Bias in the Correlated Uniqueness Model for MTMM Data

Conway, James M.; Lievens, Filip; Scullen, Steven E.; Lance, Charles E.

doi:10.1207/s15328007sem1104_3

Cited by 49 publications

(69 citation statements)

References 42 publications

(59 reference statements)

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“…In addition, the correlated uniqueness model is known to have bias when there are strong loadings on methods factors and strong correlations between method factors, which is a characteristic of our data (see Figure 2 below for loadings on method factors; Conway et al, 2004). Because the emotion factors are indicated by only two manifest variables, we added an additional constraint to adequately identify the emotion latent variable by constraining the unstandardized loadings from both manifest variables to equality forcing the affective and cognitive emotion subscale to load equally on the corresponding emotion latent factor 3 .…”

Section: Resultsmentioning

confidence: 92%

An emotion-differentiated perspective on empathy with the emotion specific empathy questionnaire

et al. 2014

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Empathy refers to the thoughts and feelings of one individual in response to the observed (emotional) experiences of another individual. Empathy, however, can occur toward persons experiencing a variety of emotions, raising the question of whether or not empathy can be emotion specific. This paper discusses theoretical and empirical support for the emotion specificity of empathy. We present a new measure, the Emotion Specific Empathy questionnaire, which assesses affective and cognitive empathy for the six basic emotions. This paper presents the measure's psychometric qualities and demonstrates, through a series of models, the discriminant validity between emotion specific empathies suggesting empathy is emotion specific. Results and implications are discussed.

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Section: Resultsmentioning

confidence: 92%

An emotion-differentiated perspective on empathy with the emotion specific empathy questionnaire

et al. 2014

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“…Similarly, the use of multisource performance ratings (e.g., by supervisors, peers, and subordinates, a.k.a., 360-degree assessment) is considered an optimal method for evaluating job performance (Conway & Huffcutt, 1997; Lance et al, 2008). Because each informant observes the target within a specific relationship and context, it is desirable to obtain ratings from multiple informants privy to different settings to obtain a comprehensive assessment, particularly when behavior may vary over contexts.…”

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confidence: 99%

A trifactor model for integrating ratings across multiple informants.

Bauer¹,

Barbaree²,

Baldasaro³

et al. 2013

Psychological Methods

137

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Psychologists often obtain ratings for target individuals from multiple informants such as parents or peers. In this paper we propose a tri-factor model for multiple informant data that separates target-level variability from informant-level variability and item-level variability. By leveraging item-level data, the tri-factor model allows for examination of a single trait rated on a single target. In contrast to many psychometric models developed for multitrait-multimethod data, the tri-factor model is predominantly a measurement model. It is used to evaluate item quality in scale development, test hypotheses about sources of target variability (e.g., sources of trait differences) versus informant variability (e.g., sources of rater bias), and generate integrative scores that are purged of the subjective biases of single informants.

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“…Instead, it is assumed that the effects of different raters are independent of each other. This is not always a plausible assumption and Conway, Lievens, Scullen, and Lance (2004) showed that biased estimates for the trait variances and covariances may result if the assumption of uncorrelated method effects is not met. Furthermore, because the method effects are only accounted for by allowing for covariances between error variables, no explanatory variables can be included in the model that may explain the interindividual differences in the method effects.…”

Section: The Correlated Trait-correlated Uniqueness Modelmentioning

confidence: 99%

Modeling Common Traits and Method Effects in Multitrait-Multimethod Analysis

Pohl

Steyer

2010

Multivariate Behavioral Research

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Method effects often occur when constructs are measured by different methods. In traditional multitrait-multimethod (MTMM) models method effects are regarded as residuals, which implies a mean method effect of zero and no correlation between trait and method effects. Furthermore, in some recent MTMM models, traits are modeled to be specific to a certain method. However, often we are not interested in a method-specific trait but in a trait that is common to all methods. Here we present the Method Effect model with common trait factors, which allows modeling "common" trait factors and method factors that represent method "effects" rather than residuals. The common trait factors are defined as the mean of the true-score variables of all variables measuring the same trait and the method factors are defined as differences between true-score variables and means of true-score variables. Because the model allows estimating mean method effects, correlations between method factors, and correlations between trait and method factors, new research questions may be investigated. The application of the model is demonstrated by 2 examples studying the effect of negative, as compared with positive, item wording for the measurement of mood states.

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Bias in the Correlated Uniqueness Model for MTMM Data

Cited by 49 publications

References 42 publications

An emotion-differentiated perspective on empathy with the emotion specific empathy questionnaire

An emotion-differentiated perspective on empathy with the emotion specific empathy questionnaire

A trifactor model for integrating ratings across multiple informants.

Modeling Common Traits and Method Effects in Multitrait-Multimethod Analysis

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