Model fitting

Clinicians and legal practitioners have often assumed, uncritically, that sensational interests are predictive of sadistic personality disorder (SADPD). This association has never been empirically tested in a non-pathological sample. Using a sample of adults (N = 219) from two countries, a four-factor SADPD structure was identified. A latent variable model was used to test the hypothesis that Sensational Interests Questionnaire (SIQ) scores would be associated with SADPD scores, after the separation of general and specific variance. The general association was not significant, but a direct pathway from SIQ Criminal Identity to SADPD Antisocial was required, to improve model fit. SADPD was predicted largely by normal personality traits. Sensational interests are therefore not a simple signifier of deviance or sadism, in clinical and legal contexts. However, Criminal Identity may emerge as a risk factor for certain forms of antisocial behaviour, and should be investigated further.

Water table across much of the western Victoria, Australia have been declining for at least the last 10-15 years, and this is attributed to the consistently low rainfall for these years, but over the same period of time there has been substantial change in land use, with grazing land replaced by cropping and tree plantations appearing in some areas. Hence,

The differential impacts risk and uncertainty have on travel decision-making were explored by examining the constructs' influence on the antecedents of intentions to visit Australia using the theory of planned behavior. Respondents were obtained from online consumer panels in South Korea, China and Japan. The South Korean and Chinese samples were general population samples, while the sample from Japan was an international travelers' sample. The extended model fitted the data well, explaining between 21 and 44 percent of ...

The differential impacts risk and uncertainty have on travel decision-making were explored by examining the constructs' influence on the antecedents of intentions to visit Australia using the theory of planned behavior. Respondents were obtained from online consumer panels in South Korea, China and Japan. The South Korean and Chinese samples were general population samples, while the sample from Japan was an international travelers' sample. The extended model fitted the data well, explaining between 21 and 44 percent of ...

A univariate time-series analysis method has been used to model and forecast the monthly number of dengue haemorrhagic fever (DHF) cases in southern Thailand. We developed autoregressive integrated moving average (ARIMA) models on the data collected between 1994-2005 and then validated the models using the data collected between January-August 2006. The results showed that the regressive forecast curves were consistent

This study examined the relationships among group therapy processes measured by the Working Alliance Inventory–Short Form, the Therapeutic Factors Inventory Cohesiveness subscale, and the Group Climate Questionnaire–Short Form in a sample of 145 patients attending 18 psychodynamic groups. Five hypothesized models were tested early in therapy (Sessions 3 and 4) using multilevel confirmatory factor analysis. Two three-factor models approached conventional standards of model fit. By merging these two models, a three-factor model consisting of member–leader alliance, positive bonding relationship, and negative relationship fit the data well. Later in therapy, member–leader bonding was no longer important to member–group cohesion, indicating that cohesion and alliance and the member–leader versus member–group bonding represent different processes.

A univariate time-series analysis method has been used to model and forecast the monthly number of dengue haemorrhagic fever (DHF) cases in southern Thailand. We developed autoregressive integrated moving average (ARIMA) models on the data collected between 1994-2005 and then validated the models using the data collected between January-August 2006. The results showed that the regressive forecast curves were consistent

A univariate time-series analysis method has been used to model and forecast the monthly number of dengue haemorrhagic fever (DHF) cases in southern Thailand. We developed autoregressive integrated moving average (ARIMA) models on the data collected between 1994-2005 and then validated the models using the data collected between January-August 2006. The results showed that the regressive forecast curves were consistent

Falls and their injury outcomes have count distributions that are highly skewed toward the right with clumping at zero, posing analytical challenges. Different modelling approaches have been used in the published literature to describe falls count distributions, often without consideration of the underlying statistical and modelling assumptions. This paper compares the use of modified Poisson and negative binomial (NB) models as alternatives to Poisson (P) regression, for the analysis of fall outcome counts. Four different count-based regression models (P, NB, zero-inflated Poisson (ZIP), zero-inflated negative binomial (ZINB)) were each individually fitted to four separate fall count datasets from Australia, New Zealand and United States. The finite mixtures of P and NB regression models were also compared to the standard NB model. Both analytical (F, Vuong and bootstrap tests) and graphical approaches were used to select and compare models. Simulation studies assessed the size and...

Spatial distribution and hazard assessment of soil lead in the mining site of Silvermines, Ireland, were investigated using statistics, geostatistics and geographic information system (GIS) techniques. Positively skewed distribution and possible outlying values of Pb and ...