A multilevel analysis of BIS sensitivity and negative affect during skill acquisition

This Study examined the muldlevel reladonships among negadve affect Behavioural Inhibidon System (BIS) Sensidvit)' and performance. It also invesdgated whether the reladonship among these variables changed across pracdce. Pardcipants performed muldple trials of a simulated air traffic control task, A single measure of BIS was taken before pracdce, while negadve affect and performance were measured at repeated intervals. As expected, negadve affect was detrimental to performance at both a between-person and withinperson level, BIS was also found to be detrimental to performance. Contrary' to expectadons, the reladonship between BIS and performance was not mediated by overall levels of negadve affect. As predicted, the effects of overall levels of negadve affect and BIS strengthened across pracdce as pardcipants gained task knowledge and skill. The findings of this study are interpreted using resource allocadon theor}' and the implicadons for skiU acquisidon discussed.

Multilevel phenomena and the implications for method: An example of knowledge transfer in sports

Multilevel theories integrate individual-level processes with those occurring at the level of the firm and above to generate richer and more complete explanations of IB phenomena than the traditional specification of IB relationships as single-level and parsimonious allows. Case study methods permit the timely collection of multiple sources of data, in context, from multiple individuals and multiple organizational units. Further, because the definitions for each level emerge from case data rather than being imposed a priori, case analysis promotes an understanding of deeper structures and cross-level processes. This paper considers the example of sport as an internationalized service to illustrate how the case method might be used to illuminate the multilevel phenomena of knowledge.

The relationships among teacher behaviours, teacher efficacy, and student quality of school life: A multilevel perspective

This Study invesdgated the impact of teacher behaviours on student quaUt}' of school Ufe (SQSL). A measure of teacher organisadonal cidzenship behaviour (OCB) was developed, tapping two dimensions of organisadon-focused OCB (OCBO) and one dimension of individual-focused OCB (OCBI). In Une with previous research suggesdng that OCBOs may consdtute efficacyenhancing experiences, as weU as studies demonstradng the posidve consequences of teacher efficacy for students, we expected teacher efficacy to mediate the reladonship between OCBO and SQSL. Hypotheses were tested in a muldlevel design in which 171 teachers and their students (N=3018) completed quesdonnaires. A significant propordon of variance in SQSL was attributable to classroom factors. Support was found for the main effects of OCBO, as well as the main and mediadng effects of teacher efficacy.

The effects of domain-specific avoid and prove goal orientations on performance: A multilevel analysis

Recently, goal orientation, a mental framework for understanding how individuals approach learning and achievement situadons, has emerged as an important predictor of performance. This study addressed the effects of domain-specific avoid and prove orientations on performance from the betweenand within-person levels of analysis. One hundred and three participants performed thirty trials of an airtraffic control task. Domain-specific avoid and prove orientations were measured before each trial to assess the effects of changes in goal orientadon on changes in performance (i.e. within-person relationships). Average levels of avoid and prove orientations were calculated to assess the effect of goal orientation on overall performance (i.e. between-person relationships). Findings from the between-person level of analysis revealed that high prove-orientated individuals performed better than low proveorientated individuals. Results also revealed that average goal orientation levels moderated the withinperson relationships. The effect of changes in avoid orientation on changes in performance was stronger for low versus high avoid-oriented individuals while the effect of changes in prove orientadon on changes in performances was stronger for low versus highprove oriented individuals. Implications of these findings are considered.

A multilevel analysis of team climate and interpersonal exchange relationships at work

This study advances research on interpersonal exchange relationships by integrating social exchange, workplace friendship and climate research to develop a multilevel model. Data were collected from 215 manager-employee dyads working within 36 teams. At the individual level, LMX was positively associated with TMX and workplace friendship. Further, workplace friendship was positively related to TMX, and mediated the LMX-TMX relationship. At the team level, HLM results demonstrated that the relationship between LMX and workplace friendship was moderated by affective climate. Findings suggest that high-quality LMX relationships are associated with enhanced employees' perceptions of workplace friendship when affective group climate was strong.

Multilevel modelling for inference of genetic regulatory networks

Time-course experiments with microarrays are often used to study dynamic biological systems and genetic regulatory networks (GRNs) that model how genes influence each other in cell-level development of organisms. The inference for GRNs provides important insights into the fundamental biological processes such as growth and is useful in disease diagnosis and genomic drug design. Due to the experimental design, multilevel data hierarchies are often present in time-course gene expression data. Most existing methods, however, ignore the dependency of the expression measurements over time and the correlation among gene expression profiles. Such independence assumptions violate regulatory interactions and can result in overlooking certain important subject effects and lead to spurious inference for regulatory networks or mechanisms. In this paper, a multilevel mixed-effects model is adopted to incorporate data hierarchies in the analysis of time-course data, where temporal and subject effects are both assumed to be random. The method starts with the clustering of genes by fitting the mixture model within the multilevel random-effects model framework using the expectation-maximization (EM) algorithm. The network of regulatory interactions is then determined by searching for regulatory control elements (activators and inhibitors) shared by the clusters of co-expressed genes, based on a time-lagged correlation coefficients measurement. The method is applied to two real time-course datasets from the budding yeast (Saccharomyces cerevisiae) genome. It is shown that the proposed method provides clusters of cell-cycle regulated genes that are supported by existing gene function annotations, and hence enables inference on regulatory interactions for the genetic network.

On the use of likert-type scales in multilevel data:Influence on aggregate variables

In multilevel analyses, problems may arise when using Likert-type scales at the lowest level of analysis. Specifically, increases in variance should lead to greater censoring for the groups whose true scores fall at either end of the distribution. The current study used simulation methods to examine the influence of single-item Likert-type scale usage on ICC(1), ICC(2), and group-level correlations. Results revealed substantial underestimation of ICC(1) when using Likert-type scales with common response formats (e.g., 5 points). ICC(2) and group-level correlations were also underestimated, but to a lesser extent. Finally, the magnitude of underestimation was driven in large part to an interaction between Likert-type scale usage and the amounts of within- and between-group variance. © Sage Publications.

Multilevel analyses in marketing research:differentiating analytical outcomes

Marketing scholars are increasingly recognizing the importance of investigating phenomena at multiple levels. However, the analyses methods that are currently dominant within marketing may not be appropriate to dealing with multilevel or nested data structures. We identify the state of contemporary multilevel marketing research, finding that typical empirical approaches within marketing research may be less effective at explicitly taking account of multilevel data structures than those in other organizational disciplines. A Monte Carlo simulation, based on results from a previously published marketing study, demonstrates that different approaches to analysis of the same data can result in very different results (both in terms of power and effect size). The implication is that marketing scholars should be cautious when analyzing multilevel or other grouped data, and we provide a discussion and introduction to the use of hierarchical linear modeling for this purpose.