The gambling related cognitions scale (GRCS): development, confirmatory factor validation and psychometric properties

Aims The aims of this study are to develop and validate a measure to screen for a range of gambling-related cognitions (GRC) in gamblers. Design and participants A total of 968 volunteers were recruited from a community-based population. They were divided randomly into two groups. Principal axis factoring with varimax rotation was performed on group one and confirmatory factor analysis (CFA) was used on group two to confirm the best-fitted solution. Measurements The Gambling Related Cognition Scale (GRCS) was developed for this study and the South Oaks Gambling Screen (SOGS), the Motivation Towards Gambling Scale (MTGS) and the Depression Anxiety Stress Scale (DASS-2 1) were used for validation. Findings Exploratory factor analysis performed using half the sample indicated five factors, which included interpretative control/bias (GRCS-IB), illusion of control (GRCS-IC), predictive control (GRCS-PC), gambling-related expectancies (GRCS-GE) and a perceived inability to stop gambling (GRCS-IS). These accounted for 70% of the total variance. Using the other half of the sample, CFA confirmed that the five-factor solution fitted the data most effectively. Cronbach's alpha coefficients for the factors ranged from 0.77 to 0.91, and 0.93 for the overall scale. Conclusions This paper demonstrated that the 23-item GRCS has good psychometric properties and thus is a useful instrument for identifying GRC among non-clinical gamblers. It provides the first step towards devising/adapting similar tools for problem gamblers as well as developing more specialized instruments to assess particular domains of GRC.

The gambling urge scale: Development, confirmatory factor validation, and psychometric properties

The urge to gamble is a physiological, psychological, or emotional motivational state, often associated with continued gambling. The authors developed and validated the 6-item Gambling Urge Questionnaire (GUS), which was based on the 8-item Alcohol Urge Questionnaire (M. J. Bohn, D. D. Krahn, & B. A. Staehler, 1995), using 968 community-based participants. Exploratory factor analysis using half of the sample indicated a 1-factor solution that accounted for 55.18% of the total variance. This was confirmed using confirmatory factor analysis with the other half of the sample. The GUS had a Cronbach's alpha coefficient of .81. Concurrent, predictive, and criterion-related validity of the GUS were good, suggesting that the GUS is a valid and reliable instrument for assessing gambling urges among nonclinical gamblers.

Transcultural properties of the Composite Scale of Morningness: The relevance of the "morning affect" factor

Morningness scales have been translated into several languages, but it lack of normative data and methodological differences make cross-cultural comparisons difficult. This study examines the psychometric properties and factor structure of the Composite Scale of Morningness (CSM) in samples from five countries: France (n = 627), Italy (n, = 702), Spain (n = 391), Thailand (n. = 503), and Australia (17 = 654). Strong national differences are identified. A quadratic relationship between age and CSM total score was apparent in the Australian data with a downward trend after age 35 yrs. There was no age effect in air), sample in the range from 18 to 29 yrs. Factor analysis identified a three-factor solution in all groups for both men and women. Tucker's congruence coefficients indicate that: (1) this solution is highly congruent between sexes in each culture, and (2) a morning affect factor is highly congruent between cultures. These results indicate there are national differences in factorial structure and that cut-off scores used to categorize participants as morning- and evening-types should be established for different cultural and age groups.

Factor structure and internal consistency of the Young Schema Questionnaire (Short Form) in Korean and Australian samples

We investigated cross-cultural differences in the factor structure and psychometric properties of the 75-item Young Schema Questionnaire-Short Form (YSQ-SF). Participants were 833 South Korean and 271 Australian undergraduate students. The South Korean sample was randomly divided into two sub-samples. Sample A was used for Exploratory Factor Analysis (EFA) and sample B was used for Confirmatory Factor Analysis (CFA). EFA for the South Korean sample revealed a 13-factor solution to be the best fit for the data, and CFA on the data from sample B confirmed this result. CFA on the data from the Australian sample also revealed a 13-factor solution. The overall scale of the YSQ-SF demonstrated a high level of internal consistency in the South Korean and Australian groups. Furthermore, adequate internal consistencies for all subscales in the South Korean and Australian samples were demonstrated. In conclusion, the results showed that YSQ-SF with 13 factors has good psychometric properties and reliability for South Korean and Australian University students. Korean samples had significantly higher YSD scores on most of the 13 subscales than the Australian sample. However, limitations of the current study preclude the generalisability of the findings to beyond undergraduate student populations. (c) 2006 Elsevier B.V. All rights reserved.

Probabilistic principal component analysis

Principal component analysis (PCA) is a ubiquitous technique for data analysis and processing, but one which is not based upon a probability model. In this paper we demonstrate how the principal axes of a set of observed data vectors may be determined through maximum-likelihood estimation of parameters in a latent variable model closely related to factor analysis. We consider the properties of the associated likelihood function, giving an EM algorithm for estimating the principal subspace iteratively, and discuss the advantages conveyed by the definition of a probability density function for PCA.

Probabilistic principal component analysis

Principal component analysis (PCA) is a ubiquitous technique for data analysis and processing, but one which is not based upon a probability model. In this paper we demonstrate how the principal axes of a set of observed data vectors may be determined through maximum-likelihood estimation of parameters in a latent variable model closely related to factor analysis. We consider the properties of the associated likelihood function, giving an EM algorithm for estimating the principal subspace iteratively, and discuss the advantages conveyed by the definition of a probability density function for PCA.

Statnote 27: Principal components analysis (PCA)

In Statnotes 24 and 25, multiple linear regression, a statistical method that examines the relationship between a single dependent variable (Y) and two or more independent variables (X), was described. The principle objective of such an analysis was to determine which of the X variables had a significant influence on Y and to construct an equation that predicts Y from the X variables. ‘Principal components analysis’ (PCA) and ‘factor analysis’ (FA) are also methods of examining the relationships between different variables but they differ from multiple regression in that no distinction is made between the dependent and independent variables, all variables being essentially treated the same. Originally, PCA and FA were regarded as distinct methods but in recent times they have been combined into a single analysis, PCA often being the first stage of a FA. The basic objective of a PCA/FA is to examine the relationships between the variables or the ‘structure’ of the variables and to determine whether these relationships can be explained by a smaller number of ‘factors’. This statnote describes the use of PCA/FA in the analysis of the differences between the DNA profiles of different MRSA strains introduced in Statnote 26.

Statnote 28:Canonical Variate Analysis (CVA)

The pattern of correlation between two sets of variables can be tested using canonical variate analysis (CVA). CVA, like principal components analysis (PCA) and factor analysis (FA) (Statnote 27, Hilton & Armstrong, 2011b), is a multivariate analysis Essentially, as in PCA/FA, the objective is to determine whether the correlations between two sets of variables can be explained by a smaller number of ‘axes of correlation’ or ‘canonical roots’.

A survey based analysis of IT adoption and 3PLs' performance

Purpose: In today's competitive scenario, effective supply chain management is increasingly dependent on third-party logistics (3PL) companies' capabilities and performance. The dissemination of information technology (IT) has contributed to change the supply chain role of 3PL companies and IT is considered an important element influencing the performance of modern logistics companies. Therefore, the purpose of this paper is to explore the relationship between IT and 3PLs' performance, assuming that logistics capabilities play a mediating role in this relationship. Design/methodology/approach: Empirical evidence based on a questionnaire survey conducted on a sample of logistics service companies operating in the Italian market was used to test a conceptual resource-based view (RBV) framework linking IT adoption, logistics capabilities and firm performance. Factor analysis and ordinary least square (OLS) regression analysis have been used to test hypotheses. The focus of the paper is multidisciplinary in nature; management of information systems, strategy, logistics and supply chain management approaches have been combined in the analysis. Findings: The results indicate strong relationships among data gathering technologies, transactional capabilities and firm performance, in terms of both efficiency and effectiveness. Moreover, a positive correlation between enterprise information technologies and 3PL financial performance has been found. Originality/value: The paper successfully uses the concept of logistics capabilities as mediating factor between IT adoption and firm performance. Objective measures have been proposed for IT adoption and logistics capabilities. Direct and indirect relationships among variables have been successfully tested. © Emerald Group Publishing Limited.

Determinants of resource development strategies in nonprofit organizations: An analysis of children, youth and family services

This study is an exploratory analysis of an operational measure for resource development strategies, and an exploratory analysis of internal organizational contingencies influencing choices of these strategies in charitable nonprofit organizations. The study provides conceptual guidance for advancing understanding about resource development in the nonprofit sector. The statistical findings are, however, inconclusive without further rigorous examination. A three category typology based on organization technology is initially presented to define the strategies. Three dimensions of internal organizational contingencies explored represent organization identity, professional staff, and boards of directors. Based on relevant literature and key informant interviews, an original survey was administered by mail to a national sample of nonprofit organizations. The survey collected data on indicators of the proposed strategy types and selected contingencies. Factor analysis extracted two of the initial categories in the typology. The Building Resource Development Infrastructure Strategy encompasses information technology, personnel, legal structures, and policies facilitating fund development. The Building Resource Development Infrastructure Strategy encompasses the mission, service niche, and type of service delivery forming the basis for seeking financial support. Linear regressions with each strategy type as the dependent variable identified distinct and common contingencies which may partly explain choices of strategies. Discriminant analysis suggests the potential predictive accuracy of the contingencies. Follow-up case studies with survey respondents provide additional criteria for operationalizing future measures of resource development strategies, and support and expand the analysis on contingencies. The typology offers a beginning framework for defining alternative approaches to resource development, and for exploring organization capacity specific to each approach. Contingencies that may be integral components of organization capacity are funding, leadership frame, background and experience, staff and volunteer effort, board member support, and relationships in the external environment. Based on these findings, management questions are offered for nonprofit organization stakeholders to consider in planning for resource development. Lessons learned in designing and conducting this study are also provided to enhance future related research. ^

Fiscal and organizational determinants of transportation outcomes: A quantitative and qualitative analysis of sustainability factors

Federal transportation legislation in effect since 1991 was examined to determine outcomes in two areas: (1) The effect of organizational and fiscal structures on the implementation of multimodal transportation infrastructure, and (2) The effect of multimodal transportation infrastructure on sustainability. Triangulation of methods was employed through qualitative analysis (including key informant interviews, focus groups and case studies), as well as quantitative analysis (including one-sample t-tests, regression analysis and factor analysis). ^ Four hypotheses were directly tested: (1) Regions with consolidated government structures will build more multimodal transportation miles: The results of the qualitative analysis do not lend support while the results of the quantitative findings support this hypothesis, possibly due to differences in the definitions of agencies/jurisdictions between the two methods. (2) Regions in which more locally dedicated or flexed funding is applied to the transportation system will build a greater number of multimodal transportation miles: Both quantitative and qualitative research clearly support this hypothesis. (3) Cooperation and coordination, or, conversely, competition will determine the number of multimodal transportation miles: Participants tended to agree that cooperation, coordination and leadership are imperative to achieving transportation goals and objectives, including targeted multimodal miles, but also stressed the importance of political and financial elements in determining what ultimately will be funded and implemented. (4) The modal outcomes of transportation systems will affect the overall health of a region in terms of sustainability/quality of life indicators: Both the qualitative and the quantitative analyses provide evidence that they do. ^ This study finds that federal legislation has had an effect on the modal outcomes of transportation infrastructure and that there are links between these modal outcomes and the sustainability of a region. It is recommended that agencies further consider consolidation and strengthen cooperation efforts and that fiscal regulations are modified to reflect the problems cited in qualitative analysis. Limitations of this legislation especially include the inability to measure sustainability; several measures are recommended. ^

Gaussian Process Kernels for Cross-Spectrum Analysis in Electrophysiological Time Series

Multi-output Gaussian processes provide a convenient framework for multi-task problems. An illustrative and motivating example of a multi-task problem is multi-region electrophysiological time-series data, where experimentalists are interested in both power and phase coherence between channels. Recently, the spectral mixture (SM) kernel was proposed to model the spectral density of a single task in a Gaussian process framework. This work develops a novel covariance kernel for multiple outputs, called the cross-spectral mixture (CSM) kernel. This new, flexible kernel represents both the power and phase relationship between multiple observation channels. The expressive capabilities of the CSM kernel are demonstrated through implementation of 1) a Bayesian hidden Markov model, where the emission distribution is a multi-output Gaussian process with a CSM covariance kernel, and 2) a Gaussian process factor analysis model, where factor scores represent the utilization of cross-spectral neural circuits. Results are presented for measured multi-region electrophysiological data.

Making personalised nutrition the easy choice: Creating policies to break down the barriers and reap the benefits

Personalised diets based on people’s existing food choices, and/or phenotypic, and/or genetic information hold potential to improve public dietary-related health. The aim of this analysis, therefore, has been to examine the degree to which factors which determine uptake of personalised nutrition vary between EU countries to better target policies to encourage uptake, and optimise the health benefits of personalised nutrition technology. A questionnaire developed from previous qualitative research was used to survey nationally representative samples from 9 EU countries (N = 9381). Perceived barriers to the uptake of personalised nutrition comprised three factors (data protection; the eating context; and, societal acceptance). Trust in sources of information comprised four factors (commerce and media; practitioners; government; family and, friends). Benefits comprised a single factor. Analysis of Variance (ANOVA) was employed to compare differences in responses between the United Kingdom; Ireland; Portugal; Poland; Norway; the Netherlands; Germany; and, Spain. The results indicated that respondents in Greece, Poland, Ireland, Portugal and Spain, rated the benefits of personalised nutrition highest, suggesting a particular readiness in these countries to adopt personalised nutrition interventions. Greek participants were more likely to perceive the social context of eating as a barrier to adoption of personalised nutrition, implying a need for support in negotiating social situations while on a prescribed diet. Those in Spain, Germany, Portugal and Poland scored highest on perceived barriers related to data protection. Government was more trusted than commerce to deliver and provide information on personalised nutrition overall. This was particularly the case in Ireland, Portugal and Greece, indicating an imperative to build trust, particularly in the ability of commercial service providers to deliver personalised dietary regimes effectively in these countries. These findings, obtained from a nationally representative sample of EU citizens, imply that a parallel, integrated, public-private delivery system would capture the needs of most potential consumers.

Analysis of the C-SOSI Instrument in a 9-1-1 Telecommunicator Population

Thesis (Master's)--University of Washington, 2016-08

The posttraumatic growth inventory: Factor structure and invariance in a sample ofbreast cancer patients and in a non-clinical sample

The Posttraumatic Growth Inventory (PTGI) is frequently used to assess positive changes following a traumatic event. The aim of the study is to examine the factor structure and the latent mean invariance of PTGI. A sample of 205 (M age = 54.3, SD = 10.1) women diagnosed with breast cancer and 456 (M age = 34.9, SD = 12.5) adults who had experienced a range of adverse life events were recruited to complete the PTGI and a socio-demographic questionnaire. We use Confirmatory Factor Analysis (CFA) to test the factor-structure and multi-sample CFA to examine the invariance of the PTGI between the two groups. The goodness of fit for the five-factor model is satisfactory for breast cancer sample (χ2(175) = 396.265; CFI = .884; NIF = .813; RMSEA [90% CI] = .079 [.068, .089]), and good for non-clinical sample (χ2(172) = 574.329; CFI = .931; NIF = .905; RMSEA [90% CI] = .072 [.065, .078]). The results of multi-sample CFA show that the model fit indices of the unconstrained model are equal but the model that uses constrained factor loadings is not invariant across groups. The findings provide support for the original five-factor structure and for the multidimensional nature of posttraumatic growth (PTG). Regarding invariance between both samples, the factor structure of PTGI and other parameters (i.e., factor loadings, variances, and co-variances) are not invariant across the sample of breast cancer patients and the non-clinical sample.