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Quantitative Methods for Analyzing Experimental Studies in Patient Ergonomics Research
Published in Richard J. Holden, Rupa S. Valdez, The Patient Factor, 2021
Kapil Chalil Madathil, Joel S. Greenstein
Analysis of variance (ANOVA) is used to analyze data with more than two conditions. Nonparametric tests are used to analyze data with non-normal distributions. Nonparametric tests often employ a ranking technique, which generates a data set with the higher scores represented by large ranks and the lower scores by small ranks. The most commonly used nonparametric tests include the Mann–Whitney test, the Wilcoxon signed-rank test, Friedman’s test, and the Kruskal–Wallis test. Cohen et al. (2002) and Cumming and Calin-Jageman (2017) present the details of the statistical analyses that have been introduced here, as well as those for independent t-tests, ANOVA, and nonparametric tests.
Work stress induced psychological disorders in construction
Published in Imriyas Kamardeen, Work Stress Induced Chronic Diseases in Construction, 2021
The influence of socio-demographic factors on psychological strain experiences were assessed using Kruskal-Wallis tests. The flowchart shown in Figure 2.7 explains the process of robustly undertaking tests for group differences. In order to run ANOVA (analysis of variance) tests, the groups need to meet criteria such as normality, minimum group size of 25 and equal group sizes. However, the groups within the respondents for different socio-demographic factors did not meet these criteria. Hence, non-parametric Kruskal-Wallis tests were undertaken. The gender, age, experience, occupation level and the organisation size of the respondents were used as fixed factors. Table 2.6 presents the test results. The p-values yielded for organisation size and occupation level are above .05 for all groups, suggesting that differences in the age, work experience, occupation level and the organisation size of the respondents do not demonstrate statistically significant differences in psychological strains reported. However, gender differences demonstrate an effect on anxiety and depression suffered though not on work stress and burnout.
Survey Research Methods in Functional GI Disorders
Published in Kevin W. Olden, Handbook of Functional Gastrointestinal Disorders, 2020
Analysis of variance (ANOVA) is a technique used to assess the reliability of a measure or value. The variability in the answers given by different respondents and in evaluations given by different observers, as well as the variability that exists randomly, are all assessed by ANOVA. An analysis of variance can quantify how each source of variability contributes to the overall variability of a . measurement. In the functional GI disorders, ANOVA has been used extensively to test for reliability of symptom measures. In one study to develop a numerical index of illness severity, ANOVA was used to compare physicians’ ratings of illness severity across several study sites to assess variability in observers (28). Another application of ANOVA techniques in the functional GI disorders is the comparison of the composition of a sample by demographic or other characteristics in order to assess the comparability of results from different study sites (28).
Formulation of novel niosomal repaglinide chewable tablets using coprocessed excipients: in vitro characterization, optimization and enhanced hypoglycemic activity in rats
Published in Drug Delivery, 2023
Shahinaze A. Fouad, Mahmoud H. Teaima, Mostafa I. Gebril, Fathy I. Abd Allah, Mohamed A. El-Nabarawi, Sammar Fathy Elhabal
A 2-factor interaction (2FI) I-Optimal statistical design was adopted to evaluate the individual and combined effects of formulation variables using the Design-Expert® 7 software. In the current design, three factors were evaluated; from which two factors were evaluated at two levels and one factor was evaluated at three levels. The studied independent variables were cholesterol concentration (X1), Span 60 concentration (X2) and peceolTM concentration (X3) (Table 1). The selected dependent variables were the particle size (Y1: PS), zeta potential (Y2: ZP), polydispersity index (Y3: PDI) and entrapment efficiency (Y4: EE %). Table 2 depicts the composition of the prepared RPG noisomes and the measured responses. Analysis of variance (ANOVA) was executed in order to determine the level of significance (α = 0.05). According to desirability calculations, the optimized formulation was chosen to attain minimized PS (< 800 nm) and PDI (< 0.5) and maximized ZP (> 25 mV; as an absolute value) and EE% (> 50%). Then, the optimized formulation was prepared and evaluated.
Ovarian reserve analysis in subfertile women based on physical, ultrasound and hormonal parameters
Published in Gynecological Endocrinology, 2023
The descriptive, inferential statistics have been utilized to determine the correlation coefficient, and consequently, the detection of association with the marker’s correlation coefficient was also performed. Various ultrasound and hormonal parameters were noted in the excel sheets, and a comparative analysis was performed with Statistical analysis. Statistical analysis was performed using Statistical Package for Social Sciences (SPSS) version 20.0. Mean standard deviation and range of values were estimated, and the correlation coefficients were determined with every set of values and Microsoft Excel 2010 was used for all statistical analyses. The frequency analysis presented the demographic, physical parameters, age and BMI as the count and percentage. ANOVA (analysis of variance) is a test used to test the significant difference among the groups. If the p value is smaller than .05, then it is shown that respondents differ significantly in this study. The two-way ANOVA test was used to assess the differences between the mean values of ultrasound and hormonal parameters in the different physical parameter groups. Bonferroni’s method revealed the significant difference between the different types of treatment. In this study, Bonferroni t test, multiple comparisons test was used to compare the physical with hormonal and ultrasound parameters and the relationship between these parameters is assessed. The study represents a multivariate analysis and is presented in a table to depict the interrelation of all the parameters to determine their corresponding significant value.
Effects of adverse childhood experiences on partnered sexual arousal appear context dependent
Published in Sexual and Relationship Therapy, 2023
N. Prause, H. Cohen, G. J. Siegle
The power differential and/or gender difference between strokers and strokees may impact relationships amongst adverse childhood experiences. Thus, parallel analyses were performed separately for strokers and strokees. After checking for linearity in scatter plot visualizations, a linear model predicting experienced sexual arousal after partnered stimulation was used. The first model entered reported sexual arousal before OM as an explanatory variable to include baseline tendencies. A second model entered both sexual arousal before the OM and the ACEs score. These models were compared using Analysis of Variance (ANOVA) F tests to test for significant differences between groups. If the mean of the ACEs group was non-significantly lower than the mean of the non-ACEs group, we resolved to perform a non-inferiority test to examine whether the groups could be thought of as similar; if the mean of the ACEs group was nonsignificantly higher than the mean of the non-ACE’s group, this conclusion would be implicit.