The authors argue that over time the difference between team members' perception of the organizational support received by the team (or team climate for organizational support) and their manager's perception of the organizational support received by the team has an effect on important outcomes and emergent states, such as team performance and team positive and negative affect above and beyond the main effects of climate perceptions themselves. With a longitudinal sample of 179 teams at Time 1 and 154 teams at Time 2, the authors tested their predictions using a combined polynomial regression and response surface analyses approach. The results supported the authors' predictions. When team managers and team members' perceptions of organizational support were high and in agreement, outcomes were maximized. When team managers and team members disagreed, team negative affect increased and team performance and team positive affect decreased. The negative effects of disagreement were most amplified when managers perceived that the team received higher levels of support than did the team itself.

We investigated whether climate uniformity (the pattern of climate perceptions of organizational support within the team) is related to task conflict, team communication quality, and team performance. We used a sample composed of 141 bank branches and collected data at 3 time points. The results obtained showed that, after controlling for aggregate team climate, climate strength, and their interaction, a type of nonuniform climate pattern (weak dissimilarity) was directly related to task conflict and team communication quality. Teams with weak dissimilarity nonuniform patterns tended to show higher levels of task conflict and lower levels of team communication quality than teams with uniform climate patterns. The relationship between weak dissimilarity patterns and team performance was fully mediated by team communication quality. (PsycINFO Database Record (c) 2014 APA, all rights reserved).

<span style="font-family: 'Garamond',serif; font-size: 8pt; mso-bidi-font-size: 10.0pt; mso-fareast-font-family: 'Times New Roman'; mso-bidi-font-family: 'Times New Roman'; mso-ansi-language: EN-US; mso-fareast-language: AR-SA; mso-bidi-language: AR-SA;" lang="EN-US">The aim of the present study is to illustrate how the appropriate or inappropriate application of exploratory factor analysis (EFA) can lead to quite different conclusions. To reach this goal, we evaluated the degree to which four different programs used to perform an EFA, specifically SPSS, FACTOR, PRELIS and MPlus, allow or limit the application of the currently recommended standards. In addition, we analyze and compare the results offered by the four programs when factor analyzing empirical data from scales that fit the assumptions of the classic linear EFA modeling adequately, ambiguously, or optimally, depending on the case, through the possibilities the different programs offer. The results of the comparison show the consequences of choosing one program or another; and the consequences of selecting some options or others within the same program, depending on the nature of the data. Finally, the study offers practical recommendations for applied researchers with a methodological orientation.</span>

Researchers have studied whether there are classes of people who differ systematically in the way they respond to polytomous ordered scales with a middle category such as ?. The mixed-partial credit model was fitted to a number of scales of a personality questionnaire. Most of the scales fit better with the use of 2 latent subpopulations. The most consistent difference among the latent classes was related to the functioning of the middle response category. For most of the examinees, the probability of choosing the middle category was very close to zero, but a nonnegligible percentage of people selected this category with much higher probability. The total scores from the 2 subpopulations were incommensurate. Some personality factors contributed to explaining class membership.

Organizations are multilevel systems. Most organizational phenomena are multilevel in nature, and their understanding involves variables (e.g., antecedents and consequences) that reside at different levels. The investigation of these phenomena requires appropriate analytical methods: multilevel modeling. These techniques are becoming increasingly popular among organizational psychology and organizational behavior (OPOB) researchers. In this article we review the literature that has evaluated the performance of multilevel modeling techniques to test multilevel direct and indirect effects and cross-level interactions. We also provide guidelines for OPOB researchers about the appropriate use of these techniques, and we suggest ways these techniques can contribute to theoretical advancement and research development in OPOB.