Tests of sphericity of normal distributions and the analysis of repeated measures designs

The locally best invariant test statistic for testing sphericity of normal distributions is shown to be a simple function of the Box/Geisser-Greenhouse degrees of freedom correction factor in a repeated measures design. Because of this relationship it provides a more intuitively appealing test of the necessary and sufficient conditions for valid F-tests in repeated measures analysis of variance than the likelihood ratio test. The properties of the two tests are compared and tables of the critical values of the Box/Geisser-Greenhouse correction factor are given.     

An alternative test for multisample sphericity

A test for multisample sphericity based on the efficient scores criterion is obtained as an alternative to the likelihood ratio test developed by Mendoza.     

A priori tests in repeated measures designs: Effects of nonsphericity

The validity conditions for univariate repeated measures designs are described. Attention is focused on the sphericity requirement. For av degree of freedom family of comparisons among the repeated measures, sphericity exists when all contrasts contained in thev dimensional space have equal variances. Under nonsphericity, upper and lower bounds on test size and power of a priori, repeated measures,F tests are derived. The effects of nonsphericity are illustrated by means of a set of charts. The charts reveal that small departures from sphericity (.97 <1.00) can seriously affect test size and power. It is recommended that separate rather than pooled error term procedures be routinely used to test a priori hypotheses.Appreciation is extended to Milton Parnes for his insightful assistance.     

The mixed model for multivariate repeated measures: validity conditions and an approximate test

Repeated measures on multivariate responses can be analyzed according to either of two models: a doubly multivariate model (DMM) or a multivariate mixed model (MMM). This paper reviews both models and gives three new results concerning the MMM. The first result is, primarily, of theoretical interest; the second and third have implications for practice. First, it is shown that, given multivariate normality, a condition called multivariate sphericity of the covariance matrix is both necessary and sufficient for the validity of the MMM analysis. To test for departure from multivariate sphericity, the likelihood ratio test can be employed. The second result is an approximation to the null distribution of the likelihood ratio test statistic, useful for moderate sample sizes. Third, for situations satisfying multivariate normality, but not multivariate sphericity, a multivariate correction factor is derived. The correction factor generalizes Box's and can be used to construct an adjusted MMM test.I am grateful to an anonymous referee for carefully attending to the mathematical details of this paper.     

Multiple Comparisons For Repeated Measures Means

The paper shows how multiple comparison procedures for repeated measures means employing a pooled estimate of error variance must conform to the sphericity assumptions of the design in order to provide a valid test. Since it is highly unlikely that behavioral science data will satisfy this condition the paper presents a test statistic that, depending upon the design, will provide either an exact or robust test and is generalizable to designs containing any number of repeated factors. Finally, various critical values are enumerated to limit the joint level of significance at α.     

Circularities in the Analysis of Counterfactuals

Expanding on a discussion by Hansson, this paper treats of Goodman's and Lewis' accounts of counterfactual conditionals, comparing the senses in which these theories may be accused of circularity. While I do maintain that in this respect Lewis has an edge over Goodman, the paper's aim is not so much to reach a firm conclusion as to disentangle some previously conflated aspects of the issue. It is also suggested that the importance of avoiding circularity may vary depending on the philosophical purpose of one's analysis. This revised version was published online in August 2006 with corrections to the Cover Date.     

Yet Another Run around the Circle

In a recent article, D. A. Truncellito (2004, ‘Running in Circles about Begging the Question’, Argumentation 18, 325–329) argues that the discussion between Robinson (1971, ‘Begging the Question’, Analysis 31, 113–117), Sorensen (1996, ‘Unbeggable Questions’, Analysis 56, 51–55) and Teng (1997, ‘Sorensen on Begging the Question’, Analysis 57, 220–222) shows that we need to distinguish between logical fallacies, which are mistakes in the form of the argument, and rhetorical fallacies, which are mistakes committed by the arguer. While I basically agree with Truncellito’s line of thinking, I believe this distinction is not tenable and offer a different view. In addition, I will argue that the conclusion to draw from the abovementioned discussion is that validity is not a sufficient criterion of begging the question, and that we should be wary of the containment-metaphor of a deductive argument.     

Modified Brown–Forsythe Procedure for Testing Interaction Effects in Split-Plot Designs

The standard univariate and multivariate methods are conventionally used to analyze continuous data from groups by trials repeated measures designs, in spite of being extremely sensitive to departures from the multisample sphericity assumption when group sizes are unequal. However, in the last 10 years several authors have offered alternative solutions to these tests that do not rest on this assumption. In an attempt to improve the precision of the Brown–Forsythe (BF) procedure, a new approximate degrees of freedom (df) approach is presented in this article. Unlike the BF test, the new method not only assures that the df will be always positive but also provides invariant solutions under linear transformations of the data. Monte Carlo methods are used to compare the new solution, in terms of control of Type I error rates, with the modified empirical generalized least squares and BF methods. Our extensive numerical studies show that the modified BF procedure outperformed the other two methods for a wide range of conditions.     

Meaning and Circular Definitions

Gupta"s and Belnap"s Revision Theory of Truth defends the legitimacy of circular definitions. Circularity, however, forces us to reconsider our conception of meaning. A readjustment of some standard theses about meaning is here proposed, by relying on a novel version of the sense–reference distinction.     

Should we rely on the Kenward–Roger approximation when using linear mixed models if the groups have different distributions?

The study explores the robustness to violations of normality and sphericity of linear mixed models when they are used with the Kenward–Roger procedure (KR) in split‐plot designs in which the groups have different distributions and sample sizes are small. The focus is on examining the effect of skewness and kurtosis. To this end, a Monte Carlo simulation study was carried out, involving a split‐plot design with three levels of the between‐subjects grouping factor and four levels of the within‐subjects factor. The results show that: (1) the violation of the sphericity assumption did not affect KR robustness when the assumption of normality was not fulfilled; (2) the robustness of the KR procedure decreased as skewness in the distributions increased, there being no strong effect of kurtosis; and (3) the type of pairing between kurtosis and group size was shown to be a relevant variable to consider when using this procedure, especially when pairing is positive (i.e., when the largest group is associated with the largest value of the kurtosis coefficient and the smallest group with its smallest value). The KR procedure can be a good option for analysing repeated‐measures data when the groups have different distributions, provided the total sample sizes are 45 or larger and the data are not highly or extremely skewed.     

The effect of skewness and kurtosis on the robustness of linear mixed models

This study analyzes the robustness of the linear mixed model (LMM) with the Kenward–Roger (KR) procedure to violations of normality and sphericity when used in split-plot designs with small sample sizes. Specifically, it explores the independent effect of skewness and kurtosis on KR robustness for the values of skewness and kurtosis coefficients that are most frequently found in psychological and educational research data. To this end, a Monte Carlo simulation study was designed, considering a split-plot design with three levels of the between-subjects grouping factor and four levels of the within-subjects factor. Robustness is assessed in terms of the probability of type I error. The results showed that (1) the robustness of the KR procedure does not differ as a function of the violation or satisfaction of the sphericity assumption when small samples are used; (2) the LMM with KR can be a good option for analyzing total sample sizes of 45 or larger when their distributions are normal, slightly or moderately skewed, and with different degrees of kurtosis violation; (3) the effect of skewness on the robustness of the LMM with KR is greater than the corresponding effect of kurtosis for common values; and (4) when data are not normal and the total sample size is 30, the procedure is not robust. Alternative analyses should be performed when the total sample size is 30.     

SOSA ON EPISTEMIC CIRCULARITY AND REFLECTIVE KNOWLEDGE

Abstract: Ernest Sosa has done important work on epistemic circularity, epistemic virtue, and reflective knowledge. He holds that epistemic circularity need not be vicious and need not prevent us from knowing that our ways of forming beliefs are reliable. In this article, I briefly explore Sosa's defense of this view and raise some questions about what is required for reflective knowledge.     

Finite Reasons Without Foundations

This article develops a theory of reasons that has strong similarities to Peter Klein's infinitism. The view it develops, Framework Reasons, upholds Klein's principles of avoiding arbitrariness (PAA) and avoiding circularity (PAC) without requiring an infinite regress of reasons. A view of reasons that holds that the “reason for” relation is constrained by PAA and that PAC can avoid an infinite regress if the “reason for” relation is contextual. Moreover, such a view of reasons can maintain that skepticism is false by the maintaining that there is more to epistemic justification than can be expressed in any reasoning session. One crucial argument for Framework Reasons is that justification depends on a background of plausibility considerations. The final section of the article applies this view of reasons to Michael Bergmann's argument that any nonskeptical epistemology must embrace epistemic circularity.     

Pritchard on Virtue Epistemology

Abstract

Peter Godfrey-Smith and Nicholas Shea have argued that standard versions of teleosemantics render explanations of successful behavior by appealing to true beliefs circular and, consequently, non-explanatory. As an alternative, Shea has recently suggested an original teleosemantic account (that he calls ‘Infotel-semantics’), which is supposed to be immune to the problem of circularity. The paper argues that the standard version of teleosemantics has a satisfactory reply to the circularity objection and that, in any case, Infotel-semantics is not better off than standard teleosemantics.     

Quantifying visual field shape for improving accuracy of search performance prediction

The current study calculated a set of 16 shape indices of six subjects (4 men and 2 women, 21-28 years old) for easy and precise quantification of visual lobe shapes using Visual Lobe Measurement System (VILOMS) software. The indices can be used in mathematical models of visual search for better understanding of search behaviour and higher accuracy of search performance prediction. General patterns in the shape indices showed that visual lobes of the subjects were slightly irregular, with low sphericity, a moderately rough boundary, horizontally elongated and slightly asymmetric along both axes. Given the complex relationships among visual lobe area, lobe shape, and search strategy, it seems that a more detailed analysis than hitherto attempted will be necessary. Quantifying lobe shape with the 16 shape indexes is a useful step to performing such analysis.     

Against the new Cartesian Circle

In two recent papers, Michael Della Rocca accuses Descartes of reasoning circularly in the Fourth Meditation. This alleged new circle is distinct from, and more vicious than, the traditional Cartesian Circle arising in the Third Meditation. We explain Della Rocca’s reasons for this accusation, showing that his argument is invalid.     

Effect of the violation of normality and sphericity in the linear mixed model in split-plot designs

This study aimed to evaluate the robustness of the linear mixed model, with the Kenward-Roger correction for degrees of freedom, when implemented in SAS PROC MIXED, using split-plot designs with small sample sizes. A Monte Carlo simulation design involving three groups and four repeated measures was used, assuming an unstructured covariance matrix to generate the data. The study variables were: sphericity, with epsilon values of 0.75 and 0.57; group sizes, equal or unequal; and shape of the distribution. As regards the latter, non-normal distributions were introduced, combining different values of kurtosis in each group. In the case of unbalanced designs, the effect of pairing (positive or negative) the degree of kurtosis with group size was also analysed. The results show that the Kenward-Roger procedure is liberal, particularly for the interaction effect, under certain conditions in which normality is violated. The relationship between the values of kurtosis in the groups and the pairing of kurtosis with group size are found to be relevant variables to take into account when applying this procedure.     

Using the linear mixed model to analyze nonnormal data distributions in longitudinal designs

Using a Monte Carlo simulation and the Kenward–Roger (KR) correction for degrees of freedom, in this article we analyzed the application of the linear mixed model (LMM) to a mixed repeated measures design. The LMM was first used to select the covariance structure with three types of data distribution: normal, exponential, and log-normal. This showed that, with homogeneous between-groups covariance and when the distribution was normal, the covariance structure with the best fit was the unstructured population matrix. However, with heterogeneous between-groups covariance and when the pairing between covariance matrices and group sizes was null, the best fit was shown by the between-subjects heterogeneous unstructured population matrix, which was the case for all of the distributions analyzed. By contrast, with positive or negative pairings, the within-subjects and between-subjects heterogeneous first-order autoregressive structure produced the best fit. In the second stage of the study, the robustness of the LMM was tested. This showed that the KR method provided adequate control of Type I error rates for the time effect with normally distributed data. However, as skewness increased—as occurs, for example, in the log-normal distribution—the robustness of KR was null, especially when the assumption of sphericity was violated. As regards the influence of kurtosis, the analysis showed that the degree of robustness increased in line with the amount of kurtosis.     

On the Theoretical Unification and Nature of Fallacies

I argue in a non-reductive sense for a plausible epistemic principle, which can (1) theoretically and instrumentally unify or systematize all fallacies, and (2) provide a justification for using such a principle for characterizing an erroneous argument as a fallacy. This plausible epistemic principle involves the idea of an error in the method of justification, which results in a failure to provide relevant evidence to satisfy certain standards of adequate proof. Thus, all fallacies are systematically disguised failures to provide substantive proof: a failure in the attempt to persuade rationally, as opposed to emotionally or rhetorically. I argue that the epistemic idea of begging the question is essential to this idea of a fallacy as an inadequate proof.     

Fitting direct covariance structures by the MSTRUCT modeling language of the CALIS procedure

This paper demonstrates the usefulness and flexibility of the general structural equation modelling (SEM) approach to fitting direct covariance patterns or structures (as opposed to fitting implied covariance structures from functional relationships among variables). In particular, the MSTRUCT modelling language (or syntax) of the CALIS procedure (SAS/STAT version 9.22 or later: SAS Institute, 2010) is used to illustrate the SEM approach. The MSTRUCT modelling language supports a direct covariance pattern specification of each covariance element. It also supports the input of additional independent and dependent parameters. Model tests, fit statistics, estimates, and their standard errors are then produced under the general SEM framework. By using numerical and computational examples, the following tests of basic covariance patterns are illustrated: sphericity, compound symmetry, and multiple‐group covariance patterns. Specification and testing of two complex correlation structures, the circumplex pattern and the composite direct product models with or without composite errors and scales, are also illustrated by the MSTRUCT syntax. It is concluded that the SEM approach offers a general and flexible modelling of direct covariance and correlation patterns. In conjunction with the use of SAS macros, the MSTRUCT syntax provides an easy‐to‐use interface for specifying and fitting complex covariance and correlation structures, even when the number of variables or parameters becomes large.