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      Time for a Measurement Check-Up: Testing the Couple’s Satisfaction Index and the Global Measure of Sexual Satisfaction Using Structural Equation Modeling and Item Response Theory

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          Abstract

          Relationship and sexual satisfaction are two central outcomes in the study of relationships and are commonly used in both academia and applied practice. However, relationship and sexual satisfaction measures infrequently undergo specific psychometric investigation. Ensuring that measures display strong psychometric performance is an important but under-tested element of replication that has come under more scrutiny lately, and adequate measurement of constructs is an important auxiliary assumption underpinning theory-testing empirical work. A measurement check-up was conducted, including Confirmatory Factor Analysis (CFA) to test factorial validity, measurement invariance to test for group comparability, and Item Response Theory (IRT) to assess the relationship between latent traits and their items/indicators. This format was used to evaluate the psychometric properties of the Couple’s Satisfaction Index (CSI) and the Global Measure of Sexual Satisfaction (GMSEX), two commonly used scales of relationship and sexual satisfaction with a sample of 640 midlife (40–59 years old) married Canadians who were recruited by Qualtrics Panels. Results of CFA suggested that both models were satisfactory. Invariance testing provided robust support for intercept invariance across all the groupings tested. IRT analysis supported the CSI and GMSEX, however, there was evidence that the GMSEX provided somewhat less information for those high on sexual satisfaction. This measurement check-up found that the CSI and GMSEX were reasonably healthy with some caveats. Implications are discussed in terms of replicability and meaning for scholars and practitioners.

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          Sensitivity of Goodness of Fit Indexes to Lack of Measurement Invariance

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            A Review and Synthesis of the Measurement Invariance Literature: Suggestions, Practices, and Recommendations for Organizational Research

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              Sample Size Requirements for Structural Equation Models: An Evaluation of Power, Bias, and Solution Propriety.

              Determining sample size requirements for structural equation modeling (SEM) is a challenge often faced by investigators, peer reviewers, and grant writers. Recent years have seen a large increase in SEMs in the behavioral science literature, but consideration of sample size requirements for applied SEMs often relies on outdated rules-of-thumb. This study used Monte Carlo data simulation techniques to evaluate sample size requirements for common applied SEMs. Across a series of simulations, we systematically varied key model properties, including number of indicators and factors, magnitude of factor loadings and path coefficients, and amount of missing data. We investigated how changes in these parameters affected sample size requirements with respect to statistical power, bias in the parameter estimates, and overall solution propriety. Results revealed a range of sample size requirements (i.e., from 30 to 460 cases), meaningful patterns of association between parameters and sample size, and highlight the limitations of commonly cited rules-of-thumb. The broad "lessons learned" for determining SEM sample size requirements are discussed.
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                Author and article information

                Journal
                J Soc Pers Relat
                J Soc Pers Relat
                spspr
                SPR
                Journal of Social and Personal Relationships
                SAGE Publications (Sage UK: London, England )
                0265-4075
                1460-3608
                12 December 2022
                July 2023
                : 40
                : 7
                : 2252-2276
                Affiliations
                [1-02654075221143360]Department of Psychology, Ringgold 7512, universityThe Memorial University of Newfoundland; , St. John’s, NL, Canada
                Author notes
                [*]Christopher Quinn-Nilas, Department of Psychology, The Memorial University of Newfoundland, 230 Elizabeth Ave, St. John’s, NL A1C 5S7, Canada. Email: cquinnnilas@ 123456mun.ca
                Author information
                https://orcid.org/0000-0002-8056-2008
                Article
                10.1177_02654075221143360
                10.1177/02654075221143360
                10333968
                37441630
                0d545c3e-51d3-44df-8871-e2e7c1ba93be
                © The Author(s) 2022

                This article is distributed under the terms of the Creative Commons Attribution 4.0 License ( https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page ( https://us.sagepub.com/en-us/nam/open-access-at-sage).

                History
                Funding
                Funded by: Social Sciences and Humanities Research Council of Canada, FundRef https://doi.org/10.13039/501100000155;
                Award ID: Doctoral Fellowship 752-2015-1326
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                ts10

                structural equation modeling,item response theory,measurement,relationship satisfaction,sexual satisfaction

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