Statistical package for the social sciences spss software version 25

An analysis of covariance (ANCOVA) was performed to test the differences among the three ADHD groups and NT group. Post hoc tests were performed to determine specific group differences. First, the homogeneity of slopes between the groups was assessed using Levene’s test; if slopes were found to be homogeneous, an ANCOVA test was performed to compare the clinical variables among the four groups. When the assumption of sphericity was violated, Greenhouse–Geisser estimates are reported. Statistical significance was set at p<0.05. To control for false positives from multiple comparisons, we used a false discovery rate correction, in which p-values were multiplied by the number of comparisons.35 All data were analyzed using the Statistical Package for the Social Sciences (SPSS) software, version 25.0 (IBM Corp., Armonk, NY, USA). To improve the clarity of the results, topographical plots were created from the results of the statistical comparisons to normative values (z-scores) using iSyncBrain (iMediSync Inc., Republic of Korea; https://isyncbrain.com/), a cloud-based, artificial intelligence EEG analysis platform.

Results are expressed as mean (standard deviation, SD) or number of participants (%). Normal distribution tests were performed on all continuous variables. Between-group differences were evaluated using an independent t-test for continuous variables and a χ² test for categorical variables. An analysis of variance (ANOVA) and analysis of covariance (ANCOVA) were also used, and Bonferroni’s post hoc analysis was conducted in order to evaluate the between-group differences. The relationship between vitamin intake and variables such as BMD, muscle mass, and strength was then analyzed using the partial correlation coefficient, after adjusting for age, sex, BMI, physical activity, energy intake, and supplement uses.
All statistical analysis was performed using the Statistical Package for the Social Sciences (SPSS) software version 25.0 (IBM Corp., Armonk, NY, USA). Two-sided p values of < 0.05 were considered as being statistically significant.

Quantitative data were analysed using IBM Statistical Package for the Social Sciences (SPSS) software version 25.0. A multinomial regression was conducted to identify which variables, if any, could predict a negative impact of the COVID-19 pandemic. A series of one-way ANOVAs were performed to explore differences in levels of MS-related concern and symptom bother between the three different levels of COVID-19 impact. Due to the uneven distribution between the three COVID-19 impact groups, Welch’s F was used as a more robust alternative. Qualitative data briefly describing reasons for the reported level of COVID-19 impact were analysed by identifying general themes and presented as frequencies and percentages. These were then compared between the three different levels of overall COVID-19 impact (positive, neutral or negative).