Integrating case study and survey research methods: An example in information systems

The case for combining research methods generally, and more specifically that for combining qualitative and quantitative methods, is strong. Yet, research designs that extensively integrate both fieldwork (e.g. case studies) and survey research are rare. More¬over, some journals tend tacitly to specialize by methodology thereby encouraging purity of method. The multi-method model of research while not new, has not been appreciated. In this respect it is useful to articulate and describe its usage through example. By reference to a recently completed study of IS consultant engagement success factors this paper presents an analysis of the benefits of integrating case study and survey research methods. The emphasis is on the qualitative case study method and how it can compliment more quantitative survey research. Benefits are demonstrated through specific examples from the reference study.</i

Measuring Subjective Wellbeing in Developing Countries.

The paper explores the conceptual and methodological issues entailed in using subjective measures of well-being in developing countries. In the first part I define, situate and contrast subjective quality of life (QoL), subjective well-being (SWB), and well-being. I also look at the conceptual and methodological shortcomings of subjective measures of well-being and suggest ways of overcoming these by combining different approaches. I then explore how an expanded concept of subjective quality of life fits into the theoretical framework of the UK-based Well-being in Developing Countries study (or WeD), specifically how it plans to produce a new, “development-related” profile of quality of life, drawing on the methodology of the WHOQOL group (1995; 1998)

A method of classification for multisource data in remote sensing based on interval-valued probabilities

An axiomatic approach to intervalued (IV) probabilities is presented, where the IV probability is defined by a pair of set-theoretic functions which satisfy some pre-specified axioms. On the basis of this approach representation of statistical evidence and combination of multiple bodies of evidence are emphasized. Although IV probabilities provide an innovative means for the representation and combination of evidential information, they make the decision process rather complicated. It entails more intelligent strategies for making decisions. The development of decision rules over IV probabilities is discussed from the viewpoint of statistical pattern recognition. The proposed method, so called evidential reasoning method, is applied to the ground-cover classification of a multisource data set consisting of Multispectral Scanner (MSS) data, Synthetic Aperture Radar (SAR) data, and digital terrain data such as elevation, slope, and aspect. By treating the data sources separately, the method is able to capture both parametric and nonparametric information and to combine them. Then the method is applied to two separate cases of classifying multiband data obtained by a single sensor. In each case a set of multiple sources is obtained by dividing the dimensionally huge data into smaller and more manageable pieces based on the global statistical correlation information. By a divide-and-combine process, the method is able to utilize more features than the conventional maximum likelihood method

Toward improved calibration of hydrologic models: Combining the strengths of manual and automatic methods

Automatic methods for model calibration seek to take advantage of the speed and power of digital computers, while being objective and relatively easy to implement. However, they do not provide parameter estimates and hydrograph simulations that are considered acceptable by the hydrologists responsible for operational forecasting and have therefore not entered into widespread use. In contrast, the manual approach which has been developed and refined over the years to result in excellent model calibrations is complicated and highly labor-intensive, and the expertise acquired by one individual with a specific model is not easily transferred to another person (or model). In this paper, we propose a hybrid approach that combines the strengths of each. A multicriteria formulation is used to "model" the evaluation techniques and strategies used in manual calibration, and the resulting optimization problem is solved by means of a computerized algorithm. The new approach provides a stronger test of model performance than methods that use a single overall statistic to aggregate model errors over a large range of hydrologic behaviors. The power of the new approach is illustrated by means of a case study using the Sacramento Soil Moisture Accounting model

Subjective Causality and Counterfactuals in the Social Sciences

The article explores the role that subjective evidence of causality and associated counterfactuals and counterpotentials might play in the social sciences where comparative cases are scarce. This scarcity rules out statistical inference based upon frequencies and usually invites in-depth ethnographic studies. Thus, if causality is to be preserved in such situations, a conception of ethnographic causal inference is required. Ethnographic causality inverts the standard statistical concept of causal explanation in observational studies, whereby comparison and generalization, across a sample of cases, are both necessary prerequisites for any causal inference. Ethnographic causality allows, in contrast, for causal explanation prior to any subsequent comparison or generalization