Jasp software

Statistical analyses were performed using JASP software (2020). Analysis of variance (ANOVA) was performed to test significant differences among groups in demographic and clinical variables. ANOVA with repeated measures and self-report traumatic experience as between-subject factors and post-hoc Tukey's test were performed. Greenhouse-Geisser sphericity correction was applied where appropriate. Demographic variables that differed among groups were added as covariates in the analyses. A statistical threshold of p < 0.05 was set as significant.

Descriptive statistics were calculated by using Microsoft Excel^® and are reported as mean ± standard deviation (SD). JASP software (version 0.13.1.0) [54 ] was used for the comparison of the mean values. Tests for normal distribution and variance homogeneity of the data were conducted using the Shapiro–Wilk test and the Levene’s test, respectively. For normally distributed data, mean values were compared by one-way ANOVA, utilizing a Welch-correction in case of variance heterogeneity. Adequate post-hoc tests (Tukey, Games–Howell) were performed to compare the mean values to each other. For non-parametric data sets, Kruskal–Wallis analysis of variance with Dunn’s post-hoc comparisons were performed. To account for alpha error accumulation Bonferroni–Holm correction was used. p-values <0.05 were considered statistically significant.

For further statistical analysis of the parameters FE and rFE (and related muscle activity), only the data of the best trial per condition were used. The best trail was defined by the trial with highest torque during stretch for the dynamic conditions and the trial with the highest torque during isometric steady‐state for the reference contractions.
Values above or below two standard deviations were treated as outliers and excluded from the statistical analysis. The normality of data was checked using the Shapiro–Wilk test. To analyze the results within a session (for electrical stimulation also between sessions one and five), paired sample t‐tests were used including Cohen's d effect size referencing 0.2 as small, 0.5 as medium, and >0.8 as large effect (Cohen, 1988 ). The development of FE, rFE, voluntary activation level, and CV for the voluntary contractions across sessions was analyzed using a one‐way repeated measures ANOVA (five levels = different sessions). If sphericity was violated, Greenhouse–Geisser correction was used. The effect size is presented as ω². The correlation between FE and rFE was performed using Spearman's rank correlation. A p ≤ 0.05 indicates a significant difference. The JASP software was used for statistical analysis (JASP Team, 2019 ). Data are presented as mean ± SD.

As the analyzed variables presented a normal distribution (Shapiro–Wilk test, p > 0.05), repeated measures analysis of variance (ANOVA) with two factors was conducted to assess the main effect of menstrual cycle phases, the main effect of trials, and the interaction effect. When significant main and/or interaction effects were found in the ANOVA, post-hoc tests with Bonferroni correction were conducted to assess differences between the trials, and between menstrual cycle phases. The statistical tests were performed using JASP software, version 0.15.0 (https://jasp-stats.org/), with a significance level set at α < 0.05.

Descriptive statistics and the comparisons of the mean values were analyzed with JASP software (version 0.13.1) [21 ] and Minitab 19 (Minitab Inc., State College, PA, USA), respectively. Parametric data were subjected to a one-way ANOVA, that was Welch-adopted in case of variance heterogeneity. Tukey post-hoc and Games–Howell post-hoc tests were performed to compare the mean values. For the nonparametric data, Kruskal–Wallis analyses of variance with Dunn‘s post-hoc tests were performed [22 (link)]. In case of multiple comparisons, the Bonferroni–Holm adjustment was applied to avoid alpha error accumulation. p -values < 0.05 were considered to be statistically significant.

Description statistics were used for continuous and categorical variables. Exploratory factor analysis (EFA) was selected for the present data, considering that the scale is relatively recent in the literature, since the original study was the only one to investigate its psychometric properties. Therefore, once EMRII-BR underwent modifications through translation and adaptation processes, EFA could achieve a new factorial model.^{(1 )}Most EMRII-BR items have positive scores, not reversed. For the analysis, the five items of reversed scoring were duly transformed in the database, for instance: item 6, when receiving a score 2 (1-5 Likert-type scale) by one given individual, was replaced by score 4 ^{(1 )}Descriptive analysis, correlation and reliability tests were made in the SPSS, while EFA was made through JASP software.^{(1 )}