Radikaler Konstruktivismus und Konstruktionismus

Both radical constructivism and constructionism are naturalized approaches to epistemology. They try to fertilize results from biology and psychology for epistemological aims. They both refuse epistemological realism as unsustainable metaphysics. This raises the problem of the range of the naturalistic approach to epistemology. Constructivism, in both forms, turns out to be untenable because it runs in an aporia: it must borrow from realism either, or it must qualify its own position as a metaphysical one. But therewith, constructivism would be blamed to be metaphysical itself.     

Using item-specific instructional information in achievement modeling

The problem of detecting instructional sensitivity (item basis) in test items is considered. An illustration is given which shows that for tests with many biased items, traditional item bias detection schemes give a very poor assessment of bias. A new method is proposed instead. This method extends item response theory (IRT) by including item-specific auxiliary measurement information related to opportunity-to-learn. Item-specific variation in measurement relations across students with varying opportunity-to-learn is allowed for.This paper was presented at the 1987 AERA meeting in Washington, DC. This research was supported by grant OERI-G-86-003 from the Office of Educational Research and Improvement, Department of Education. The author thanks Michael Hollis and Chih-fen Kao for valuable research assistance, and appreciates valuable comments made by an anonymous reviewer.     

Empirical tests of scientific realism: A quantitative framework

The scientific realism debate in philosophy of science raises some intriguing methodological issues. Scientific realism posits a link between a scientific theory's observational and referential success. This opens the possibility of testing the thesis empirically, by searching for evidence of such a link in the record of theories put forward in the history of science. Many realist philosophers working today propose case study methodology as a way of carrying out such a test. This article argues that a qualitative method such as case study methodology is not adequate for this purpose, for two reasons: to test scientific realism is to pose an effects-of-causes question, and observational and referential success are quantities that theories possess to a greater or lesser degree. The article concludes that an empirical test of scientific realism requires a quantitative method.     

What analytic metaphysics can do for scientific metaphysics

The apparent chasm between two camps in metaphysics, analytic metaphysics and scientific metaphysics, is well recognized. I argue that the relationship between them is not necessarily a rivalry; a division of labour that resembles the relationship between pure mathematics and science is possible. As a case study, I look into the metaphysical underdetermination argument for ontic structural realism, a well-known position in scientific metaphysics, together with an argument for the position in analytic metaphysics known as ontological nihilism. I argue that we can ascribe the same schema to both arguments, which indicates that analytic metaphysics can offer an abstract model that scientific metaphysics may find useful.     

Three problems for the evolutionary debunking argument

In attempting to debunk moral realism through an appeal to evolutionary facts, debunkers face a series of problems, which we label the problems of scope, corrosiveness, and post-hoc justification. To overcome these problems, debunkers must assume certain metaphysical or epistemological positions, or otherwise pre-establish them. In doing so, they must assume or pre-establish the very conclusion they seek in advancing the argument. This means that such debunking arguments either beg the question against the moral realist or are undermined as standalone metaethical arguments.     

Premature conclusions about the signal-to-noise ratio in structural equation modeling research: A commentary on Yuan and Fang (2023)

In a recent article published in this journal, Yuan and Fang (British Journal of Mathematical and Statistical Psychology, 2023) suggest comparing structural equation modeling (SEM), also known as covariance-based SEM (CB-SEM), estimated by normal-distribution-based maximum likelihood (NML), to regression analysis with (weighted) composites estimated by least squares (LS) in terms of their signal-to-noise ratio (SNR). They summarize their findings in the statement that “[c]ontrary to the common belief that CB-SEM is the preferred method for the analysis of observational data, this article shows that regression analysis via weighted composites yields parameter estimates with much smaller standard errors, and thus corresponds to greater values of the [SNR].” In our commentary, we show that Yuan and Fang have made several incorrect assumptions and claims. Consequently, we recommend that empirical researchers not base their methodological choice regarding CB-SEM and regression analysis with composites on the findings of Yuan and Fang as these findings are premature and require further research.     

Structural connectivity between the hippocampus and cortical/subcortical area relates to cognitive impairment in schizophrenia but not in mood disorders

Cognitive impairment in schizophrenia and other psychiatric disorders is a challenge to be overcome in order to maintain patients' quality of life and social function. The neurological pathogenesis of cognitive impairment requires further elucidation. In general, the hippocampus interacts between the cortical and subcortical areas for information processing and consolidation and has an important role in memory. We examined the relationship between structural connectivity of the hippocampus and cortical/subcortical areas and cognitive impairment in schizophrenia, major depressive disorder and bipolar disorder. Subjects comprised 21 healthy controls, 19 patients with schizophrenia, 20 patients with bipolar disorder and 18 patients with major depressive disorder. Diffusion-weighted tensor images data were processed using ProbtrackX2 to calculate the structural connectivity between the hippocampus and cortical/subcortical areas. Cognitive function was assessed using the Brief Assessment of Cognition in schizophrenia composite score. Hippocampal structural connectivity index was significantly correlated with composite score in the schizophrenia group but not in the healthy control, major depressive disorder or bipolar disorder groups. There were no statistically significant differences in hippocampal structural connectivity index between the four groups. Structural connectivity between the hippocampus and cortical/subcortical areas is suggested to be a pathophysiological mechanism of comprehensive cognitive impairment in schizophrenia.