Statistical package for the social sciences version 22

Analysis was conducted using the Statistical Package for the Social Sciences, version 22 (SPSS Inc., Chicago, IL, USA). For each task, the main group effects of adaptation were analysed using repeated-measures 2 × 3 analysis of variance (ANOVA) with factors of: phase (baseline, top-up) and task (eye-gaze direction, head direction, chair direction). In Experiment 2 a between-subjects factor of group (ASD, NT) was added to the ANOVA. Independent samples t-tests were used to compare differences in adaptation magnitude where a significant interaction with group was identified in Experiment 2. All statistical tests are reported at a 2-tailed level of significance unless otherwise stated. Wherever the relationship between adaptation magnitudes was examined as an a priori hypothesised negative predictor of autistic traits/sensory sensitivity (Experiment 1) or ASD symptoms/sensory sensitivity (Experiment 2) bivariate correlations were conducted in line with our hypotheses (1-tailed). Steiger’s Z-test for correlated correlations was also used to investigate whether correlation coefficients for each task were statistically significantly different to one another (Steiger, 1980 ). Stieger’s Z test compares the equality of two correlation coefficients that share one variable in common while accounting for the correlation between the unshared variables.

Data analysis was performed using the Statistical Package for the Social Sciences, version 22 (SPSS Inc., Illinois, USA). Percentage and counts were used to report categorical data. Continuous data were described using mean and standard deviation (SD), or median and interquartile range (IR) if the data did not follow a normal distribution. The Kolmogorov-Smirnov test was used to determine whether the data were normally distributed. The chi-square test and unpaired t-test were used to assess the significance of the proportions or mean differences among individuals with or without TNs, respectively. Odd ratios (ORs) and 95% confidence intervals (95% CIs) were calculated using binary logistic regression analyses, in order to determine associations of TNs with BMI and VFA. Twelve probable risk factors, that is, TC, HDL-C, LDL-C, TG, FBG, UA, DBP, SDP, high salt intake and smoking status, age and gender were considered using adjusted logistic regression models. Subgroups analyses were performed using VFA and BMI as categorical variables and dividing the participants by four probable risk factors: gender, age (< 50 or ≥ 50 years), FBG (< 6.1 or ≥ 6.1 mmol/L), and TG (< 1.7 or ≥ 1.7 mmol/L). P < 0.05 was regarded as statistically significant. Forest plots were drawn using R software, version 3.6.0 (R Foundation for Statistical Computing, Vienna, Austria).

Analyses were performed using Statistical Package for the Social Sciences version 22 (SPSS), Chicago, IL, USA and two-sided p values of <0.05 were considered statistically significant. Differences in demographic and clinical characteristics between respondents, non-respondents and cancer survivors with unverifiable addresses and between the three age groups (AYA, adults, elderly) were compared using chi-square and analysis of variance (ANOVA), where appropriate.
ANOVAs were performed to investigate mean differences between the age categories of lymphoma survivors (independent variable) and the IOCv2 total and subscale scores (dependent variables). To counteract the problem of multiple comparisons (type-1 error), Bonferroni correction was used (p < 0.001).
Multiple linear regression analyses were used to compare AYA lymphoma survivor characteristics (independent variables) and the mean IOCv2 summary and subscale scores (dependent variables).
Additionally, multiple linear regression analyses were performed to investigate the independent association between IOCv2 total and subscales, and the HRQoL subscales for the AYA sample. All demographic and clinical variables were included as confounders; this was determined a priori based on hypotheses.

As an exploratory aim, we conducted Partial Pearson correlations to examine the relationship between the specific resting-state functional connectivity of male perpetrators and the executive and socioemotional processes. Before performing the correlation analyses, we first extracted the mean value of each seed’s total functional connectivity (network) separately (i.e. the mean value of the connectivity network of the right PI seed). For that, we created a mask containing the total significant between-group functional connectivity differences of each seed.
Second, those behavioral variables that did not follow normal distribution, were normalized using the adequate formula in each case. More concretely, Distorted Thoughts about Women (IPDM) and the Use of Violence (IPDV) were normalized by applying the Napierian logarithm and cognitive reappraisal variable was normalized by squaring the original value.
Third, Partial Pearson correlations were performed between each seed’s rsFC networks and the executive functions and the socio-emotional variables, controlling for age and drug severity. The Statistical Package for the Social Sciences, version 22 (SPSS; Chicago, IL, USA) was used for these analyses based on a threshold at p < 0.05 and Bonferroni correction for multiple comparisons was performed.