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Journal ArticleDOI

Do partners agree about abuse in their relationship?: A psychometric evaluation of interpartner agreement.

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TLDR
In this paper, the authors examined whether partners can be relied on to provide congruent reports about abuse in their relationship, and they found that abusive behavior reported by victims (both men and women) reported somewhat more abuse than did their perpetrators.
Abstract
This study tested whether partners can be relied on to provide congruent reports about abuse in their relationship. The authors examined whether interpartner agreement (IA) varies as a function of whether the perpetrator is the man or the woman, and by whether the abusive behavior being reported is physical or psychological. Guided by psychometric test theory, the authors examined whether weak IA about specific behaviors can be improved by aggregating behavior items into scales and by controlling for random measurement error. A representative sample of 360 young couples was studied. IA did not vary with the perpetrator's gender or with the nature of the abusive behaviors, but victims (both men and women) reported somewhat more abuse than did their perpetrators. IA about specific abusive behaviors was only poor to fair, but it became very good when items were aggregated into scales and even better when measurement errors were removed from the reports. The findings suggest that reports of abuse can be aggregated to form internally consistent scales that show strong IA, thereby fulfilling criteria for reliability. Moreover, under research conditions that guarantee confidentiality, either abuser reports or victim reports are suitable methods for use in research on partner abuse.

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References
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Book

Statistical Power Analysis for the Behavioral Sciences

TL;DR: The concepts of power analysis are discussed in this paper, where Chi-square Tests for Goodness of Fit and Contingency Tables, t-Test for Means, and Sign Test are used.
Journal ArticleDOI

The measurement of observer agreement for categorical data

TL;DR: A general statistical methodology for the analysis of multivariate categorical data arising from observer reliability studies is presented and tests for interobserver bias are presented in terms of first-order marginal homogeneity and measures of interob server agreement are developed as generalized kappa-type statistics.
Book

Testing Structural Equation Models

TL;DR: In this paper, Bollen et al. proposed a model fitting metric for Structural Equation Models, which is based on the Monte Carlo evaluation of Goodness-of-Fit measures.
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