Economic accountability in the context of local governance in the Philippines : a structural equation modelling approach

This study posits that by virtue of the enabling role of local governments, the economic development of their locality must be at the core of their public accountability, which is referred to here as “economic accountability”. Grounded on this idea of accountability, along with enabling theory and institutional theory, the study presents empirical evidence supportive of the argument that the enabling role of local governments, as manifested in a capacity to establish or adhere to formal institutional arrangements, has a direct impact on the entrepreneurial strategic posture and performance of local small and medium enterprises (SMEs) which are key players in local economic development.The results of the structural equation modelling support the view that institutional arrangements as manifestations of the enabling role of city governments are positively associated with an entrepreneurial strategic posture of local firms, which consequently improves the firms’ overall economic performance. Therefore, SME development in particular, and local economic development in general, should be part of the economic accountability of local governments in the Philippine context of local governance.

Fast cross-validation of kernel fisher discriminant classifiers

Given n training examples, the training of a Kernel Fisher Discriminant (KFD) classifier corresponds to solving a linear system of dimension n. In cross-validating KFD, the training examples are split into 2 distinct subsets for a number of times (L) wherein a subset of m examples is used for validation and the other subset of(n - m) examples is used for training the classifier. In this case L linear systems of dimension (n - m) need to be solved. We propose a novel method for cross-validation of KFD in which instead of solving L linear systems of dimension (n - m), we compute the inverse of an n × n matrix and solve L linear systems of dimension 2m, thereby reducing the complexity when L is large and/or m is small. For typical 10-fold and leave-one-out cross-validations, the proposed algorithm is approximately 4 and (^4/9n) times respectively as efficient as the naive implementations. Simulations are provided to demonstrate the efficiency of the proposed algorithms.

Structural equation modeling for evaluating the user perceptions of E-learning effectiveness in higher education

A crucial prerequisite for sustainable e-learning is the understanding of learners’ preferences for various pedagogical strategies, technologies, and the management of learning resources. This paper presents an empirical study aiming to empirically test the theoretical (pedagogies, technologies and management) (PTM) model on the preference of learners and on the perceived impact of the effectiveness of e-learning. This study uses structural equation modelling (SEM) to identify the critical dimensions in the PTM model for augmenting the effectiveness of e-learning. This leads to the development of a PTM model with the path coefficients showing weak to strong relationships ranging from 0.15 to 0.42 with acceptable significance levels. The results support the hypothesis that management, technology, resources and metadata ontology dimensions affect the effectiveness of elearning both directly and indirectly through enhancing the management effectiveness of learning resources. However, the result does not support positive influence of pedagogical strategy per se on e-learning effectiveness. The implications of this study indicate the criticality of effective management of learning resources to enhance e-learning effectiveness.

Power, effects, confidence, and significance: an investigation of statistical practices in nursing research

Objectives: To (a) assess the statistical power of nursing research to detect small, medium, and large effect sizes; (b) estimate the experiment-wise Type I error rate in these studies; and (c) assess the extent to which (i) a priori power analyses, (ii) effect sizes (and interpretations thereof), and (iii) confidence intervals were reported. Design: Statistical review. Data sources: Papers published in the 2011 volumes of the 10 highest ranked nursing journals, based on their 5-year impact factors. Review methods: Papers were assessed for statistical power, control of experiment-wise Type I error, reporting of a priori power analyses, reporting and interpretation of effect sizes, and reporting of confidence intervals. The analyses were based on 333 papers, from which 10,337 inferential statistics were identified. Results: The median power to detect small, medium, and large effect sizes was .40 (interquartile range [. IQR]. = .24-.71), .98 (IQR= .85-1.00), and 1.00 (IQR= 1.00-1.00), respectively. The median experiment-wise Type I error rate was .54 (IQR= .26-.80). A priori power analyses were reported in 28% of papers. Effect sizes were routinely reported for Spearman's rank correlations (100% of papers in which this test was used), Poisson regressions (100%), odds ratios (100%), Kendall's tau correlations (100%), Pearson's correlations (99%), logistic regressions (98%), structural equation modelling/confirmatory factor analyses/path analyses (97%), and linear regressions (83%), but were reported less often for two-proportion z tests (50%), analyses of variance/analyses of covariance/multivariate analyses of variance (18%), t tests (8%), Wilcoxon's tests (8%), Chi-squared tests (8%), and Fisher's exact tests (7%), and not reported for sign tests, Friedman's tests, McNemar's tests, multi-level models, and Kruskal-Wallis tests. Effect sizes were infrequently interpreted. Confidence intervals were reported in 28% of papers. Conclusion: The use, reporting, and interpretation of inferential statistics in nursing research need substantial improvement. Most importantly, researchers should abandon the misleading practice of interpreting the results from inferential tests based solely on whether they are statistically significant (or not) and, instead, focus on reporting and interpreting effect sizes, confidence intervals, and significance levels. Nursing researchers also need to conduct and report a priori power analyses, and to address the issue of Type I experiment-wise error inflation in their studies. © 2013 .

Panel cointegration tests of the Fisher effect

Most empirical evidence suggests that the Fisher effect, stating that inflation and nominal interest rates should cointegrate with a unit slope on inflation, does not hold, a finding at odds with many theoretical models. This paper argues that these results can be attributed in part to the low power of univariate tests, and that the use of panel data can generate more powerful tests. For this purpose, we propose two new panel cointegration tests that can be applied under very general conditions, and that are shown by simulation to be more powerful than other existing tests. These tests are applied to a panel of quarterly data covering 20 OECD countries between 1980 and 2004. The evidence suggest that the Fisher effect cannot be rejected once the panel evidence on cointegration has been taken into account. Copyright © 2008 John Wiley & Sons, Ltd.

Panel cointegration tests of the Fisher hypothesis

Recent empirical studies suggest that the Fisher hypothesis, stating that inflation and nominal interest rates should cointegrate with a unit parameter on inflation, does not hold, a finding at odds with many theoretical models. This paper argues that these results can be explained in part by the low power inherent in univariate cointegration tests and that the use of panel data should generate more powerful tests. In doing so, we propose two new panel cointegration tests, which are shown by simulation to be more powerful than other existing tests. Applying these tests to a panel of monthly data covering the period 1980:1 to 1999:12 on 14 OECD countries, we find evidence supportive of the Fisher hypothesis.