Fundamentals of Quantitative Research Methods in Mental Health Nursing

These series of papers are designed to provide readers with useful information on key concepts, issues and theories when engaging in quantitative research and statistical analysis. The points identified are not exhaustive but are designed to provide the reader with key learning points, as well as direct the reader to additional reading. The papers are designed to be incremental in learning outcomes, with the first two papers providing an overview of rudimentary principles underpinning quantitative research, data handling and quality assurance, before allowing the reader to choose which statistical techniques they need to draw upon to conduct the appropriate statistical analysis. The statistical papers use the software package JASP 18.3.0. It is free of charge and chosen to facilitate practitioners with no access to expensive statistical software packages. Hopefully, this will promote engagement in quantitative research, service evaluation or quality improvement projects. Hopefully, we will see the fruits of these initiatives published in the Journal of Psychiatric and Mental Health Nursing. The influence of positivism and quantitative research in psychiatric and mental health nursing research is evident across the most fundamental aspects of care provision. Be it in the establishment of clinical conditions diagnostic/classification criteria such as the DSM V or ICD 11; in the development and use of screening tools in practice such as the PHQ9 or the GAD7; or impactful policy change and the use of evidence-based practice such as recover rates, remissions, etc. (Kutney 2006). At an international, national and regional level, we have also seen a growth of digital technology, data linkage and ethical data sharing of quantitative healthcare information to better inform service provision and provide support for evidence-based practice. This is also the case within psychiatry and mental health nursing. Given this reliance on quantitative research, there is a clear necessity to better understand the key concepts, definitions and terms underpinning both positivism and its practical application using quantitative research. Positivism is defined as ‘a philosophical system recognising only that which can be scientifically verified or which is capable of logical or mathematical proof’ (Oxford Dictionary). It is a translation of the ‘scientifically verified’ and ‘mathematical proof’ component of this definition that encapsulates quantitative research methodology and methods. This research paradigm ‘Positivism’ has the ontological (how reality is viewed) philosophical view that reality as a single, objective reality, experienced by all people, definable, measurable and ready for change, and; epistemologically, (how the nature of knowledge is conceived) positivism seeks to describe the laws of nature in a scientifically verifiable manner, with a reliance on measurement (scientifically verifiable observations). The translation of the ontological and epistemological views of reality and how we conceive knowledge give rise to the hypothetico-deductive model of science and from this model all quantitative research methodologies and methods emerge. A Working Example: In Chang et al. (2022) paper the authors used the Social Support Deterioration Model (Kaniasty and Norris 1993) to underpin the cross-sectional survey of 300 participants with substance use disorders. Its contribution to the generation of hypothesises, instrument selection and statistical analysis. Chang, C.W., Chang, K.C., Griffiths, M.D., Chang, C.C., Lin, C.Y. and Pakpour, A.H., 2022. The mediating role of perceived social support in the relationship between perceived stigma and depression among individuals diagnosed with substance use disorders. Journal of Psychiatric and Mental Health Nursing, 29(2), pp. 307–316. Quantitative research can be challenging and the reliance on numerical data deters many would-be researchers from using it as a methodology. Figure 2 shows the main elements involved in quantitative research from theory to report writing. A key feature of quantitative research is that much of the work is completed before data is collected such as hypothesis generation, research design selection, instrument selection and recruitment. The collection of data and its statistical analysis form the latter elements of the research process and focus on hypothesis testing. It is clearly an extension of the hypothetico-deductive model and forms the template for quantitative research design. HELPFUL HINT It is very important and beneficial to involve and methodologist and/or statistician as early as possible in a research study. They will bring valuable insight and advice to help improve the strength and generalisability of the research study findings. Waiting to the data analysis stage is often too late and many earlier mistakes cannot be corrected. Remember—statistical techniques can only correct for so many mistakes! This paper provides a brief overview of the philosophical tenets of positivism and the hypothetico-deductive model of science, and how they underpin quantitative research methodologies and methods. The aim being to produce ‘scientifically verifiable’ and ‘mathematical proof’ to examine hypotheses. Further exploration of each area identified in the paper is required to ensure that the most appropriate methodology and methods are selected to address the study objectives. This paper is the first in a series of papers to cover key areas of quantitative research methodologies and methods, intended to help promote a better understanding and increase usage of quantitative research. First, we will look at the conceptualisation phase of a research project. Let us assume, based on our work with patients reporting high levels of anxiety and note that many report having experiences childhood traumas that are clearly anxiety-evoking. Based on this hunch, we wish to move beyond subjective reporting, and to statistically examine the impact of childhood trauma has on development of anxiety in later life. The conceptual model identified in Figure 4 is an example of a cross-sectional survey where participants would be asked to complete two short, 4-item questionnaires, each examining the two constructs: childhood trauma and anxiety. In this model, childhood trauma is the independent variable and influences anxiety (the dependent variable) as indicated by the directional arrow. Using this simple diagrammatical representation allows the identification and definition of the key variables (childhood trauma and anxiety), the relationship between the two variables as a testable hypothesis, and the identification of instruments to measure both variables. This conceptualised model is established a priori and informs and guides all further aspects of a quantitative study. The author has nothing to report. The author declares no conflicts of interest. The author has nothing to report.