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Utilizing Educational Media of Disaster Mitigation on Earthquake and Tsunami Preparedness for Inpatient Families in Hospital
Published in Teuku Tahlil, Hajjul Kamil, Asniar, Marthoenis, Challenges in Nursing Education and Research, 2020
Cut Husna, Muzar Hafni, Mustanir Yahya, Hajjul Kamil, Teuku Tahlil
The results of the data of earthquake and tsunami disaster preparedness variables conducted in 18 wards were nine rooms for each group. Intervention group A disaster mitigation education activities used media in the form of a leaflet and group B used flip chart. The normality tested of the data was carried out through the Kolmogorov-Smirnov test. The results showed that all the data were normally distributed. The category levels of disaster preparedness are as follows: very ready if 80–100, ready 65–79, almost ready 55–64, less ready 40–54 and not ready <40, as shown as the table 2.
Association between nutritional and serum zinc levels amongst children aged six months to five years old
Published in Cut Adeya Adella, Stem Cell Oncology, 2018
W. Agusthin, T. Sembiring, P. Sianturi
Normality tests were conducted using the Kolmogorov-Smirnov test. A Kruskal-Wallis test was performed in order to determine the association between nutritional status, and serum albumin and zinc levels. To determine the correlation between serum albumin and zinc levels, the Spearman test was used. A 95% confidence interval was chosen and p < 0.05 was considered significant.
Data checking
Published in Antony Stewart, Basic Statistics and Epidemiology, 2018
Although you can visually inspect the data, for example, by using a histogram (Petrie & Sabin, 2009), to check whether it resembles the symmetrical bell-shaped pattern described in Chapter 11, normality is often checked using one of two just previously mentioned tests: Kolmogorov-Smirnov — for large samples (e.g. 50 or more)Shapiro-Wilk — best for sample sizes of less than 50.
Serum Cortistatin Levels in Patients with Ocular Active and Ocular Inactive Behçet Disease
Published in Ocular Immunology and Inflammation, 2020
Mehmet Balbaba, Fatih Ulaş, Sevinç Arzu Postacı, Burak Öz, Süleyman Aydın
All data were analyzed using the SPSS statistical software package, version 25.0 (SPSS Inc., Chicago, IL, USA). For general statistical reporting, the mean values from each data set were calculated with standard deviations (SDs). A level of P < .05 was accepted as statistically significant. The Kolmogorov–Smirnov test was used to check the normality of the sample distribution. The p-value for the Kolmogorov– Smirnov test was >0.05 (the actual values ranged from 0.072 to 0.326) for all of the continuous parameters analyzed. One-way analysis of variance test and post hoc Tukey test, independent samples t-test, Spearman and Pearson correlation analysis were performed for the statistical analysis of the data. For the significantly correlated parameters, logistic regression models were used to identify associations between presence of BD and the studied parameters. Logistic regression models were also used to identify associations between activity of BD and the correlated studied parameters.
Comparing exercise responses to aerobic plus resistance training between postmenopausal breast cancer survivors undergoing aromatase inhibitor therapy and healthy women
Published in Disability and Rehabilitation, 2019
Thais R. S. de Paulo, Kerri M. Winters-Stone, Juliana Viezel, Fabricio E. Rossi, Bruna L. Aro, Ana Carolina A. C. Trindade, Jamile S. Codogno, Ismael F. Freitas Junior
The Kolmogorov-Smirnov test was applied to analyze the sample distribution. To compare groups at baseline, the Student’s t test for independent samples was used. In the longitudinal analysis, “linear general mixed models”, estimated by a restricted maximum likelihood algorithm, were used to compare the effects in postmenopausal breast cancer survivors and healthy postmenopausal women over zero, three, six, and nine months on muscle strength, aerobic capacity, load progression of resistance exercises, and body composition. Training group was included as the between-subject factor, time (zero, three, six, and nine months) was included as the repeated within-subjects factor, time × group was included as the interaction, and subject was included as a random effect. When a significant interaction was observed, a Bonferroni post hoc test was applied. For all measured variables, the estimated sphericity was verified according to Mauchly’s W test and the Greenhouse-Geisser correction was used when necessary and all estimated sphericity was assumption. The effect size was calculated via Cohen’s d and significance was set at alpha =0.05. The data were analyzed using Statsoft Statistic software (StatSoft, Tulsa, OK, version 10).
Expanding the Clinical Spectrum of Multiple Evanescent White Dot Syndrome with Overlapping Multifocal Choroiditis
Published in Ocular Immunology and Inflammation, 2022
Hyun Goo Kang, Tae Young Kim, Min Kim, Suk Ho Byeon, Sung Soo Kim, Hyoung Jun Koh, Sung Chul Lee, Christopher Seungkyu Lee
We performed statistical analyses using the Statistical Package for the Social Sciences software version 22.0 (IBM Corp., Armonk, NY, USA). The Kolmogorov–Smirnov test was used to analyze the distribution of samples. The independent Student’s t-test, non-parametric Mann–Whitney U test, and chi-square test were used to compare the groups. The Wilcoxon signed-rank test was used to compare the initial and final choroidal thicknesses. A logistic regression analysis was performed to assess the impacts of patient selection and treatment factors on the outcome. All data are presented as mean ± standard deviation. P values <.05 were considered statistically significant.