Spss 23.0 statistics software

All measurements were carried out three-fold (n = 3) with the exception of mechanical properties carried out five-fold (n = 5). Analytical data were treated using the SPSS 23.0 Statistics software (IBM, Armonk, NY, USA) as described by Papapetros et al. [17 (link)].

The analysis was performed with IBM SPSS 23.0 Statistics software (IBM Inc., Chicago, IL, USA), setting the critical level of significance at p < 0.05. The descriptive data are described as mean and standard deviation. Kolmogorov Smirnoff analysis showed a non-normal behavior of variables. Therefore, non-parametric tests were performed. A Wilcoxon test was used for inference analysis in comparisons between importance and performance, while a Spearman test was applied for correlational analyses. The sociodemographic variables considered were gender and age. Regarding the IPA model, the data interpretation was based on Ábalo et al. [30 (link)].

The evaluation has been made basing on the unit of measurement “patient”, since all the patients had only one lesion in the moment of diagnosis. The descriptive statistic was performed using frequencies and percentages for the categorical variables and averages and standard deviations for the quantitative variables. The Kolmogorov-Smirnov test was used to verify the normality of age variance. Contingency tables were drawn up using the Chi-squared test. The analytical statistic was performed by comparing the variables using the ANOVA test for independent samples. Cohen’s Kappa index was calculated to determine the degree of agreement between the clinical and histopathological diagnosis. All of the divergences in which the value of p was less than or equal to 0.05 were considered to be statistically significant. The SPSS 23.0 statistics software was used.

Analytical data were treated using the SPSS 23.0 Statistics software [42 ]. Comparison of the means was achieved by MANOVA in order to determine those parameters that are significant in differentiating cheeses according to geographical origin. The independent variable was the geographical origin, while CQPs (pH, TA, moisture, protein, fat dwb, ash), FAs, VCs, and minerals were taken as the dependent variables. Pillai’s trace and Wilk’s lambda indices were computed in order to determine possible significant effects of conventional quality, chromatographic parameter, and element values on the geographical origins of graviera cheeses. LDA was then applied using the selected dependent variables to explore the possibility of classifying cheeses according to geographical origin. Both the original and leave-one-out cross validation methods were used to test the prediction ability. Cross validation is a more conservative method for correct classification, but at the same time is a more reliable one. In addition, the homogeneity of the variability was tested by application of the Box M index [43 ].

The demographic and clinical characteristics of the participants were analyzed by one-way analysis of variance (ANOVA) with a post-hoc Scheffe comparison using the NW group as the reference group. To test for the differences in biochemical and hematologic parameters, and BMD among the groups (UW, OW, AN, and BN groups), we used the logistic regression model with the group as the response variable and the NW group as the reference. Statistical significance was set at the 5% level (p < 0.05). All data were analyzed by SPSS 23.0 statistics software (SPSS Inc., New York, NY, USA).

To examine the association between dietary behaviors, food intake, health behaviors, and psychological features, and the group (UW, OW, AN, and BN groups), we used the logistic regression model and the group as a response variable with the NW group as the reference. If an independent variable was categorical, the last category of the variable was used as a reference category in logistic regression analysis. Statistical significance was set at the 5% level (p < 0.05). We used two-sided p-values and a 95% confidence interval (CI). All data were analyzed by SPSS 23.0 statistics software (SPSS Inc., New York, NY, USA).

Demographics and work characteristics, job stress, affect, and life satisfaction were analyzed using descriptive statistics. To determine the mediation effect, we identified the relationships between the variables. To do this, the variables of job stress, coping, affect, and life satisfaction were analyzed using a Pearson’s correlation coefficient. We used Hayes’s PROCESS macro and a modal 4 bootstrap method to examine the mediating effect of coping in the relationship between job stress and SWB. Unlike a causal statistical approach, the PROCESS macro approach offers indirect mediating effects and can be applied to a small sample size [27 (link), 28 (link)]. In the first step, we defined job stress as a dependent variable (X) and SWB (PA, NA, life satisfaction) as an independent variable (Y) (Fig. 1). In the second step, we tested the mediating effect of X on Y (a × b) and obtained a 95% CI. If the 95% CI did not cross zero, there was a significant mediating effect. For this study, we assumed that SWB includes three elements: PA, NA, and life satisfaction, based on our literature review. We therefore analyzed the mediating effects to determine if there is a correlation among the factors (such as job stress), the sub-categories of coping, and any of the three SWB elements. For data analysis, we used SPSS 23.0 statistics software.