Spss statistics 24 for window

Data analysis was handled accordingly based on the normality of the data. Hedonic data from the sensory evaluation was analyzed using a non-parametric Kruskal–Wallis test (α = 0.05), with post hoc Mann-Whitney to identify the significant differences between samples. Bonferroni adjustment was applied to account for type 1 error, therefore working at an alpha level of 0.017. Analysis of variance (ANOVA) with post hoc Tukey significant test was applied to RDA and descriptive analysis data, working at an alpha level of 0.05. Just-about-right (JAR) data was assessed using chi-square statistic. A combination of parametric and non-parametric tests was used to evaluate the volatile data. All parametric and non-parametric tests were performed using SPSS IBM SPSS Statistics 24 for windows (SPSS Inc., IBM Corporation, NY, USA).

First, the correlations between TGP, FGA, life-space mobility, and perceived autonomy in participation outdoors were tested with Pearson’s correlation. Thereafter, to identify the different coping profiles in our sample, we performed a cluster analysis of TGP and FGA using two-step clustering. Two-step clustering identifies groupings by first running pre-clustering and then running hierarchical methods. Log-likelihood was used as a distance measure and the number of clusters was not determined beforehand. Since cluster solutions can depend on the order of cases, the order was randomized before the analysis. To test the stability of the given solution, cluster analysis was executed four additional times using different randomizations of cases. Differences in background characteristics between the resulting coping profiles were analyzed with chi square test and one-way analysis of variance.
Finally, general linear modeling was used to study the associations of the coping profiles with life-space mobility and perceived autonomy in participation outdoors. The base model was adjusted for age and sex. SPPB, MMSE, environmental barriers, and depressive symptoms were added to the base model one at a time to see which one of them possibly affects the associations. All analyses were performed with SPSS Statistics 24 for Windows.

A one-way ANOVA was used to detect significant differences between fresh and ozone-processed peel paste for 30 and 60 min concerning all of the analysed characteristics. Tukey’s test was posteriorly performed to assess pairwise differences of means. The normality and homoscedasticity of the data were assessed using Shapiro-Wilk and Levene’s tests, respectively.
When the normality of data was not verified, the nonparametric Kurskal-Wallis test was carried out alternatively to a one-way ANOVA with a Mann–Whitney test for the further detection of differences. The significance level was set at 5% in all cases. The results were expressed as mean ± margin of confidence interval at 95%. All analyses were performed using IBM SPSS Statistics 24 for Windows^® (SPSS Inc., Chicago, IL, USA).

Frequency and descriptive statistics were calculated on all variables. Independent samples t-tests and chi-squares were utilized to evaluate differences in sociodemographic and clinical history characteristics between groups at baseline. Percent change was calculated for the difference between (a) baseline and 45-day follow-up, (b) baseline and 90-day follow-up, and (c) 45- and 90-day follow-up for all biomarkers. Then, the percent change dependent variables were evaluated in one-way analysis of variance to compare differences between the placebo and RBAC groups. IBM SPSS Statistics 24 for Windows (IBM, Inc., Chicago, IL, USA) was used for statistical analyses, and α < 0.05 was considered statistically significant.

Descriptive statistics were applied to characterize our sample and are presented as number and percentages. Mann-Whitney U tests were used to compare age (months) at first visit to a physiotherapist based on referral source. Linear regression analyses were performed to explore the relationship between age at first visit to a physiotherapist (months) and referral source (categorical). The model was adjusted for number of risk factors (continuous) and severity of motor impairment (categorical). Additionally, we investigated the interaction between referral source and number of risk factors. Gestational age was not included in the model, as preterm infants born before GW 32 are routinely enrolled in the neonatal follow-up and are hence, unless otherwise indicated, assessed by a physiotherapist at 2 months corrected age regardless of the degree of prematurity. The children’s neonatal clinical history and CP profile were compared based on referral source and access to physiotherapy before or after 5 months of age using the Mann-Whitney U test, Chi-square analysis or Fischer’s exact test. For the analysis of severity of motor impairment, the children were categorized into either ambulatory or non-ambulatory. A two-tailed p-value of <0.05 was considered statistically significant. All calculations were performed using IBM SPSS Statistics 24 for Windows (SPSS Inc., Chicago, IL, USA).