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In this commentary, the authors discuss the implications of A. S. Green, E. Rafaeli. N. Bolger, P. E. Shrout, and H. T. Reis's (2006) diary studies with respect to memory. Researchers must take 2 issues into account when determining whether paper-and-pencil or handheld electronic diaries gather more trustworthy data. The first issue is a matter of prospective memory, and the second is a matter of reconstructive memory. The authors review the research on these issues and conclude that regardless of the type of diary researchers use, several factors can conspire to produce prompt--but inaccurate--data. [ABSTRACT FROM AUTHOR]

The authors applaud A. S. Green, E. Rafaeli, N. Bolger, P. E. Shrout, and H. T. Reis's (2006) response to one-sided comparisons of paper versus electronic (plastic) diary methods and hope that it will stimulate more balanced considerations of the issues involved. The authors begin by highlighting areas of agreement and disagreement with Green et al. The authors review briefly the broader literature that has compared paper and plastic diaries, noting how recent comparisons have relied on study designs and methods that favor investigators' allegiances. The authors note some sorely needed data for the evaluation of the implications of paper versus plastic for the internal and external validity of research. To facilitate evaluation of the existing literature and assist in the design of future studies, the authors offer a balanced comparison of paper and electronic diary methods across a range of applications. Finally, the authors propose 2 study designs that offer fair comparisons of paper and plastic diary methods. [ABSTRACT FROM AUTHOR]

This commentary discusses 4 issues relevant to interpretation of A. S. Green. E. Rafaeli, N. Bolger, P. E. Shrout, and H, T. Reis's (2006) article: (a) Self-reported compliance in medical settings has generally been substantially higher than verified compliance, suggesting that this is not a rare phenomenon; (b) none of the studies reported in Green et al. explicitly verified paper diary compliance; (c) the impact of participant motivation on diary compliance is unknown, and it may be difficult for researchers to accurately assess it in their own studies; and (d) without objective verification of diary compliance, analysis of the effects of noncompliance on data quality is difficult to interpret. The authors conclude that compliance in paper diaries and the effects of noncompliance on data quality are still unsettled issues. [ABSTRACT FROM AUTHOR]

The authors review commentaries by J. E. Broderick and A. A. Stone (2006); H. Tennen, G. Aftteck, J. C. Coyne, R. J. Laden. and A. DeLongis (2006); and M. K. T. Takarangi, M. Garry, and E. F. Loftus (2006) on their original article (A. S. Green. E. Rafaeli. N. Bolger, P. Shrout, & H. T. Reis. 2006). The authors were pleased to find more agreement than disagreement regarding the choice of methods for conducting diary studies. It is clear that continued critical evaluation of all diary methods, both paper and plastic, is warranted. However, on the basis of their initial findings, the authors conclude that paper diaries are still likely to have a valuable place in researchers' toolboxes. [ABSTRACT FROM AUTHOR]

Steinley and Brusco (2011) presented the results of a huge simulation study aimed at evaluating cluster recovery of mixture model clustering (MMC) both for the situation where the number of clusters is known and is unknown. They derived rather strong conclusions on the basis of this study, especially with regard to the good performance of "K"-means (KM) compared with MMC. I agree with the authors' conclusion that the performance of KM may be equal to MMC in certain situations, which are primarily the situations investigated by Steinley and Brusco. However, a weakness of the paper is the failure to investigate many important real-world situations where theory suggests that MMC should outperform KM. This article elaborates on the KM-MMC comparison in terms of cluster recovery and provides some additional simulation results that show that KM may be much worse than MMC. Moreover, I show that KM is equivalent to a restricted mixture model estimated by maximizing the classification likelihood and comment on Steinley and Brusco's recommendation regarding the use of mixture models for clustering. (Contains 2 figures and 3 tables.)