Issues in Statistical Inference

The APA Task Force’s treatment of research methods is critically examined. The present defense of the experiment rests on showing that (a) the control group cannot be replaced by the contrast group, (b) experimental psychologists have valid reasons to use non-randomly selected subjects, (c) there is no evidential support for the experimenter expectancy effect, (d) the Task Force had misrepresented the role of inductive and deductive logic, and (e) the validity of experimental data does not require appealing to the effect size or statistical power

Where do statistical models come from? Revisiting the problem of specification

R. A. Fisher founded modern statistical inference in 1922 and identified its fundamental problems to be: specification, estimation and distribution. Since then the problem of statistical model specification has received scant attention in the statistics literature. The paper traces the history of statistical model specification, focusing primarily on pioneers like Fisher, Neyman, and more recently Lehmann and Cox, and attempts a synthesis of their views in the context of the Probabilistic Reduction (PR) approach. As argued by Lehmann [11], a major stumbling block for a general approach to statistical model specification has been the delineation of the appropriate role for substantive subject matter information. The PR approach demarcates the interrelated but complemenatry roles of substantive and statistical information summarized ab initio in the form of a structural and a statistical model, respectively. In an attempt to preserve the integrity of both sources of information, as well as to ensure the reliability of their fusing, a purely probabilistic construal of statistical models is advocated. This probabilistic construal is then used to shed light on a number of issues relating to specification, including the role of preliminary data analysis, structural vs. statistical models, model specification vs. model selection, statistical vs. substantive adequacy and model validation.Comment: Published at http://dx.doi.org/10.1214/074921706000000419 in the IMS Lecture Notes--Monograph Series (http://www.imstat.org/publications/lecnotes.htm) by the Institute of Mathematical Statistics (http://www.imstat.org

Methods in Psychological Research

Psychologists collect empirical data with various methods for different reasons. These diverse methods have their strengths as well as weaknesses. Nonetheless, it is possible to rank them in terms of different critieria. For example, the experimental method is used to obtain the least ambiguous conclusion. Hence, it is the best suited to corroborate conceptual, explanatory hypotheses. The interview method, on the other hand, gives the research participants a kind of emphatic experience that may be important to them. It is for the reason the best method to use in a clinical setting. All non-experimental methods owe their origin to the interview method. Quasi-experiments are suited for answering practical questions when ecological validity is importa

Experimentation in Psychology--Rationale, Concepts and Issues

An experiment is made up of two or more data-collection conditons that are identical in all aspects, but one. It owes its design to an inductive principle and its hypothesis to deductive logic. It is the most suited for corroborating explanatory theries , ascertaining functional relationship, or assessing the substantive effectiveness of a manipulation. Also discussed are (a) the three meanings of 'control,' (b) the issue of ecological validity, (c) the distinction between theory-corroboration and agricultural-model experiments, and (d) the distinction among the hypotheses at four levels of abstraction that are implicit in an experiment

Metaphysics and Law

The dichotomy between questions of fact and questions of law serves as a starting point for the following discussion of the nature of legal reasoning. In the course of the dialogue the author notes similarities and dissimilarities between legal reasoning and philosophical and mathematical reasoning. In the end we are left with a clearer insight into the distinctive features of the adjudicative process

Building and Using Models as Examples

Sometimes, theoreticians explicitly state that they consider their models as examples. When this is not the case, it is fairly common for theoreticians to attribute to their models the characteristics and objectives of illustrative examples. However, this way of understanding models has not received enough attention in the methodological literature focused on economics. Given that didactic examples and their properties are extremely familiar in practice, considering theoretical models as examples can offer a useful perspective on models and their properties. On the basis of both explanatory and exemplifying role played by the deductive arguments by which results are proved, the paper emphasizes also the importance of understanding in theoretical work, the analogical and tentative character of the application of models, the central role played by the above mentioned arguments in such application, the didactic function of theory, and the transmision of plausibility from those arguments to the results obtained.models; examples; explanatory arguments; theoretical understanding; analogical application

Thinking skills in the context of Formal Logic, Informal Logic and Critical Thinking19

The aim of this essay is to explore the concept of thinking skills in three different contexts, i.e. Formal Logic, Informal Logic and Critical Thinking. The essay traces some contemporary historical connections between these approaches and illustrates differences and overlap between them by referring to the content pages of textbooks which are representative of the different approaches. In evaluating the historical developments sketched in the essay, the conclusion is reached that the open and pragmatic way in which Critical Thinking handles the topic of thinking skills has advantages for interdisciplinary contact and cooperation. However, this pragmatic approach also has a possible downside: the concept of thinking skills can become so vague as to be of no use