All data analyses were completed using SPSS version 25 (IBM SPSS Statistics 2016 ), and Gephi2 (Bastian et al. 2009 ) to calculate SNS density and visualise social network structure. We used an alpha level of .05 and used Bonferroni corrections to adjust for multiple comparisons where appropriate. We used parametric tests for analysing data from TD students, and non-parametric test for data from students on the autism spectrum, due to the relatively smaller sample size for students on the autism spectrum (n = 21). Analyses included only students who completed the study and were completed in three steps for each study. First, we assessed changes in social anxiety over time, using either repeated measures ANOVA (TD group), or Friedman’s Test (autism group). Second, we investigated changes in perceived distress frequency, SNS and PSS over time, using either repeated measures ANOVA (TD group), or Friedman’s tests (autism group). Third, we explored how levels of autistic traits, social anxiety, as well as changes in SNS and PSS might influence different aspects of students’ transition outcomes, using either stepwise linear regressions (TD group), or Kendall’s tau-b correlations (autism group). See Appendix 2 for additional details.
Spss statistics 2016
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SPSS Statistics 2016 is a comprehensive software package for statistical analysis. It provides a wide range of tools and techniques for data management, analysis, and presentation. The software is designed to handle a variety of data types and can be used for both simple and complex statistical analyses.
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Lab products found in correlation
6 protocols using spss statistics 2016
1
Transition Outcomes of Autistic Students
2
Statistical Analysis of Neonatal Necrotizing Enterocolitis
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Statistical analysis was performed using SPSS® statistics 2016 (IBM, NY, USA). Mean and standard deviation were used to describe continuous variables. Percentage and number of events were used for quantitative variables. The Student t-test or Mann–Whitney U test were used to perform two-group comparisons for quantitative variables. The χ-square (Mantel–Haenszel) test was used to perform two-group comparisons for qualitative variables, or the Fisher exact test was used when the expected count was <5. A multivariate analysis using logistic regression was performed to identify independent risk factors for NEC. Variables clinically relevant and associated (p < 0.10) with NEC in univariate analysis were used in the model. A p value < 0.05 was considered statistically significant.
3
Diversity Analysis of Microbial Species
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Descriptive statistics included percentages for the categorical variables and medians with interquartile ranges (IQR) for the non-normally distributed continuous variables. The comparisons were performed using the Chi-square test or Fisher's exact test for the categorical variables. The correlation analysis was performed by assessing the Spearman's rho coefficient of correlation. A P-value < 0.05 was considered to be statistically significant. Statistical analysis was performed using SPSS statistics 2016 (IBM, NY, USA). The principal component analysis was performed with XLSTAT 2017 (Addinsoft, Paris, France). Because richness (defined as the number of different species) does not take into account the abundance of species, we used the Shannon index to compare diversity (taking abundance into account), as previously described (Wang et al., 2008 (link)).
4
Statistical Analysis of Research Data
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Statistical analysis of all the data in this study was conducted using Microsoft Excel 2016 and IBM SPSS Statistics 2016.
5
Youth Custodial Staff Survey: Insights and Ethics
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The Western Australian Aboriginal Health Ethics Committee (#582) and The University of Western Australia Human Research Ethics Committee (#RA/4/1/7116) provided ethical approval for this study. Secure data collection methods were employed to ensure survey responses both in paper and online form were kept anonymous and confidential. This was important given that early consultations with custodial staff accentuated the need for anonymity to foster candid responses.
When completed paper surveys were placed in a locked box or handed to the researchers in person, responses were entered into the online system and checked by two researchers for accuracy and interpretation in the case of ambiguous responses. Survey data were converted into SPSS format and descriptive statistics produced using IBM SPSS Statistics (2016). Cross-tabulations of respondent gender, age, length of employment and education level by knowledge, attitude and practice variables were also conducted. Demographic information for the overall youth custodial workforce, such as for gender, Aboriginality and the distribution of positions, were made available to researchers via the Department of Justice (DoJ; formerly the Department of Corrective Services).
When completed paper surveys were placed in a locked box or handed to the researchers in person, responses were entered into the online system and checked by two researchers for accuracy and interpretation in the case of ambiguous responses. Survey data were converted into SPSS format and descriptive statistics produced using IBM SPSS Statistics (2016). Cross-tabulations of respondent gender, age, length of employment and education level by knowledge, attitude and practice variables were also conducted. Demographic information for the overall youth custodial workforce, such as for gender, Aboriginality and the distribution of positions, were made available to researchers via the Department of Justice (DoJ; formerly the Department of Corrective Services).
6
Statistical Analyses for Comparing Datasets
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First, we tested if each of the samples represented a Gaussian distribution and whether the two samples had equal variances. In case either of these hypotheses was rejected, we followed with nonparametric testing; otherwise, we applied parametric testing. We used the Levene's test to examine whether the variances of the compared groups were equal. In the cases where equal variances were verified, a Student's t-test or post-hoc Tukey's test was performed to test differences in the means of two or more groups, respectively. If there was evidence to support non-equal variances, a nonparametric Wilcoxon-Mann-Whitney's test or Tamhane's test were applied to evaluate statistical differences between two or more groups, respectively. A paired t-test was applied to groups of paired data variables. Results are presented as a mean ± SEM. Differences with p < 0.05 were considered statistically significant (IBM SPSS Statistics, 2016).
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