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      The GRoLTS-Checklist: Guidelines for Reporting on Latent Trajectory Studies

      , , , ,
      Structural Equation Modeling: A Multidisciplinary Journal
      Informa UK Limited

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          Health Measurement Scales : A Practical Guide to Their Development and Use

          Clinicians and those in health sciences are frequently called upon to measure subjective states such as attitudes, feelings, quality of life, educational achievement and aptitude, and learning style in their patients. This fifth edition of Health Measurement Scales enables these groups to both develop scales to measure non-tangible health outcomes, and better evaluate and differentiate between existing tools.<br> <br> Health Measurement Scales is the ultimate guide to developing and validating measurement scales that are to be used in the health sciences. The book covers how the individual items are developed; various biases that can affect responses (e.g. social desirability, yea-saying, framing); various response options; how to select the best items in the set; how to combine them into a scale; and finally how to determine the reliability and validity of the scale. It concludes with a discussion of ethical issues that may be encountered, and guidelines for reporting the results of the scale development process. Appendices include a comprehensive guide to finding existing scales, and a brief introduction to exploratory and confirmatory factor analysis, making this book a must-read for any practitioner dealing with this kind of data.<br>
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            Latent Class Modeling with Covariates: Two Improved Three-Step Approaches

            J. Vermunt (2010)
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              Statistical Power to Detect the Correct Number of Classes in Latent Profile Analysis.

              Little research has examined factors influencing statistical power to detect the correct number of latent classes using latent profile analysis (LPA). This simulation study examined power related to inter-class distance between latent classes given true number of classes, sample size, and number of indicators. Seven model selection methods were evaluated. None had adequate power to select the correct number of classes with a small (Cohen's d = .2) or medium (d = .5) degree of separation. With a very large degree of separation (d = 1.5), the Lo-Mendell-Rubin test (LMR), adjusted LMR, bootstrap likelihood-ratio test, BIC, and sample-size adjusted BIC were good at selecting the correct number of classes. However, with a large degree of separation (d = .8), power depended on number of indicators and sample size. The AIC and entropy poorly selected the correct number of classes, regardless of degree of separation, number of indicators, or sample size.
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                Author and article information

                Journal
                Structural Equation Modeling: A Multidisciplinary Journal
                Structural Equation Modeling: A Multidisciplinary Journal
                Informa UK Limited
                1070-5511
                1532-8007
                March 04 2017
                November 11 2016
                : 24
                : 3
                : 451-467
                Article
                10.1080/10705511.2016.1247646
                7eed1ca2-94f2-4a59-a2aa-664f552fb7bd
                © 2017
                History

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