In Brief: Statistics in Brief: Statistical Power: What Is It and When Should It Be Used?

Dorey, Frederick J.

doi:10.1007/s11999-010-1435-0

Cited by 23 publications

(18 citation statements)

References 2 publications

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“…Post‐positivist researchers relying on an objectivist orientation and inferential statistics endeavour to draw inferences from samples of individuals to larger populations. To build defensible arguments for these inferences, researchers seek to ensure that samples have been randomly selected, are reasonably representative of larger populations, and are sufficiently large to support, or ‘power’, 18 analytic claims. In essence, the assumption that inferences made from samples generalise to the populations from which they are drawn means that phenomena are replicable to other contexts.…”

Section: Generalisabilitymentioning

confidence: 99%

The applicability of generalisability and bias to health professions education's research

et al. 2020

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Context Research in health professions education (HPE) spans an array of topics and draws from a diversity of research domains , which brings richness to our understanding of complex phenomena and challenges us to appreciate different approaches to studying them. To fully appreciate and benefit from this diversity, scholars in HPE must be savvy to the hallmarks of rigour that differ across research approaches. In the absence of such recognition, the valuable contributions of many high‐quality studies risk being undermined. Methods In this article, we delve into two constructs‐‐‐generalisability and bias‐‐that are commonly invoked in discussions of rigour in health professions education research. We inspect the meaning and applicability of these constructs to research conducted from different paradigms (i.e., positivist and constructivist) and orientations (i.e., objectivist and subjectivist) and then describe how scholars can demonstrate rigour when these constructs do not align with the assumptions underpinning their research. Conclusions A one‐size‐fits‐all approach to evaluating the rigour of HPE research disadvantages some approaches and threatens to reduce the diversity of research in our field. Generalisability and bias are two examples of problematic constructs within paradigms that embrace subjectivity; others are equally problematic. As a way forward, we encourage HPE scholars to inspect their assumptions about the nature and purpose of research—both to defend research rigour in their own studies and to ensure they apply standards of rigour that align with research they read and review.

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Section: Generalisabilitymentioning

confidence: 99%

The applicability of generalisability and bias to health professions education's research

et al. 2020

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“…Due to lack of any study that focusing on correlation between QoL, depression, anxiety, stress, and SWB scores of patients HF and their caregivers, the authors could not utilize a priori power analysis. [26] After completing data collection, the power of the study was calculated using the G Power 3.0 program. In this context, the power of this study was defined as 95% based on completed data set (60 HF patientsfamily caregiver dyads): with 0.80 correlation between the WHOQOL-BREF and the DASS scores, minimum effect size (0.3), and alpha value 0.05.…”

Section: Study Sample and Settingmentioning

confidence: 99%

Kalp Yetersizliği Hastaları ve Bakım Veren Aile Üyelerinde Yaşam Kalitesi, Depresyon, Anksiyete, Stres ve Spiritüel İyilik Hali Arasındaki İlişki

Metin¹

2020

J Cardiovasc Nurs

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Objective: This study aimed to examine the correlation between quality of life, depression, anxiety, stress, and spiritual well-being in patients with heart failure and their family caregivers. Methods: This descriptive, correlational study was carried out in a coronary outpatient clinic, located in Ankara between March-October 2018. Sixty patients with heart failure, and 60 family caregivers who providing care for these individuals, a total of 120 participants were included in the study. The Kansas City Cardiomyopathy Questionnaire, the Depression Anxiety Stress Scale, the World Health Organization Quality of Life Short Form, and the Functional Assessment of Chronic Illness Therapy-Spiritual Well-Being Scale were utilized for data collection. The data were analyzed using descriptive and correlational statistics. The Pearson correlation test was used to determine the association between the Kansas City Cardiomyopathy Questionnaire, the Depression Anxiety Stress Scale, the World Health Organization Quality of Life Short Form, and the Functional Assessment of Chronic Illness Therapy-Spiritual Well-Being Scale scores. Results: As quality of life scores increased in patients with heart failure, depression, anxiety, stress scores increased (p<0.05). On the other hand, as quality of life scores increased in patients with heart failure, spiritual well-being scores increased (p<0.05). With regard to family caregivers, as quality of life scores decreased, depression, anxiety, stress scores increased, and spiritual well-being scores decreased (p<0.05). Moreover, as quality of life and spiritual well-being scores of patients with heart failure decreased, depression, anxiety, stress scores of family caregivers increased (p<0.05). Conclusion: This study revealed that as quality of life and spiritual well-being scores decreased, depression, anxiety, stress scores of both patients with heart failure and family caregivers increased. Further education and counseling programs targeting patients with heart failure and their family caregivers are recommended to assess both patients' and family caregivers' psychological health and spiritual well-being.

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“…The real-world effectiveness of a healthcare intervention can depend on many characteristics such as age, sex, race/ethnicity, disease severity, prior treatments and insurance status [14]. In oncology, these and other characteristics (e.g., tumor stage, tumor characteristics [e.g., genomics and biomarker status] and line of therapy) are likely to have an impact on outcomes.…”

Section: Are the Right Patients Being Studied?mentioning

confidence: 99%

Evaluation of comparative effectiveness research: a practical tool

et al. 2018

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Comparative effectiveness research (CER) guidelines have been developed to direct the field toward the most rigorous study methodologies. A challenge, however, is how to ensure the best evidence is generated, and how to translate methodologically complex or nuanced CER findings into usable medical evidence. To reach that goal, it is important that both researchers and end users of CER output become knowledgeable about the elements that impact the quality and interpretability of CER. This paper distilled guidance on CER into a practical tool to assist both researchers and nonexperts with the critical review and interpretation of CER, with a focus on issues particularly relevant to CER in oncology.

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In Brief: Statistics in Brief: Statistical Power: What Is It and When Should It Be Used?

Cited by 23 publications

References 2 publications

The applicability of generalisability and bias to health professions education's research

The applicability of generalisability and bias to health professions education's research

Kalp Yetersizliği Hastaları ve Bakım Veren Aile Üyelerinde Yaşam Kalitesi, Depresyon, Anksiyete, Stres ve Spiritüel İyilik Hali Arasındaki İlişki

Evaluation of comparative effectiveness research: a practical tool

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