Analysis of Australian Research Council Grants Awarded for Social Work Projects 2008–2017

ABSTRACT

The quantity and quality of social work research is central to creating knowledge for the profession and maintaining social work’s presence and status in universities. This study examines Australian Research Council grants awarded for social work projects for the 10-year period 2008–2107. It investigates the quantum of grants and the topics addressed, and compares social work to related social sciences. The field of social work was awarded 84 research grants over the 10-year period, a total of $23 million and an average of 8.4 grants per year. However, this did not match the grant successes of the two comparator fields of criminology and social policy and administration. Having a clear picture of research achievements, including grants, is necessary to enable the discipline to plot a strategic way forward, addressing gaps and deficits, and building on strengths.

IMPLICATIONS

• Increasing research grant success is vital to the ongoing development of the social work knowledge base, and bolsters the standing of social work in universities.

• Maximising the use of social work Field of Research codes will increase the visibility of social work research, especially in multidisciplinary projects.

• Social work researchers should publicise competitive grant successes

     

Allocation Processes in Mergers and Acquisitions: An Organizational Justice Perspective

This paper contributes to the understanding of allocation processes in mergers and acquisitions by applying an organizational justice perspective. This is a perspective that has been neglected in past research on mergers and acquisitions, but which is important in understanding the challenges managers face in allocating human resources and other resources of symbolic importance. In particular, the paper examines the trade-offs between multiple goals in selecting distributive and procedural justice rules, and identifies factors that constrain these choices. Mergers and acquisitions are typically driven by multiple motives. The findings presented in this paper suggest that there are different approaches to applying justice rules in mergers and acquisitions and that these approaches have different implications for meeting multiple goals. The choice of equity in combinations with fair procedural rules is an approach which complies with the two goals of maximizing economic productivity and fostering relationships. However, the choice of this approach rests upon an unbalanced power relationship and relatively low ambiguity. When there is a high degree of ambiguity and a balanced power relationship, management has to make trade-offs between maximizing economic productivity and fostering relationships.     

Making Radical Change Happen Through Selective Inclusion and Exclusion of Stakeholders*

The purpose of this paper is to explore how political leaders can gain acceptance for radical reforms through selective inclusion and exclusion of stakeholders. We had inside access to the formulation of a radical reform in the Scandinavian context and found that radical change was not secured through extensive participation, but rather through selective inclusion and exclusion of stakeholders. Change leaders cannot always choose who to include and exclude in participative processes and both proponents and opponents can influence the content in ways that make it less radical. In the context of the public sector, it becomes particularly challenging to gain acceptance for radical reforms because of the influence from multiple stakeholders, many of whom have the right to be included in the process. Based on a study of a Norwegian regulatory reform, we identify five tactics change leaders can use to manage selective inclusion and exclusion. These include deleting stages in the process, including new stakeholders, alternating the process sequence, suppressing issues, and controlling the kind of influence. By consciously including and excluding stakeholders, change leaders can secure that the radicalness of a reform is preserved in the formulation process.     

Explaining Family Change and Variation: Challenges for Family Demographers

Twenty years ago, the National Institute of Child Health and Human Development (NICHD) issued a request for proposals that resulted in the National Survey of Families and Households (NSFH), a unique survey valuable to a wide range of family scholars. This paper describes the efforts of an interdisciplinary group of family demographers to build on the progress enabled by the NSFH and many other theoretical and methodological innovations. Our work, also supported by NICHD, will develop plans for research and data collection to address the central question of what causes family change and variation. We outline the group's initial assessments of orienting frameworks, key aspects of family life to study, and theoretical and methodological challenges for research on family change. Finally, we invite family scholars to follow our progress and to help develop this shared public good.     

Marital Quality and Personal Well‐Being: A Meta‐Analysis

This study examines the association between marital quality and personal well‐being using meta‐analytic techniques. Effects from 93 studies were analyzed. The average weighted effect size r was .37 for cross‐sectional and .25 for longitudinal effects. Results indicate that several variables moderate the association between marital quality and personal well‐being, including gender, participants’ marital duration, source of measurement, data collection year, and dependent variable. These results suggest that longitudinal effects are more likely to be uncovered when using standard measurement and that future research should use samples homogenous in marital length. The longitudinal finding that the strength of the association is stronger when personal well‐being is treated as the dependent variable supports previous theorizing.     

Likelihood-based estimation for Gaussian MRFs

Markov random fields (MRFs) express spatial dependence through conditional distributions, although their stochastic behavior is defined by their joint distribution. These joint distributions are typically difficult to obtain in closed form, the problem being a normalizing constant that is a function of unknown parameters. The Gaussian MRF (or conditional autoregressive model) is one case where the normalizing constant is available in closed form; however, when sample sizes are moderate to large (thousands to tens of thousands), and beyond, its computation can be problematic. Because the conditional autoregressive (CAR) model is often used for spatial-data modeling, we develop likelihood-inference methodology for this model in situations where the sample size is too large for its normalizing constant to be computed directly. In particular, we use simulation methodology to obtain maximum likelihood estimators of mean, variance, and spatial-depencence parameters (including their asymptotic variances and covariances) of CAR models.