Summary of Guo (2014): Shaping Social Work Science: What Should Quantitative Researchers Do

Summary of Guo (2014):

Shaping Social Work Science: What Should Quantitative Researchers Do?

             Social work has not contributed greatly to scientific knowledge, lagging behind other professions such as nursing, clinical psychology and psychiatry, and this, despite the recent national and international “…movement towards shaping the science of social work”, and also despite the fact that “…the call for strengthening the scientific base of social work practice was declared 50 years ago”.  This is Guo’s (2014) opening message in Shaping Social Work Science: What Should Quantitative Researchers Do? As a social worker, it really hits home for me. My first instinct is a defensive rant, as social work has long been considered a “soft science” and I often find myself defending the profession’s integrity and ‘honor’. As an aspiring researcher, I read on as Guo borrows from Brekke (2012), to bring forth questions he feels have not been addressed in literature, and proceeds to try and answer them:

What role should quantitative methods play in shaping the science of social work?

Is social work different from other scientific disciplines? If so, in which ways?

To enhance the level of scientific research, what should quantitative researchers do?

Guo introduces the reader to the notion of striving for a scientific social work by discussing economics, a field that experienced similar challenges for attaining “respectability”, an ongoing journey that for it, began in the 1940s with a focus on the advantages and disadvantages of incorporating mathematics in the field. Advocates of mathematics believe (d) that elaborate usage of quantitative techniques are necessary in the “development of any scientific discipline”, while mathematic rivals fear(ed) that math “…has made contemporary economics less relevant to the social problems that formed the subject matter of classical political economy”. It is mathematic techniques that Guo suggests need to be at the basis of social work inquiry, and it seems to be these two voices that are currently present in social work discourse; the seeming battle between the concreteness hence ‘coldness’ of numbers and the humanity and ‘warmth’ of ‘doing’ social work. How can these two polls coexist?

To ease the transition into empirical social work research, Guo recommends that social work researchers engage in three main actions:

1) Social work researchers should “Follow the positivist tradition and use empirical data to test theories.” Evidence-based practice as the new buzz words in social work practice has pushed a greater usage of quantitative research methods, which aligns with the positivist paradigm of using “…empirical data to test theoretically derived research hypotheses”. Utilizing techniques for observation, experimentation and comparison, social work does not differ from the ‘natural sciences’, where empirical data are used to test theories. Guo states that fundamentally, “…any theory remains to be hypothetical and cannot be termed as theory if it is not tested by empirical data” and social workers should not stray from empirical data because of its potential complexity.

2) Social work researchers should “incorporate the latest developments of methods from adjacent disciplines”. Adopting the understanding that determining causation is at the basis of most sciences, and that the “gold standard for research” randomized clinical trail is not always possible, Guo suggests borrowing from other disciplines that have “recognized the need for more efficient, effective approaches for assessing treatment effects when evaluating programs based on quasi-experimental design”. Namely, Guo discusses the propensity score analysis (PSA), a technique for “estimating causal effects from observational data” a contribution from Paul Rosenbaum and Donald Rubin (statisticians) and James Heckman (economist)[i].

3) Social worker researchers should “address the most pressing and challenging issues of social work research and practice”. Despite “lagging behind its adjacent disciplines”, Guo states that social work research has evolved and has added to the rigor in its research in 3 significant ways: a) The Society for Social Work and Research, created in 1994 has promoted social work research through annual conferences and other scientific activities; b) Advanced quantitative methods are being used and are published in social work publications; c) The quality of social work research has been enhanced by the “information technology revolution”.

Despite these accomplishments, Guo states that quantitative methods are unevenly distributed among substantive areas and there remains the need for more “rigorous quantitative research”. To illustrate, he presents the following results from in a review of 213 articles published between January1, 2012, to December 16, 2013[ii]:

1) Quantitative methods exceed all other research methods: 173 (81.2%) quantitative; 24 (11.3%) qualitative; 16 (7.5%)    mixed methods.

2) The mathematical methods used in social work research have been for “producing knowledge  or … hypothetic-deductive tests as opposed to deriving theory.

3) A wide variety of statistical methods have been used in social work research, most using descriptive or bivariate methods (22 studies), the other 167 using at least one multivariate statistical model.

4) Of 14 areas of social work research, only 3 (health/mental health, 50 studies; social welfare and poverty, 35 studies; and child welfare, 30 studies) used quantitative methods extensively.

5) More rigour is required as most studies use non-probability sampling methods and “low-level design” such as cross-sectional (41.3%).

Play nice now. Until recently, I lived on that very fine line between holding onto (what I consider to be) ‘human’ social work practices, and transforming lived experience into analyzable numbers; seeing the benefit of both yet not wanting to ‘sell my soul to the devil’ by venturing from interacting with people to quantifying them.  Guo’s push for empirical social work research has not been gentle, but although his usage of statistical language and detailed explications of analytical procedures may have been enough to scare any social worker away from ‘crossing over’ into the world of empirical data, which is counterproductive to his goal, I welcomed the brain-twisting linguistics and concepts, as I find the more I sit back and allow myself to ‘make friends’ with these concepts, the less overwhelming they are and the easier they are for me to wrap my head around.


[i] Many other statistical methods, sometimes used in conjunction with PSA have also been developed: hierarchical linear modeling; robust standard error estimation; SEM analyzing latent variables ; methods for analyzing categorical and limited dependent variables; and methods for analyzing time-to-event data as well as its marginal approaches to clustered event data.

[ii] Articles were selected from these journals: Social Work; Social Work Research; Research on Social Work Practice; Social Service Review; and the Journal of the Society for Social Work and Research.

Leave a Reply

Blog authors are solely responsible for the content of the blogs listed in the directory. Neither the content of these blogs, nor the links to other web sites, are screened, approved, reviewed or endorsed by McGill University. The text and other material on these blogs are the opinion of the specific author and are not statements of advice, opinion, or information of McGill.