Integrated learning communities as a peer support initiative for first year university students

Peer Mentoring schemes tend to be developed as retention strategies, however, they can also serve other purposes (psychosocial or career-related). However, evidence of the effectiveness of these presents mixed results and less is known about the horizontal peer support schemes which may help students capitalize on existing peer relationships. We developed an integrated learning communities (ILC) peer support scheme, building on the theoretical principles of social identity theory, which we embedded within our existing teaching framework and designed functional activities. Collective activities were undertaken to promote the processes of social identity with the intention that these may foster social and academic integration experiences. This intervention was undertaken with an entire cohort of first year undergraduate psychology students. We conducted semi-structured interviews with a self-selected sample of these students (N = 17). Thematic analysis revealed two main themes, each with two sub-themes. These were: “Divergent Experiences” with the sub-themes of “dependent on people” and “types of support”, and “Good idea in principle” with the sub-themes of “Theory ≠ Practice” and “Dependent on student engagement”. Although identifying with a peer group was not transparent in the interviews, the existence of a peer support scheme was perceived positively by students which might explain the success of the newly developed student-led Psychology Society. Indeed, this Psychology Society can provide a lasting framework for further amplification of the student voice. We conclude that our embedded ILC was both feasible and potentially valuable, but it is crucial for the peer support approach to have transactional significance.     

Estimation of maximal reliability for multiple‐component instruments in multilevel designs

A procedure for point and interval estimation of maximal reliability of multiple‐component measuring instruments in multi‐level settings is outlined. The approach is applicable to hierarchical designs in which individuals are nested within higher‐order units and exhibit possibly related performance on components of a given homogeneous scale. The method is developed within the framework of multi‐level factor analysis. The proposed procedure is illustrated with an empirical example.     

A method of bias correction for maximal reliability with dichotomous measures

This paper is concerned with the reliability of weighted combinations of a given set of dichotomous measures. Maximal reliability for such measures has been discussed in the past, but the pertinent estimator exhibits a considerable bias and mean squared error for moderate sample sizes. We examine this bias, propose a procedure for bias correction, and develop a more accurate asymptotic confidence interval for the resulting estimator. In most empirically relevant cases, the bias correction and mean squared error correction can be performed simultaneously. We propose an approximate (asymptotic) confidence interval for the maximal reliability coefficient, discuss the implementation of this estimator, and investigate the mean squared error of the associated asymptotic approximation. We illustrate the proposed methods using a numerical example.     

Testing multivariate mean collinearity via latent variable modelling

A procedure for testing mean collinearity in multidimensional spaces is outlined, which is applicable in settings with missing data and can be used when examining group mean differences. The approach is based on non‐linear parameter restrictions and is developed within the framework of latent variable modelling. The method provides useful information about the constellation of multiple response centroids in the populations studied, and is illustrated with an example.