Amy Liang, Kristopher J Preacher, Nathaniel J Williams, Paul D Allison, Steven C Marcus, Sonya K Sterba
{"title":"Determining the power of a 1-sided z-test given only the power of the corresponding 2-sided test.","authors":"Amy Liang, Kristopher J Preacher, Nathaniel J Williams, Paul D Allison, Steven C Marcus, Sonya K Sterba","doi":"10.1007/s10865-025-00595-6","DOIUrl":null,"url":null,"abstract":"<p><p>Estimating statistical power is essential for designing behavioral medicine studies efficiently and conserving finite resources. Sometimes behavioral medicine researchers are interested in calculating power for 1-sided z-tests of individual parameters (e.g., slopes) in complex models such as multilevel structural equation models or multilevel mixture regression models. For such models, calculating power for 1-sided z-tests is cumbersome because: (a) online z-test power calculator tools are inapplicable, (b) commonly-used power analysis software provides power only for 2-sided z-tests and does not allow changing alpha, and (c) published power tables typically provide power results only for 2-sided z-tests. Hence, here we introduce straightforward and resource-efficient conversion formulas to estimate the power of 1-sided z-tests of individual parameters in any model by using direct power conversions from the corresponding 2-sided tests. We then implement these conversion formulas in accessible R and Excel software. This brief report thus provides behavioral medicine researchers with a convenient and practical solution for power calculation that minimizes the time, financial, and computational resources typically needed for power estimation.</p>","PeriodicalId":48329,"journal":{"name":"Journal of Behavioral Medicine","volume":" ","pages":""},"PeriodicalIF":2.9000,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Behavioral Medicine","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1007/s10865-025-00595-6","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PSYCHOLOGY, CLINICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Estimating statistical power is essential for designing behavioral medicine studies efficiently and conserving finite resources. Sometimes behavioral medicine researchers are interested in calculating power for 1-sided z-tests of individual parameters (e.g., slopes) in complex models such as multilevel structural equation models or multilevel mixture regression models. For such models, calculating power for 1-sided z-tests is cumbersome because: (a) online z-test power calculator tools are inapplicable, (b) commonly-used power analysis software provides power only for 2-sided z-tests and does not allow changing alpha, and (c) published power tables typically provide power results only for 2-sided z-tests. Hence, here we introduce straightforward and resource-efficient conversion formulas to estimate the power of 1-sided z-tests of individual parameters in any model by using direct power conversions from the corresponding 2-sided tests. We then implement these conversion formulas in accessible R and Excel software. This brief report thus provides behavioral medicine researchers with a convenient and practical solution for power calculation that minimizes the time, financial, and computational resources typically needed for power estimation.
期刊介绍:
The Journal of Behavioral Medicine is a broadly conceived interdisciplinary publication devoted to furthering understanding of physical health and illness through the knowledge, methods, and techniques of behavioral science. A significant function of the journal is the application of this knowledge to prevention, treatment, and rehabilitation and to the promotion of health at the individual, community, and population levels.The content of the journal spans all areas of basic and applied behavioral medicine research, conducted in and informed by all related disciplines including but not limited to: psychology, medicine, the public health sciences, sociology, anthropology, health economics, nursing, and biostatistics. Topics welcomed include but are not limited to: prevention of disease and health promotion; the effects of psychological stress on physical and psychological functioning; sociocultural influences on health and illness; adherence to medical regimens; the study of health related behaviors including tobacco use, substance use, sexual behavior, physical activity, and obesity; health services research; and behavioral factors in the prevention and treatment of somatic disorders. Reports of interdisciplinary approaches to research are particularly welcomed.