Although much research exists suggesting affect is effectively transmitted in computer-mediated communication (CMC), much of it focuses on how a writer encodes affect in a message or how a reader decodes affect in a message, rather than the communication process as a whole. For example, Lo (2008) created messages that either had an emoticon or did not. The author found that the presence of emoticons significantly changed the reader's perception of the writer's affect. Walther and D' Addario (2001), using the same experimental set-up, found that the influence varied by the emoticon type and message valence. However, because in both cases the messages were researcher-generated, the only conclusion that can be made is that readers attribute affective information to the emoticon; we cannot know whether this interpretation is equal to the writer's intent. Indeed, emoticons can serve as indicators of pragmatic meaning, motives, intentions, or even adherence to politeness rules,

Factor rotation is conducted to aid interpretation in exploratory factor analysis (EFA). Target rotation allows researchers to directly examine the match between the rotated factor loading matrix and their expected factor loading pattern. In some EFA applications, however, researchers have expectations on both the factor loading pattern and the factor correlation pattern. We propose to extend target rotation such that target values can be specified for both factor loadings and factor correlations. We illustrate extended target rotation with a memory study and a personality study with the multitrait-multimethod design. We also explore the statistical properties of extended target rotation using simulated data.

This article is concerned with standard errors (SEs) and confidence intervals (CIs) for exploratory factor analysis (EFA) in different situations. The authors adapt a sandwich SE estimator for EFA parameters to accommodate nonnormal data and imperfect models, factor extraction with maximum likelihood and ordinary least squares, and factor rotation with CF-varimax, CF-quartimax, geomin, or target rotation. They illustrate the sandwich SEs and CIs using nonnormal continuous data and ordinal data. They also compare SE estimates and CIs of the conventional information method, the sandwich method, and the bootstrap method using simulated data. The sandwich method and the bootstrap method are more satisfactory than the information method for EFA with nonnormal data and model approximation error.

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