Factor Analysis Research Articles

Reliability and validity indices of the Cognitive Failure Scale (CFS) in an Egyptian-Saudi sample

This study evaluated the reliability and validity of the Cognitive Failure Scale (CFS) among university students in Egypt (Menoufia University) and Saudi Arabia (Umm Al-Qura University) to provide a culturally appropriate assessment tool for Arab contexts. The sample included 557 Egyptian students and 200 Saudi female students who completed the scale via an online questionnaire. Statistical analyses assessed internal consistency (Cronbach’s alpha, split-half reliability) and construct validity (exploratory and confirmatory factor analysis). Results showed a convergence in the averages of cognitive failures between the two environments, with a high internal consistency (correlation coefficients significant at 0.01). Cronbach's alpha and split-half reliability values were 0.943 and 0.921 for the Egyptian environment and 0.967 and 0.941 for the Saudi environment, confirming the reliability of the scale. Factor analysis revealed two factors in the Egyptian sample and three in the Saudi sample, indicating slight cultural variations. Confirmatory factor analysis supported the model’s fit with acceptable goodness-of-fit indices. The findings demonstrate the CFS as a reliable and valid tool for assessing cognitive failures in Arab university students, suitable for exploring factors like stress, learning strategies, and academic performance. The study recommends educational and psychological interventions to reduce cognitive failures and enhance student outcomes.

Evaluating the factors influencing artificial intelligence technology uptake in health and safety management within the Ghanaian construction industry

Purpose The construction industry in Ghana faces significant challenges in managing health and safety risks, leading to high rates of accidents and fatalities. Despite the potential of artificial intelligence (AI) technologies to improve health and safety management, their adoption in the Ghanaian construction industry remains limited. This paper aims to identify and evaluate key factors influencing the uptake of AI technologies in construction health and safety management within the Ghanaian industry. Design/methodology/approach The study adopts a rigorous two-step qualitative approach to identify a set of 17 variables. First, an extensive analysis of scholarly publications was conducted to compile an initial variable list. Secondly, a pilot survey involving both academic and industry professionals assisted in refining the identified variables. Subsequently, a questionnaire survey involving 219 Ghanaian construction professionals then collects quantitative assessments of each variable using the purposive sampling technique. Statistical modelling using factor analysis and fuzzy synthetic evaluation (FSE) was applied to process the survey data and determine the criticality of the factor categories. Findings The factor analysis yielded a three-factor solution underlying the 17 adoption variables: Extensive technological requirements and costs, resistance to change and AI adoption and uncertainty about AI outcomes and value. Subsequently, FSE confirmation showed the Extensive Technological Requirements category as the most critical, with specialized algorithmic demands, infrastructure limitations and expert support needs presenting major obstacles Ghanaian firms face in AI adoption. Originality/value This research contributes robust empirical evidence and novel factor-based statistical analysis to augment the theoretical discourse surrounding construction safety technology integration and change dynamics. The developed fuzzy quantitative methodology offers a model for assessing complex innovation adoption decisions in the face of uncertainty. The research addresses a gap in existing literature by providing a comprehensive assessment of the technological, organizational and environmental factors shaping AI adoption decisions and offering practical strategies for overcoming adoption barriers.

Business process innovation through digital strategy: unveiling the adoption of big data analytics and the evolution of digital culture

PurposeThis research aims to elucidate the interplay between implementing digital strategies, adopting big data analytics–artificial intelligence (BDAAI), and business process innovation, with a particular emphasis on assessing the moderating impact of digital culture. This article explores how big data analytics can provide organizations with the tools and resources to utilize their data assets effectively, fostering novel and innovative processes.Design/methodology/approachThe research employed a questionnaire-based approach to collect data from managers in Pakistan’s telecom sector. Data analysis was conducted using SPSS and AMOS software. The measurement model’s suitability was assessed via confirmatory factor analysis (CFA) using AMOS.FindingsPreliminary results indicate a correlation between these critical factors: digital strategy prioritization, big data analytics incorporation and digital culture cultivation. The study results confirm the effect of digital strategy on business process innovation and support the positive mediating role of the adoption of BDAAI and the moderating role of digital culture.Research limitations/implicationsThe research offers insights but is limited to Pakistan’s telecom industry. Digital strategy is crucial for innovation amid digital transformation, with BDAAI enhancing process innovation. The digital culture in telecom will drive industry digitalization through BDAAI in business innovation.Originality/valueLeveraging artificial intelligence within a digital culture context could be a productive tool for improving business processes. This research represents a novel exploration of the intersection between digital strategy, BDAAI and digital culture within the context of the telecommunications industry.

Investigating the effect of Industry 4.0 technologies on Lean bundles: a stakeholder perspective for quality improvement

PurposeThis study aims to investigate the role of Industry 4.0 (I4.0) technologies (base and front-end) on the deployment of Lean bundles (just-in-time, total productive maintenance and total quality management) to achieve operational performance.Design/methodology/approachWe adopt a stakeholder perspective by considering the role of leaders, employees and customers when engaged in quality improvement. The objective is to compare moderation and mediation models and address a gap in literature related to the contrasting outcomes from I4.0 technologies and Lean deployment on firm performance. We perform survey research involving 270 manufacturing firms. We use factor analysis, hierarchical ordinary least squares regression and bootstrapping techniques to evaluate the hypothesised relationships.FindingsOur findings confirm I4.0’s mediating influence, suggesting that organisations focusing on stakeholders’ engagement in quality more effectively develop Lean bundles and achieve operational performance. Regarding moderation models, we find no significant relationship between base technologies (Internet of Things, big data, cloud computing and artificial intelligence) and contrasting results from front-end technologies (robotics, simulation, augmented and/or virtual reality and 3D printing) on deployment of Lean bundles.Originality/valueOur work theoretically contributes to the current operations management field by showing that organisations should first engage stakeholders for quality improvement instead of trying to engage stakeholders and adopt digital technologies simultaneously to deploy Lean bundles.

Competitiveness Evaluation of Listed Logistics Companies Based on Factor Analysis Method

With the rapid development of the times, the logistics industry, as an important link connecting production and consumption and promoting the economic cy-cle, is thriving at an unprecedented speed and scale. This research constructs a com-petitiveness evaluation system covering profitability, operations, debt re-payment, and research and development capabilities. We deeply analyzes 74 listed logistics companies using factor analysis to accurately rank their compet-itiveness. This approach aims to provide scientific evidence for strategic ad-justments, assist companies in formulating precise strategies, strengthen their advantages, and ef-fectively respond to market challenges. Through continuous optimization and in-novation, the study contributes to the steady advancement of logistics companies, enabling them to distinguish themselves in the highly competitive market and achieve sustainable growth.

Does students’ understanding of test designs and demands of a high-stakes test influence their learning practices: a washback study

This study aimed to explore the relationship between students’ understanding of the National Matriculation English Test (NMET) and their learning practices through standard multiple regression (SMR) and structural equation modeling (SEM) with the purpose of unraveling the working mechanism of washback. A total number of 3105 Chinese senior high school students participated in the questionnaire survey. Data were processed and analyzed using SPSS 21.0 software to conduct descriptive statistics, exploratory factor analysis, and standard multiple regression and using AMOS 21.0 software to perform confirmatory factor analysis (CFA) and SEM. The findings revealed that students demonstrated a better understanding of test designs than test demands, and that their understanding of the test significantly predicted their learning practices. Specifically, students’ understanding of both test designs and test demands significantly predicted teaching-related learning and test preparation. However, understanding of test demands was found to be a stronger predictor of teaching-related learning and test preparation than understanding of test designs and was the only predictor of communicative learning. These findings have significant implications for students, teachers, and educational authorities, highlighting the need for targeted support and guidance to enhance students’ understanding of high-stakes testing and its impact on learning behaviors.

Optimization of the process of acetylation and carboxymethylation for a polysaccharide from Gastrodia elata and antioxidant and immunomodulatory activities test

A water-soluble polysaccharide extracted from Gastrodia elata was chemically modified to generate acetylated and carboxymethylated derivatives. Thourshe conditions for the two modification methods were optimized through single factor and response surface analyses. The optimized conditions for acetylation were 3.4 mL of acetic anhydride, a reaction temperature of 63.4 °C, and a reaction duration of 4 h. For carboxymethylation, the optimal modification parameters were 2.2 g of chloroacetic acid, a temperature of 62.3 °C and a NaOH concentration of 2.2 mol/L. The results of chemical analysis and Fourier transform infrared spectroscopy revealed that these modifications were successfully added, although the molecular weights of the derivatives decreased because of slight degradation during the reactions. The antioxidant and immunomodulatory potentials of the polysaccharide derivatives were assayed to delineate the structure-bioactivity correlations. The results revealed that both acetylation and carboxymethylation of GEP-1 augmented its scavenging efficacy against DPPH radicals, hydroxyl radicals, and superoxide anions, surpassing the antioxidant capacity of the unmodified polysaccharide. Furthermore, the modified derivatives demonstrated minimal cytotoxicity against RAW264.7 macrophages and significantly potentiated the secretion of TNF-α and IL-6 by these cells, indicative of a pronounced immunostimulatory response. These findings suggest that chemical modifications can significantly improve the solubility, antioxidant capacity, and immunomodulatory activity of polysaccharides, thereby enhancing their potential for development and application in the utilization of natural polysaccharide resources.

11 Cross-Cultural Adaptation and External Validation of the Locally Recurrent Rectal Cancer – Quality of Life (LRRC-QoL) Measure

Abstract Background Establishing an international measure of health-related quality of life (HrQoL) for patients with LRRC is central to understanding its impact from a patient perspective. The aim of this study was to cross-culturally adapt and externally validate the LRRC-QoL as an international measure of HrQoL in LRRC. Methods A multi-phase mixed methodological study was conducted. Cross-cultural adaptation was undertaken through Translatability Assessment (TA), forward-backward translation, and pre-testing interviews to establish content validity and conceptual equivalence. External validation included assessing scale structure through confirmatory factor analysis (CFA), reliability using Cronbach’s Alpha and Intraclass Correlation Coefficient (ICC). Convergent validity was assessed against EORTC QLQ-CR29 and FACT-C scales using Pearson’s Correlation Coefficient (r). Known groups comparison through independent t-tests and ANOVA. Responsiveness through effect sizes (ES) and standardised response means (SRM). Results Sixty-seven patients and 6 clinicians were recruited to pre-testing interviews to cross-culturally adapt the LRRC-QoL for use in 10 languages. Overall, 321 patients were included in the validation of the LRRC-QoL from 16 countries. The scale structure demonstrated excellent goodness of fit. Most scales demonstrated good reliability (Cronbach’s Alpha and ICC >0.7). Convergent validity confirmed hypothesised correlations. Known groups validity was demonstrated for gender, pattern of recurrence, pre-operative treatment, treatment intent, and metastases. The LRRC-QoL demonstrated excellent responsiveness; patients undergoing surgery reported worse HrQoL at 3- (p=0.00, ES 0.57, SRM 0.50) and 6-months (p=0.001, ES 0.37, SRM 1.11). Conclusion The LRRC-QoL has demonstrated excellent psychometric properties in this international validation analysis and is now validated for use in 16 countries.

Data-driven supply chain orientation and supply chain performance: empirical investigation using a contingent resource-based view perspective

PurposeBased on a contingent resource-based view, this paper explores whether and how data-driven supply chain orientation (DDSCO) affects supply chain performance (SCP).Design/methodology/approachSurvey data were collected from 307 manufacturers in China, Confirmatory factor analysis confirmed the reliability and validity of measures, and bootstrapping was used to test all hypotheses.FindingsDDSCO positively and significantly influences SCP and supply chain risk management capabilities (i.e. robustness and resilience). Additionally, supply chain risk management capabilities partially mediate the relationship between DDSCO and SCP. Furthermore, the findings provide evidence for the moderating effect of supply dynamism on the DDSCO–SCP relationship, with robustness and resilience capability functioning as mediators.Originality/valueBuilding upon existing digital strategies and supply chain performance literature, this study provides a new perspective (supply chain risk management capabilities) on maintaining a sustainable supply chain under supply dynamism. These findings offer new theoretical and managerial contributions that add value to the supply chain management literature.

Pedagogical practices promoting the development of wisdom in university students

ABSTRACT Dealing with complex problems requires of future professionals more than knowledge, skills and expertise; it requires wisdom. Recently, the question how to develop wisdom in university students has been raised. Most theoretical models of wisdom include cognitive, affective-social, ethical-existential and action dimensions. The aim of this study was to examine (1) whether the four-dimensional theoretical structure of the wisdom concept can be empirically verified in the higher education context, and (2) what kinds of pedagogical practices explain the development of attributes related to wisdom in university students. Participants of the study were third-year university students (n = 535) representing several disciplines. Data were collected through a survey in which students evaluated their development in wisdom attributes with 46 items and answered 57 items concerning pedagogical practices used on their courses. Exploratory factor analysis and regression analysis were used to analyse the data. The analyses concerning the four-dimensional model of wisdom yielded four factors: cognitive, ethical–existential, affective, and social dimensions, thus partly verifying the theoretical structure. Regarding the pedagogical practices, activities aimed at developing scientific thinking, and sharing and learning from practical experiences explained most the development of attributes of wisdom. Collaboration between students, independent studying, and integration of theory and practice also played an important role. In general, diverse forms of teaching and learning seem to be crucial for the development of prerequisites of wisdom in higher education. Practical implications of the findings are discussed.

Validity and reliability study of the Turkish version of the multidimensional outcome expectations for exercise scale (MOEES) in patients with cardiovascular diseases

Outcome expectations are a multidimensional construct encompassing physical, social, and self-evaluative components, significantly influencing adults’ exercise behaviors. The study aimed to develop and validate a Turkish version of the ‘Multidimensional Outcome Expectations for Exercise Scale’ (MOEES) and assess its reliability in adults with cardiovascular disease (CVD). This cross-sectional validation included 150 participants. The MOEES was translated into Turkish, ensuring cultural and linguistic appropriateness, and reviewed by 15 healthcare professionals. Participants’ demographic data were collected, and they completed the MOEES, International Physical Activity Questionnaire (IPAQ), and Tampa Scale for Kinesiophobia-Heart (TSK-H) twice, 15 days apart. The internal consistency of the MOEES was excellent, with a Cronbach’s Alpha of 0.919. Test-retest reliability showed high intraclass correlation coefficients (ICC) for the physical (0.924), self-evaluative (0.921), and social (0.929) subscales. Confirmatory factor analysis (CFA) indicated an excellent fit (X2/df = 1.782, GFI = 0.908, AGFI = 0.856, CFI = 0.962, RMSEA = 0.072, NFI = 0.918, SRMR = 0.052). Strong correlations were found between MOEES sub-dimensions and IPAQ total and TSK-H scores (p < 0.01), confirming the scale’s validity. The Turkish MOEES is a reliable tool for evaluating physical, social, and self-evaluative outcome expectations regarding exercise in CVD patients, making it suitable for clinical and research applications.

The Influence of Working Capital Management, Market Risk, and Investment Strategy on Profitability With Financial Distress As an Intervening Variable (Study on Companies With Special Notations Listed on The Indonesia Stock Exchange For The 2019-2023 Perio

This study aims to examine the influence of working capital management, market risk, and investment strategy on profitability, with financial distress as an intervening variable. The research is conducted on companies with special notations listed on the Indonesia Stock Exchange (IDX) during the period of 2019-2023. Quantitative Research Methods The research uses quantitative analysis with an associative quantitative approach, which is research that asks about the relationship between two or more variables. The analysis is carried out using a quantitative approach with Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM-PLS) through Smart PLS 4.0 software. The results show that working capital management and investment strategy have a positive and significant impact on profitability, while market risk and financial distress negatively affect profitability. Furthermore, financial distress plays a significant mediating role between the independent variables and profitability. The model demonstrates a strong predictive ability with an R² value of 0.65 for profitability and 0.57 for financial distress. These findings suggest that efficient working capital management and appropriate investment strategies contribute to higher profitability, while market risks and financial distress pose a threat to the financial stability of companies.

Factors Influencing Students' Satisfaction with Using Mosoteach for Online Learning in the Culture and Arts Disciplines at Vocational Colleges in Chengdu, China

Background and Aim: With the development of mobile Internet and the popularity of smart terminal devices, online learning platforms such as Mosoteach have been widely used in vocational colleges and universities in China. In the context of student-centered education, the researchers conducted this study to better meet students' online learning needs. This study aimed to explore the factors affecting students' satisfaction with using Mosoteach for online learning in the disciplines of culture and arts in a representative vocational college in Chengdu, Sichuan Province, China. These included computer anxiety, instructor innovation, student engagement, interaction, motivation to learn, and academic self-efficacy. Materials and Methods: The quantitative study used a combination of purposive sampling and stratified sampling to distribute questionnaires to a total of 504 students in the three target disciplines who fulfilled the eligibility criteria and 476 valid questionnaires were returned. For the data collected, the researchers analyzed the reliability, validity, and goodness of fit of the proposed model and tested the hypotheses using Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM). Results: The results of this study showed that student engagement, motivation, and academic self-efficacy had a significant positive effect on satisfaction and that interaction was positively related to student engagement and teacher innovation was positively related to academic self-efficacy. There is no significant relationship between the effect of interaction on student satisfaction and the effect of computer anxiety on student engagement. Conclusion: In the future, vocational colleges and universities should pay more attention to the role of online teaching interactions and instructor innovations in student engagement and students' academic self-efficacy when teaching online for arts and culture students. By enhancing students' engagement, motivation, and academic self-efficacy, they can improve their satisfaction with online learning, thus improving the quality of education.

Validation of the Brazilian Portuguese Version of the Barcelona Music Reward Questionnaire (BMRQ)

Musical enjoyment varies among individuals due to personal and cultural factors. While some find emotional upliftment and social connection in music, others, affected by anhedonia, struggle to derive pleasure from it. The Barcelona Music Reward Questionnaire (BMRQ) is designed to assess sensitivity to music rewards. This study validates the Brazilian Portuguese version of the BMRQ, acknowledging the linguistic importance for countries like Brazil. The process involved translating the BMRQ and administering it to 209 participants. Analytical methods, including confirmatory factor analysis and reliability checks, were used to confirm the construct’s validity and reliability. The findings demonstrated that the Brazilian Portuguese version aligns closely with the original Spanish version. Now available in several languages, the BMRQ is applicable in various fields such as music emotion research, clinical settings, music therapy, educational contexts, and cross-cultural studies. Its broad applicability underscores its significance in understanding the intricate relationship between humans and music.

Psychometric properties of the Chinese version of the Quiet Quitting Scale

BackgroundThe phenomenon of quiet quitting has attracted considerable attention within the nursing community. Elevated quiet quitting levels can weaken the work environment, reducing healthcare efficiency and quality. However, appropriate assessment tools to measure this phenomenon remain unavailable in China. This study aimed to translate the Quiet Quitting Scale into Chinese and evaluate its psychometric properties.MethodsThe Brislin translation model was used to translate the Quiet Quitting Scale into Chinese. A total of 420 nurses were recruited from hospitals in Central China. Item analysis was conducted using the Critical Rate, correlation coefficient, and internal consistency methods. Reliability was evaluated through internal consistency, split-half reliability, and test-retest reliability. The content validity of the Chinese version was assessed using the Delphi method. Exploratory factor analysis and confirmatory factor analysis were performed to evaluate the construct validity of the Chinese version. Data were analyzed using SPSS 29.0 and Mplus 8.0 software.ResultsThe Chinese version of the Quiet Quitting Scale demonstrated robust content validity (S-CVI/Ave: 0.989, CVR:0.800-1.000). The three-factors model was obtained by using exploratory factor analysis, explaining 77.93%, and confirmatory factor analysis supported acceptable construct validity (χ2/df = 2.224, CFI = 0.963, TLI = 0.945, RMSEA = 0.077). Additionally, the scale demonstrated a Cronbach’s α of 0.856, split-half reliability was 0.921, test-retest reliability was 0.851, and McDonald’s Omega was 0.887.ConclusionsThe Chinese version of the Quiet Quitting Scale (QQS) demonstrated strong reliability and validity, making it a valuable tool for assessing quiet quitting behaviors among nurses in both clinical and managerial contexts. This scale can help identify at-risk individuals and support the development of targeted interventions aimed at improving work quality and efficiency. Future research should assess the scale’s applicability across various healthcare settings and its long-term impact.

Transcultural Adaptation, Validation, Psychometric Analysis, and Interpretation of the 22-Item Thai Senior Technology Acceptance Model for Mobile Health Apps: Cross-Sectional Study.

The rapid advancement of technology has made mobile health (mHealth) a promising tool to mitigate health problems, particularly among older adults. Despite the numerous benefits of mHealth, assessing individual acceptance is required to address the specific needs of older people and promote their intention to use mHealth. This study aims to adapt and validate the senior technology acceptance model (STAM) questionnaire for assessing mHealth acceptance in the Thai context. In this cross-sectional study, we adapted the original, 38-item, English version of the STAM using a 10-point Likert scale for mHealth acceptability among the Thai population. We translated the mHealth STAM into Thai using forward and backward translation. A total of 15 older adults and experts completed the pilot questionnaire and were interviewed to assess its validity. The pilot items of the Thai mHealth STAM were then reworded and revised for better comprehension and cross-cultural compatibility. The construct validity of the Thai mHealth STAM was evaluated by a multidimensional approach, including exploratory and confirmatory factor analysis and nonparametric item response theory analysis. Discriminative indices consisting of sensitivity, specificity, and area under the receiver operating characteristic (AUROC) were used to determine appropriate banding and discriminant validity for the intention to use mHealth. Internal consistency was assessed using Cronbach α and McDonald ω coefficients. Out of the 1100 participants with a mean age of 62.3 (SD 8.8) years, 360 (32.7%) were adults aged 45-59 years, and 740 (67.3%) were older adults aged 60 years and older. Of the 40-item pilot questionnaire, exploratory factor analysis identified 22 items with factor loadings >0.4 across 7 principal components, explaining 91.45% of the variance. Confirmatory factor analysis confirmed that 9-dimensional sets of 22 items had satisfactory fit indices (comparative fit index=0.976, Tucker-Lewis index=0.968, root mean square error of approximation=0.043, standardized root mean squared residual=0.044, and R2 for each item>0.30). The score banding D (low≤151, moderate 152-180, and high≥181) was preferred as the optimal 22-item Thai mHealth STAM cutoff score based on the highest sensitivity of 89% (95% CI 86.1%-91.5%) and AUROC of 72.4% (95% CI 70%-74.8%) for predicting the intention to use mHealth. The final Thai mHealth STAM, consisting of 22 items, exhibited remarkable internal consistency, as evidenced by a Cronbach α of 0.88 (95% CI 0.87-0.89) and a McDonald ω of 0.85 (95% CI 0.83-0.87). For all 22 items, the corrected item-total correlations ranged between 0.26 and 0.71. The 22-item Thai mHealth STAM demonstrated satisfactory psychometric properties in both validity and reliability. The questionnaire has the potential to serve as a practical questionnaire in assessing the acceptance and intention to use mHealth among pre-older and older adults.

Development and psychometric analysis of the traumatic childbirth perception scale in men

Abstract This study aimed to develop the Traumatic Childbirth Perception Scale for men and to examine its psychometric properties. This methodological study was conducted in three stages. Participants included all men aged 18 and older who consented to participate. Ethical approval was obtained. In the first stage, 512 participants were included, with 255 participants and 257 participants in the second stage. Exploratory Factor Analysis (EFA) was conducted in the first stage using Sample 1 data. Following the analysis in the first stage, data were collected for Sample 2. Discriminant Function Analysis (DFA) was performed on the Sample 2 data. Considering that perceptions of traumatic birth in men might vary according to marital status, Sample 2 data were analyzed in three groups: single, married, and all participants. Data collection was conducted online via Google Forms. The research instruments included a Participant Information Form, a draft version of the Traumatic Childbirth Perception Scale for Men, and the Posttraumatic Stress Disorder Short Scale as an equivalent scale. Exploratory and confirmatory factor analyses were used for validity analysis. Reliability coefficients were calculated using Cronbach's alpha and parallel test reliability coefficients. The developed scale consisted of 23 items and four sub-dimensions (anxiety, trauma, privacy, and comfort). The Cronbach's alpha internal reliability coefficient of the scale was 0.868, and the reliability coefficients of the sub-dimensions ranged from 0.602 to 0.851, indicating good reliability. In the second stage of the Traumatic Childbirth Perception Scale for Men, the first-level factorial structure of the scale (× 2 (221, N = 257) p &lt; 0.000; × 2/sd = 1.578; CMIN:348.87; RMSEA = 0.048; CFI = 0.931; GFI = 0.900) showed excellent fit across the entire sample. The results indicate that the Traumatic Childbirth Perception Scale for men has good reliability and validity and can be used to determine the perceived level of traumatic birth in men.

Unveiling the power of R: a comprehensive perspective for laboratory medicine data analysis

Abstract R language has gained traction in laboratory medicine for its statistical power and dynamic tools like RMarkdown and RShiny. However, there is limited literature summarizing R packages and functions tailored for laboratory medicine, making it difficult for clinical laboratory workers to access these tools. Additionally, varying algorithms across R packages can lead to inconsistencies in published reports. This review addresses these challenges by providing an overview of R’s evolution and its key features, followed by a summary of statistical methods implemented in R, including platform comparisons, precision verification, factor analysis, and the establishment of reference intervals (RIs). We also highlight the development and validation of predictive models using techniques such as linear and logistic regression, decision trees, random forests, support vector machines, naive Bayes, K-Nearest Neighbors, k-means clustering, and backpropagation neural networks – all implemented in R. To ensure transparency and reproducibility in research, a checklist is provided for authors publishing papers using R for data analysis in laboratory medicine. In the final section, the potential of R in big data analytics is explored, focusing on standardized reporting through RMarkdown and the creation of user-friendly data visualization platforms with RShiny. Moreover, the integration of large language models (LLMs), such as ChatGPT, is discussed for their benefits in enhancing R programming, automating reporting, and offering insights from data analysis, thus improving the efficiency and accuracy of laboratory data analysis.

Development of new bilingual oral health behavior social support (OHBSS) scales in English and Spanish.

This paper describes the simultaneous co-development of Oral Health Behavior Social Support (OHBSS) scales in English and Spanish. OHBSS scales assess social support for toothbrushing, flossing, and dental care utilization, which are targets for interpersonal-level interventions to promote oral health among Hispanic/Latino adults. The focus was on Mexican-origin adults, who comprise the largest United States Hispanic/Latino subgroup and experience a high oral disease burden. All participants self-identified as Mexican-origin adults (ages 21-40 years old), living along the California-Arizona-Mexico border. Independent samples were recruited for each study partnering with Federally Qualified Health Centers. First, we conducted semi-structured interviews about social support for oral health behaviors in August to November 2018 (Study 1, N = 72). Interviews were audio recorded, transcribed (in original language, Spanish or English), and qualitative data were coded and analyzed in Dedoose following three topical codebooks; excerpts were used to co-create the large bilingual item data bank (OHBSSv1). The item bank was pre-tested via 39 cognitive interviews between December 2019 to March 2020, reviewed by an expert panel with several bilingual members, reduced to 107 Spanish/109 English items (OHBSSv2), then pilot tested in January to December 2021 (Study 2, N = 309). Pilot survey data were analyzed through Exploratory Factor Analysis and Horn's parallel analysis, overall and by language, to examine response patterns and inform item selection (OHBSSv3). The scales queried social support for toothbrushing, flossing, and dental care utilization across 39 items from three sources (family, health providers, others/friends), plus up to nine optional dental care-related items (Study 3, conducted April 2022 to February 2023, N = 502). Confirmatory Factor Analysis (CFA) assessed model fit, overall and by language (multiple group CFA). Final OHBSS scales include 37 items, plus seven optional items. Acceptable model fit for three-factor structures for each oral health behavior was found, providing evidence of the scales' construct validity. Cronbach's alphas and McDonald's omegas were tabulated; all were above 0.95, overall and by language, supporting scales' internal consistency.

Cross-cultural validation of acculturation measures: Expanding the East Asian acculturation framework for global applicability.

In a globalised world, understanding acculturation, the process by which individuals adapt to new cultural environments, is crucial, especially in multicultural societies experiencing increased migration. The East Asian Acculturation Measure (EAAM), based on Berry's acculturation model, has been a cornerstone for assessing acculturation strategies among East Asian populations in the United States; however, its cultural specificity limits utility in broader contexts. This study addresses this gap by adapting and validating the EAAM for diverse populations, producing the Shortened Adapted Acculturation Scale (SAAS). Across two phases involving 490 university students from 87 nationalities in Germany and 329 university students from 25 nationalities in South Africa, both Confirmatory Factor Analysis (CFA) and Exploratory Factor Analysis (EFA) identified a five-factor structure: Social Disconnection, cultural adaptation, Social Perception, Interpersonal Comfort, and Language Integration. The SAAS showed high internal consistency and measurement invariance across genders. These results highlight the importance of culturally adapting psychological measures to ensure their relevance and reliability in global contexts. The SAAS offers practical benefits for clinicians, educators, and policymakers who serve multicultural populations. By illuminating specific dimensions of acculturation, the scale can help identify areas where targeted interventions such as mental health counselling, cultural orientation programs, or inclusive campus policies may foster better social integration and well-being. Although the present study focused on structural validity, future research should examine the SAAS's predictive utility for mental health and social integration outcomes. These findings contribute to cross-cultural psychology and underline the need to refine and validate tools for assessing acculturation in an increasingly interconnected world.