Spss version 27

TKA and healthy subjects were matched to the BCA-M cohort in IBM^® SPSS^® Version 27 for age (p = 0.3), sex (p = 1), body mass index (p = 0.9) and height (p = 0.5) from the prospectively collected database (Table 1). Gait output data were averaged using a custom MathWorks^® MatLab^® script and analysed in IBM^® SPSS^® Version 27. The Shapiro–Wilk test demonstrated that a number of variables were not normally distributed; therefore, all variables were compared using Kruskal–Wallis, then Mann–Whitney test with Bonferroni correction where differences were detected. Significance was set at α = 0.05.

The technological processing of the samples was performed in duplicate as well as all the chemical determinations (n = 4). All the data were analysed using SPSS version 27.0. Multivariate general linear models were used to determine the influence of HPP pressure, HPP time, and extraction conditions (conventional extraction and UAE) on protein recovery, protein purity, and amino acid composition of the protein isolates. The differences between groups were further analysed by either Student’s t-test or Games–Howell post hoc tests for comparisons between 2 or more than 2 groups, respectively. In all cases, the criterion for statistical significance is p < 0.05. The variance in the data set was analysed by principal component analysis (PCA) using direct Varimax rotation with Kaiser normalisation to calculate the expected weight for each component with eigenvalues higher than 1. PCA scatter plots were generated by SPSS version 27.0, while all the other graphics were produced in Microsoft Excel.

The age, gender, and regional distributions were used to calibrate the data weights. Percentages and significance (p) were calculated with Chi-Squared and Fisher’s exact tests. All selected variables were dichotomized and added simultaneously into the logistic regression models. Furthermore, health-related harms, work/study harms, and social deviance harms were combined for the model and named other harms due to the small number of respondents in some subcategories. To optimize the model, gender and age were added. All analyses were done using SPSS version 27.0 (IBM Corporation, Chicago, IL, USA).

Participants’ responses were first preprocessed and then exported to IBM SPSS version 27.0 (IBM Corp. Armonk, NY, USA) for statistical analyses. Incomplete responses and those with invalid data entries were excluded. Mean and standard deviation were used to represent continuous variables. Counts and proportions were used to express categorical variables. Inferential statistics were conducted through independent samples-t-tests to perform group-wise comparisons. Cronbach’s alpha values were computed for the entire scale and subscales to assess the internal consistency. Two hierarchical regression models (HRM) were fit to explain the variance in the likelihood of initiation and sustenance of sunscreen use behavior by MTM individual constructs, besides the demographic variables. Structural equation modeling (SEM) was utilized for the construct validation. The Analysis of Moment Structure, AMOS (Chicago, IL, USA) was used for SEM [27 ]. We used indices such as chi-square (χ²), root mean square error of approximation (RMSEA), comparative fit index (CFI), and Tucker–Lewis (TLI) to assess how well our models fit the data [31 ,32 ,33 (link)]. Models were considered to have adequate fit if they met the less stringent, but traditionally accepted, values of 0.90 or greater for CFI and TLI, and values less than 0.08 for RMSEA. P-values less than 0.05 were considered statistically significant.

Alongside descriptive statistics, we used multiple logistic regressions to measure any associations between negative attitudes toward or reported negative experiences of the copper IUD and sociodemographic and other variables. Associations were expressed as odds ratios (ORs) with 95% confidence intervals (95% CIs). All statistical analyses were conducted using IBM SPSS Version 27.0 (SPSS Inc., Chicago, IL, USA). Open-survey responses were analyzed thematically (75 (link)), i.e., divided into themes and categories, using NVivo.

The data were derived from two independent experiments with biological and technical replicates and are presented as a mean + standard error of the mean (SEM). A one-way ANOVA in conjunction with the Bonferroni post-hoc test from SPSS version 27.0 (IBM Corp., Armonk, NY, USA) was used in all cases, except for the proliferation and scratch assays, where the multiple measures were also accounted for. In cases where the parametric testing of multiple samples was not permissible, the Kruskal–Wallis or Friedman non-parametric approaches were used. The significance was set at p < 0.05.