light on the conceptual distinctions between them. A targeted illustration is then presented to demonstrate the factors which differentially influence CSs and FPEs, further highlighting their theoretical differences. With three empirical examples on initiating a conversation with a stranger (Bagozzi & Warshaw, 1988), posttraumatic growth of caregivers in the context of pediatric palliative care (Cadell et al., 2014), and the direct and indirect effects of spirituality on thriving among youth (Dowling et al., 2004), we illustrate how CSs and FPEs provide unique information which lead to better informed scientific conclusions. Finally, we discuss the importance of considering information afforded by CSs and FPEs in strengthening the basis of interpreting statistical results in substantive research, conclude with future research directions, and provide example OpenMx code for the computation of CSs and FPEs.Keywords: confidence sets, fungible estimates, sensitivity analysis, profile likelihood, structural equation modeling CONFIDENCE SETS AND FUNGIBLE ESTIMATES 3 Parameter uncertainty in structural equation models:Confidence sets and fungible estimates Statistical practice in psychological science is undergoing reform in response to concerns over the dependability of findings (Harlow, Mulaik, & Steiger, 2016; Open Science Collaboration, 2015;Pashler & Wagenmakers, 2012;Simmons, Nelson, & Simonsohn, 2011;Sijtsma, 2015). In response to these concerns, the reporting of effect sizes or focal parameter estimates and their confidence intervals (CIs) have been recommended as best practice (Cumming, 2014; Wilkinson & the Task Force on Statistical Inference, 1999). Confidence intervals communicate precision in estimation, providing information to researchers on whether inferences about their commensurate parameters can be drawn. Note that confidence regions (CRs) are an extension of CIs from a single parameter to a set of multiple parameters, and we use the term confidence sets (CSs) to collectively refer to CIs and CRs. Additionally, recent quantitative developments show that examining parameter sensitivity (T. Lee & MacCallum, 2015) and fungible parameter estimates (FPEs; T. Lee et al., in press;MacCallum, Browne, & Lee, 2009;Waller, 2008) can add to substantive researchers' diagnostic toolkit in terms of generating more information about the validity of their statistical results. Rel...

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Section: Empirical Data Illustrationssupporting

confidence: 76%

supporting

confidence: 73%

Section: Example 1: Conversation With a Strangermentioning

confidence: 99%

Section: Empirical Data Illustrationsmentioning

confidence: 99%

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Parameter uncertainty in structural equation models: Confidence sets and fungible estimates.

Pek

2018

Psychological Methods

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“…All these conditions are satisfied as presented in Table 5. In other words, the outer model (measurement model) has appropriate convergent validity (Bagozzi& Yi, 1988). SEM literature refers to discriminant validity as the level of items that can differentiate a construct from other constructs in the model.…”

Section: The Convergent Validitymentioning

confidence: 99%

The Association between Communication, Customer Relationship Management and Organisational Performance: Evidence from the Saudi Arabia Hospitals

Alsharari¹,

Aziz²,

Taib³

et al. 2017

jmr

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This research aims to examine the influence of the communication, customer relationship management on organisational performance among Saudi Arabia Hospitals. Furthermore, to exam the study model, the survey questionnaire was used to collect data 154 hospitals randomly chosen from 259 government hospitals in Saudi Arabia. For data analysis, the Partial Least Squares (PLS) structural equation modeling was used. In addition, this study found a positive and significant association between communication, customer relationship management, and organisational performance. Ultimately, this study offers some limitations and suggestions for future researchers.

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Strategic consensus and performance: the role of strategy type and market-related dynamism

Homburg,

Krohmer,

Workman. Jr

1999

Strat. Mgmt. J.

224

138

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This paper examines the link between consensus among senior managers and performance at the SBU level and considers factors which may moderate the strength of this relationship. Using data from a cross-national study in three industry sectors, the authors find that consensus increases the performance of the SBU in the case of a differentiation strategy but not in the case of a low-cost strategy. Additionally, the relationship between consensus on a differentiation strategy and performance is negatively influenced by dynamism of the market. This research thus clarifies and extends prior consensus research by indicating the conditions under which consensus positively affects performance. For managers, our results indicate that investing managerial time in obtaining consensus is more important for a differentiation than for a lowcost strategy and is particularly important when using a differentiation strategy in a stable environment.

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On the evaluation of structural equation models

Cited by 17,827 publications

References 33 publications

Parameter uncertainty in structural equation models: Confidence sets and fungible estimates.

Parameter uncertainty in structural equation models: Confidence sets and fungible estimates.

The Association between Communication, Customer Relationship Management and Organisational Performance: Evidence from the Saudi Arabia Hospitals

Strategic consensus and performance: the role of strategy type and market-related dynamism

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