A study of the power associated with testing factor mean differences under violations of factorial invariance

Kaplan, David; George, Rani Mary

doi:10.1080/10705519509539999

Cited by 78 publications

(79 citation statements)

References 39 publications

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“…Based on the current study and the previous ones (Chen, 2008;Kaplan & George, 1995;Wang et al, 2012;Whittaker, 2013), non-invariant loadings and/or intercepts is likely to lead to biased estimates of factor mean differences and other undesired model estimation properties if researchers fail to adequately model non-invariance in parameters. The effect of misspecification of models with non-invariance of indicator intercepts is particularly problematic on the assessment of factor mean differences.…”

Section: Discussionmentioning

confidence: 99%

“…Previous studies on testing factor mean differences using SEM have been focusing on the impact of different levels and patterns of non-invariance in generated data (Chen, 2008;Kaplan & George, 1995;Wang et al, 2012;Whittaker, 2013). One common feature of these studies is that the analysis models fit to data were fixed in terms of their specifications of invariance constraints.…”

Section: Discussionmentioning

confidence: 99%

“…The impact of inappropriate invariance constraints on statistical properties of latent mean estimation has been studied as well. Kaplan and George (1995) conducted a study to investigate the impact of degrees of non-invariance of factor loadings on the power associated with the test of factor mean differences. In their study, the proportion and magnitude of uniform non-invariance in cross-group loading pairs were manipulated for data generation, while all loading pairs were constrained to be equal in model analyses.…”

Section: Bias Of Differences In Factor Meansmentioning

confidence: 99%

“…If scalar invariance is violated and the non-invariance is ignored for analysis models, estimated differences in factor means across groups could be biased (Chen, 2008). Additionally, Type I and Type II error rates associated with testing factor mean differences could be distorted (Kaplan & George, 1995;Whittaker, 2013).…”

mentioning

confidence: 99%

“…Studies on latent factor mean modeling have been focusing on the impact of violating invariance assumptions (Chen, 2008;Kaplan & George, 1995;Wang, Whittaker, & Beretvas, 2012;Whittaker, 2013). Specifically, existing studies examined the effect of an analysis model with assumed full scalar invariance when data were generated with varying levels of non-invariance of measurement parameters.…”

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confidence: 99%

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The Impact of Varying the Number of Measurement Invariance Constraints on the Assessment of Between-Group Differences of Latent Means

2015

Structural Equation Modeling: A Multidisciplinary Journal

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Structural equation modeling is potentially useful for assessing mean differences between groups on latent variables (i.e., factors). However, to evaluate these differences accurately, the parameters of the indicators of these latent variables must be specified correctly. The focus of the current research is on the specification of between-group equality constraints on the loadings and intercepts of indicators. These equality constraints are referred to as invariance constraints. Previous simulation studies in this area focused on fitting a particular model to data that were generated to have various levels and patterns of non-invariance. Results from these studies were interpreted from a viewpoint of assumption violation rather than model misspecification. In contrast, the current study investigated analysis models with varying number of invariance constraints given data that were generated based on a model with indicators that were invariant, partially invariant, or non-invariant. More broadly, the current simulation study was conducted to examine the effect of correctly or incorrectly imposing invariance constraints as well as correctly or incorrectly not imposing invariance constraints on the assessment of factor mean differences. The results indicated that different types of analysis models yield different results in terms of Type I error rates, power, bias in estimation of factor mean difference, and model fit. Benefits and risks are associated with imposing or reducing invariance constraints on models. In addition, model fit or lack of fit can lead to wrong decisions concerning invariance constraints.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Bias Of Differences In Factor Meansmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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The Impact of Varying the Number of Measurement Invariance Constraints on the Assessment of Between-Group Differences of Latent Means

2015

Structural Equation Modeling: A Multidisciplinary Journal

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A national study of the mental health status of Asian international students in the United States

Xiong

Pillay

2022

J Multicult Couns & Deve

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This study compared the mental health status of Asian international students (AIS) in the United States with American students and other international students by analyzing 4 years of data (2011-2014) from the American College Health Association-National College Health Assessment. No significant differences were found in the ***AIS' psychological distress, but their self-reported selfharm ideation and behavior were higher than the other two groups. Predictors of psychological distress were analyzed and implications for stakeholders are offered. K E Y W O R D S mental health status, psychological distress, Asian international students ResumenEste estudio comparó el estado de salud mental de estudiantes internacionales asiáticos (AIS, por sus siglas en inglés) en Estados Unidos con el de estudiantes americanos y de otros grupos internacionales mediante el análisis de cuatro años de datos (2011-2014) obtenidos de las Evalua-

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Does worksite social capital enhance retention into a worksite weight‐loss programme?

Hill

Wilson

Harden

et al. 2016

Obesity Science & Practice

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SummaryObjectiveTo determine if worksite social capital predicted retention in a worksite‐based weight‐loss programme using structural equation modelling. A secondary aim was to determine if worksite social capital was related to changes in weight at 6 months.MethodsOverweight or obese employees from 28 worksites enrolled in a larger 12‐month worksite weight‐loss trial. Workplace social capital was assessed using an eight‐item scale specific to the workplace. Weight was measured using a HealthSpottm, and change in weight was computed from weigh‐ins at baseline and 6 months and reported as pounds (lbs) lost. Retention was defined as those employees who completed a weigh‐in at 6 months.ResultsAcross the trial, N = 1,790; age = 46.6 ± 11; 73% women; 73% White overweight or obese employees participated. The odds of participant attrition were 1.12 times greater with each unit decrease in social capital score at baseline (p < 0.05), and while the model testing the direct effect of social capital at baseline on weight loss at 6 months demonstrated acceptable fit, social capital was not a significant predictor of weight loss (p > 0.05).ConclusionsIncreased worksite social capital was predictive of retention in a worksite weight‐loss programme. To maximize return on investments for employee wellness and weight‐loss programmes, employers may benefit from understanding the facets of the ‘social’ environment such as social capital that may increase the likelihood of sustained participation.

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A study of the power associated with testing factor mean differences under violations of factorial invariance

Cited by 78 publications

References 39 publications

The Impact of Varying the Number of Measurement Invariance Constraints on the Assessment of Between-Group Differences of Latent Means

The Impact of Varying the Number of Measurement Invariance Constraints on the Assessment of Between-Group Differences of Latent Means

A national study of the mental health status of Asian international students in the United States

Does worksite social capital enhance retention into a worksite weight‐loss programme?

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