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Basic Research Design:
Published in Lynne M. Bianchi, Research during Medical Residency, 2022
Lynne M. Bianchi, Luke J. Rosielle, Justin Puller, Kristin Juhasz
Statistical analysis: Data are typically analyzed using inferential statistics that allow the investigator to formally test a hypothesis and make comparisons between groups. The statistical tests used will be determined by the types of measurements made (categorical, continuous) and the number of groups in the study. For example, when the data from each group have a normal distribution and the variables measured are continuous (interval, ratio) then parametric tests, such as Student'st test, Pearson's correlation coefficient, or analysis of variance (ANOVA), might be used. When the data are not normally distributed or the variables are categorical (nominal, ordinal), then an appropriate non-parametric test will be used, such as the Chi-squared test, Fisher's exact test, Spearman's rank correlation coefficient, or Mann-WhitneyUtest.
Nonparametric Methods
Published in Marcello Pagano, Kimberlee Gauvreau, Heather Mattie, Principles of Biostatistics, 2022
Marcello Pagano, Kimberlee Gauvreau, Heather Mattie
Nonparametric methods also have disadvantages. If the assumptions underlying a parametric test are satisfied, the nonparametric test is less powerful than the comparable parametric technique. This means that if the null hypothesis is false, the nonparametric test would require a larger sample to provide sufficient evidence to reject it. This loss of power is not substantial, however. If the sample data do come from an underlying normal population, the power of the Wilcoxon tests is approximately 95% of that for the t tests. In other words, if the t test requires 19 observations to achieve a particular level of power, the Wilcoxon test would need 20 observations to have the same power. Another disadvantage is that the hypotheses tested by nonparametric techniques tend to be less specific than those tested by traditional methods, focusing on medians rather than means. Because they rely on ranks rather than on the actual values of the observations, nonparametric tests do not use all the information known about a distribution. This, of course, presumes that our information about the underlying population is correct. Finally, if a large proportion of the observations are tied, then σT and σW overestimate the standard deviations of T and W, respectively. To compensate for this, a correction term must be added to the calculations [222]. These correction terms are beyond the scope of this text, but they are implemented in most statistical packages that perform nonparametric hypothesis tests.
The Importance of Temperature Control When Investigating High Threshold Calcium Currents in Mammalian Neurones
Published in Avital Schurr, Benjamin M. Rigor, BRAIN SLICES in BASIC and CLINICAL RESEARCH, 2020
R. Hamish McAllister-Williams, John S. Kelly
where k1 and k2 are measurements observed at T1, and T2, respectively,53 and all are quoted for the temperature range 15 to 25°C. Arrhenius plots were made to check that the associated Q10 was approximately constant over this temperature range. It was found that Q10 values were clearly not normally distributed, and as a result, average Q10 values were calculated as geometric means. In addition, due to this nonsymmetrical distribution of Q10 values, 95% confidence intervals (CI) are quoted. All other values are arithmetic means ± standard errors (SEM), including normalized values used in Arrhenius plots. Note that all Q10 values quoted are positive, irrespective of whether the measurement increased or decreased with temperature. Arrhenius plots, on the other hand, reflect the direction of the change. The number of experiments averaged for any given measurement is given in parentheses. For Q10 values, these are for the number of values averaged and the number of cells from which these had been obtained. Statistical analysis was by means of the Mann-Whitney-Wilcox nonparametric test, and significance was adjudged if p <0.01.
Comparison of the effects of aerobic training alone versus aerobic training combined with clinical Pilates exercises on the functional and psychosocial status of patients with ankylosing spondylitis: A randomized controlled trial
Published in Physiotherapy Theory and Practice, 2023
We reevaluated 26 patients with AS who had completed the 8-week exercise program (Figure 1). Descriptive statistics were used to determine information about the general characteristics of the patients who completed the study. The continuous variables were represented as mean and standard deviation (X ± SD). The frequency and percentage (%) distributions were reported for the qualitative data. The relationship between the independent variables was evaluated using the Chi-square test. Mann–Whitney U Test was used as the non-parametric test, because the Kolmogorov–Smirnov test showed a non-normal distribution. Similarly, the Wilcoxon paired-sample test was used to determine any difference between the repetitions in each group. The significance levels of p < .05 were considered statistically significant. Statistical analysis was performed using a statistical software (IBM SPSS Statistics 22 [Demo version], SPSS Inc., IBM Co., Somers, NY). Effect sizes were calculated to test clinical significance. The formula r = z/√(nx2) was used to calculate the effect size. A value of r ≥ 0.5 was interpreted as a large effect, r = 0.3 was interpreted as medium effect, and r ≤ 0.1 was interpreted as a small effect (Rosenthal, 1994).
Evaluation of the relationship between para-aortic adipose tissue and ascending aortic diameter using a new method
Published in Acta Cardiologica, 2022
Adem Adar, Orhan Onalan, Fahri Cakan, Hakan Keles, Ertan Akbay, Sinan Akıncı, Ali Coner, Cevahir Haberal, Haldun Muderrisoglu
Data were analysed using the SPSS 23 (IBM Statistical Package for Social Sciences version 23) software package. Categorical variables were expressed using frequency distributions and numerical variables using descriptive statistics (mean ± standard deviation). The Kolmogorov–Smirnov test was used to determine whether the data conformed to the normal distribution. Subsequently, a parametric test was used to analyse normally distributed data and data that did not confirm to the normal distribution were analysed using a nonparametric test. The independent samples t-test and Mann–Whitney U test were used to check whether there was a difference between the measurements of the two independent groups. Additionally, multivariate logistic regression analyses were conducted to assess the relationship between PAT and AAD. In multivariate regression models, the effect size was adjusted for variables with a significance level ≤ 0.10 in the univariate analysis. Adjusted odds ratios (ORs) and their corresponding confidence intervals (CI) were given. 2-tailed probability (p) values of< 0.05 were considered statistically significant.ICC was used to determine the in-class reliability of PAT measurement (95% CI).
Nurse-delivered outpatient asthma education for children and caregivers: a pilot study to promote shared asthma management
Published in Journal of Asthma, 2021
Sean M. Frey, Nicholas C. Contento, Jill S. Halterman
We used descriptive statistics to report participant characteristics, and conducted a descriptive analysis of feasibility using process measures documented by nurses in the EMR. Next, we performed a series of pre-post analyses to examine the preliminary effectiveness of the intervention on caregivers and children. Due to the relatively small sample size, we did not assume that data would be normally distributed and therefore decided to use an appropriate non-parametric test. Non-parametric tests additionally reduce the impact of outlier data when analyzing results. All preliminary outcomes of interest (e.g. responsibility, self-efficacy, caregiver quality of life, and caregiver-reported symptoms) involve continuous data. Accordingly, we used Wilcoxon signed rank tests for comparisons of non-parametric paired continuous data. Each of these analyses were completed within subject, comparing responses at the 1-month follow-up (caregiver and child) and final follow-up (caregiver only) with baseline measurements. All analyses were performed using SPSS (version 25.0). An exact 2-sided alpha <0.05 was considered statistically significant.