Thoughts on Social Work Knowledge Development Activities within a Quantitative Framework

Abstract

As a result of many years of serving as reviewers for numerous journals from multiple professions and disciplines and through our own experience as researchers and authors, we offer the following thoughts on conducting research within a quantitative framework. We hope these ideas can further strengthen knowledge development activities by social work researchers who rely on quantitative methods. The focus on quantitative methods is largely because this is the work we do, and hence we believe we are in a strong position to offer ideas that can serve to strengthen this line of work. Colleagues who can make suggestions along the lines of qualitative research will be sought out to speak on these issues in a subsequent editorial. In this editorial, we make a two-stage argument. First, we argue that quantitative research in social work must increase in its statistical sophistication if social work research is to make robust and widely read conclusions about the social problems and issues that social workers care about. Second, we argue that technical sophistication is not enough. Social workers and social work researchers must think carefully about the research questions, methodologies, and conclusions that underlie social work research. For many research questions, simple univariate statistics such as means, medians, standard deviations, and percentages, or bivariate statistics such as correlations, t tests, and chi-squares are often not sufficient to address the research question of interest. Such univariate and bivariate statistics provide critical pieces of information about the study sample and about basic relationships among variables, but they are only a first step in the process of uncovering more complex relationships. In the case of bivariate statistics alone, for example, these estimates do not afford the ability to control for the effects of other variables that might affect, or account for, the relationships of interest. For example, in a study of the relationship of a particular kind of parenting with children's behavior problems, it would be important to control for other variables. Without such statistical controls it would be possible that any observed bivariate relationship could be attributed to an unobserved third factor. As an illustration, in a recent article, Grogan-Kaylor (2004) used regression methods to demonstrate that a relationship between parental use of corporal punishment and children's antisocial behavior persisted even when factors such as children's age, or initial levels of antisocial behavior were accounted. Through the use of more sophisticated statistical techniques, the author was able to account for a number of factors that are sometimes suggested as explanations for the observed relationship between parental use of corporal punishment and higher levels of children's behavior problems and to provide stronger evidence that parental use of corporal punishment has undesirable effects on children's behavior. The particular regression models were fixed-effect regression models, an extension of ordinary least squares regression that is able to account for both observed and some unobserved variables. We acknowledge that the use of more sophisticated statistical methods that can rule out alternative explanations in social work research is hindered by several factors. First of all, authors may not always have the necessary expertise to carry out the appropriate statistical analyses. Graduate programs in social work at both the master's and doctoral levels are encouraged to provide social work students with at least some training in multivariate methods. At the same time, we recognize that it may often be beneficial for social workers to collaborate with researchers such as statisticians, among others, who have the necessary complementary expertise. In fact, this is yet another example of the importance of engaging in multidisciplinary or interdisciplinary collaborations. …