Explaining usage of process modeling grammars : comparing three theoretical models in the study of two grammars

Our objective was to determine the factors that lead users to continue working with process modeling grammars after their initial adoption. We examined the explanatory power of three theoretical models of IT usage by applying them to two popular process modeling grammars. We found that a hybrid model of technology acceptance and expectation-confirmation best explained user intentions to continue using the grammars. We examined differences in the model results, and used them to provide three contributions. First, the study confirmed the applicability of IT usage models to the domain of process modeling. Second, we discovered that differences in continued usage intentions depended on the grammar type instead of the user characteristics. Third, we suggest implications and practice.

Making use of pedagogic models as reflective catalysts for investigating pedagogic practice

The notion of pedagogy for anyone in the teaching profession is innocuous. The term itself, is steeped in history but the details of the practice can be elusive. What does it mean for an academic to be embracing pedagogy? The problem is not limited to academics; most teachers baulk at the introduction of a pedagogic agenda and resist attempts to have them reflect on their classroom teaching practice, where ever that classroom might be constituted. This paper explores the application of a pedagogic model (Education Queensland, 2001) which was developed in the context of primary and secondary teaching and was part of a schooling agenda to improve pedagogy. As a teacher educator I introduced the model to classroom teachers (Hill, 2002) using an Appreciative Inquiry (Cooperrider and Srivastva 1987) model and at the same time applied the model to my own pedagogy as an academic. Despite being instigated as a model for classroom teachers, I found through my own practitioner investigation that the model was useful for exploring my own pedagogy as a university academic (Hill, 2007, 2008). Cooperrider, D.L. and Srivastva, S. (1987) Appreciative inquiry in organisational life, in Passmore, W. and Woodman, R. (Eds) Research in Organisational Changes and Development (Vol 1) Greenwich, CT: JAI Press. Pp 129-69 Education Queensland (2001) School Reform Longitudinal Study (QSRLS), Brisbane, Queensland Government. Hill, G. (2002, December ) Reflecting on professional practice with a cracked mirror: Productive Pedagogy experiences. Australian Association for Research in Education Conference. Brisbane, Australia. Hill, G. (2007) Making the assessment criteria explicit through writing feedback: A pedagogical approach to developing academic writing. International Journal of Pedagogies and Learning 3(1), 59-66. Hill, G. (2008) Supervising Practice Based Research. Studies in Learning, Evaluation, Innovation and Development, 5(4), 78-87

Density variation of different shaped food particulates in fluid bed drying : empirical models

Three particular geometrical shapes of parallelepiped, cylindrical and spheres were selected from potatoes (aspect ratio = 1:1, 2:1, 3:1), cut beans (length:diameter = 1:1, 2:1, 3:1) and peas respectively. The density variation of food particulates was studied in a batch fluidised bed dryer connected to a heat pump dehumidifier system. Apparent density and bulk density were evaluated with non-dimensional moisture at three different drying temperatures of 30, 40 and 50 o C. Relative humidity of hot air was kept at 15% in all drying temperatures. Several empirical relationships were developed for the determination of changes in densities with the moisture content. Simple mathematical models were obtained to relate apparent density and bulk density with moisture content.

Interrogating statistical models of music perception

We have developed a new experimental method for interrogating statistical theories of music perception by implementing these theories as generative music algorithms. We call this method Generation in Context. This method differs from most experimental techniques in music perception in that it incorporates aesthetic judgments. Generation In Context is designed to measure percepts for which the musical context is suspected to play an important role. In particular the method is suitable for the study of perceptual parameters which are temporally dynamic. We outline a use of this approach to investigate David Temperley’s (2007) probabilistic melody model, and provide some provisional insights as to what is revealed about the model. We suggest that Temperley’s model could be improved by dynamically modulating the probability distributions according to the changing musical context.

Bayesian models for longitudinal data

Longitudinal data, where data are repeatedly observed or measured on a temporal basis of time or age provides the foundation of the analysis of processes which evolve over time, and these can be referred to as growth or trajectory models. One of the traditional ways of looking at growth models is to employ either linear or polynomial functional forms to model trajectory shape, and account for variation around an overall mean trend with the inclusion of random eects or individual variation on the functional shape parameters. The identification of distinct subgroups or sub-classes (latent classes) within these trajectory models which are not based on some pre-existing individual classification provides an important methodology with substantive implications. The identification of subgroups or classes has a wide application in the medical arena where responder/non-responder identification based on distinctly diering trajectories delivers further information for clinical processes. This thesis develops Bayesian statistical models and techniques for the identification of subgroups in the analysis of longitudinal data where the number of time intervals is limited. These models are then applied to a single case study which investigates the neuropsychological cognition for early stage breast cancer patients undergoing adjuvant chemotherapy treatment from the Cognition in Breast Cancer Study undertaken by the Wesley Research Institute of Brisbane, Queensland. Alternative formulations to the linear or polynomial approach are taken which use piecewise linear models with a single turning point, change-point or knot at a known time point and latent basis models for the non-linear trajectories found for the verbal memory domain of cognitive function before and after chemotherapy treatment. Hierarchical Bayesian random eects models are used as a starting point for the latent class modelling process and are extended with the incorporation of covariates in the trajectory profiles and as predictors of class membership. The Bayesian latent basis models enable the degree of recovery post-chemotherapy to be estimated for short and long-term followup occasions, and the distinct class trajectories assist in the identification of breast cancer patients who maybe at risk of long-term verbal memory impairment.

Compliance with international financial reporting standards (IFRS) and the value relevance of accounting information in emerging stock markets : evidence from Kuwait

Since the 1960s, the value relevance of accounting information has been an important topic in accounting research. The value relevance research provides evidence as to whether accounting numbers relate to corporate value in a predicted manner (Beaver, 2002). Such research is not only important for investors but also provides useful insights into accounting reporting effectiveness for standard setters and other users. Both the quality of accounting standards used and the effectiveness associated with implementing these standards are fundamental prerequisites for high value relevance (Hellstrom, 2006). However, while the literature comprehensively documents the value relevance of accounting information in developed markets, little attention has been given to emerging markets where the quality of accounting standards and their enforcement are questionable. Moreover, there is currently no known research that explores the association between level of compliance with International Financial Reporting Standards (IFRS) and the value relevance of accounting information. Motivated by the lack of research on the value relevance of accounting information in emerging markets and the unique institutional setting in Kuwait, this study has three objectives. First, it investigates the extent of compliance with IFRS with respect to firms listed on the Kuwait Stock Exchange (KSE). Second, it examines the value relevance of accounting information produced by KSE-listed firms over the 1995 to 2006 period. The third objective links the first two and explores the association between the level of compliance with IFRS and the value relevance of accounting information to market participants. Since it is among the first countries to adopt IFRS, Kuwait provides an ideal setting in which to explore these objectives. In addition, the Kuwaiti accounting environment provides an interesting regulatory context in which each KSE-listed firm is required to appoint at least two external auditors from separate auditing firms. Based on the research objectives, five research questions (RQs) are addressed. RQ1 and RQ2 aim to determine the extent to which KSE-listed firms comply with IFRS and factors contributing to variations in compliance levels. These factors include firm attributes (firm age, leverage, size, profitability, liquidity), the number of brand name (Big-4) auditing firms auditing a firm’s financial statements, and industry categorization. RQ3 and RQ4 address the value relevance of IFRS-based financial statements to investors. RQ5 addresses whether the level of compliance with IFRS contributes to the value relevance of accounting information provided to investors. Based on the potential improvement in value relevance from adopting and complying with IFRS, it is predicted that the higher the level of compliance with IFRS, the greater the value relevance of book values and earnings. The research design of the study consists of two parts. First, in accordance with prior disclosure research, the level of compliance with mandatory IFRS is examined using a disclosure index. Second, the value relevance of financial statement information, specifically, earnings and book value, is examined empirically using two valuation models: price and returns models. The combined empirical evidence that results from the application of both models provides comprehensive insights into value relevance of accounting information in an emerging market setting. Consistent with expectations, the results show the average level of compliance with IFRS mandatory disclosures for all KSE-listed firms in 2006 was 72.6 percent; thus, indicating KSE-listed firms generally did not fully comply with all requirements. Significant variations in the extent of compliance are observed among firms and across accounting standards. As predicted, older, highly leveraged, larger, and profitable KSE-listed firms are more likely to comply with IFRS required disclosures. Interestingly, significant differences in the level of compliance are observed across the three possible auditor combinations of two Big-4, two non-Big 4, and mixed audit firm types. The results for the price and returns models provide evidence that earnings and book values are significant factors in the valuation of KSE-listed firms during the 1995 to 2006 period. However, the results show that the value relevance of earnings and book values decreased significantly during that period, suggesting that investors rely less on financial statements, possibly due to the increase in the available non-financial statement sources. Notwithstanding this decline, a significant association is observed between the level of compliance with IFRS and the value relevance of earnings and book value to KSE investors. The findings make several important contributions. First, they raise concerns about the effectiveness of the regulatory body that oversees compliance with IFRS in Kuwait. Second, they challenge the effectiveness of the two-auditor requirement in promoting compliance with regulations as well as the associated cost-benefit of this requirement for firms. Third, they provide the first known empirical evidence linking the level of IFRS compliance with the value relevance of financial statement information. Finally, the findings are relevant for standard setters and for their current review of KSE regulations. In particular, they highlight the importance of establishing and maintaining adequate monitoring and enforcement mechanisms to ensure compliance with accounting standards. In addition, the finding that stricter compliance with IFRS improves the value relevance of accounting information highlights the importance of full compliance with IFRS and not just mere adoption.

Hierarchical models for 2D presence/absence data having ambiguous zeroes: With a biogeographical case study on dingo behaviour

This dissertation is primarily an applied statistical modelling investigation, motivated by a case study comprising real data and real questions. Theoretical questions on modelling and computation of normalization constants arose from pursuit of these data analytic questions. The essence of the thesis can be described as follows. Consider binary data observed on a two-dimensional lattice. A common problem with such data is the ambiguity of zeroes recorded. These may represent zero response given some threshold (presence) or that the threshold has not been triggered (absence). Suppose that the researcher wishes to estimate the effects of covariates on the binary responses, whilst taking into account underlying spatial variation, which is itself of some interest. This situation arises in many contexts and the dingo, cypress and toad case studies described in the motivation chapter are examples of this. Two main approaches to modelling and inference are investigated in this thesis. The first is frequentist and based on generalized linear models, with spatial variation modelled by using a block structure or by smoothing the residuals spatially. The EM algorithm can be used to obtain point estimates, coupled with bootstrapping or asymptotic MLE estimates for standard errors. The second approach is Bayesian and based on a three- or four-tier hierarchical model, comprising a logistic regression with covariates for the data layer, a binary Markov Random field (MRF) for the underlying spatial process, and suitable priors for parameters in these main models. The three-parameter autologistic model is a particular MRF of interest. Markov chain Monte Carlo (MCMC) methods comprising hybrid Metropolis/Gibbs samplers is suitable for computation in this situation. Model performance can be gauged by MCMC diagnostics. Model choice can be assessed by incorporating another tier in the modelling hierarchy. This requires evaluation of a normalization constant, a notoriously difficult problem. Difficulty with estimating the normalization constant for the MRF can be overcome by using a path integral approach, although this is a highly computationally intensive method. Different methods of estimating ratios of normalization constants (N Cs) are investigated, including importance sampling Monte Carlo (ISMC), dependent Monte Carlo based on MCMC simulations (MCMC), and reverse logistic regression (RLR). I develop an idea present though not fully developed in the literature, and propose the Integrated mean canonical statistic (IMCS) method for estimating log NC ratios for binary MRFs. The IMCS method falls within the framework of the newly identified path sampling methods of Gelman & Meng (1998) and outperforms ISMC, MCMC and RLR. It also does not rely on simplifying assumptions, such as ignoring spatio-temporal dependence in the process. A thorough investigation is made of the application of IMCS to the three-parameter Autologistic model. This work introduces background computations required for the full implementation of the four-tier model in Chapter 7. Two different extensions of the three-tier model to a four-tier version are investigated. The first extension incorporates temporal dependence in the underlying spatio-temporal process. The second extensions allows the successes and failures in the data layer to depend on time. The MCMC computational method is extended to incorporate the extra layer. A major contribution of the thesis is the development of a fully Bayesian approach to inference for these hierarchical models for the first time. Note: The author of this thesis has agreed to make it open access but invites people downloading the thesis to send her an email via the 'Contact Author' function.