Experiences with systematic triangulation at the Global Environment Facility

Systematic triangulation may address common challenges in evaluation, such as the scarcity or unreliability of data, or the complexities of comparing and cross-checking evidence from diverse disciplines. Used to identify key evaluation findings, its application has proven to be effective in addressing the limitations encountered in country-level evaluation analysis conducted by the Independent Evaluation Office of the Global Environment Facility (GEF). These include the scarcity or unreliability of national statistics on environmental indicators and data series, especially in Least Developed Countries; challenges in evaluating the impacts of GEF projects; and inherent difficulties in defining the GEF portfolio of projects prior to the undertaking of the evaluation. In addition to responding to the need for further developing triangulation protocols, procedures and/or methodologies advocated by some authors, the approach offers a contribution to evaluation practice. This applies particularly to those evaluation units tasked with country-level evaluations in international organizations, facing similar constraints.     

Measurement of the subjective work environment

Abstract

This paper considers the various issues that frame the development and use of measures of the subjective work environment. It begins by questioning the role of the work environment in determining occupational health, and explores the possible mechanisms by which that environment might exert its influence. It concludes that one of the important final common pathways is psycho-physiological in nature, and is rooted in individual perception and cognition and the experience of stress. Important for this model are the concepts of mediation and moderation. The measurement of the subjective work environment has often been idiosyncratic to the study in hand, and there are few well-established measures in common use. It is argued that researchers should be careful when deciding not to use established measures and effectively ‘invent’ their own. In developing new measures, decisions have to be made concerning the nature of the measure along with its ‘granularity’ and complexity. It is also argued that it is important that researchers should follow good psychometric practice in the development of those measures. Suggestions for ‘good practice’ are discussed. Attention is drawn to the issues of reliability and validity, and this paper discusses the role of triangulation in the planning and execution of data collection and analysis. The paper ends by reviewing the recommendations made towards the development and use of measures of the subjective work environment.     

Multi-method analysis of transparency in social media practices: Survey,interviews and content analysis

With the decline of public trust in corporate America, organizations must consider ways to improve their relationships with key publics. One of the best strategies to build trust is through engaging in transparent communication (Rawlins, 2009). The increase in the popularity of social media has brought both challenges and opportunities for organizational transparency. This study used multiple methods to explore whether public relations professionals believe they are using social media to communicate transparently, to investigate how social media can be used to improve transparency along with its benefits and challenges, and to identify how transparency is used in social media campaigns.     

Statistical Inference Enables Bad Science; Statistical Thinking Enables Good Science

ABSTRACT

Scientific research of all kinds should be guided by statistical thinking: in the design and conduct of the study, in the disciplined exploration and enlightened display of the data, and to avoid statistical pitfalls in the interpretation of the results. However, formal, probability-based statistical inference should play no role in most scientific research, which is inherently exploratory, requiring flexible methods of analysis that inherently risk overfitting. The nature of exploratory work is that data are used to help guide model choice, and under these circumstances, uncertainty cannot be precisely quantified, because of the inevitable model selection bias that results. To be valid, statistical inference should be restricted to situations where the study design and analysis plan are specified prior to data collection. Exploratory data analysis provides the flexibility needed for most other situations, including statistical methods that are regularized, robust, or nonparametric. Of course, no individual statistical analysis should be considered sufficient to establish scientific validity: research requires many sets of data along many lines of evidence, with a watchfulness for systematic error. Replicating and predicting findings in new data and new settings is a stronger way of validating claims than blessing results from an isolated study with statistical inferences.     

Simplex based space filling designs

Space filling designs are important for deterministic computer experiments. Even a single experiment can be very time consuming and can have many input parameters. Furthermore the underlying function generating the output is often nonlinear. Thus, the computer experiment has to be designed carefully. There exist many design criteria, which can be numerically optimized. Here, a method is developed, which does not need algorithmic optimization. A mesh of nearly regular simplices is constructed and the vertices of the simplices are used as potential design points. The extraction of a design from these meshes is very fast and easy to implement once the underlying mesh has been constructed. The extracted designs are highly competitive regarding the maximin design criterion and it is easy to extract designs for nonstandard design spaces.     

Bayesian image analysis with Markov chain Monte Carlo and coloured continuum triangulation models

It is now possible to carry out Bayesian image segmentation from a continuum parametric model with an unknown number of regions. However, few suitable parametric models exist. We set out to model processes which have realizations that are naturally described by coloured planar triangulations. Triangulations are already used, to represent image structure in machine vision, and in finite element analysis, for domain decomposition. However, no normalizable parametric model, with realizations that are coloured triangulations, has been specified to date. We show how this must be done, and in particular we prove that a normalizable measure on the space of triangulations in the interior of a fixed simple polygon derives from a Poisson point process of vertices. We show how such models may be analysed by using Markov chain Monte Carlo methods and we present two case-studies, including convergence analysis.     

Constrained clustering of irregularly sampled spatial data

Many spatial data such as those in climatology or environmental monitoring are collected over irregular geographical locations. Furthermore, it is common to have multivariate observations at each location. We propose a method of segmentation of a region of interest based on such data that can be carried out in two steps: (1) clustering or classification of irregularly sample points and (2) segmentation of the region based on the classified points.

We develop a spatially-constrained clustering algorithm for segmentation of the sample points by incorporating a geographical-constraint into the standard clustering methods. Both hierarchical and nonhierarchical methods are considered. The latter is a modification of the seeded region growing method known in image analysis. Both algorithms work on a suitable neighbourhood structure, which can for example be defined by the Delaunay triangulation of the sample points. The number of clusters is estimated by testing the significance of successive change in the within-cluster sum-of-squares relative to a null permutation distribution. The methodology is validated on simulated data and used in construction of a climatology map of Ireland based on meteorological data of daily rainfall records from 1294 stations over the period of 37 years.     

从科学哲学视角看“主体间性”

从科学哲学视角看,“主体间性”包括六个方面：物理学研究的“物理学家眼光中的自然”;库恩的“集团合理性”;从“纽拉特之船”看科学的经验基础;从实在论与非实在论之争看主体间性;按照戴维森“三角测量”的观点来理解科学实验;从相对论看主体性、主体间性与客观性三者的统一。     

Measurement of the subjective work environment

This paper considers the various issues that frame the development and use of measures of the subjective work environment. It begins by questioning the role of the work environment in determining occupational health, and explores the possible mechanisms by which that environment might exert its influence. It concludes that one of the important final common pathways is psycho-physiological in nature, and is rooted in individual perception and cognition and the experience of stress. Important for this model are the concepts of mediation and moderation. The measurement of the subjective work environment has often been idiosyncratic to the study in hand, and there are few well-established measures in common use. It is argued that researchers should be careful when deciding not to use established measures and effectively 'invent' their own. In developing new measures, decisions have to be made concerning the nature of the measure along with its 'granularity' and complexity. It is also argued that it is important that researchers should follow good psychometric practice in the development of those measures. Suggestions for 'good practice' are discussed. Attention is drawn to the issues of reliability and validity, and this paper discusses the role of triangulation in the planning and execution of data collection and analysis. The paper ends by reviewing the recommendations made towards the development and use of measures of the subjective work environment.