Psychological capital serves as a psychological asset facilitating personal growth and enhanced performance. It aids individuals in navigating life’s myriad challenges and adversities, promoting psychological well-being and adaptability. Despite the widely recognized importance of psychological capital in enhancing competitiveness and work performance, existing assessment tools, such as the PCQ-24 developed by Luthans et al. (2007), have not been adequately validated in the Chinese university student population. To explore the factor structure of the Psychological Capital Questionnaire (PCQ-24) developed by Luthans et al., the researchers employed Principal Component Analysis (PCA) and Exploratory Factor Analysis (EFA), extracting factors with eigenvalues greater than 1. The results revealed a six-factor structure, which is inconsistent with the original four-factor structure of the PCQ-24 measurement tool. Therefore, Luthans’ psychological capital scale is not suitable for university students. Additionally, the indigenous scale developed by Chinese researchers Ke Jianglin et al (2009) is more suitable for organizational employees rather than university students. Hence, it is essential to revise and validate a psychological capital measurement tool appropriate for Chinese university students. To fill this gap, the researchers aimed to revise and validate a psychological capital scale suitable for Chinese university students based on the four structural dimensions proposed by Luthans et al. In this study, a large-scale survey of Chinese university students (N = 2780) was conducted, and SPSS 26.0 and AMOS 24.0 were used to statistically analyze the data and assess the psychometric properties of the Revised Mental Capital Scale. The results indicate that the revised psychological capital scale meets the psychometric requirements. The study concluded that the psychological capital scale revised and validated by the researcher can be used as an instrument to measure and assess the psychological capital of university students.

In our submission to the Journal of Software and Systems, titled “The Power of Words in Agile vs. Waterfall Development: Written Communication in Hybrid Software Teams,” we present an exploratory case study conducted in a large software organization, AFAS Software. Our study investigates the influence of the development paradigm and the formality of communication channels on written communication within hybrid development teams.

JSS Online Appendix

This online appendix contains supplementary material to uphold transparency and facilitate the reproduction of the statistical analysis. Please refer to Section 2.1 for the research questions and hypotheses.

The root folder includes the “Project Teams Composition.pdf” file, which contains the anonymized compositions of 20 project teams: 11 agile (PrAG) projects and 9 waterfall (PrWF) projects. This file lists the project team members involved in communication within the Microsoft Teams and Insite channels.

Subfolder "SPSS Files"

This subfolder includes the SPSS files, which contain the normality test and Mann-Whitney U Tests.

• Normality Test.spv: This file contains the results of the normality test.

We performed the normality test. In most cases, the significance of Shapiro-Wilk is below 0.05; thus, the data is not normally distributed and fails to meet the assumption for the t-test. We, therefore, opted for the Mann-Whitney U test, the non-parametric alternative of the t-test.

The files below contain the results of Mann-Whitney U Tests, which are presented in Appendix A - Tables A.7 (a), A.8 (a), A.9 (a):

• H1 - PrAG vs PrWF Product.spv: This file contains the results for hypothesis 1.
• H1.1 - PrAG vs PrWF Formal.spv: This file contains the results for hypothesis 1.1.
• H1.2 - PrAG vs PrWF Informal.spv: This file contains the results for hypothesis 1.2.

The files below contain the results of Mann-Whitney U Tests, which are presented in Appendix A - Tables A.7 (b), A.8 (b), A.9 (b):

• H2 - PrAG vs PrWF Formality.spv: This file contains the results for hypothesis 2.
• H2.1 - PrWF Formal vs Informal.spv: This file contains the results for hypothesis 2.1.
• H2.2 - PrAG Formal vs Informal.spv: This file contains the results for hypothesis 2.2.

Subfolder "Excel Files"

• JSS Paper.xlsx: This file contains the absolute numbers, which are used as input for the calculations.
• JSS-Statistics.py: This Python script uses the “JSS Paper.xlsx” file to perform calculations based on the hypotheses for Project Life Cycle Management (PMLC), Software Development Life Cycle (SDLC), and Speech Acts.

The files below contain the results of the calculations:

• PMLC (RQ1A & RQ1B)_v1.xlsx: This file contains the results for the PMLC phases. The results are presented in Appendix A.1.
• SDLC (RQ2A & RQ2B)_v1.xlsx: This file contains the results for the SDLC phases. The results are presented in Appendix A.2.
• Speech Acts (RQ3A & RQ3B)_v1.xlsx: This file contains the results for the speech act types. The results are presented in Appendix A.3.

Assessment of the normality assumption and the homogeneity of variances assumption for two-sample t-tests.

Continuous data were indicated with mean±SD (standard deviation) while categorical data were indicated with number and percentage (%). For comparisons of means between groups, Mann-Whitney U test or student’s independent t-test was used depends on normality assumption. Categorical data were tested using Chi-square test or Fisher’s exact text (if expected value ≤ 5 was found). Spearman’s correlation coefficient was used to observe the relation among independent variables. Further, univariate and multivariate logistic regression models were used to analyze the association between independent variables and survival results.The independent variables which were significant in univariate were entered into a multivariate model. Two kinds of multivariate models were used, including the enter method and forward (Wald test) method. In the enter method, significant variables were recognized as associated factors. In the forward method with Wald test, the combination of independent variables with best explained variation were reported. The estimated odds ratio (OR) and its 95% confidence interval (CI) were reported in all logistic regression results. The probabilities generated from the final multivariate logistic regression model was further validated by ROC analysis. The AUC and its 95% confidence interval (CI) were reported. All above analyses were performed using IBM SPSS Version 25 (SPSS Statistics V25, IBM Corporation, Somers, New York).

Parameters used for printing the models and repositioning guide.

Five files, one of which is a ZIP archive, containing data that support the findings of this study. PDF file "IA screenshots CSU Libraries search config" contains screenshots captured from the Internet Archive's Wayback Machine for all 24 CalState libraries' homepages for years 2017 - 2019. Excel file "CCIHE2018-PublicDataFile" contains Carnegie Classifications data from the Indiana University Center for Postsecondary Research for all of the CalState campuses from 2018. CSV file "2017-2019_RAW" contains the raw data exported from Ex Libris Primo Analytics (OBIEE) for all 24 CalState libraries for calendar years 2017 - 2019. CSV file "clean_data" contains the cleaned data from Primo Analytics which was used for all subsequent analysis such as charting and import into SPSS for statistical testing. ZIP archive file "NonparametricStatisticalTestsFromSPSS" contains 23 SPSS files [.spv format] reporting the results of testing conducted in SPSS. This archive includes things such as normality check, descriptives, and Kruskal-Wallis H-test results.

Data gathering The researchers invited the students to answer an online form - through Google Forms virtual platform - containing the questionnaires: sociodemographic information, FANTASTIC questionnaire on Lifestyle, and a questionnaire on Sense of Coherence. The researchers clearly explained the research objectives and collection procedures on the home page, and the participants were given the Free and Informed Consent Form. The data gathered in the online form were transferred to a spreadsheet in Microsoft Excel. The results were filtered, classified, and treated in order to be in line with the desired statistical analysis and could feed the statistical programs used. Statistical analysis The statistical analyses were performed by the JASP statistical software, and part of the graphics by the SPSS software. First, the researchers submitted the results to normality (Shapiro Wilk) and homogeneity (Levene test) analysis. Next, the normal homogeneous data were submitted to the ANOVA anal...

Background: This study aimed to assess breast cancer awareness among patients undergoing active treatment for breast cancer at Day Hospital, assess their quality of life (QoL), and explore the association between minor knowledge of the disease and higher levels of anxiety. Methods: This prospective observational study included patients with breast cancer undergoing active treatment at the Instituto Português de Oncologia Coimbra. The EORTC QLQ-C30, QLQ-INFO25, and Anxiety and Depression Scale (HADS) were completed, and demographic and clinical data were collected and processed using SPSS. Results: In total, 188 patients with breast cancer were included. A vast majority had a positive perception of their QoL, with a higher average value compared to “cognitive functioning” (X=77.22±22.53), “social functioning” (76.86±25.41), and “physical functioning” (75.67±17.24). Regarding the information received, an overall “score” below the “cut-off line” (47.96±14.40) was observed. When evaluating ...

Mean values of surface deviation (µm) ± standard deviation.

The objective of this research was to examine the Love-Hate and Identification Relationship of Individuals Participating in Euroleague Match for Recreational Purposes. The study was conducted using a relational survey methodology. The study’s population comprises persons who watching recreational purpose part in the Euroleague match held in Istanbul in 2023–2024 season, while the sample consists of 178 voluntary participants selected through convenience sampling. The participants completed the Fan Love-Hate Scale and Fan Identification Scale, in addition to being asked about their gender, marital status, age, educational status, and frequency of attending football matches per week. The data collected from the personal information form and scales was entered into the IBM SPSS 24.0 software package for analysis. Statistical analyses were conducted using the Independent Sample T test and One-way Anova methods. The LSD test was employed to ascertain the dissimilarity between the groups. The Pearson correlation analysis was utilized to ascertain the association between the variables of love-hate and identity. In summary, it is evident that demographic factors, including gender and age, significantly influence fan perceptions and sports identification. In contrast, there is no substantial correlation observed between attributes such as level of education achieved and the frequency of engaging in sports activities, and the aforementioned outcomes. The significant associations identified between the Fan Love-Hate Scale and the Sports Fan Identification Scale underscore the complex relationship between fans’ emotional experiences and their connection to sports. Further investigations could be conducted to go deeper into the underlying causes that contribute to these relationships and inequalities, so resulting in a more thorough understanding of fan psychology.

In this study, both quantitative and qualitative data were analysed. SPSS version 24 was used to analyse the quantitative data. First, the Shapiro-Wilk Normality Tests were performed. The total mean score of SRL skills in six variants was then calculated for each pre-test and post-test. A significant difference in SRL skills was then tested using a dependent t-test for paired samples and 0.05 as the significance level. To triangulate the information gathered in the OSLQ, a qualitative analysis was performed on the open-ended questionnaire data.

Abstract Background: Physical activity reduces the risk of coronary heart disease, one of the leading causes of death in the world. Objectives: This study intends to correlate physical activity and clinical variables of the patients hospitalized in public hospitals of Santa Catarina after the first acute myocardial infarction. Methods: The selected patients answered a questionnaire on different clinical variables. Physical activity was measured using the Baecke's questionnaire. Data were tabulated and analyzed using the SPSS 13.0 for Windows software. Normality was assessed using the Kolmogorov-Smirnov test. Correlations between two quantitative variables were evaluated by Pearson's correlation. Values of p < 0.05 were considered statistically significant. Results: The study showed a weak positive correlation between the Baecke score and years of schooling (r = 0.361; p = 0.001). There was a weak negative correlation between the Baecke score and the PHQ9 depression score (r = -0.252; p = 0.009). The study also showed a weak negative correlation between the PHQ9 depression score and the Mini Mental score (r = -0.258; p = 0.007), as well as a weak negative correlation between PHQ9 and schooling years with (r = -0.199, p = 0.039). Conclusions: There is a positive correlation between physical activity and years of schooling in hospitalized patients with first acute myocardial infarction. Negative correlations were found between physical activity and depression, between depression and the Mini-Mental State Examination, and between depression and years of schooling in these patients.