Introduction
The Society for Social Work and Research is determined to advance social work research in all spheres of life. The organization focuses on designing and transferring rigorous research practices that influence policy decisions that impact social welfare programs. Therefore, it is vital to explicate the role statistics plays in social work and the relevance of outliers in contextualizing different phenomena. I believe that advancing social sciences and resolving society’s challenges lie in using scientifically robust statistical techniques to develop effective programs and evidence-based policies.
The Role of Statistics in Social Work Research
The degree to which a discipline applies statistical tools impacts its epistemic and scientific development. Statistics are important in social work because they provide a measure of evidence for a specific hypothesis, or as Dunleavy and Lacasse (2021) point out, they “provide a measure of fit for competing models” (p. 1). When statistics are not applied in social work, practitioners risk proposing or delivering policies and interventions that are of little benefit to clients because they have not been empirically evaluated for effectiveness.
Statistics are important because they allow social scientists to critique empirical research, evaluate practice, and disseminate research. Using numerical evidence to reach important conclusions is essential in social work. The ability to analyze claims based on quantitative evidence is an essential skill because it allows practitioners to differentiate between dubious and reasonable assertions.
The usefulness of statistics is demonstrated in analyzing various forms of information. All social sciences rely on accurate results to inform decisions in different contexts. Statistics help practitioners navigate the pitfalls associated with assessing large quantities of data. For instance, individuals are equipped with the skills to identify biased samples, overgeneralization, and incorrect analyses. It is vital to ascertain the accuracy of the information in order to avoid making wrong decisions or proposing ineffective interventions.
The view that statistics are important in social sciences is based on empirical research findings. Dunleavy and Lacasse (2021) demonstrate the applications and relevance of various statistical principles in social work. The researchers highlight the centrality of statistics in the social sciences by providing a nuanced description of relevant concepts and their applications.
Contemporary society and the challenges it presents are best understood through the evaluation of quantitative data. All practitioners in the field of social sciences must be equipped with essential statistical skills to make sense of the intricacies that characterize modern society.
The Role of Outliers in Social Work Research
Researchers must choose the most effective technique to apply in analyzing specific datasets. The process of assessing the data for errors or test assumptions often involves decisions regarding handling outliers. Leys et al. (2019) note that a researcher’s choice regarding managing outliers leads to different datasets that may alter the study’s outcome. The failure to detect and manage outliers may create inaccuracies in statistical analyses and reduce the external validity of the study’s results (Mowbray et al., 2019).
Outliers are “extremely distant from most of the other data points” (Leys et al., 2019, p. 1). It is often the case that outliers cause a problematic influence on developing substantive assessments of the associations between variables. Despite the challenges above, outliers should be included in the results. This is because careful examination of these data points may result in new theoretical insights forming the foundations for future studies. Figure 1 below demonstrates outliers in a histogram and boxplot of age frequency distribution among frontline nurses.
Outliers make significant contributions to the understanding of various phenomena. Leys et al. (2019) propose a two-step process to address outliers in any research endeavor. Firstly, researchers must always strive to detect possible outliers using reliable quantitative tools. Secondly, researchers must decide how to address outliers by using qualitative information to keep, record, or remove them from the dataset (Leys et al., 2019). It should be noted that if the detection and management procedures are conducted post hoc to select a modality that yields a desired outcome, bias is inadvertently introduced in the results.
There are multiple options available for managing outliers. I agree with the proposal by Leys et al. (2019) that one should ask judges, such as colleagues or interns, who are blind to the study hypotheses to decide whether outliers that do not correspond to selection criteria should be included. The process must be conducted prior to further data analysis.
The second proposed solution is to stick to the pre-registered decision regardless of other arguments to maintain credibility. Error outliers can be removed from the sample provided the process is relayed transparently (Gibbert et al., 2021). The impact of outliers on research findings must not be ignored. The seemingly misplaced data points play a critical role in advancing the science of social work because they contextualize phenomena in ways that could lead to the discovery of novel theoretical perspectives.
The handling of outliers when conducting disparities-related research is critical. The oppressed, marginalized, and vulnerable populations correspond to the notion of outliers because the correct interpretation of data and the resultant programs is essential for fulfilling all their needs. It is worth considering that outliers may result from measurement errors, chance variations, or true heterogeneity in a given dataset.
The Relationship Between Oppressed, Marginalized, and Vulnerable Populations and the Concept of Outliers
When conducting research related to oppressed, marginalized, and vulnerable populations, it is vital to ascertain whether the outliers result from true heterogeneity by analyzing any systematic patterns in the extreme values. In the event outliers are the result of true heterogeneity, they could be interesting.
Interesting outliers have significant implications in oppressed, marginalized, and vulnerable populations because they could represent specific realities such as extremities of marginalization, severe vulnerability, or a high degree of oppression. Identifying why outliers exist in these populations could help identify important phenomena in disparities research.
Interesting outliers in oppressed, marginalized, and vulnerable populations represent a critical sub-population that requires careful analysis to ascertain the factors that contribute to their poor outcomes. Therefore, the consideration of extreme values is important because they may represent opportunities for developing effective interventions for vulnerable sub-populations.
Conclusion
The advancement of the social sciences depends upon the sound application of scientific principles and methods of inquiry. I believe that statistics contribute immensely to this endeavor because they facilitate the extraction of meaning from large chunks of quantitative data. This allows practitioners to formulate theories and create interventions that significantly impact society.
Outliers must be included in results because they often represent sub-populations that may provide insight into previously unknown phenomena. Outliers may represent specific realities that, if ignored, may leave critical social problems unchecked. Statistics plays a critical role in explicating phenomena and addressing some of society’s most pressing challenges.
References
Dunleavy, D. J., & Lacasse, J. R. (2021). The use and misuse of classical statistics: A primer for social workers. Research on Social Work Practice, 31(5), 438–453. Web.
Gibbert, M., Balachandran Nair, L., Weiss, M., & Hoegl, M. (2021). Using Outliers for Theory Building. Organizational Research Methods, 24(1), 172–181. Web.
Leys, C., Delacre, M., Mora, Y. L., Lakens, D., & Ley, C. (2019). How to classify, detect, and manage univariate and multivariate outliers, emphasizing pre-registration. International Review of Social Psychology, 32(1), 1–10. Web.
Mowbray, F. I., Fox-Wasylyshyn, S. M., & El-Masri, M. M. (2019). Univariate outliers: A conceptual overview for the nurse researcher. Canadian Journal of Nursing Research, 51(1), 31–37. Web.