Evaluation of a skin self examination attitude scale using an item response theory model approach

Introduction The Skin Self-Examination Attitude Scale (SSEAS) is a brief measure that allows for the assessment of attitudes in relation to skin self-examination. This study evaluated the psychometric properties of the SSEAS using Item Response Theory (IRT) methods in a large sample of men ≥ 50 years in Queensland, Australia. Methods A sample of 831 men (420 intervention and 411 control) completed a telephone assessment at the 13-month follow-up of a randomized-controlled trial of a video-based intervention to improve skin self-examination (SSE) behaviour. Descriptive statistics (mean, standard deviation, item–total correlations, and Cronbach’s alpha) were compiled and difficulty parameters were computed with Winsteps using the polytomous Rasch Rating Scale Model (RRSM). An item person (Wright) map of the SSEAS was examined for content coverage and item targeting. Results The SSEAS have good psychometric properties including good internal consistency (Cronbach’s alpha = 0.80), fit with the model and no evidence for differential item functioning (DIF) due to experimental trial grouping was detected. Conclusions The present study confirms the SSEA scale as a brief, useful and reliable tool for assessing attitudes towards skin self-examination in a population of men 50 years or older in Queensland, Australia. The 8-item scale shows unidimensionality, allowing levels of SSE attitude, and the item difficulties, to be ranked on a single continuous scale. In terms of clinical practice, it is very important to assess skin cancer self-examination attitude to identify people who may need a more extensive intervention to allow early detection of skin cancer.

Bayesian latent trait modeling of migraine symptom data

Definition of disease phenotype is a necessary preliminary to research into genetic causes of a complex disease. Clinical diagnosis of migraine is currently based on diagnostic criteria developed by the International Headache Society. Previously, we examined the natural clustering of these diagnostic symptoms using latent class analysis (LCA) and found that a four-class model was preferred. However, the classes can be ordered such that all symptoms progressively intensify, suggesting that a single continuous variable representing disease severity may provide a better model. Here, we compare two models: item response theory and LCA, each constructed within a Bayesian context. A deviance information criterion is used to assess model fit. We phenotyped our population sample using these models, estimated heritability and conducted genome-wide linkage analysis using Merlin-qtl. LCA with four classes was again preferred. After transformation, phenotypic trait values derived from both models are highly correlated (correlation = 0.99) and consequently results from subsequent genetic analyses were similar. Heritability was estimated at 0.37, while multipoint linkage analysis produced genome-wide significant linkage to chromosome 7q31-q33 and suggestive linkage to chromosomes 1 and 2. We argue that such continuous measures are a powerful tool for identifying genes contributing to migraine susceptibility.

Bayesian methodology for genetics of complex diseases

Genetic research of complex diseases is a challenging, but exciting, area of research. The early development of the research was limited, however, until the completion of the Human Genome and HapMap projects, along with the reduction in the cost of genotyping, which paves the way for understanding the genetic composition of complex diseases. In this thesis, we focus on the statistical methods for two aspects of genetic research: phenotype definition for diseases with complex etiology and methods for identifying potentially associated Single Nucleotide Polymorphisms (SNPs) and SNP-SNP interactions. With regard to phenotype definition for diseases with complex etiology, we firstly investigated the effects of different statistical phenotyping approaches on the subsequent analysis. In light of the findings, and the difficulties in validating the estimated phenotype, we proposed two different methods for reconciling phenotypes of different models using Bayesian model averaging as a coherent mechanism for accounting for model uncertainty. In the second part of the thesis, the focus is turned to the methods for identifying associated SNPs and SNP interactions. We review the use of Bayesian logistic regression with variable selection for SNP identification and extended the model for detecting the interaction effects for population based case-control studies. In this part of study, we also develop a machine learning algorithm to cope with the large scale data analysis, namely modified Logic Regression with Genetic Program (MLR-GEP), which is then compared with the Bayesian model, Random Forests and other variants of logic regression.

The effects of linguistic factors on student performance on economics multiple choice questions

This paper proposes a framework to analyse performance on multiple choice questions with the focus on linguistic factors. Item Response Theory (IRT) is deployed to estimate ability and question difficulty levels. A logistic regression model is used to detect Differential Item Functioning questions. Probit models testify relationships between performance and linguistic factors controlling the effects of question construction and students’ background. Empirical results have important implications. The lexical density of stems affects performance. The use of non-Economics specialised vocabulary has differing impacts on the performance of students with different language backgrounds. The IRT-based ability and difficulty help explain performance variations.

Non-response bias in randomized control experiments in criminology : putting the Queensland Community Engagement Trial (QCET) under a microscope

Objectives The goal of this article is to examine whether or not the results of the Queensland Community Engagement Trial (QCET)-a randomized controlled trial that tested the impact of procedural justice policing on citizen attitudes toward police-were affected by different types of nonresponse bias. Method We use two methods (Cochrane and Elffers methods) to explore nonresponse bias: First, we assess the impact of the low response rate by examining the effects of nonresponse group differences between the experimental and control conditions and pooled variance under different scenarios. Second, we assess the degree to which item response rates are influenced by the control and experimental conditions. Results Our analysis of the QCET data suggests that our substantive findings are not influenced by the low response rate in the trial. The results are robust even under extreme conditions, and statistical significance of the results would only be compromised in cases where the pooled variance was much larger for the nonresponse group and the difference between experimental and control conditions was greatly diminished. We also find that there were no biases in the item response rates across the experimental and control conditions. Conclusion RCTs that involve field survey responses-like QCET-are potentially compromised by low response rates and how item response rates might be influenced by the control or experimental conditions. Our results show that the QCET results were not sensitive to the overall low response rate across the experimental and control conditions and the item response rates were not significantly different across the experimental and control groups. Overall, our analysis suggests that the results of QCET are robust and any biases in the survey responses do not significantly influence the main experimental findings.

The ZMET Technique: A New Paradigm for Improving Marketing and Marketing Research

For decades, marketing and marketing research have been based on a concept of consumer behaviour that is deeply embedded in a linear notion of marketing activities. With increasing regularity, key organising frameworks for marketing and marketing activities are being challenged by academics and practitioners alike. In turn, this has led to the search for new approaches and tools that will help marketers understand the interaction among attitudes, emotions and product/brand choice. More recently, the approach developed by Harvard Professor, Gerald Zaltman, referred to as the Zaltman Metaphor Elicitation Technique (ZMET) has gained considerable interest. This paper seeks to demonstrate the effectiveness of this alternative qualitative method, using a non-conventional approach, thus providing a useful contribution to the qualitative research area.

Deriving a preference-based measure for cancer using the EORTC QLQ-C30 : a confirmatory versus exploratory approach

Background: To derive preference-based measures from various condition-specific descriptive health-related quality of life (HRQOL) measures. A general 2-stage method is evolved: 1) an item from each domain of the HRQOL measure is selected to form a health state classification system (HSCS); 2) a sample of health states is valued and an algorithm derived for estimating the utility of all possible health states. The aim of this analysis was to determine whether confirmatory or exploratory factor analysis (CFA, EFA) should be used to derive a cancer-specific utility measure from the EORTC QLQ-C30. Methods: Data were collected with the QLQ-C30v3 from 356 patients receiving palliative radiotherapy for recurrent or metastatic cancer (various primary sites). The dimensional structure of the QLQ-C30 was tested with EFA and CFA, the latter based on a conceptual model (the established domain structure of the QLQ-C30: physical, role, emotional, social and cognitive functioning, plus several symptoms) and clinical considerations (views of both patients and clinicians about issues relevant to HRQOL in cancer). The dimensions determined by each method were then subjected to item response theory, including Rasch analysis. Results: CFA results generally supported the proposed conceptual model, with residual correlations requiring only minor adjustments (namely, introduction of two cross-loadings) to improve model fit (increment χ2(2) = 77.78, p < .001). Although EFA revealed a structure similar to the CFA, some items had loadings that were difficult to interpret. Further assessment of dimensionality with Rasch analysis aligned the EFA dimensions more closely with the CFA dimensions. Three items exhibited floor effects (>75% observation at lowest score), 6 exhibited misfit to the Rasch model (fit residual > 2.5), none exhibited disordered item response thresholds, 4 exhibited DIF by gender or cancer site. Upon inspection of the remaining items, three were considered relatively less clinically important than the remaining nine. Conclusions: CFA appears more appropriate than EFA, given the well-established structure of the QLQ-C30 and its clinical relevance. Further, the confirmatory approach produced more interpretable results than the exploratory approach. Other aspects of the general method remain largely the same. The revised method will be applied to a large number of data sets as part of the international and interdisciplinary project to develop a multi-attribute utility instrument for cancer (MAUCa).

Moving beyond transition pedagogy: Maturity models and student engagement

Australian higher education institutions (HEIs) have entered a new phase of regulation and accreditation which includes performance-based funding relating to the participation and retention of students from social and cultural groups previously underrepresented in higher education. However, in addressing these priorities, it is critical that HEIs do not further disadvantage students from certain groups by identifying them for attention because of their social or cultural backgrounds, circumstances which are largely beyond the control of students. In response, many HEIs are focusing effort on university-wide approaches to enhancing the student experience because such approaches will enhance the engagement, success and retention of all students, and in doing so, particularly benefit those students who come from underrepresented groups. Measuring and benchmarking student experiences and engagement that arise from these efforts is well supported by extensive collections of student experience survey data. However no comparable instrument exists that measures the capability of institutions to influence and/or enhance student experiences where capability is an indication of how well an organisational process does what it is designed to do (Rosemann & de Bruin, 2005). This paper proposes that the concept of a maturity model (Marshall, 2010; Paulk, 1999) may be useful as a way of assessing the capability of HEIs to provide and implement student engagement, success and retention activities. We will describe the Student Engagement, Success and Retention Maturity Model (SESR-MM), (Clarke, Nelson & Stoodley, 2012; Nelson, Clarke & Stoodley, 2012) we are currently investigating. We will discuss if our research may address the current gap by facilitating the development of an SESR-MM instrument that aims (i) to enable institutions to assess the capability of their current student engagement and retention programs and strategies to influence and respond to student experiences within the institution; and (ii) to provide institutions with the opportunity to understand various practices across the sector with a view to further improving programs and practices relevant to their context. The first aim of our research is to extend the generational approach which has been useful in considering the evolutionary nature of the first year experience (FYE) (Wilson, 2009). Three generations have been identified and explored: First generation approaches that focus on co-curricular strategies (e.g. orientation and peer programs); Second generation approaches that focus on curriculum (e.g. pedagogy, curriculum design, and learning and teaching practice); and third generation approaches—also referred to as transition pedagogy—that focus on the production of an institution-wide integrated holistic intentional blend of curricular and co-curricular activities (Kift, Nelson & Clarke, 2010). The second aim of this research is to move beyond assessments of students’ experiences to focus on assessing institutional processes and their capability to influence student engagement. In essence, we propose to develop and use the maturity model concept to produce an instrument that will indicate the capability of HEIs to manage and improve student engagement, success and retention programs and strategies. References Australian Council for Educational Research. (n.d.). Australasian Survey of Student Engagement. Retrieved from http://www.acer.edu.au/research/ausse/background Clarke, J., Nelson, K., & Stoodley, I. (2012, July). The Maturity Model concept as framework for assessing the capability of higher education institutions to address student engagement, success and retention: New horizon or false dawn? A Nuts & Bolts presentation at the 15th International Conference on the First Year in Higher Education, “New Horizons,” Brisbane, Australia. Kift, S., Nelson, K., & Clarke, J. (2010) Transition pedagogy - a third generation approach to FYE: A case study of policy and practice for the higher education sector. The International Journal of the First Year in Higher Education, 1(1), pp. 1-20. Department of Education, Employment and Workplace Relations. (n.d.). The University Experience Survey. Advancing quality in higher education information sheet. Retrieved from http://www.deewr.gov.au/HigherEducation/Policy/Documents/University_Experience_Survey.pdf Marshall, S. (2010). A quality framework for continuous improvement of e-Learning: The e-Learning Maturity Model. Journal of Distance Education, 24(1), 143-166. Nelson, K., Clarke, J., & Stoodley, I. (2012). An exploration of the Maturity Model concept as a vehicle for higher education institutions to assess their capability to address student engagement. A work in progress. Submitted for publication. Paulk, M. (1999). Using the Software CMM with good judgment, ASQ Software Quality Professional, 1(3), 19-29. Wilson, K. (2009, June–July). The impact of institutional, programmatic and personal interventions on an effective and sustainable first-year student experience. Keynote address presented at the 12th Pacific Rim First Year in Higher Education Conference, “Preparing for Tomorrow Today: The First Year as Foundation,” Townsville, Australia. Retrieved from http://www.fyhe.com.au/past_papers/papers09/ppts/Keithia_Wilson_paper.pdf

Building adaptable agent-based models – application to the electricity distribution network

Agent-based modelling (ABM), like other modelling techniques, is used to answer specific questions from real world systems that could otherwise be expensive or impractical. Its recent gain in popularity can be attributed to some degree to its capacity to use information at a fine level of detail of the system, both geographically and temporally, and generate information at a higher level, where emerging patterns can be observed. This technique is data-intensive, as explicit data at a fine level of detail is used and it is computer-intensive as many interactions between agents, which can learn and have a goal, are required. With the growing availability of data and the increase in computer power, these concerns are however fading. Nonetheless, being able to update or extend the model as more information becomes available can become problematic, because of the tight coupling of the agents and their dependence on the data, especially when modelling very large systems. One large system to which ABM is currently applied is the electricity distribution where thousands of agents representing the network and the consumers’ behaviours are interacting with one another. A framework that aims at answering a range of questions regarding the potential evolution of the grid has been developed and is presented here. It uses agent-based modelling to represent the engineering infrastructure of the distribution network and has been built with flexibility and extensibility in mind. What distinguishes the method presented here from the usual ABMs is that this ABM has been developed in a compositional manner. This encompasses not only the software tool, which core is named MODAM (MODular Agent-based Model) but the model itself. Using such approach enables the model to be extended as more information becomes available or modified as the electricity system evolves, leading to an adaptable model. Two well-known modularity principles in the software engineering domain are information hiding and separation of concerns. These principles were used to develop the agent-based model on top of OSGi and Eclipse plugins which have good support for modularity. Information regarding the model entities was separated into a) assets which describe the entities’ physical characteristics, and b) agents which describe their behaviour according to their goal and previous learning experiences. This approach diverges from the traditional approach where both aspects are often conflated. It has many advantages in terms of reusability of one or the other aspect for different purposes as well as composability when building simulations. For example, the way an asset is used on a network can greatly vary while its physical characteristics are the same – this is the case for two identical battery systems which usage will vary depending on the purpose of their installation. While any battery can be described by its physical properties (e.g. capacity, lifetime, and depth of discharge), its behaviour will vary depending on who is using it and what their aim is. The model is populated using data describing both aspects (physical characteristics and behaviour) and can be updated as required depending on what simulation is to be run. For example, data can be used to describe the environment to which the agents respond to – e.g. weather for solar panels, or to describe the assets and their relation to one another – e.g. the network assets. Finally, when running a simulation, MODAM calls on its module manager that coordinates the different plugins, automates the creation of the assets and agents using factories, and schedules their execution which can be done sequentially or in parallel for faster execution. Building agent-based models in this way has proven fast when adding new complex behaviours, as well as new types of assets. Simulations have been run to understand the potential impact of changes on the network in terms of assets (e.g. installation of decentralised generators) or behaviours (e.g. response to different management aims). While this platform has been developed within the context of a project focussing on the electricity domain, the core of the software, MODAM, can be extended to other domains such as transport which is part of future work with the addition of electric vehicles.

Hybrid frequency–time domain models for dynamic response analysis of marine structures

Time-domain models of marine structures based on frequency domain data are usually built upon the Cummins equation. This type of model is a vector integro-differential equation which involves convolution terms. These convolution terms are not convenient for analysis and design of motion control systems. In addition, these models are not efficient with respect to simulation time, and ease of implementation in standard simulation packages. For these reasons, different methods have been proposed in the literature as approximate alternative representations of the convolutions. Because the convolution is a linear operation, different approaches can be followed to obtain an approximately equivalent linear system in the form of either transfer function or state-space models. This process involves the use of system identification, and several options are available depending on how the identification problem is posed. This raises the question whether one method is better than the others. This paper therefore has three objectives. The first objective is to revisit some of the methods for replacing the convolutions, which have been reported in different areas of analysis of marine systems: hydrodynamics, wave energy conversion, and motion control systems. The second objective is to compare the different methods in terms of complexity and performance. For this purpose, a model for the response in the vertical plane of a modern containership is considered. The third objective is to describe the implementation of the resulting model in the standard simulation environment Matlab/Simulink.

Comparison of eight published lumbar spine finite element models

Due to its ability to represent intricate systems with material nonlinearities as well as irregular loading, boundary, geometrical and material domains, the finite element (FE) method has been recognized as an important computational tool in spinal biomechanics. Current FE models generally account for a single distinct spinal geometry with one set of material properties despite inherently large inter-subject variability. The uncertainty and high variability in tissue material properties, geometry, loading and boundary conditions has cast doubt on the reliability of their predictions and comparability with reported in vitro and in vivo values. A multicenter study was undertaken to compare the results of eight well-established models of the lumbar spine that have been developed, validated and applied for many years. Models were subjected to pure and combined loading modes and their predictions were compared to in vitro and in vivo measurements for intervertebral rotations, disc pressures and facet joint forces. Under pure moment loading, the predicted L1-5 rotations of almost all models fell within the reported in vitro ranges; their median values differed on average by only 2° for flexion-extension, 1° for lateral bending and 5° for axial rotation. Predicted median facet joint forces and disc pressures were also in good agreement with previously published median in vitro values. However, the ranges of predictions were larger and exceeded the in vitro ranges, especially for facet joint forces. For all combined loading modes, except for flexion, predicted median segmental intervertebral rotations and disc pressures were in good agreement with in vivo values. The simulations yielded median facet joint forces of 0 N in flexion, 38 N in extension, 14 N in lateral bending and 60 N in axial rotation that could not be validated due to the paucity of in vivo facet joint forces. In light of high inter-subject variability, one must be cautious when generalizing predictions obtained from one deterministic model. This study demonstrates however that the predictive power increases when FE models are combined together. The median of individual numerical results can hence be used as an improved tool in order to estimate the response of the lumbar spine.