Ideas and approaches towards strengthening the quantitative science for the interprofessional field

In the past year I have worked as a statistical consultant on the Journal of Interprofessional Care, overseeing many of the Journal’s manuscripts submitted as articles that used statistical methods and tests. As a result of this role, I have identified some recurring issues with the use and presentation of statistical methodology in quantitative submissions to the Journal. In this editorial, I aim to address a few of these concerns in order to encourage potential authors to present their empirical work in ways that will help readers and reviewers understand and appreciate quantitative contributions to the interprofessional literature, regardless of the level of their statistical training and interest. There have been rapid advances in quantitative methods for the academic disciplines spanned by JIC, such as education, sociology, psychology, and health sciences generally. To some extent the content of the Journal’s research articles should reflect these developments. To start with fundamentals first, authors should remember that statistical methods must be selected and used in service of research questions. Statistical methods follow from research questions. There must therefore be a convincing connection between the research question(s), the units of analysis, and the statistical procedures. We have seen many submissions, for example, comparing equality of group means on continuous measures and chi-square statistics on categorical measures that treat the groups and categories like they are naturally occurring entities whose location/distribution differences are unquestionably interesting and in need of analysis, but fail to offer a clear connection with the research question posed. Statistical questions can be formulated all too easily about possible differences between the multiple obvious and identifiable groups that interprofessionality involves, such as professions and professionals, faculties and departments, levels of training, gender, and so on. But the reasons for making the comparisons in the first place should be based on theory, systematic reviews, meta-analyses, case studies, or any other reasonable expectation that can be argued persuasively. To the greatest degree possible – and in line with the research question – there should be clear a priori hypotheses about the direction and magnitude of expected group differences. These expectations should be stated in the manuscript, with the sources where they were derived from – be they theories or other bodies of literature. Statistical methodology for socio-behavioral and health science research has had explosive growth in the area of multilevel models (Goldstein, 2011). Such models go by different names – linear mixed models (Verbeke & Molenberghs, 2000; West, Welch, & Galecki, 2006), random coefficient models (Longford, 1993), and hierarchical linear models (Raudenbush & Bryk, 2002) – but are essentially the same regardless of the name. Among the many advantages of multilevel models, one significant implication stands out: the recognition that units of analysis are often ‘nested’ and observations on them are correlated in a relevant explanatory or contextual manner. In some well-known research settings involving the institutions where interprofessional care is practiced and interprofessional training is given, like colleges and universities, hospitals, wards, and specialty teams, correlated observations may be the norm rather than the exception. Longitudinal data and repeated measurements (Hedeker & Gibbons, 2006; Singer & Willett, 2003) are special cases of nested observations. Groupand clusterrandomization is another (Donner & Klar, 2000). There is greater recognition now that nested data often require statistical approaches based on multilevel modeling. Authors should become aware of the potential multilevel structure of their research designs and should apply contemporary methods as needed. The clarity, precision, and level of detail of statistical and associated material (e.g. tables and figures) in quantitative manuscripts should be improved considerably. One specific area involves measurement scales and survey questionnaires. Care is needed when describing scales. When referring to scales by name, consistency throughout the manuscript is very desirable. In some applications, scales measure related but different concepts, such as ‘communication’ and ‘interprofessional interaction’, for example. The difference must be made evident in terminology. On the other hand, reviewers and readers alike need to be able to make easy connections between the first usage of a scale’s name and later references to it. Authors need to ensure that they report the internal consistency reliability of measurement scale data in their studies. Cronbach’s coefficient a (Cronbach, 1951) is the most popular statistic, though not all manuscripts convey a clear understanding of its meaning. A high a value does not mean that scale items are unidimensional or homogeneous; it only means that the items are interrelated (Schmitt, 1996). One should remember that to use coefficient a is to assume that the items are unidimensional, i.e. that they measure a single latent variable (Graham, 2006, pp. 934–935). Since a assumes unidimensionality, it cannot also show it. Furthermore, it is not sufficient simply to state that a scale’s Journal of Interprofessional Care, 2011, 25: 239–24