Concept: Factor analysis
BACKGROUND: Dengue, a mosquito-borne febrile viral disease, is found in tropical and sub-tropical regions and is now extending its range to temperate regions. The spread of the dengue viruses mainly depends on vector population (Aedes aegypti and Aedes albopictus), which is influenced by changing climatic conditions and various land-use/land-cover types. Spatial display of the relationship between dengue vector density and land-cover types is required to describe a near-future viral outbreak scenario. This study is aimed at exploring how land-cover types are linked to the behavior of dengue-transmitting mosquitoes. METHODS: Surveys were conducted in 92 villages of Phitsanulok Province Thailand. The sampling was conducted on three separate occasions in the months of March, May and July. Dengue indices, i.e. container index (C.I.), house index (H.I.) and Breteau index (B.I.) were used to map habitats conducible to dengue vector growth. Spatial epidemiological analysis using Bivariate Pearson’s correlation was conducted to evaluate the level of interdependence between larval density and land-use types. Factor analysis using principal component analysis (PCA) with varimax rotation was performed to ascertain the variance among land-use types. Furthermore, spatial ring method was used as to visualize spatially referenced, multivariate and temporal data in single information graphic. RESULTS: Results of dengue indices showed that the settlements around gasoline stations/workshops, in the vicinity of marsh/swamp and rice paddy appeared to be favorable habitat for dengue vector propagation at highly significant and positive correlation (p = 0.001) in the month of May. Settlements around the institutional areas were highly significant and positively correlated (p = 0.01) with H.I. in the month of March. Moreover, dengue indices in the month of March showed a significant and positive correlation (p <= 0.05) with deciduous forest. The H.I. of people living around horticulture land were significantly and positively correlated (p = 0.05) during the month ofMay, and perennial vegetation showed a highly significant and positive correlation (p = 0.001) in the month of March with C.I. and significant and positive correlation (p <= 0.05) with B.I., respectively. CONCLUSIONS: The study concluded that gasoline stations/workshops, rice paddy, marsh/swamp and deciduous forests played highly significant role in dengue vector growth. Thus, the spatio-temporal relationships of dengue vector larval density and land-use types may help to predict favorable dengue habitat, and thereby enables public healthcare managers to take precautionary measures to prevent impending dengue outbreak.
BACKGROUND: Negative affect and difficulties in its regulation have been connected to several adverse psychological consequences. While several questionnaires exist, it would be important to have a theory-based measure that includes clinically relevant items and shows good psychometric properties in healthy and patient samples. This study aims at developing such a questionnaire, combining the two Gross  scales Reappraisal and Suppression with an additional response-focused scale called Externalizing Behavioral Strategies covering clinically relevant items. METHODS: The samples consisted of 684 students (mean age = 23.3, SD = 3.5; 53.6% female) and 369 persons with mixed mental disorders (mean age = 36.0 SD = 14.6; 71.2% female). Items for the questionnaire were derived from existing questionnaires and additional items were formulated based on suggestions by clinical experts. All items start with “When I don’t feel well, in order to feel better…”. Participants rated how frequently they used each strategy on a 5-point Likert scale. Confirmatory Factor Analyses were conducted to verify the factor structure in two separate student samples and a clinical sample. Group comparisons and correlations with other questionnaires were calculated to ensure validity. RESULTS: After modification, the CFA showed good model fit in all three samples. Reliability scores (Cronbach’s alpha) for the three NARQ scales ranged between .71 and .80. Comparisons between students and persons with mental disorders showed the postulated relationships, as did comparisons between male and female students and persons with or without Borderline Personality Disorder. Correlations with other questionnaires suggest the NARQ’s construct validity. CONCLUSIONS: The results indicate that the NARQ is a psychometrically sound and reliable measure with practical use for therapy planning and tracking of treatment outcome across time. We advocate the integration of the new response-focused strategy in the Gross’s model of emotion regulation.
BACKGROUND: Static posture, repetitive movements and lack of physical variation are known risk factors for work-related musculoskeletal disorders, and thus needs to be properly assessed in occupational studies. The aims of this study were (i) to investigate the effectiveness of a conventional exposure variation analysis (EVA) in discriminating exposure time lines and (ii) to compare it with a new cluster-based method for analysis of exposure variation. METHODS: For this purpose, we simulated a repeated cyclic exposure varying within each cycle between “low” and “high” exposure levels in a “near” or “far” range, and with “low” or “high” velocities (exposure change rates). The duration of each cycle was also manipulated by selecting a “small” or “large” standard deviation of the cycle time. Theses parameters reflected three dimensions of exposure variation, i.e. range, frequency and temporal similarity.Each simulation trace included two realizations of 100 concatenated cycles with either low (rho = 0.1), medium (rho = 0.5) or high (rho = 0.9) correlation between the realizations. These traces were analyzed by conventional EVA, and a novel cluster-based EVA (C-EVA). Principal component analysis (PCA) was applied on the marginal distributions of 1) the EVA of each of the realizations (univariate approach), 2) a combination of the EVA of both realizations (multivariate approach) and 3) C-EVA. The least number of principal components describing more than 90% of variability in each case was selected and the projection of marginal distributions along the selected principal component was calculated. A linear classifier was then applied to these projections to discriminate between the simulated exposure patterns, and the accuracy of classified realizations was determined. RESULTS: C-EVA classified exposures more correctly than univariate and multivariate EVA approaches; classification accuracy was 49%, 47% and 52% for EVA (univariate and multivariate), and C-EVA, respectively (p < 0.001). All three methods performed poorly in discriminating exposure patterns differing with respect to the variability in cycle time duration. CONCLUSION: While C-EVA had a higher accuracy than conventional EVA, both failed to detect differences in temporal similarity. The data-driven optimality of data reduction and the capability of handling multiple exposure time lines in a single analysis are the advantages of the C-EVA.
- International journal for quality in health care : journal of the International Society for Quality in Health Care / ISQua
- Published about 6 years ago
OBJECTIVE: /st>To study the psychometric properties of a translated version of the Agency for Healthcare Research and Quality Hospital Survey on Patient Safety Culture (HSOPSC) in the Slovenian setting. DESIGN: /st>A cross-sectional psychometric study including principal component and confirmatory factor analysis. The percentage of positive responses for the 12 dimensions (42 items) of patient safety culture and differences at unit and hospital-level were calculated. SETTING: /st>Three acute general hospitals. PARTICIPANTS: /st>Census of clinical and non-clinical staff (n = 976). MAIN OUTCOME MEASURES: /st>Model fit, internal consistency and scale score correlations. RESULTS: /st>Principal component analysis showed a 9-factor model with 39 items would be appropriate for a Slovene sample, but a Satorra-Bentler scaled χ(2) difference test demonstrated that the 12-factor model fitted Slovene data significantly better. Internal consistency was found to be at an acceptable level. Most of the relationships between patient safety culture dimensions were strong to moderate. The relationship between all 12 dimensions and the patient safety grade was negative. The unit-level dimensions of patient safety were perceived better than the dimensions at the hospital-level. CONCLUSION: /st>The original 12-factor model for the HSOPSC was a good fit for a translated version of the instrument for use in the Slovene setting.
Although pettiness, defined as the tendency to get agitated over trivial matters, is a facet of neuroticism which has negative health implications, no measure exists. The goal of the current study was to develop, and validate a short pettiness scale. In Study 1 (N = 2136), Exploratory Factor Analysis distilled a one-factor model with five items. Convergent validity was established using the Big Five Inventory, DASS, Satisfaction with Life Scale, and Conner-Davidson Resilience Scale. As predicted, pettiness was positively associated with neuroticism, depression, anxiety and stress but negatively related to extraversion, agreeableness, conscientiousness, openness, life satisfaction and resilience. Also, as predicted, pettiness was not significantly related to physical functioning, or blind and constructive patriotism, indicating discriminant validity. Confirmatory Factor Analysis in Study 2 (N = 734) revealed a stable one-factor model of pettiness. In Study 3 (N = 532), the scale, which showed a similar factor structure in the USA and Singapore, also reflected predicted cross-cultural patterns: Pettiness was found to be significantly lower in the United States, a culture categorized as “looser” than in Singapore, a culture classified as “tighter” in terms of Gelfand and colleagues' framework of national tendencies to oppose social deviance. Results suggest that this brief 5-item tool is a reliable and valid measure of pettiness, and its use in health research is encouraged.
Family dogs and dog owners offer a potentially powerful way to conduct citizen science to answer questions about animal behavior that are difficult to answer with more conventional approaches. Here we evaluate the quality of the first data on dog cognition collected by citizen scientists using the Dognition.com website. We conducted analyses to understand if data generated by over 500 citizen scientists replicates internally and in comparison to previously published findings. Half of participants participated for free while the other half paid for access. The website provided each participant a temperament questionnaire and instructions on how to conduct a series of ten cognitive tests. Participation required internet access, a dog and some common household items. Participants could record their responses on any PC, tablet or smartphone from anywhere in the world and data were retained on servers. Results from citizen scientists and their dogs replicated a number of previously described phenomena from conventional lab-based research. There was little evidence that citizen scientists manipulated their results. To illustrate the potential uses of relatively large samples of citizen science data, we then used factor analysis to examine individual differences across the cognitive tasks. The data were best explained by multiple factors in support of the hypothesis that nonhumans, including dogs, can evolve multiple cognitive domains that vary independently. This analysis suggests that in the future, citizen scientists will generate useful datasets that test hypotheses and answer questions as a complement to conventional laboratory techniques used to study dog psychology.
Citizen science-the involvement of volunteers in data collection, analysis and interpretation-simultaneously supports research and public engagement with science, and its profile is rapidly rising. Citizen science represents a diverse range of approaches, but until now this diversity has not been quantitatively explored. We conducted a systematic internet search and discovered 509 environmental and ecological citizen science projects. We scored each project for 32 attributes based on publicly obtainable information and used multiple factor analysis to summarise this variation to assess citizen science approaches. We found that projects varied according to their methodological approach from ‘mass participation’ (e.g. easy participation by anyone anywhere) to ‘systematic monitoring’ (e.g. trained volunteers repeatedly sampling at specific locations). They also varied in complexity from approaches that are ‘simple’ to those that are ‘elaborate’ (e.g. provide lots of support to gather rich, detailed datasets). There was a separate cluster of entirely computer-based projects but, in general, we found that the range of citizen science projects in ecology and the environment showed continuous variation and cannot be neatly categorised into distinct types of activity. While the diversity of projects begun in each time period (pre 1990, 1990-99, 2000-09 and 2010-13) has not increased, we found that projects tended to have become increasingly different from each other as time progressed (possibly due to changing opportunities, including technological innovation). Most projects were still active so consequently we found that the overall diversity of active projects (available for participation) increased as time progressed. Overall, understanding the landscape of citizen science in ecology and the environment (and its change over time) is valuable because it informs the comparative evaluation of the ‘success’ of different citizen science approaches. Comparative evaluation provides an evidence-base to inform the future development of citizen science activities.
- Proceedings of the National Academy of Sciences of the United States of America
- Published almost 5 years ago
First impressions of social traits, such as trustworthiness or dominance, are reliably perceived in faces, and despite their questionable validity they can have considerable real-world consequences. We sought to uncover the information driving such judgments, using an attribute-based approach. Attributes (physical facial features) were objectively measured from feature positions and colors in a database of highly variable “ambient” face photographs, and then used as input for a neural network to model factor dimensions (approachability, youthful-attractiveness, and dominance) thought to underlie social attributions. A linear model based on this approach was able to account for 58% of the variance in raters' impressions of previously unseen faces, and factor-attribute correlations could be used to rank attributes by their importance to each factor. Reversing this process, neural networks were then used to predict facial attributes and corresponding image properties from specific combinations of factor scores. In this way, the factors driving social trait impressions could be visualized as a series of computer-generated cartoon face-like images, depicting how attributes change along each dimension. This study shows that despite enormous variation in ambient images of faces, a substantial proportion of the variance in first impressions can be accounted for through linear changes in objectively defined features.
To what extent is there a general factor of risk preference, R, akin to g, the general factor of intelligence? Can risk preference be regarded as a stable psychological trait? These conceptual issues persist because few attempts have been made to integrate multiple risk-taking measures, particularly measures from different and largely unrelated measurement traditions (self-reported propensity measures assessing stated preferences, incentivized behavioral measures eliciting revealed preferences, and frequency measures assessing actual risky activities). Adopting a comprehensive psychometric approach (1507 healthy adults completing 39 risk-taking measures, with a subsample of 109 participants completing a retest session after 6 months), we provide a substantive empirical foundation to address these issues, finding that correlations between propensity and behavioral measures were weak. Yet, a general factor of risk preference, R, emerged from stated preferences and generalized to specific and actual real-world risky activities (for example, smoking). Moreover, R proved to be highly reliable across time, indicative of a stable psychological trait. Our findings offer a first step toward a general mapping of the construct risk preference, which encompasses both general and domain-specific components, and have implications for the assessment of risk preference in the laboratory and in the wild.
Schools are common sites for obesity prevention interventions. Although many theories suggest that the school context influences weight status, there has been little empirical research. The objective of this study was to explore whether features of the school context were consistently and meaningfully associated with pupil weight status (overweight or obese). Exploratory factor analysis of routinely collected data on 319 primary schools in Devon, England, was used to identify possible school-based contextual factors. Repeated cross-sectional multilevel analysis of five years (2006/07-2010/11) of data from the National Child Measurement Programme was then used to test for consistent and meaningful associations. Four school-based contextual factors were derived which ranked schools according to deprivation, location, resource and prioritisation of physical activity. None of which were meaningfully and consistently associated with pupil weight status, across the five years. The lack of consistent associations between the factors and pupil weight status suggests that the school context is not inherently obesogenic. In contrast, incorporating findings from education research indicates that schools may be equalising weight status, and obesity prevention research, policy and practice might need to address what is happening outside schools and particularly during the school holidays.