scholarly journals Statistical Primer for Athletic Trainers: Understanding the Role of Statistical Power in Comparative Athletic Training Research

2018 ◽  
Vol 53 (7) ◽  
pp. 716-719
Author(s):  
Monica R. Lininger ◽  
Bryan L. Riemann
Keyword(s):  
Sample Size ◽  
Athletic Training ◽  
Treatment Effect ◽  
Statistical Power ◽  
Athletic Trainers ◽  
A Priori ◽  
Significant Treatment ◽  
Training Research ◽  
Α Level

Objective: To describe the concept of statistical power as related to comparative interventions and how various factors, including sample size, affect statistical power.Background: Having a sufficiently sized sample for a study is necessary for an investigation to demonstrate that an effective treatment is statistically superior. Many researchers fail to conduct and report a priori sample-size estimates, which then makes it difficult to interpret nonsignificant results and causes the clinician to question the planning of the research design.Description: Statistical power is the probability of statistically detecting a treatment effect when one truly exists. The α level, a measure of differences between groups, the variability of the data, and the sample size all affect statistical power.Recommendations: Authors should conduct and provide the results of a priori sample-size estimations in the literature. This will assist clinicians in determining whether the lack of a statistically significant treatment effect is due to an underpowered study or to a treatment's actually having no effect.

scholarly journals A Multi-faceted Mess: A Review of Statistical Power Analysis in Psychology Journal Articles

2019 ◽  
Author(s):  
Rob Cribbie ◽  
Nataly Beribisky ◽  
Udi Alter
Keyword(s):  
Sample Size ◽  
Effect Size ◽  
Power Analysis ◽  
Statistical Power ◽  
Type I Error ◽  
A Priori ◽  
Type I ◽  
Specific Level ◽  
Maximum Sample Size ◽  
Power Analyses

Many bodies recommend that a sample planning procedure, such as traditional NHST a priori power analysis, is conducted during the planning stages of a study. Power analysis allows the researcher to estimate how many participants are required in order to detect a minimally meaningful effect size at a specific level of power and Type I error rate. However, there are several drawbacks to the procedure that render it “a mess.” Specifically, the identification of the minimally meaningful effect size is often difficult but unavoidable for conducting the procedure properly, the procedure is not precision oriented, and does not guide the researcher to collect as many participants as feasibly possible. In this study, we explore how these three theoretical issues are reflected in applied psychological research in order to better understand whether these issues are concerns in practice. To investigate how power analysis is currently used, this study reviewed the reporting of 443 power analyses in high impact psychology journals in 2016 and 2017. It was found that researchers rarely use the minimally meaningful effect size as a rationale for the chosen effect in a power analysis. Further, precision-based approaches and collecting the maximum sample size feasible are almost never used in tandem with power analyses. In light of these findings, we offer that researchers should focus on tools beyond traditional power analysis when sample planning, such as collecting the maximum sample size feasible.

Testing for qualitative heterogeneity: An application to composite endpoints in survival analysis

2017 ◽  
Vol 28 (1) ◽  
pp. 151-169
Author(s):  
Abderrahim Oulhaj ◽  
Anouar El Ghouch ◽  
Rury R Holman
Keyword(s):  
Sample Size ◽  
Treatment Effect ◽  
Statistical Power ◽  
Controlled Trial ◽  
Composite Endpoint ◽  
Testing Procedure ◽  
Parameters Estimation ◽  
Type I ◽  
Data Set ◽  
Composite Endpoints

Composite endpoints are frequently used in clinical outcome trials to provide more endpoints, thereby increasing statistical power. A key requirement for a composite endpoint to be meaningful is the absence of the so-called qualitative heterogeneity to ensure a valid overall interpretation of any treatment effect identified. Qualitative heterogeneity occurs when individual components of a composite endpoint exhibit differences in the direction of a treatment effect. In this paper, we develop a general statistical method to test for qualitative heterogeneity, that is to test whether a given set of parameters share the same sign. This method is based on the intersection–union principle and, provided that the sample size is large, is valid whatever the model used for parameters estimation. We propose two versions of our testing procedure, one based on a random sampling from a Gaussian distribution and another version based on bootstrapping. Our work covers both the case of completely observed data and the case where some observations are censored which is an important issue in many clinical trials. We evaluated the size and power of our proposed tests by carrying out some extensive Monte Carlo simulations in the case of multivariate time to event data. The simulations were designed under a variety of conditions on dimensionality, censoring rate, sample size and correlation structure. Our testing procedure showed very good performances in terms of statistical power and type I error. The proposed test was applied to a data set from a single-center, randomized, double-blind controlled trial in the area of Alzheimer’s disease.

scholarly journals Causality in Statistical Power: Isomorphic Properties of Measurement, Research Design, Effect Size, and Sample Size

Scientifica ◽  
2016 ◽  
Vol 2016 ◽  
pp. 1-5 ◽  
Author(s):  
R. Eric Heidel
Keyword(s):  
Sample Size ◽  
Cognitive Dissonance ◽  
Research Design ◽  
Effect Size ◽  
Statistical Power ◽  
Research Study ◽  
A Priori ◽  
Sample Size Calculation ◽  
Design Effect

Statistical power is the ability to detect a significant effect, given that the effect actually exists in a population. Like most statistical concepts, statistical power tends to induce cognitive dissonance in hepatology researchers. However, planning for statistical power by ana priorisample size calculation is of paramount importance when designing a research study. There are five specific empirical components that make up ana priorisample size calculation: the scale of measurement of the outcome, the research design, the magnitude of the effect size, the variance of the effect size, and the sample size. A framework grounded in the phenomenon of isomorphism, or interdependencies amongst different constructs with similar forms, will be presented to understand the isomorphic effects of decisions made on each of the five aforementioned components of statistical power.

Sample size estimates for determining treatment effects in high-risk patients with early relapsing-remitting multiple sclerosis

2003 ◽  
Vol 9 (3) ◽  
pp. 289-292 ◽  
Author(s):  
Thomas F Scott ◽  
Carol J Schramke ◽  
Gary Cutter
Keyword(s):  
Risk Factors ◽  
High Risk ◽  
Sample Size ◽  
Treatment Effect ◽  
Significant Treatment Effect ◽  
Significant Treatment ◽  
High Risk Patients ◽  
Treatment Trials ◽  
Relapsing Remitting ◽  
Risk Patients

Background: Risk factors for short-term progression in early relapsing-remitting MS have been identified recently. Previously we determined potential risk factors for rapid progression of early relapsing-remitting MS and identified three groups of high-risk patients. These non-mutually exclusive groups of patients were drawn from a consecutively studied sample of 98 patients with newly diagnosed MS. High-risk patients had a history of either poor recovery from initial attacks, more than two attacks in the first two years of disease, or a combination of at least four other risk factors. Objective: To determine differences in sample sizes required to show a meaningful treatment effect when using a high-risk sample versus a random sample of patients. Methods: Power analyses were used to calculate the different sample sizes needed for hypothetical treatment trials. Results: We found that substantially smaller numbers of patients should be needed to show a significant treatment effect by employing these high-risk groups of patients as compared to a random population of MS patients (e.g., 58% reduction in sample size in one model). Conclusion: The use of patients at higher risk of progression to perform drug treatment trials can be considered as a means to reduce the number of patients needed to show a significant treatment effect for patients with very early MS.

Quantifying annual patterns in the frequency of mammalian births: do goodness-of-fit tests provide adequate inferences?

2012 ◽  
Vol 60 (6) ◽  
pp. 381 ◽  
Author(s):  
Evan Watkins ◽  
Julian Di Stefano
Keyword(s):  
Sample Size ◽  
Effect Size ◽  
Statistical Power ◽  
Goodness Of Fit ◽  
A Priori ◽  
Simulated Data ◽  
Maximum Effect ◽  
Chi Square ◽  
Goodness Of Fit Tests ◽  
Power Studies

Hypotheses relating to the annual frequency distribution of mammalian births are commonly tested using a goodness-of-fit procedure. Several interacting factors influence the statistical power of these tests, but no power studies have been conducted using scenarios derived from biological hypotheses. Corresponding to theories relating reproductive output to seasonal resource fluctuation, we simulated data reflecting a winter reduction in birth frequency to test the effect of four factors (sample size, maximum effect size, the temporal pattern of response and the number of categories used for analysis) on the power of three goodness-of-fit procedures – the G and Chi-square tests and Watson’s U2 test. Analyses resulting in high power all had a large maximum effect size (60%) and were associated with a sample size of 200 on most occasions. The G-test was the most powerful when data were analysed using two temporal categories (winter and other) while Watson’s U2 test achieved the highest power when 12 monthly categories were used. Overall, the power of most modelled scenarios was low. Consequently, we recommend using power analysis as a research planning tool, and have provided a spreadsheet enabling a priori power calculations for the three tests considered.

scholarly journals Positive Factors Influencing the Advancement of Women to the Role of Head Athletic Trainer in the National Collegiate Athletic Association Divisions II and III

2016 ◽  
Vol 51 (7) ◽  
pp. 550-556 ◽  
Author(s):  
Stephanie M. Mazerolle ◽  
Christianne M. Eason
Keyword(s):  
Athletic Training ◽  
National Collegiate Athletic Association ◽  
Athletic Trainers ◽  
Organizational Factors ◽  
Division I ◽  
Athletic Trainer ◽  
Work Experiences ◽  
Training Experience ◽  
Individual Level

Context: Research suggests that women do not pursue leadership positions in athletic training due to a variety of reasons, including family challenges, organizational constraints, and reluctance to hold the position. The literature has been focused on the National Collegiate Athletic Association Division I setting, limiting our full understanding. Objective: To examine factors that help women as they worked toward the position of head athletic trainer. Design: Qualitative study. Setting: Divisions II and III. Patients or Other Participants: Seventy-seven women who were employed as head athletic trainers at the Division II or III level participated in our study. Participants were 38 ± 9 (range = 24−57) years old and had an average of 14 ± 8 (range = 1−33) years of athletic training experience. Data Collection and Analysis: We conducted online interviews. Participants journaled their reflections to a series of open-ended questions pertaining to their experiences as head athletic trainers. Data were analyzed using a general inductive approach. Credibility was secured by peer review and researcher triangulation. Results: Three organizational facilitators emerged from the data, workplace atmosphere, mentors, and past work experiences. These organizational factors were directly tied to aspects within the athletic trainer's employment setting that allowed her to enter the role. One individual-level facilitator was found: personal attributes that were described as helpful for women in transitioning to the role of the head athletic trainer. Participants discussed being leaders and persisting toward their career goals. Conclusions: Women working in Divisions II and III experience similar facilitators to assuming the role of head athletic trainer as those working in the Division I setting. Divisions II and III were viewed as more favorable for women seeking the role of head athletic trainer, but like those in the role in the Division I setting, women must have leadership skills.

scholarly journals The Sampling Conundrum in Qualitative Research: Can Saturation Help Alleviate the Controversy and Alleged Subjectivity in Sampling?

2021 ◽  
Vol 9 (5) ◽  
pp. 11
Author(s):  
Favourate Y Sebele-Mpofu
Keyword(s):  
Qualitative Research ◽  
Sample Size ◽  
Sampling Methods ◽  
Research Question ◽  
A Priori ◽  
Sampling Techniques ◽  
Whole Process ◽  
Sampling Process ◽  
Adequate Sample Size

Sampling is one of the most controversial matters in qualitative research. Qualitative researchers have often been denounced for not giving adequate rationalisations for their sample size resolutions. This study aimed to provide an extensive review of sampling methods used in qualitative research and discuss the extent to which saturation might help alleviate the issues concerning these methods, sample size sufficiency and when to sample. The study specifically honed on the sampling adequacy (how big or how small should a sample be), the sampling techniques used and whether sample sizes should be delineated a priori, posteriori or during analysis. Having highlighted, the paradoxically nature of these aspects, through an overview of the sampling process, the researcher explored saturation as a tool to alleviate the challenges and the lack of objectivity in sampling in qualitative research. The overall findings were that, saturation does provide same degree of transparency and quality in sampling, but the concept is not immune to controversy, guidelines on how to apply it or achieve it remain foggy and contestable among researchers. Discussions are in most cases oversimplified and comparatively unknowledgeable. The answer to the research question, was that, what really constitutes an adequate sample size is only answerable within the context of the study, scientific paradigm, epistemological stance, ontological and methodological assumptions of the research conducted. Contextualisation of the mode of saturation adopted, clear articulation of the research methodology and transparent reporting of the whole process is key to enhance the role of saturation in alleviating subjectivity in sampling. This paper sought to make a contribution to the on-going methodological discourse on how qualitative researchers can justify their sampling decisions.

scholarly journals Career and Family Aspirations of Female Athletic Trainers Employed in the National Collegiate Athletic Association Division I Setting

2015 ◽  
Vol 50 (2) ◽  
pp. 170-177 ◽  
Author(s):  
Stephanie M. Mazerolle ◽  
Christianne M. Eason ◽  
Elizabeth M. Ferraro ◽  
Ashley Goodman
Keyword(s):  
Family Planning ◽  
Athletic Training ◽  
National Collegiate Athletic Association ◽  
Athletic Trainers ◽  
Division I ◽  
Life Balance ◽  
Factors Influencing ◽  
Data Collection And Analysis ◽  
Collegiate Level

Context: Female athletic trainers (ATs) tend to depart the profession of athletic training after the age of 30. Factors influencing departure are theoretical. Professional demands, particularly at the collegiate level, have also been at the forefront of anecdotal discussion on departure factors. Objective: To understand the career and family intentions of female ATs employed in the collegiate setting. Design: Qualitative study. Setting: National Collegiate Athletic Association Division I. Patients or Other Participants: Twenty-seven female ATs (single = 14, married with no children = 6, married with children = 7) employed in the National Collegiate Athletic Association Division I setting. Data Collection and Analysis: All female ATs responded to a series of open-ended questions via reflective journaling. Data were analyzed via a general inductive approach. Trustworthiness was established by peer review, member interpretive review, and multiple-analyst triangulation. Results: Our participants indicated a strong desire to focus on family or to start a family as part of their personal aspirations. Professionally, many female ATs were unsure of their longevity within the Division I collegiate setting or even the profession itself, with 2 main themes emerging as factors influencing decisions to depart: family planning persistence and family planning departure. Six female ATs planned to depart the profession entirely because of conflicts with motherhood and the role of the AT. Only 3 female ATs indicated a professional goal of persisting at the Division I setting regardless of their family or marital status, citing their ability to maintain work-life balance because of support networks. The remaining 17 female ATs planned to make a setting change to balance the roles of motherhood and AT because the Division I setting was not conducive to parenting. Conclusions: Our results substantiate those of previous researchers, which indicate the Division I setting can be problematic for female ATs and stimulate departure from the setting and even the profession.

Statistical Power in Experimental Audit Studies

2015 ◽  
Vol 45 (2) ◽  
pp. 260-303 ◽  
Author(s):  
Mike Vuolo ◽  
Christopher Uggen ◽  
Sarah Lageson
Keyword(s):  
Sample Size ◽  
Statistical Power ◽  
A Priori ◽  
Present Sample ◽  
Power Calculations ◽  
Dichotomous Outcome ◽  
The Social ◽  
Key Factor ◽  
Dichotomous Outcomes ◽  
And Control

Given their capacity to identify causal relationships, experimental audit studies have grown increasingly popular in the social sciences. Typically, investigators send fictitious auditors who differ by a key factor (e.g., race) to particular experimental units (e.g., employers) and then compare treatment and control groups on a dichotomous outcome (e.g., hiring). In such scenarios, an important design consideration is the power to detect a certain magnitude difference between the groups. But power calculations are not straightforward in standard matched tests for dichotomous outcomes. Given the paired nature of the data, the number of pairs in the concordant cells (when neither or both auditor receives a positive response) contributes to the power, which is lower as the sum of the discordant proportions approaches one. Because these quantities are difficult to determine a priori, researchers must exercise particular care in experimental design. We here present sample size and power calculations for McNemar’s test using empirical data from an audit study on misdemeanor arrest records and employability. We then provide formulas and examples for cases involving more than two treatments (Cochran’s Q test) and nominal outcomes (Stuart–Maxwell test). We conclude with concrete recommendations concerning power and sample size for researchers designing and presenting matched audit studies.

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