Despite recurring concerns about common method variance (CMV) in survey research, the information systems (IS) community remains largely uncertain of the extent of such potential biases. To address this uncertainty, this paper attempts to systematically examine the impact of CMV on the inferences drawn from survey research in the IS area. First, we describe the available approaches for assessing CMV and conduct an empirical study to compare them. From an actual survey involving 227 respondents, we find that although CMV is present in the research areas examined, such biases are not substantial. The results also suggest that few differences exist between the relatively new marker-variable technique and other well-established conventional tools in terms of their ability to detect CMV. Accordingly, the marker-variable technique was employed to infer the effect of CMV on correlations from previously published studies. Our findings, based on the reanalysis of 216 correlations, suggest that the inflated correlation caused by CMV may be expected to be on the order of 0.10 or less, and most of the originally significant correlations remain significant even after controlling for CMV. Finally, by extending the marker-variable technique, we examined the effect of CMV on structural relationships in past literature. Our reanalysis reveals that contrary to the concerns of some skeptics, CMV-adjusted structural relationships not only remain largely significant but also are not statistically differentiable from uncorrected estimates. In summary, this comprehensive and systematic analysis offers initial evidence that (1) the marker-variable technique can serve as a convenient, yet effective, tool for accounting for CMV, and (2) common method biases in the IS domain are not as serious as those found in other disciplines.

Common Method Variance in IS Research: A Comparison of Alternative Approaches and a Reanalysis of Past Research

We believe that journal reviewers (as well as editors and dissertation or thesis committee members) have to some extent perpetuated misconceptions about common method bias in self-report measures, including (a) that relationships between self-reported variables are necessarily and routinely upwardly biased, (b) other-reports (or other methods) are superior to self-reports, and (c) rating sources (e.g., self, other) constitute measurement methods. We argue against these misconceptions and make recommendations for what reviewers (and others) should reasonably expect from authors regarding common method bias. We believe it is reasonable to expect (a) an argument for why self-reports are appropriate, (b) construct validity evidence, (c) lack of overlap in items for different constructs, and (d) evidence that authors took proactive design steps to mitigate threats of method effects. We specifically do not recommend post hoc statistical control strategies; while some statistical strategies are promising, all have significant drawbacks and some have shown poor empirical results.

https://www.springer.com/cda/content/document/cda_downloaddocument/Common%20Method%20BIAS%20in.pdf?SGWID=0-0-45-1109143-p35536793

What Reviewers Should Expect from Authors Regarding Common Method Bias in Organizational Research

Geometric morphometrics is the statistical analysis of form based on Cartesian landmark coordinates. After separating shape from overall size, position, and orientation of the landmark configurations, the resulting Procrustes shape coordinates can be used for statistical analysis. Kendall shape space, the mathematical space induced by the shape coordinates, is a metric space that can be approximated locally by a Euclidean tangent space. Thus, notions of distance (similarity) between shapes or of the length and direction of developmental and evolutionary trajectories can be meaningfully assessed in this space. Results of statistical techniques that preserve these convenient properties—such as principal component analysis, multivariate regression, or partial least squares analysis—can be visualized as actual shapes or shape deformations. The Procrustes distance between a shape and its relabeled reflection is a measure of bilateral asymmetry. Shape space can be extended to form space by augmenting the shape coordinates with the natural logarithm of Centroid Size, a measure of size in geometric morphometrics that is uncorrelated with shape for small isotropic landmark variation. The thin-plate spline interpolation function is the standard tool to compute deformation grids and 3D visualizations. It is also central to the estimation of missing landmarks and to the semilandmark algorithm, which permits to include outlines and surfaces in geometric morphometric analysis. The powerful visualization tools of geometric morphometrics and the typically large amount of shape variables give rise to a specific exploratory style of analysis, allowing the identification and quantification of previously unknown shape features.

/pdf/advances-in-geometric-morphometrics-2qr05ayqqi.pdf

Advances in Geometric Morphometrics

Wittgenstein's work remains, undeniably, now, that off one of those few philosophers who will be read by all future generations.

Remarks on the Foundations of Mathematics. By L. Wittgenstein. Edited by G. H. Von Wright, R. Rhees and G. E. M. Anscombe. Translated by G. E. M. Anscombe. Pp. 400. 37s. 6d. 1956. (Basil Blackwell, Oxford)

http://eprints.teachingandlearning.ie/2753/1/Little%201995.pdf

Learning as dialogue: the dependence of learner autonomy on teacher autonomy*

An Alternative Approach

Assertion and belief

Whereas arithmetical quantification is substitutional in the sense that a particular quantification is true only if some instance of it is true, it does not follow (and, in fact, is not true) that an account of the truth-conditions of the sentences of a language of arithmetic can be given by a substitutional semantics. A substitutional semantics fails in a most fundamental fashion: It fails to articulate the truth-conditions of the quantifications with which it is concerned. This is what is defended in the paper. In particular, it is defended against remarks to the contrary by Saul Kripke in a well known paper on the topic.

There Is A Problem with Substitutional Quantification

Conversational implicatures are easy to grasp for the most part. But it is another matter to give a rational reconstruction of how they are grasped. We argue that Grice's attempt to do this fails. We distinguish two sorts of cases: (1) those in which we grasp the implicature by asking ourselves what would the speaker have to believe given that what he saidis such as is required by the talk exchange; (2) those in which we grasp the implicature by asking ourselves why it is that what the speaker said is so obviouslynot such as is required by the talk exchange. We argue that Grice's account does not fit those cases falling under (2).

A problem about conversational implicature

1. Consider the following argument. Suppose there is a domain of discourse of English. Then everything of which any predicate of English is true is a member of that domain. But if English has a domain of discourse, then, since 'is a domain of discourse of English' it itself a predicate of English and true of that domain, that domain is a member of itself. But nothing is a member of itself. Thus, English has no domain of discourse. We can put the argument this way. First note that the schema

Charles Sayward

Papers

An Alternative Approach

Assertion and belief

There Is A Problem with Substitutional Quantification

A problem about conversational implicature

Domains