Moderation in management research: What, why, when and how.

Many theories in management, psychology, and other disciplines rely on moderating variables: those which affect the strength or nature of the relationship between two other variables. Despite the near-ubiquitous nature of such effects, the methods for testing and interpreting them are not always well understood. This article introduces the concept of moderation and describes how moderator effects are tested and interpreted for a series of model types, beginning with straightforward two-way interactions with Normal outcomes, moving to three-way and curvilinear interactions, and then to models with non-Normal outcomes including binary logistic regression and Poisson regression. In particular, methods of interpreting and probing these latter model types, such as simple slope analysis and slope difference tests, are described. It then gives answers to twelve frequently asked questions about testing and interpreting moderator effects

Predicting Group-Level Outcome Variables: An Empirical Comparison of Analysis Strategies

This study provides a review of two methods for analyzing multilevel data with group-level outcome variables and compares them in a simulation study. The analytical methods included an unadjusted ordinary least squares (OLS) analysis of group means and a two-step adjustment of the group means suggested by Croon and van Veldhoven (2007). The Type I error control, power, bias, standard errors, and RMSE in parameter estimates were compared across design conditions that included manipulations of number of predictor variables, level of correlation between predictors, level of intraclass correlation, predictor reliability, effect size, and sample size. The results suggested that an OLS analysis of the group means, with White’s heteroscedasticity adjustment, provided more power for tests of group-level predictors, but less power for tests of individual-level predictors. Furthermore, this simple analysis avoided the extreme bias in parameter estimates and inadmissible solutions that were encountered with other strategies. These results were interpreted in terms of recommended analytical methods for applied researchers

Evaluating the Change in Space in a Technology-Enabled Primary Years Setting

There has been considerable attention in the literature postulating the potential effects of contemporary, technology-enabled new generation learning spaces (NGLS) on both teaching and learning (Brooks, 2011, 2012). This has, in part, been driven by the pervasive and transformative potentiall of ubiquitous access to and use of digital technology in the classroom (Chan et al., 2006). Increased access to mobile technology in recent years has freed students from the restrictive nature of shared access in tradiitional computer laboratories (Blackmore, Bateman, O'Mara, & Loughlin, 2011). Students now have personal 'anywhere, anytime' access to a boundless library of highly indexed information (Beichner, 2014), which in turn challenges the highly sequential style of instruction that has allowed teachers to preserve their historically authoritative role. Personal access to technology can support more adaptive and connected learning experiences. These experiences are created by connecting teachers and students within multimodalities of teaching and },earning that may have not been possible before (Bocconi, Karnpylis, & Punie, 2012; Hall-van den Elsen & Palaskas, 2014; Swan, van'T Hooft, Kratcoski, & Schenk,er, 2007). Multimodalities afford teachers the ability to orchestrate adaptive learning opportunities using a range of physical, text and visual tools, whilst connecting students with each other. A key element is the connectivity between teachers-students and students-students is established through the creaition of technology-enabled NGLS. The technology-enabled spaces have ubiquitous access to digital technology through one-to-one digital devices connected through wireless infrastructure. The affordances of a NGLS environment has the potential to revolutionize how, where and with whom students learn (Mouza & Lavigne, 2013; Thomas & Brown, 2011). It has the potential to sup,port contemporary pedagogical practices that facilitate highly personalised models of student learning (Bocconi et al., 2012; Magee, 2009; Zandvliet & Fraser, 2004). This personalised model includes learning outside the primacy of the traditional classroom forum (Mouza & Lavigne, 2013). Together these elements intertwine to create a model of teaching and learning that can be radically different to prevailing school cultures (Mouza & Lavigne, 2013)