Event-related brain potentials: report order and task switch on multidimensional stimuli identification

An experiment was conducted to evaluate the effect of order of reporting stimulus dimension in multidimensional stimulus identification using a switch-task paradigm. Participants were required to identify each two-dimensional symbol by pushing the corresponding buttons on the keypad on which there were two columns representing the two dimensions, part and shape. The two orders of report were Order Part/Shape and Order Shape/Part. There was a task cue prior to each presentation of a symbol indicating the particular report order the participants should perform. The cue changed randomly. Both behavioral data and event-related potentials were recorded from 12 college students. Analysis of behavioral data showed switch cost indicated by increase in response time was greater for Order Shape/Part, a less appropriate order of reporting dimensional values, than Order Part/Shape (88 msec. vs 9 msec. for response time for the first stimulus dimension; 96 msec. vs -1 msec. for response time total). Neural activities under Order Shape/Part and Order Part/Shape were different, and it seems plausible that participants put more effort into selection and showed more related semantic activation in Order Shape/Part than in Order Part/ Shape which fits the Chinese adjective-then-noun language habit.     

Sample size determination for confidence intervals of interaction effects in moderated multiple regression with continuous predictor and moderator variables

Moderated multiple regression (MMR) has been widely employed to analyze the interaction or moderating effects in behavior and related disciplines of social science. Much of the methodological literature in the context of MMR concerns statistical power and sample size calculations of hypothesis tests for detecting moderator variables. Notably, interval estimation is a distinct and more informative alternative to significance testing for inference purposes. To facilitate the practice of reporting confidence intervals in MMR analyses, the present article presents two approaches to sample size determinations for precise interval estimation of interaction effects between continuous moderator and predictor variables. One approach provides the necessary sample size so that the designated interval for the least squares estimator of moderating effects attains the specified coverage probability. The other gives the sample size required to ensure, with a given tolerance probability, that a confidence interval of moderating effects with a desired confidence coefficient will be within a specified range. Numerical examples and simulation results are presented to illustrate the usefulness and advantages of the proposed methods that account for the embedded randomness and distributional characteristic of the moderator and predictor variables.     

Power Analysis and Sample Size Planning in ANCOVA Designs

The analysis of covariance (ANCOVA) has notably proven to be an effective tool in a broad range of scientific applications. Despite the well-documented literature about its principal uses and statistical properties, the corresponding power analysis for the general linear hypothesis tests of treatment differences remains a less discussed issue. The frequently recommended procedure is a direct application of the ANOVA formula in combination with a reduced degrees of freedom and a correlation-adjusted variance. This article aims to explicate the conceptual problems and practical limitations of the common method. An exact approach is proposed for power and sample size calculations in ANCOVA with random assignment and multinormal covariates. Both theoretical examination and numerical simulation are presented to justify the advantages of the suggested technique over the current formula. The improved solution is illustrated with an example regarding the comparative effectiveness of interventions. In order to facilitate the application of the described power and sample size calculations, accompanying computer programs are also presented.

     

On the Conceptual Foundations of Anti-Realism

The central premise of Michael Dummett's global argument for anti-realism is the thesis that a speaker's grasp of the meaning of a declarative, indexical-free sentence must be manifested in her uses of that sentence. This enigmatic thesis has been the subject of a great deal of discussion, and something of a consensus has emerged about its content and justification. The received view is that the manifestation thesis expresses a behaviorist and reductive theory of meaning, essentially in agreement with Quine's view of language, and motivated by worries about the epistemology of communication. In the present paper I begin by arguing that this standard interpretation of the manifestation thesis is neither particularly faithful to Dummett's writings nor philosophically compelling. I then continue by reconstructing, from Dummett's texts, an account of the manifestation thesis, and of its justification, that differ sharply from the received view. On my reading, the thesis is motivated not epistemologically, but conceptually. I argue that connections among our conceptions of meaning, assertion, and justification lead to a conclusion about the metaphysics of meaning: we cannot form a clearly coherent conception of how two speakers can attach different meanings to a sentence without at the same time differing in what they count as justifying assertions made with that sentence. I conclude with some suggestions about how Dummett's argument for global anti-realism should be understood, given my account of the manifestation thesis. This revised version was published online in August 2006 with corrections to the Cover Date.     

Methodological and computational considerations for multiple correlation analysis

The squared multiple correlation coefficient has been widely employed to assess the goodness-of-fit of linear regression models in many applications. Although there are numerous published sources that present inferential issues and computing algorithms for multinormal correlation models, the statistical procedure for testing substantive significance by specifying the nonzero-effect null hypothesis has received little attention. This article emphasizes the importance of determining whether the squared multiple correlation coefficient is small or large in comparison with some prescribed standard and develops corresponding Excel worksheets that facilitate the implementation of various aspects of the suggested significance tests. In view of the extensive accessibility of Microsoft Excel software and the ultimate convenience of general-purpose statistical packages, the associated computer routines for interval estimation, power calculation, a nd samplesize determination are alsoprovided for completeness. The statistical methods and available programs of multiple correlation analysis described in this article purport to enhance pedagogical presentation in academic curricula and practical application in psychological research.     

Effects of display characteristics and individual differences on preferences of VDT icon design

This study explored the effects of display characteristics such as target/background color combination, single/simultaneous presentation, and individual differences by sex, and design specialty on preferences of VDT icon design. The results indicated black targets (black-on-white, black-on-yellow) and black backgrounds (red-on-black, yellow-on-black) were the most popular and white targets (white-on-red, white-on-black) and white backgrounds (blue-on-white, red-on-white) were the second most popular. As for the chromatic color combinations, yellow-on-blue was the most favored. Subjects rated color combinations under single presentation higher than those under simultaneous presentation. Women rated purplish targets and rated purplish and blue backgrounds higher than men. Subjects with design background favored black more either as a target or background, but they favored turquoise less than those without design background.     

Confidence intervals and sample size calculations for the weighted eta-squared effect sizes in one-way heteroscedastic ANOVA

Effect size reporting and interpreting practices have been extensively recommended in academic journals when primary outcomes of all empirical studies have been analyzed. This article presents an alternative approach to constructing confidence intervals of the weighted eta-squared effect size within the context of one-way heteroscedastic ANOVA models. It is shown that the proposed interval procedure has advantages over an existing method in its theoretical justification, computational simplicity, and numerical performance. For design planning, the corresponding sample size procedures for precise interval estimation of the weighted eta-squared association measure are also delineated. Specifically, the developed formulas compute the necessary sample sizes with respect to the considerations of expected confidence interval width and tolerance probability of interval width within a designated value. Supplementary computer programs are provided to aid the implementation of the suggested techniques in practical applications of ANOVA designs when the assumption of homogeneous variances is not tenable.     

The impact of ignoring random features of predictor and moderator variables on sample size for precise interval estimation of interaction effects

The influence of the joint distribution of predictor and moderator variables on the identification of interactions has been well described, but the impact on sample size determinations has received rather limited attention within the framework of moderated multiple regression (MMR). This article investigates the deficiency in sample size determinations for precise interval estimation of interaction effects that can result from ignoring the stochastic nature of continuous predictor and moderator variables in MMR. The primary finding of our examinations is that failure to accommodate the distributional properties of regressors can lead to underestimation of the necessary sample size and distortion of the desired interval precision. In order to take account of the randomness of regressor variables, two general and effective procedures for computing sample size estimates are presented. Moreover, corresponding programs are provided to facilitate use of the suggested approaches. This exposition helps to correct drawbacks in the existing techniques and to advance the practice of reporting confidence intervals in MMR analyses.     

What Anit-realist Intuitionism Could Not Be

One of the two major parts of Dummett’s defense of intuitionism is the rejection of classical in favor of intuitionistic reasoning in mathematics, given that mathematical discourse is anti-realist. While there have been illuminating discussions of what Dummett’s argument for this might be, no consensus seems to have emerged about its overall form. In this paper I give an account of this form, starting by investigating a fundamental, but little discussed question: to what view of the relation between deductive principles and meaning is anti-realism committed? The result of this investigation is a constraint on meaning theoretic assessments of logical laws. Given this constraint, I show that, surprisingly, a consistent anti-realist critique of classical logic could not rely on the rejection of bivalence. Moreover, a consistent anti-realist defense of intuitionism must begin with a radical rejection of the very conception of logical consequence that underlies realist classical logic. It follows from these conclusions that anti-realist intuitionism seems committed to proceeding by proof theoretic means.     

Effect size,statistical power,and sample size for assessing interactions between categorical and continuous variables

The reporting and interpretation of effect size estimates are widely advocated in many academic journals of psychology and related disciplines. However, such concern has not been adequately addressed for analyses involving interactions between categorical and continuous variables. For the purpose of improving current practice, this article presents fundamental features and theoretical developments for the variance of standardized slopes as a desirable standardized effect size measure for the degree of disparity between several slope coefficients. To estimate the effect size, a consistent and nearly unbiased estimator is described and a simple refinement is emphasized for extreme situations whenever appropriate. The essential problems of power and sample size calculations for testing the equality of slope coefficients are also considered. According to the analytic justification and empirical assessment, the exact approach has a clear advantage over the approximate methods. Both SAS and R computer codes are provided to facilitate practical accessibility of the proposed techniques in interaction studies.