Spss statistical package version 17

Data analysis was performed using SPSS statistical package version 17 (SPSS, Inc., Chicago, IL, USA). The data were examined for normal distribution using Kolmogorov–Smirnov test. Continuous and parametric data were displayed as mean ± standard deviation, while continuous nonparametric or categorical data as percentages. Student t-test for quantitative data and paired sample t-test for significant differences in one group. VAS scores were assessed using one-way analysis of variance with repeated measures followed by Tukey's post hoc test. For nonparametric data, Mann–Whitney test is used. Chi-square test is used for qualitative data. P < 0.05 is considered statistically significant.

Data for the current study were collected for the spiritual intelligence scale and personality traits scale. The questionnaire was distributed by the researchers during March and April during the academic year of 2013–2014. The researchers selected six courses for university optional requirements during class sessions, explained the purpose and instruction of the study, assured confidentiality of results, and handed the scales to students. At the end of class sessions, scales were collected by researchers. Seven hundred sixteen scales were distributed at class sessions. Of these, 34 were unusable, resulting in a return rate of 95%.
The alpha level was set at 0.05 a priori. Procedures for the statistical analysis are discussed by research questions. Research question one was to determine the level of spiritual intelligence among undergraduate students at Hashemite University in Jordan. Descriptive statistics were used to answer these questions by computing means and standard deviations for the four dimensions of students’ spiritual intelligence. Research question two was to investigate the relationship between spiritual intelligence and personality traits. Person correlation was used to answer this question. The SPSS statistical package version 17 was employed to carry out these analyses.

The study was conducted during the second semester of the academic year 2014–2015. The researchers selected four courses of the university optional requirement during class sessions, and explained the purpose and format of the study. The participants were asked to complete the demographic data in the questionnaire before proceeding to give their response to items in the PFAI. They were then asked to complete the PFAI by reading each item and then answering it based on their experience.
Procedures for the statistical analysis were discussed based on the research questions. Research question 1 was to determine the level of FoF among undergraduate students at HU. Descriptive statistics, mean and SD, for the five dimensions of PFAI were used to answer this question. Research question 2 was used to determine the differences in students’ FoF based on their sex, academic level, and GPA. In the case of sex, an independent sample t-test was used, whereas in the case of academic level and GPA, one-way analysis of variance (ANOVA) was used. The SPSS statistical package Version 17 (SPSS Inc., Chicago, IL, USA) was used to carry out the analysis. The α-level was set at 0.05 a priori.

The results were collected, tabulated, and analyzed using SPSS Statistical Package Version 17 (SPSS Inc. SPSS Statistics for Windows, Chicago, IL, United States). Descriptive data were expressed as median/range (Minimum - maximum) for quantitative non-parametric data, Mean ± SD for quantitative parametric data and frequency (number/percent). Mann-Whitney test was used to compare between two groups of numerical (non-parametric) data. Kruskal-Wallis test was used to compare between more than two groups of numerical (non-parametric) data. Inter-group comparison of categorical data was performed by using χ² test. Some investigated parameters were entered into a logistic regression model to determine which of the factors would be considered as a significant risk factor and identify its odds ratio. Also, some investigated parameters were entered into forward logistic regression to detect a binary response based on one or more predictor variables (risk factors). All parameters were entered into post hoc analysis model. P value was consideredstatistically significant if < 0.05.