The collected data were analyzed using SPSS/WIN 25.0. The participants’ general characteristics, learning attitudes, problem-solving abilities, and empathy were analyzed using real numbers, percentages, means, and standard deviations. Homogeneity between the experimental and control groups was tested using Fisher’s exact test, x2 test, and t-test. Additionally, the Kolmogorov–Smirnov test was used to analyze normality. The post-test pre-test values were analyzed using an independent sample t-test to determine the program’s effectiveness. Furthermore, the variables with non-homogeneous post-test values between the two groups were tested using an ANCOVA.
Spss win 25
Manufactured by IBM
Sourced in United States
SPSS/WIN 25.0 is a software package for statistical analysis. It provides a comprehensive set of tools for data management, analysis, and visualization. The software is designed to handle a wide range of data types and can be used for a variety of statistical applications, including regression analysis, hypothesis testing, and multivariate analysis.
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35 protocols using spss win 25
1
Evaluating Empathy and Problem-Solving in Education
2
Improving Nurse-to-Nurse Communication through SBAR Training
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Primary data were analysed using SPSS/WIN 25.0:
Descriptive statistical analysis determined participants' general characteristics, their awareness of handover SBAR, communication self‐efficacy and satisfaction with handover education.
For normality testing of the dependent variables, the Shapiro–Wilk test and skewness were used for pre‐test scores. Shapiro‐Wilk test results showed that normality was met for the pre‐test scores (p > .05) and the resulting skewness, ranging from −2 to + 2, also satisfied normality. Hence, we performed a parametric statistical analysis. Furthermore, Mauchly's sphericity test examined homoscedasticity, which was met for all variables, except for communication self‐efficacy (p ≥ .05)
Repeated measures ANOVA verified the effects of the proposed education programme on the dependent variables at different points of time during the proposed programme and Bonferroni correction was used for pairwise comparisons between the time points.
3
COVID-19 Risk Perception and Prevention
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The data were analyzed using SPSS/WIN 25.0. Frequency and descriptive statistics analyses were performed to identify the general characteristics of the participants. Descriptive statistical analysis was performed to identify COVID-19 risk perception, crisis communication, health literacy, and infection prevention behaviors. To test whether there are significant differences in COVID-19 risk perception, crisis communication, health literacy, and infection prevention behaviors according to the general characteristics of the participants, independent sample t-test and one-way analysis of variance (ANOVA) were performed. Scheffé post-hoc test was performed on variables that showed a significant difference. Pearson’s correlation analysis was performed to identify the correlations among COVID-19 risk perception, crisis communication, health literacy, and infection prevention behaviors. Multiple regression analysis was performed to identify the influencing factors of COVID-19 infection prevention behaviors.
4
Evaluating Intervention Effectiveness
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In the pre-test, an independent sample T-test was conducted on the basic characteristics of the experimental group and control group. In terms of age, height, weight, and basic motor skills, there was no statistical difference between the groups (p > 0.05). Therefore, the experimental group and control group were considered homogeneous. In the post-test, the basic data of the experimental group and control group were analyzed for mean and standard deviation. An independent sample T-test was conducted between the two groups. Additionally, a paired sample T-test was performed on the pre-post data in the groups, while an independent sample T-test was conducted to ascertain the differences between the experimental group and control group in the pre-test and post-test. The T-test indicated that the differences had statistical significance (p < 0.05). In this study, SPSS WIN 25.0 was used for statistical analysis.
5
Dietary Habits, Emotional Eating, and Stress
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Data were analyzed using SPSS Win 25.0. For general characteristics, the frequency and percentage were calculated, and continuous variables were analyzed using independent samples t-test. One-way analysis of variance test was performed to obtain stress score according to dietary habits, emotional dietary behavior, and insomnia level, and post hoc analysis was performed using Duncan's new multiple range test. Pearson's correlation coefficient test was performed to find the association among dietary habits, emotional dietary behavior, insomnia, and stress. All results were determined the statistical significance at P < 0.05.
6
Burnout Predictors among Healthcare Professionals
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Data were analyzed using SPSS/WIN25.0 software. Participants’ general characteristics, work-related characteristics, traumatic event experience, compassion satisfaction, secondary traumatic stress, and burnout were analyzed using descriptive statistics. Differences in burnout according to general characteristics and work-related characteristics were analyzed using t-tests, χ2 tests, and ANOVA, followed by Scheffé’s test for post hoc comparisons. Correlations between age, clinical career length, traumatic event experience, compassion satisfaction, secondary traumatic stress, and burnout were analyzed using Pearson’s correlation coefficients. Predictors of burnout were analyzed using multiple regression, and to examine the goodness of fit of the regression model, normality and equal variance were tested using the Kolmogorov-Smirnov test and the Breusch-Pagan test, respectively.
7
Academic Burnout and Mental Health
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SPSS/WIN 25.0 program was used to analyze the collected data. Compliance to normal distribution was assessed via descriptive statistics and the Shapiro–Wilk test. Data were not normally distributed; therefore, non-parametric statistical tests were used to analyze study data. T-test and χ2-test were conducted to compare differences in general characteristics according to clinical practice experience, and t-test and Mann–Whitney U test were conducted to assess differences in stress, depression, anxiety, and academic burnout. T-test, analysis of variance (ANOVA), and Tukey post-hoc test were conducted to evaluate differences in academic burnout according to the general characteristics, and Spearman correlation coefficient analysis was performed to assess the correlation between stress, depression, anxiety, and burnout. To identify factors influencing academic burnout, stepwise multiple regression analysis was performed with entry level below 0.05 and exit level above 0.1.
8
Sexting Behavior and Factors
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The collected data were analyzed using SPSS Win 25.0. The specific analysis methods were as follows. (a) Descriptive statistics were used to calculate the frequency and percentage of the participants’ demographic and sexual characteristics. (b) A chi-square test was used to analyze the differences in sending and receiving sexts according to participants’ demographic and sexual characteristics. (c) Logistic regression analysis was used to identify the influencing factors of sending and receiving sexts.
9
PTSD Symptoms and Influencing Factors in ICU Nurses
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The collected data were analyzed using SPSS/WIN 25.0. The personal and environmental characteristics of ICU nurses were analyzed using descriptive statistics, specifically, frequencies, percentages, means, and standard deviations. To determine the extent of PTSD symptoms according to the individual and environmental characteristics of ICU nurses, independent two-sample t-tests and one-way analysis of variance were conducted. Pearson's correlation coefficient was used to examine the correlation of PTSD symptoms with the main variables of this study: traumatic-event experience, cognitive flexibility, and coworker support. Multiple linear regression was used to identify the influencing variables of PTSD symptoms among ICU nurses.
10
Factors Influencing Sleep Disturbance among Professionals
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The collected data were analyzed using SPSS/WIN 25.0 as follows:
The participants' general characteristics and the degree of job stress, health promotion behavior, resilience, sleep disturbance, and occupational safety were analyzed based on frequency, percentage, mean, standard deviation, and minimum and maximum values.
In accordance with the general characteristics, the difference in sleep disturbance was analyzed using the independent t test and one-way analysis of variance. The Scheffé test was used as a post hoc test.
To confirm the internal consistency of the measurement tool, it was analyzed with the Cronbach alpha coefficient. The Cronbach alpha value is ‘0-1’; ‘0’ means no internal consistency at all, and ‘1' means complete internal consistency.
The correlation between job stress, health promotion behavior, resilience, and sleep disturbance was analyzed using Pearson's correlation coefficients. In addition, to reduce the probability of incorrectly rejecting the null hypothesis, the p-value was taken as less than 0.05.
Finally, the factors influencing sleep disturbance of the participants were analyzed using stepwise multiple regression analysis after verifying the histogram and normal probability plot.
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