Identifying reliable, valid markers of authorship: a response to Chaski

In response to Chaski’s article (published in this volume) an examination is made of the methodological understanding necessary to identify dependable markers for forensic (and general) authorship attribution work. This examination concentrates on three methodological areas of concern which researchers intending to identify markers of authorship must address. These areas are sampling linguistic data, establishing the reliability of authorship markers and establishing the validity of authorship markers. It is suggested that the complexity of sampling problems in linguistic data is often underestimated and that theoretical issues in this area are both difficult and unresolved. It is further argued that the concepts of reliability and validity must be well understood and accounted for in any attempts to identify authorship markers and that largely this is not done. Finally, Principal Component Analysis is identified as an alternative approach which avoids some of the methodological problems inherent in identifying reliable, valid markers of authorship.

Innovation in the pharmaceutical industry:a study of the effects of regulation on the U.K. pharmaceutical industry

A history of government drug regulation and the relationship between the pharmaceutical companies in the U.K. and the licensing authority is outlined. Phases of regulatory stringency are identified with the formation of the Committees on Safety of Drugs and Medicines viewed as watersheds. A study of the impact of government regulation on industrial R&D activities focuses on the effects on the rate and direction of new product innovation. A literature review examines the decline in new chemical entity innovation. Regulations are cited as a major but not singular cause of the decline. Previous research attempting to determine the causes of such a decline on an empirical basis is given and the methodological problems associated with such research are identified. The U.K. owned sector of the British pharmaceutical industry is selected for a study employing a bottom-up approach allowing disaggregation of data. A historical background to the industry is provided, with each company analysed or a case study basis. Variations between companies regarding the policies adopted for R&D are emphasised. The process of drug innovation is described in order to determine possible indicators of the rate and direction of inventive and innovative activity. All possible indicators are considered and their suitability assessed. R&D expenditure data for the period 1960-1983 is subsequently presented as an input indicator. Intermediate output indicators are treated in a similar way and patent data are identified as a readily-available and useful source. The advantages and disadvantages of using such data are considered. Using interview material, patenting policies for most of the U.K. companies are described providing a background for a patent-based study. Sources of patent data are examined with an emphasis on computerised systems. A number of searches using a variety of sources are presented. Patent family size is examined as a possible indicator of an invention's relative importance. The patenting activity of the companies over the period 1960-1983 is given and the variation between companies is noted. The relationship between patent data and other indicators used is analysed using statistical methods resulting in an apparent lack of correlation. An alternative approach taking into account variations in company policy and phases in research activity indicates a stronger relationship between patenting activity, R&D Expenditure and NCE output over the period. The relationship is not apparent at an aggregated company level. Some evidence is presented for a relationship between phases of regulatory stringency, inventive and innovative activity but the importance of other factors is emphasised.

The MIMIC model and formative variables:problems and solutions

The use of the multiple indicators, multiple causes model to operationalize formative variables (the formative MIMIC model) is advocated in the methodological literature. Yet, contrary to popular belief, the formative MIMIC model does not provide a valid method of integrating formative variables into empirical studies and we recommend discarding it from formative models. Our arguments rest on the following observations. First, much formative variable literature appears to conceptualize a causal structure between the formative variable and its indicators which can be tested or estimated. We demonstrate that this assumption is illogical, that a formative variable is simply a researcher-defined composite of sub-dimensions, and that such tests and estimates are unnecessary. Second, despite this, researchers often use the formative MIMIC model as a means to include formative variables in their models and to estimate the magnitude of linkages between formative variables and their indicators. However, the formative MIMIC model cannot provide this information since it is simply a model in which a common factor is predicted by some exogenous variables—the model does not integrate within it a formative variable. Empirical results from such studies need reassessing, since their interpretation may lead to inaccurate theoretical insights and the development of untested recommendations to managers. Finally, the use of the formative MIMIC model can foster fuzzy conceptualizations of variables, particularly since it can erroneously encourage the view that a single focal variable is measured with formative and reflective indicators. We explain these interlinked arguments in more detail and provide a set of recommendations for researchers to consider when dealing with formative variables.