Principal Component Factor Analysis Research Articles

Structural equation modelling of the critical barriers influencing the adoption of green building concepts and technologies in Ghana

Purpose In Ghana, the adoption and application of green building concepts and technologies have not been fully explored. The study aimed to look into the key barriers and how they affect this. Design/methodology/approach Purposive and snowball sampling techniques were used to select a total of 292 construction industry stakeholders in Ghana who provided information via a questionnaire survey used for the data collection. Exploratory factor analysis and Partial least squares structural equation modelling (PLS-SEM) were used for computing the data analyses. Findings According to the study findings, the top five most critical barriers to the uptake of green building concepts and technologies (GBCs and Ts) in Ghana are: lack of government incentives/supports for implementing green building technologies (GBTs), lack of knowledge and awareness of GBTs and their benefits, lack of GBTs databases and information, Lack of green building (GB) expertise/skilled labour and Higher costs of GBTs. Principal Component Factor Analysis was used to further analyse the data, which allowed for the reduction of the 27 (27) factors to just four (4) underlying critical barriers: (1) government and knowledge-associated barriers, (2) technical barriers, (3) cost and finance barriers and (4) stakeholders’ attitude barriers. PLS-SEM techniques were used to analyse this collection of barriers, and the results showed that stakeholders’ attitude-associated barriers and cost and finance-related barriers have a significant negative influence on the uptake of GBCs and Ts in Ghana. This study’s findings have provided empirical evidence of the critical barriers to the uptake of GBCs and Ts from all stakeholders. Stakeholders desirous of implementing GBCs and Ts would work against the negative influences on the uptake of GBCs and TS. Originality/value Although there has been an abundance of research to examine the critical barriers to GB, however, the uniqueness of this study is nested in modelling the influence of the barriers on the adoption of GBCs and Ts using the PLS-SEM path modelling.

Quantifying of morphological character for Kacang goat using principal component factor analysis

The study’s objective was to estimate the association among various linear body measurements and body weights of adult Kacang goats. The data was obtained from 209 heads of adult Kacang Goat, compressed 78 bucks and 131 ewes. The morphological evaluation was performed by measuring body weight (BW), body length (BL), chest depth (CD), chest girth (CG), chest width (CW), and withers height (WH). Factor PROCEDURE was performed to estimate the principal component. The result of factor analysis was used to determine the independent variable for linear regression analysis. BW has a favorable correlation with CG, BL, CD, CW, and WH for bucks and ewes. PC 1 accounts for 55.62% of the variation in bucks, while PC 2 accounts for an additional 18.34%. PC 1 accounts for just 0.45% of the overall variation in ewes, whereas PC 2 accounts for 0.24%. The R-squared (R2) values for bucks and ewes in the regression equation with CG as the independent variable are 0.32 and 0.41, respectively. For both bucks and ewes, the regression equation with CW as the independent variable had a higher R2 of 0.52 and 0.20, respectively. For bucks and ewes, the regression equation'sR2 values are 0.54 and 0.44, respectively, with combined CG and CW acting as independent variables. This integrated approach to analyzing body measurements in Kacang Goats provides a robust foundation for making informed decisions in goat farming.

Culex quinquefasciatus Density Associated with Socioenvironmental Conditions in a Municipality with Indeterminate Transmission of Lymphatic Filariasis in Northeastern Brazil.

Lymphatic filariasis (LF) is a neglected tropical disease associated with poverty and poor environmental conditions. With the inclusion of vector control activities in LF surveillance actions, there is a need to develop simple methods to identify areas with higher mosquito density and thus a higher consequent risk of W. bancrofti transmission. An ecological study was conducted in Igarassu, which is in the metropolitan region of Recife, Pernambuco, Brazil. The mosquitoes were captured in 2060 houses distributed across 117 census tracts. The vector density index (VDI), which measures the average number of lymphatic-filariasis-transmitting mosquitoes per number of houses collected in the risk stratum, was constructed. Moreover, the social deprivation indicator (SDI) was constructed and calculated through principal component factor analysis. An average of 242 female C. quinquefasciatus were found in the high-risk stratum, while the average in the low-risk stratum was 108. The overall VDI was 6.8 mosquitoes per household. The VDI for the high-risk stratum was 13.2 mosquitoes per household, while for the low/medium-risk stratum, it was 5.2. This study offers an SDI for the density of C. quinquefasciatus mosquitoes, which can help reduce the costs associated with data collection and allows for identifying priority areas for vector control actions.

Auditing students’ perceptions of online assessments and e-proctoring systems

The COVID-19 pandemic accelerated the implementation of online assessments at an unprecedented pace. The pandemic required most higher education institutions worldwide to implement online assessments almost overnight. The study aimed to gain an understanding of auditing students’ perceptions of online assessments and e-proctoring systems during the pandemic. A web-based questionnaire was administered to postgraduate auditing students who had taken part in remote online assessments at an open distance and e-learning university in South Africa, invigilated via e-proctoring software, which was introduced in the 2020 academic year. A total of 238 students participated in the study. Factor analysis, using principal component factor analysis, was performed to identify the factors or components that possibly caused the variation in these auditing students’ perceptions of online assessments and e-proctoring. Varimax with Kaiser normalisation was used as a rotation method. This process revealed five main components that explained variations in participants’ perceptions, namely: Component 1—emotions (assessments), appropriateness (assessments), fairness (assessments), monetary aspects, and time; Component 2—practical IT matters, academic integrity (e-proctoring) and cheating (e-proctoring); Component 3—practical IT matters (e-proctoring) and emotions (e-proctoring); Component 4—practical IT matters (assessments) and time (assessments); and Component 5—cheating (assessments). Implications of the findings for educators are offered, along with recommendations for future research.

Measuring Self-Efficacy to Request Cancer-Related Work Accommodations: Development and Validation of a Brief Survey Instrument.

Access to work accommodations, such as time off to attend medical appointments, is a key predictor of cancer-related job loss. We aimed to develop and validate a measure of self-efficacy to request and obtain work accommodations related to diagnosis of breast cancer and need for treatment. The 5-item Self-efficacy to Ask for Work Accommodations (SAWA) tool was adapted from a scale that measures self-efficacy in patient-physician interactions. English-speaking, employed women, aged 18-64, who were undergoing treatment for stage I-III breast cancer completed surveys during treatment (chemotherapy, radiation, and/or < 60days since surgery) and 4months after its completion. Post-treatment surveys were the primary validation dataset; analyses were repeated using baseline data to further evaluate validity/reliability. Psychometric analyses included internal consistency, principal component factor analysis, convergent and divergent validity, and test-retest reliability (in a subset of patients, 2weeks after post-treatment surveys). Among 362 participants, the SAWA measure had a Cronbach's alpha of 0.93, indicating high scale reliability. Factor analysis revealed one summary measure which could be divided into two subscales. Overall, the SAWA demonstrated convergent and divergent validity based on the hypothesized variables. Similar patterns were observed when analyses were repeated using baseline survey data. In the test-retest subset, the Pearson correlation between SAWA assessed 4months post-treatment versus retest 2weeks later was 0.79. The SAWA is a validated survey instrument that can be used to measure self-efficacy in employed cancer patients to ask for and obtain employment accommodations.

Impact of Mental Health on Academic Performance: A Study on the Risk of Failure in Higher Education

Building healthy spaces and adequate mental health in the university context is of vital importance for optimal, quality and warm academic development, both for students and teachers. The purpose of the research is to determine the state of mental health of students as a risk factor for academic failure in higher education. For the methodological procedure, the “GHQ-28 Mental Health Assessment Scale and Questionnaire” was applied and a socio-educational form was used to measure academic performance. The level of mental health of students was estimated by applying the principal component factor analysis (PCA). To determine the variance of the factor analysis, Bartlett's sphericity test was used, then the factorization process was carried out by the PCA method with a varimax rotation. They were selected for the sample by the simple random method, resulting in a total of 212 cases. The results obtained revealed that the “mental health status” variable has a significant effect on academic risk, with an estimated coefficient of -0.0878 and an associated p-value of 0.014. This indicates that as the mental health status of students decreases, an increase in the probability of repeating the academic semester is observed.

Exploring adult hearing aid user experiences: meaning extraction methods, content patterns, and associations with demographic and outcome variables

Objective Employing automated language analysis, specifically Meaning Extraction Method (MEM) and Principal Component Analysis (PCA), to identify key factors in open-text responses about hearing aid experiences. Design Exploratory, cross-sectional design, using an online questionnaire. Responses to a single open-ended question were analysed using MEM, PCA, regression, and correlation analyses. Study sample Participants (n = 538) included adult hearing aid users sampled from the Hearing Tracker website community and Lexie Hearing user databases in the United States. Results The MEM-derived items revealed six factors related to hearing aid experiences: (1) life change, (2) social situation, (3) quality of life, (4) impact and speech understanding, (5) communication and interaction, and (6) music and environmental sounds. IOI-HA item 3 had the most statistically significant correlations with PCA factors. Quantile regression revealed that factors one and two significantly predicted the IOI-HA total score. Positive correlations were observed between self-reported hearing difficulty and factors one, four, and five, as well as between factor one and general health and factor two and physical activity. Conclusion Natural language analysis of open-ended textual responses can offer valuable insights into hearing aid users’ experiences. Future studies should aim to refine this methodology to enhance clinical relevance and generalisability.

Self-Regulation of Internet Behaviors on Social Media Platforms

The current research sought a comprehensive understanding about the consequences of information-sharing behavior on social media, given public concerns about privacy violations. We used a mixed-methods approach to investigate the influence of the self on “revealing” and emotional “healing” experiences online. Respondents completed a survey measuring sense of self and motivations for using social media, as well as revealing and healing attitudes and behavior. We conducted a principal component factor analysis on separate parts of the survey and ran Pearson correlations of the emerging factors. Qualitative data describing experiences of online self-disclosure were used to illustrate the correlational findings. The “revealing” factors contrasted adaptive with maladaptive and naïve posting. The sense of self, as well as motivations for social media use, influenced whether users engaged in destructive posting behaviors. The “healing” factors were associated with positive motivations for self-disclosure, seeking a supportive online community, and building resilience. Correlational data revealed that respondents with an insecure or asocial sense of self felt the greater need for online self-disclosure. Motivations to self-disclose online and experiences of “healing”, with the help of a supportive online community, depended on whether the sense of self was secure, insecure, or asocial.

Negative Beliefs About Working with Health Problems and Support at Work as Predictors for Return to Work for People Struggling with Common Mental Disorders.

The purpose of this study was to investigate predictors for return to work for people struggling with common mental disorders on sick leave or at risk of sick leave. The first aim of this study was to evaluate the psychometric properties of a set of statements exploring different conditions at the workplace and assumptions about working with health problems, by investigating the factor structure, reliability and construct validity of these statements. The second aim of this study was to investigate the predictive value of the identified factors. A total of 797 patients from an outpatient mental health clinic were included in a naturalistic observational study. The study design was longitudinal. The participants filled out self-report questionnaires pre- and post-treatment. A principal component factor analysis with a varimax rotation identified two factors, Negative beliefs about working with health problems and Support at work, displaying high internal consistency, 0.83 and 0.84, respectively. Separately, both factors were significant predictors of full return to work after treatment. The final multivariable analysis including both factors left Negative beliefs about working with health problems as a significant predictor explaining unique variance. Negative beliefs about working with health problems and Support at work are important predictors for work status after treatment and should therefore be addressed during treatment for common mental disorders to assist people return to work.

Assessment of socioeconomic and health vulnerability among urban slum dwellers in Bangladesh: a cross-sectional study

BackgroundBangladesh is rapidly urbanizing and approximately half of its urban population resides in deprived slums with limited access to basic needs. However, there is a dearth of information on vulnerability levels among slum dwellers. We aimed to assess the level of vulnerability within and between slums via various socioeconomic and health indicators.MethodsA cross-sectional survey of 810 randomly selected households was conducted in two purposively selected slums, Korail and Shyampur, in Dhaka from November to December 2021. Data was collected on various indicators, including demographics, education, employment, access to utility, and healthcare services. Principal component factor analysis was employed to identify the key indicators to construct the socioeconomic and health vulnerability index for the urban slums of Bangladesh (SEHVI-BD). Kaiser-Meyer-Olkin, Bartlett’s test, and Cronbach’s alpha coefficient test were used to assess indicators’ suitability. The selected indicators were used to generate an index on a scale of 100, with a higher index value indicating a higher level of vulnerability. The estimated scores were used to categorize the vulnerability status into three levels: mild, moderate, and severe vulnerability. The Mann-Whitney-U test and Kruskal-Wallis test were applied between the generated index and other socioeconomic variables to validate the relationship.ResultsA total of 27 socioeconomic and health indicators were identified that explained 60% of the variance. The indicators were then grouped into six domains on the basis of their relevance. The prevalence of severe vulnerability in the Korail slum was approximately 9% and moderate vulnerability was 30% whereas these values were approximately 58% and 37%, respectively, in the Shyampur slum. The difference in the vulnerability level between the two slums was also evident across the domains. Households in the poorest wealth quintile, with lower education levels of household heads, and having irregular income experienced higher levels of vulnerability.ConclusionsThe SEHVI-BD offers a critical tool for policymakers to identify and address vulnerabilities, facilitating more targeted public health interventions in urban Bangladesh and similar low-income settings. This study further emphasizes the importance of integrating comprehensive vulnerability assessments into public health policies to reduce inequalities and improve well-being, especially for the urban marginalized slum population.

Psychometric Evaluation of a Measure of Evidence-Based Practice in Occupational Health.

Occupational Health Services (OHS) are obliged to follow the principles of evidence-based medicine. However, there needs to be tools to measure this. Therefore, we developed and validated a questionnaire for evaluating OHS practitioners' attitudes, competence, and organisational support to perform evidence-based practice (EBP-OHS) following the JBI Model of Evidence-Based Healthcare. The questionnaire's content validity was assessed by 12 experts in the field. Then, an opportunity sample of 549 OHS practitioners completed the questionnaire. We examined the questionnaire's psychometric properties using exploratory factor analysis and subjected it to construct validity and reliability testing. The content validity index of the chosen items was 0.78 or higher. Exploratory factor analysis revealed that the measure's construct validity was adequate (KMO 0.9). Principal component factor analysis supported a three-factor structure (all eigenvalues 1.3 or more), which explained 60.3 % of the total variance. Aligned with these three factors, the EBP-OHS consists of three domains: Organisational support (seven items), OHS practitioners' competence (six items) and OHS practitioners' attitudes (two items). The scale's reliability is good (Cronbach alpha 0.88). The EBP-OHS is a valid tool for measuring occupational health services' evidence-based practice and enabling the implementation of research into practice. It embodies the phases of evidence transfer and implementation described in the JBI Model of Evidence-Based Healthcare and translates them into concrete measurable activities in the OHS context.

Enhancing predictive accuracy of electoral outcomes: A comparative study of dimension reduction techniques in linear regression models

The effectiveness of Principal Component Analysis (PCA) and Factor Analysis (FA) in enhancing the predictive accuracy and interpretability of linear regression models for electoral outcomes is examined in this study, with a focus on the Croatian presidential election. Challenges such as multicollinearity and model complexity were addressed through the application of PCA and FA to socio-demographic and political variables, resulting in their transformation into a simpler, more manageable form. Multicollinearity was significantly reduced by PCA, and model stability was improved. Deeper insights into voter behavior were provided by FA through the identification of latent variables. Improvements were noted; however, issues with homoscedasticity were identified, which affected the predictive reliability of the models. The optimized linear regression model, specifically designed to address homoscedasticity (LR2), was found to demonstrate superior performance in terms of predictive accuracy and stability when compared to models that utilized PCA and FA alone. The importance of maintaining homoscedasticity in regression models is underscored by the findings, and the potential of dimension reduction techniques to improve electoral predictions is highlighted. The integration of these techniques in forecasting models is advocated for, with the aim of assisting political analysts and policymakers. It is suggested that future research could explore the combination of these methods with other advanced modeling approaches to further enhance predictive capabilities.

The Classification of Altruism Forms South Korean Coaches' Perspective.

Altruism is an important element that enables coaches to achieve their clients' coaching goals. Using Q methodology, which enables the examination of individuals' subjectivity, this study investigated South Korean coaches' perceptions of altruism. Through a literature review, interviews, and surveys, 204 statements were collected from the Q population, and 40 statements were selected to form Q Samples. P samples were organized with 31 coaches registered with the Korea Coach Association, and principal component factor analysis using the Quanl program was applied to assess the data. Based on the results of this study, South Korean coaches' perceptions of altruism were sorted into four types: type 1, 'a perception type that considers altruism to lead to respect for the client's presence'; type 2, 'a perception type that considers altruism to be meaningful when practiced'; type 3, 'a perception type that considers altruism is a mindset that leads to the development of happiness'; and type 4, 'a perception type that considers altruism to be a basic attitude that coaches should have'. The results of this study will further clarify coaches' perceptions of altruism, enabling them to receive the necessary training, develop their character, and achieve inner maturity. This, in turn, will help them improve their attitudes towards clients and further enhance their ethics and professionalism.

Backyard Activities as Sources of Social and Personal Well-Being: A Study of the Mexican Population (Guasave, Sinaloa)

Isolation due to the COVID-19 pandemic has heightened daily tension, leading to stress and other illnesses. This research examined the impact of backyard activities on social and personal well-being, focusing on agricultural (gardens, vegetables, medicinal plants) and livestock activities (chickens, goats, sheep, cows). An exploratory study was conducted using a Likert-scale survey of 387 people, with 22 items covering economic growth, social well-being, and human development. The results showed that 50% of the respondents experienced pleasant emotions through sensory engagement with home gardens, leading to well-being, reduced anxiety and stress, better concentration, and improved family harmony. McDonald’s Omega (ω) value for item consistency was 0.964. Strong correlations were found between economic growth, social well-being, and human development with social and personal well-being, at 0.719, 0.819, and 0.812, respectively. These results are validated by an exploratory principal component factor analysis, which shows the correlation of the classes that integrate backyard activities on a principal axis. Backyard livestock activities had a lesser impact on personal and social well-being compared to agricultural activities, which were valued more for their economic and nutritional contributions. Thus, backyard activities, especially those involving home gardens, significantly enhance social and personal well-being alongside their economic and food benefits.

Assessment of water quality and source apportionment of pollution in a tropical river in eastern India: A study utilizing multivariate statistical tools and the APCS-MLR receptor model.

The Mundeswari River, an ecologically distressed river in eastern India, has been subjected to water quality deterioration largely due to anthropogenic activities in its vicinity. This study aimed to comprehensively evaluate the current state of pollution in the river and assess the appropriateness of river water for irrigation, given its extensive use for agricultural purposes. Monthly water quality monitoring was undertaken at four distinct sampling sites (SP1-SP4) over a two-year period (2020-2022), considering seventeen water quality parameters. This research employed principal component analysis/factor analysis (PCA/FA) and absolute principal component score-multiple linear regression (APCS-MLR) receptor modelling. These methodologies were used to discern and quantify potential sources of pollution influencing the water quality of the Mundeswari River. The study revealed that the water quality of the Mundeswari River was most degraded during the pre-monsoon season. Among the four sampling sites, SP3 exhibited the highest level of pollution with mean biochemical oxygen demand (BOD) and chemical oxygen demand (COD) values of 5.36mg/L and 44.72mg/L, respectively. According to the one-way analysis of variance (ANOVA), there was considerable spatial and seasonal disparities (P < 0.05) in most water quality parameters. The PCA/FA extracted four latent pollution sources, accounting for 81.5% of the total variance. The primary factors influencing the quality of river water are natural weathering processes, discharge of domestic effluent and waste, and agricultural runoff. The APCS-MLR receptor model further revealed that agricultural drainage factors and the discharge of domestic effluent and waste had a greater impact on the Mundeswari River. The investigation concluded that the mean values of all indicators for irrigation suitability were below the defined threshold limits, indicating that the water of the studied river appears suitable for irrigation. The outcomes of this study may significantly contribute to the formulation of sustainable strategies for the ecological rejuvenation of the Mundeswari River.

Supervised Dynamic PCA: Linear Dynamic Forecasting with Many Predictors

This paper proposes a novel dynamic forecasting method using a new supervised Principal Component Analysis (PCA) when a large number of predictors are available. The new supervised PCA provides an effective way to bridge the gap between predictors and the target variable of interest by scaling and combining the predictors and their lagged values, resulting in an effective dynamic forecasting. Unlike the traditional diffusion-index approach, which does not learn the relationships between the predictors and the target variable before conducting PCA, we first rescale each predictor according to their significance in forecasting the targeted variable in a dynamic fashion, and a PCA is then applied to a rescaled and additive panel, which establishes a connection between the predictability of the PCA factors and the target variable. We also propose to use penalized methods such as the LASSO to select the significant factors that have superior predictive power over the others. Theoretically, we show that our estimators are consistent and outperform the traditional methods in prediction under some mild conditions. We conduct extensive simulations to verify that the proposed method produces satisfactory forecasting results and outperforms most of the existing methods using the traditional PCA. An example of predicting U.S. macroeconomic variables using a large number of predictors showcases that our method fares better than most of the existing ones in applications. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Addressing type Ia supernova color variability with a linear spectral template

Type Ia Supernovae (SNeIa) provided the first evidence of an accelerated expansion of the universe and remain a valuable probe to cosmology. They are deemed standardizable candles due to the observed correlations between their luminosity and photometric quantities. This characteristic can be exploited to estimate cosmological distances after accounting for the observed variations. There is however a remaining dispersion unaccounted for in the current state-of-the-art standardization methods. In an attempt to explore this issue, we propose a simple linear 3-component rest-frame flux description for a light-curve fitter. Since SNIa intrinsic color index variations are expected to be time-dependent, our description builds upon the mathematical expression of the well-known Spectral Adaptive Light Curve Template 2 (SALT2) for rest-frame flux, whilst we drop the exponential factor and add an extra model component with time and wavelength dependencies. The model components are obtained by performing either Principal Component Analysis (PCA) or Factor Analysis (FA) onto a representative training set. The constraining power of the model dubbed Pure Expansion Template for Supernovae (PETS) is evaluated and we found compatible results with SALT2 for Ωm0 and ΩΛ0 within 68% uncertainty between the two models, with PETS’ fit parameters exhibiting non-negligible linear correlations with SALT2’ parameters. For both PCA and FA model versions we verified that the first component mainly describes color index variations, proving it is a dominant effect on SNIa spectra. The model nuisance parameter which multiplies the color index variation-like fit parameter shows evolution with redshift in an initial binned cosmology analysis. This behavior can be due to selection effects and should be further investigated with higher redshift SNeIa samples. Overall, our model shows promise, as there are still a few aspects to be refined; however, it still falls short in reducing the unaccounted dispersion.

Acculturating to multiculturalism: a new dimension of dietary acculturation among Asian American, Native Hawaiian, and Pacific Islander women in the San Francisco Bay Area, USA

BackgroundDietary acculturation is the process by which diet and dietary practises from the environment of origin are retained or changed and/or those prevalent in a new environment are adopted. Despite rapid population growth the U.S., knowledge gaps exist on characterising dietary acculturation among Asian American, Native Hawaiian, and Pacific Islander communities (AANHPI). This study characterise dietary patterns in a sample representative of AANHPI on key demographic characteristics.MethodsData were from a 2013–2014 population-based case-control study in the San Francisco Bay Area, U.S. Survey items were adapted from dietary acculturation scales developed for AANHPI populations. Validated measures assessed social capital, social standing, discrimination and immigration experiences. A principal components factor analysis was conducted to characterise dietary patterns of acculturation.ResultsThree dietary patterns were identified: “Asian,” “Western,” and a distinct “Multicultural” factor. Respondents reporting a high-Asian diet tended to also report smaller social networks, higher levels of stress, and, among those born outside of the U.S., an educational standing that was better before immigration. Respondents reporting a high-Western diet tended to also report the highest level of discrimination. Those reporting a high-Multicultural diet tended to report higher neighbourhood collective efficacy.ConclusionsThe finding of a distinct “Multicultural” factor beyond the typical “Asian” and “Western” factors may reflect the multidirectional relationships between culture, diet, and dietary behavior, in which origin and destination cultures interact in complex ways and where foods from multiple ethnicities intermix.

Validity and Reliability of the German Vocal Fatigue Index in Adults With Voice Disorders.

Vocal fatigue-related symptoms, frequent in patients with voice disorders, are reliably quantifiable with the Vocal Fatigue Index (VFI) in several languages. The main aim of this work was to investigate the content and construct validity of the German Vocal Fatigue Index (VFI-D) by comparison with the German Voice Handicap Index 9 international (VHI9i) and the Vocal Tract Discomfort Scale (VTD). Moreover, the scale and retest reliability were investigated. In a prospective cohort study at two tertiary care hospitals, 93 adults with all types of voice complaints filled out the VFI-D, the VHI-9i, and the VTD. Eighty-one adults (Mage = 53.5 years, SD = 15.7) completed them one time, and additional 12 patients (Mage = 53.8 years, SD = 15.6) did a test-retest, completed them twice at an interval of 1 week. The validity was assessed by Spearman's rho and Mokken scaling and the reliability by a factor analysis and a two-tailed t test. Mokken scaling has proven the VFI-D as one-dimensional questionnaire (one cluster, r = .95). All VFI-D items could be assigned to one of three factors according to principal component and principal axis factor analysis. Although Factor 1 related best to the VHI9i (∅ rs = .76) and Factor 2 to the VTD (∅ rs = .74), Factor 3 correlated moderately with the VHI9i (rs = .34) and weakly with the VTD (rs = .25). Results from the first and second VFI-D completion agreed strongly (t = -1.78, p = .10). In this study, the content and construct validity of the VFI-D were confirmed. Also, both scale reliability with identical factors as those in the original study and retest reliability were verified. The VHI9i and VTD were highly indicative of VFI-D Factors 1 and 2, respectively. No correlation between Factor 3 and the other questionnaires was determined. Future research should evaluate which characteristics are specific to vocal fatigue versus general voice disorder-related symptoms.

Waste separation intention of households in Sri Lanka

Abstract The effective practice of waste separation at the source is crucial for achieving sustainable development and promoting the reduce, reuse, and recycle concept. This practice should be adopted by both households and business organisations. The primary objective of this study is to investigate the factors that influence the intention of waste separation at the household level in Sri Lanka. For this purpose, a random selection of 388 households representing the entire country was undertaken. Independent variables, including attitude, perceived cost benefits, moral norms, inconvenience, and rules and regulations, were examined in relation to their impact on the dependent variable, which was the intention of waste separation. A 5-point Likert scale questiommaire was utilised to assess each of these variables. The methodology employed in this study included principal component analysis, factor analysis, and Structural Equation Model (SEM) to establish the underlying model. The results of the analysis revealed the significance of attitude, perceived cost benefits, and moral norms at a 5% significance level. Interestingly, although inconvenience exhibited a negative influence on intention, its significance was not established. Furthermore, while existing literature commonly attributed local government rules and regulations as a significant factor, the model indicated a positive and insignificant relationship. In conclusion, this study underscores the importance of positive attitudes, perceived benefits in terms of cost, and adherence to moral norms in fostering the intention for waste separation. Despite the negative impact of inconvenience not reaching statistical significance, it remains a noteworthy consideration. Additionally, the unexpected positive and insignificant relationship with local government rules and regulations prompts further exploration.