Evaluating the role of quantitative modeling in language evolution

Models are a flourishing and indispensable area of research in language evolution. Here we highlight critical issues in using and interpreting models, and suggest viable approaches. First, contrasting models can explain the same data and similar modelling techniques can lead to diverging conclusions. This should act as a reminder to use the extreme malleability of modelling parsimoniously when interpreting results. Second, quantitative techniques similar to those used in modelling language evolution have proven themselves inadequate in other disciplines. Cross-disciplinary fertilization is crucial to avoid mistakes which have previously occurred in other areas. Finally, experimental validation is necessary both to sharpen models' hypotheses, and to support their conclusions. Our belief is that models should be interpreted as quantitative demonstrations of logical possibilities, rather than as direct sources of evidence. Only an integration of theoretical principles, quantitative proofs and empirical validation can allow research in the evolution of language to progress

Assessing the quality of a student-generated question repository

We present results from a study that categorizes and assesses the quality of questions and explanations authored by students, in question repositories produced as part of the summative assessment in introductory physics courses over the past two years. Mapping question quality onto the levels in the cognitive domain of Bloom's taxonomy, we find that students produce questions of high quality. More than three-quarters of questions fall into categories beyond simple recall, in contrast to similar studies of student-authored content in different subject domains. Similarly, the quality of student-authored explanations for questions was also high, with approximately 60% of all explanations classified as being of high or outstanding quality. Overall, 75% of questions met combined quality criteria, which we hypothesize is due in part to the in-class scaffolding activities that we provided for students ahead of requiring them to author questions.Comment: 24 pages, 5 figure

Topics in inference and decision-making with partial knowledge

Two essential elements needed in the process of inference and decision-making are prior probabilities and likelihood functions. When both of these components are known accurately and precisely, the Bayesian approach provides a consistent and coherent solution to the problems of inference and decision-making. In many situations, however, either one or both of the above components may not be known, or at least may not be known precisely. This problem of partial knowledge about prior probabilities and likelihood functions is addressed. There are at least two ways to cope with this lack of precise knowledge: robust methods, and interval-valued methods. First, ways of modeling imprecision and indeterminacies in prior probabilities and likelihood functions are examined; then how imprecision in the above components carries over to the posterior probabilities is examined. Finally, the problem of decision making with imprecise posterior probabilities and the consequences of such actions are addressed. Application areas where the above problems may occur are in statistical pattern recognition problems, for example, the problem of classification of high-dimensional multispectral remote sensing image data

Miniature mobile sensor platforms for condition monitoring of structures

In this paper, a wireless, multisensor inspection system for nondestructive evaluation (NDE) of materials is described. The sensor configuration enables two inspection modes-magnetic (flux leakage and eddy current) and noncontact ultrasound. Each is designed to function in a complementary manner, maximizing the potential for detection of both surface and internal defects. Particular emphasis is placed on the generic architecture of a novel, intelligent sensor platform, and its positioning on the structure under test. The sensor units are capable of wireless communication with a remote host computer, which controls manipulation and data interpretation. Results are presented in the form of automatic scans with different NDE sensors in a series of experiments on thin plate structures. To highlight the advantage of utilizing multiple inspection modalities, data fusion approaches are employed to combine data collected by complementary sensor systems. Fusion of data is shown to demonstrate the potential for improved inspection reliability

RAMESES publication standards: realist syntheses

PMCID: PMC3558331This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Commentary on the evaluation of teacher effectiveness through student test scores

The Tennessee Value-added Assessment System is claimed to be able to estimate the impact of teachers on their students’ progress. This has led to further claims, such as that teacher quality is paramount in improving student progress. However, TVAAS and similar schemes should not be relied upon. Explanations of TVASS in the public domain are incomplete and poorly presented. TVAAS is not a ‘test’ of anything, and other analysts have attributed the same student progress residuals as used in TVAAS to school, classroom, district, leadership, social and economic factors. The analysis appears to be circular – effective teachers are defined by progress of students so students making progress have necessarily effective teachers. The analysis anyway cannot be sustained with the kinds of data available. The estimated level of missing data, and of measurement and representational error in the data that is present, suggest that the estimated residuals for each student are composed largely of error terms. Sanders and colleagues make elementary statistical errors, such as using significance tests with population data

Hermeneutic single-case efficacy design

In this article, I outline hermeneutic single-case efficacy design (HSCED), an interpretive approach to evaluating treatment causality in single therapy cases. This approach uses a mixture of quantitative and qualitative methods to create a network of evidence that first identifies direct demonstrations of causal links between therapy process and outcome and then evaluates plausible nontherapy explanations for apparent change in therapy. I illustrate the method with data from a depressed client who presented with unresolved loss and anger issues

Effects of alternative elicitation formats in discrete choice experiments

An elicitation format prevalently applied in DCE is to offer each respondent a sequence of choice tasks containing more than two choice options. However, empirical evidence indicates that repeated choice tasks influence choice behavior through institutional learning, fatigue, value learning, and strategic response. The study reported in this paper employs a split sample approach based on field surveys using a single binary elicitation format with a majority vote implementation as the baseline to expand the research on effects of sequential binary DCE formats. We provide evidence for effects caused by institutional learning and either strategic behavior or value learning after respondents answered repeated choice questions. However, we did not find any indications for strategic behavior caused by awareness of having multiple choices. The choice between a sequential and a single elicitation format may thus imply a trade-off between decreased choice accuracy and potentially increased strategic behavior due to an incentive incompatible mechanism. Further research is needed to explore strategic behavior induced by incentive incompatible elicitation formats using alternative approaches that are not compromised by a confounded baseline, that facilitate the differentiation between value learning and strategic behavior, and that allow the use of less restrictive model specifications. Such research should also investigate the effects of varying incentives induced by the order in which choice questions are presented to respondents.discrete choice experiments, split sample approach, elicitation format, incentive compatibility, strategic behavior, learning effects, panel mixed logit models, Environmental Economics and Policy,