Spss statistic 22

Statistical analysis was performed using the IBM SPSS statistic 22 (Chicago, IL, USA). All the quantitative data was presented as the mean ± standard deviation (SD). For statistical analysis, differences in continuous variables between two independent samples were evaluated by the Mann-Whitney U test. Dichotomous variables were compared by the χ² test. Association between continuous variables was assessed by means of Spearman and partial correlation. Pearson's rank correlation analysis was used to evaluate the associations between dichotomous variables and protein levels. Covariance analysis was used. The statistical software GraphPad Prism 6.0 (GraphPad Software, La Jolla, CA) and IBM SPSS statistic 22 (Chicago, IL, USA) were used for graph creation.

A nested ANOVA (IBM SPSS statistic 22, Armonk, NY, USA)) with photo captures nested within fish within cohort and time was used to investigate if MMCs in the spleen of SBT changes over time in each group and differences between the two groups at the same time point. Because the test was robust regarding the assumption of normal distribution, so this assumption was not assessed [17 ,18 (link),19 ]. The severity of infection was analysed using two-factor ANOVA with cohort and time as orthogonal factors and comparing between each sampling point for both cohorts. Homogeneity assumption was checked using a Levene test applying on means of MMCs quantified for each fish [20 ]. For comparison of the two cohorts at transfer, t-test was used. Total mortalities between the two cohorts were compared using χ² test.
Possible associations between the percentage area of splenic MMCs and intensity of blood flukes in the heart and gills of SBT were assessed using Pearson correlation analysis (IBM SPSS statistic 22). p < 0.05 was considered statistically significant.

Statistical analyses were performed using procedures in IBM SPSS Statistic 22.0 and IBM SPSS Amos 22.0. First, descriptive statistics (means, standard deviations, correlations, and checks for normality) were computed for variables using IBM SPSS Statistic 22.0. Second, confirmatory factor analyses (CFA) were conducted for the measurement model. Then, structural equation modeling (SEM) analyses examining direct and indirect effects in the proposed conceptual model were performed by using procedures in IBM SPSS Amos 22.0 with the maximum likelihood (ML) estimation. The measurement property of the structural model was first examined, followed by an assessment of the proposed structural relationships (Anderson and Gerbing, 1988 (link)). Goodness of fit indices were assessed by using the comparative fit index (CFI), normed chi-square (χ²/df), standardized root mean square residual (SRMR), and root mean square error of approximation (RMSEA). The reliability of the measures was assessed by Cronbach’s alpha, Average Variance Extracted (AVE), and composite reliability. The convergent validity and discriminant validity were examined by factor loadings and the inter-factor correlations (Fornell and Larcker, 1981 (link); Kline, 2005 ; Hair et al., 2010 ).