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One possible reason for the continued neglect of statistical power analysis in research in the behavioral sciences is the inaccessibility of or difficulty with the standard material. A convenient, although not comprehensive, presentation of required sample sizes is provided here. Effect-size indexes and conventional values for these are given for operationally defined small, medium, and large effects. The sample sizes necessary for .80 power to detect effects at these levels are tabled for eight standard statistical tests: (a) the difference between independent means, (b) the significance of a product-moment correlation, (c) the difference between independent rs, (d) the sign test, (e) the difference between independent proportions, (f) chi-square tests for goodness of fit and contingency tables, (g) one-way analysis of variance, and (h) the significance of a multiple or multiple partial correlation.
Depression, anxiety and somatization are the most common mental disorders in primary care as well as medical specialty populations; each is present in at least 5-10% of patients and frequently comorbid with one another. An efficient means for measuring and monitoring all three conditions would be desirable. Evidence regarding the psychometric and pragmatic characteristics of the Patient Health Questionnaire (PHQ)-9 depression, generalized anxiety disorder (GAD)-7 anxiety and PHQ-15 somatic symptom scales are synthesized from two sources: (1) four multisite cross-sectional studies (three conducted in primary care and one in obstetric-gynecology practices) comprising 9740 patients, and (2) key studies from the literature that have studied these scales. The PHQ-9 and its abbreviated eight-item (PHQ-8) and two-item (PHQ-2) versions have good sensitivity and specificity for detecting depressive disorders. Likewise, the GAD-7 and its abbreviated two-item (GAD-2) version have good operating characteristics for detecting generalized anxiety, panic, social anxiety and post-traumatic stress disorder. The optimal cutpoint is > or = 10 on the parent scales (PHQ-9 and GAD-7) and > or = 3 on the ultra-brief versions (PHQ-2 and GAD-2). The PHQ-15 is equal or superior to other brief measures for assessing somatic symptoms and screening for somatoform disorders. Cutpoints of 5, 10 and 15 represent mild, moderate and severe symptom levels on all three scales. Sensitivity to change is well-established for the PHQ-9 and emerging albeit not yet definitive for the GAD-7 and PHQ-15. The PHQ-9, GAD-7 and PHQ-15 are brief well-validated measures for detecting and monitoring depression, anxiety and somatization. Copyright 2010. Published by Elsevier Inc.
Measurement invariance assesses the psychometric equivalence of a construct across groups or across time. Measurement noninvariance suggests that a construct has a different structure or meaning to different groups or on different measurement occasions in the same group, and so the construct cannot be meaningfully tested or construed across groups or across time. Hence, prior to testing mean differences across groups or measurement occasions (e.g., boys and girls, pretest and posttest), or differential relations of the construct across groups, it is essential to assess the invariance of the construct. Conventions and reporting on measurement invariance are still in flux, and researchers are often left with limited understanding and inconsistent advice. Measurement invariance is tested and established in different steps. This report surveys the state of measurement invariance testing and reporting, and details the results of a literature review of studies that tested invariance. Most tests of measurement invariance include configural, metric, and scalar steps; a residual invariance step is reported for fewer tests. Alternative fit indices (AFIs) are reported as model fit criteria for the vast majority of tests; χ(2) is reported as the single index in a minority of invariance tests. Reporting AFIs is associated with higher levels of achieved invariance. Partial invariance is reported for about one-third of tests. In general, sample size, number of groups compared, and model size are unrelated to the level of invariance achieved. Implications for the future of measurement invariance testing, reporting, and best practices are discussed.
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