Exploring MSW students’ attitudes toward research: What predicts them and do they change over time?

Authors

DOI:

https://doi.org/10.1921/jpts20572622

Keywords:

Attitudes towards research, social work practice, social work education, evidence-based practice

Abstract

Students’ attitudes toward specific content areas can shape their academic performance and future professional competence. Identifying students who hold negative perceptions of subjects such as research methods allows educators to determine whether additional support is needed. This study examined attitudes toward research among a diverse sample of master’s-level social work students (n = 113). Participants completed a demographic questionnaire and three subscales of the Revised Attitudes Toward Research Scale (R-ATR) at the beginning and end of the semester to (1) assess changes in research-related attitudes and (2) determine whether demographic characteristics predicted these changes. Students’ positive research dispositions were associated with perceptions of research usefulness and lower levels of research-related anxiety. Overall, research anxiety decreased over the semester, and several demographic variables were linked to variation in gain scores. Findings highlight the utility of the R-ATR for monitoring students’ attitudes in research courses and future engagement in evidence-based practice. Recommendations are offered to strengthen students’ capacity to engage in evidence-based practice by enhancing their ability to integrate research knowledge into decision-making and outcome evaluation.

Author Biographies

Micki Washburn, University of Texas at Arlington

Dr. Micki Washburn is an Associate Professor at the University of Texas at Arlington School of Social Work.  She frequently integrates aspects of technology into her scholarship and has expertise in virtual reality (VR) intervention development and telehealth. Her current research focuses on the development of innovative culturally grounded interventions for individuals experiencing co-occurring mental health and substance use disorders. One of her current projects focuses on the development and testing of a modular virtual reality digital therapeutic that can be customized to the individual client for the adjunctive treatment of substance use disorders. Dr. Washburn’s other work focuses on the implementation of large scale system improvements to better serve LGBTQ+ youth and families with child welfare system involvement. For over 20 years, Dr. Washburn has worked with people experiencing co-occurring mental health, substance abuse and physical health concerns.  In addition, she has substantial program evaluation experience related to mental health, substance abuse and child welfare system improvement projects. Her extensive practice experience brings real world knowledge to the classroom to further develop the knowledge base and skills of the next generation of social work professionals.

Christian Carr, University of Houston

Dr. Christian Carr was a Post-doctoral fellow at the University of Houston at the time of manuscript preparation.  Dr. Carr now works in private industry.  He has significant expertise in advanced statistical analysis and novel analytic methods. 

Rebecca Mauldin

Rebecca Mauldin, PhD, LMSW, studies social connectedness. She focuses on the social relationships of older adults, the factors that support them forming and maintaining positive relationships, and the ways in which their relationships affect their health, well-being, and access to vital resources and information. In addition, she uses social network analysis to investigate human and organizational networks and their role in contributing to individual and community well-being. In her teaching, Rebecca is passionate about using and developing open educational resources. She earned her doctorate in Social Work from the University of Houston, her Master in Social Work from the University of Houston, and her Bachelor of Arts in Political Science from the University of North Carolina at Chapel Hill.

Wen Xu, City University of Macau, Faculty of Health and Wellness, Department of Innovative Social Work

Dr. Wen Xu is an Assistant Professor Faculty of Health and Wellness at the City University of Macau.  Her research interests include: Maternal and child health, Mental Health, Perinatal Mood and Anxiety Disorders, Social Determinants of Health, Implementation Research, Social Work Education, Intervention Development and Evaluation

Joshua Awua, University of Texas at Arlington

Dr. Joshua Awua is a Postdoctoral Research Associate at the University of Texas at Arlington School of Social Work. He received his PhD in Addictive Disorders and Recovery Studies from Texas Tech University in 2024 and an MPhil in Clinical Health Psychology from the University of Cape Coast, Ghana, in 2018. His research focuses on understanding how social networks impact youth and young adults’ substance use to identify areas for intervention development. Dr Awua uses advanced statistical methods such as social network analysis, multilevel modeling, and structural equation models to understand social factors that influence substance use. He is specifically interested in understanding how interpersonal communication and perceived norms are associated with substance use. His current project focuses on the development and testing of virtual reality digital therapies that can be customized to individual clients for the treatment of substance use disorders.

References

Aiken, L. S., & West, S. G. (1990). Invalidity of true experiments: Self-report pretest biases. Evaluation Review, 14(4), 374–390.

Bolin, B. L., Lee, K. H., GlenMaye, L. F., & Yoon, D. P. (2012). Impact of research orientation on attitudes toward research of social work students. Journal of Social Work Education, 48(2), 223–243. https://doi.org/10.5175/JSWE.2012.200900120

Brown, T. A. (2015). Confirmatory factor analysis for applied research (2nd ed.). Guilford Press.

Carr, L. C., Washburn, M., Xu, W., & Mauldin, R. L. (2025). Evaluating the psychometric properties of the Revised Attitudes Toward Research Scale for use with MSW students. Journal of Evidence-Based Social Work, 22(1), 1-19. https://doi.org/10.1080/26408066.2024.2418103

Castro-Schilo, L., & Grimm, K. J. (2018). Using residualized change versus difference scores for longitudinal research. Journal of Social and Personal Relationships, 35(1), 32–58. https://doi.org/10.1177/0265407517718387

Chen, F. F. (2007). Sensitivity of goodness of fit indexes to lack of measurement invariance. Structural Equation Modeling: A Multidisciplinary Journal, 14(3), 464–504. https://doi.org/10.1080/10705510701301834

Coman, E. N., Picho, K., McArdle, J. J., Villagra, V., Dierker, L., & Iordache, E. (2013). The paired t-test as a simple latent change score model. Frontiers in Psychology, 4, 738. https://doi.org/10.3389/fpsyg.2013.00738

Council on Social Work Education Commission on Accreditation and Commission on Educational Policy. (2022). 2022 Educational Policy and Accreditation Standards for baccalaureate and master’s social work programs. Council on Social Work Education. https://www.cswe.org/getmedia/bb5d8afe-7680-42dc-a332-a6e6103f4998/2022-EPAS.pdf

Drake, B., & Jonson-Reid, M. (2019). Social work research methods: From conceptualization to dissemination (1st ed.). Pearson.

Drisko, J. W., & Grady, M. D. (2015). Evidence-based practice in social work: A contemporary perspective. Clinical Social Work Journal, 43(3), 274–282. https://doi.org/10.1007/s10615-015-0548-z

Einbinder, S. D. (2014). Reducing research anxiety among MSW students. Journal of Teaching in Social Work, 34(1), 2–16. https://doi.org/10.1080/08841233.2013.863263

Engel, R. J., & Schutt, R. K. (2016). The practice of research in social work (4th ed.). SAGE Publications.

Godsil, R. D., Tropp, L. R., Goff, P. A., & Powell, J. A. (2014). Addressing implicit bias, racial anxiety, and stereotype threat in education and health care (The Science of Equality, 1). Perception Institute. No DOI.

Grady, M. D., Wike, T., Putzu, C., Field, S., Hill, J., Bledsoe, S. E., Bellamy, J., & Massey, M. (2017). Recent social work practitioners’ understanding and use of evidence-based practice and empirically supported treatments. Journal of Social Work Education, 54(1), 163–177. https://doi.org/10.1080/10437797.2017.1299063

Hardy, M. A. (1993). Regression with dummy variables. Sage.

Joo, S., Ali, U., Robin, F., & Shin, H. J. (2022). Impact of differential item functioning on group score reporting in the context of large-scale assessments. Large-Scale Assessments in Education, 10(1), 18. https://doi.org/10.1186/s40536-022-00135-7

Kagan, M. (2022). Social workers’ attitudes toward evidence-based practice: The mediating role of work-related self-efficacy. Social Work Research, 46(3), 217–228. https://doi.org/10.1093/swr/svac018

Little, T. D. (2013). Longitudinal structural equation modeling. Guilford Press.

Martinková, P., Drabinová, A., Liaw, Y. L., Sanders, E. A., McFarland, J. L., & Price, R. M. (2017). Checking equity: Why differential item functioning analysis should be a routine part of developing conceptual assessments. CBE—Life Sciences Education, 16(2), rm2. https://doi.org/10.1187/cbe.16-10-0307

McArdle, J. J. (2009). Latent variable modeling of differences and changes with longitudinal data. Annual Review of Psychology, 60, 577–605. https://doi.org/10.1146/annurev.psych.60.110707.163612

McNeece, C. A., & Thyer, B. A. (2004). Evidence-based practice and social work. Journal of Evidence-Based Social Work, 1(1), 7–25. https://doi.org/10.1300/J394v01n01_02

McNeish, D. (2017). Thanks coefficient alpha, we’ll take it from here. Psychological Methods, 23(3), 412–433. https://doi.org/10.1037/met0000144

Melnyk, B. M., Fineout-Overholt, E., Stillwell, S. B., & Williamson, K. M. (2010). Evidence-based practice: Step by step: The seven steps of evidence-based practice. AJN: The American Journal of Nursing, 110(1), 51–53. https://doi.org/10.1097/01.NAJ.0000366056.06605.d2

Mullen, E. J., Bledsoe, S. E., & Bellamy, J. L. (2008). Implementing evidence-based social work practice. Research on Social Work Practice, 18(4), 325–338. https://doi.org/10.1177/1049731506297827

O’Halloran, P., Porter, S., & Blackwood, B. (2010). Evidence based practice and its critics: What is a nurse manager to do? Journal of Nursing Management, 18(1), 90–95. https://doi.org/10.1111/j.1365-2834.2009.01068.x

Oort, F. J. (1998). Simulation study of item bias detection with restricted factor analysis. Structural Equation Modeling: A Multidisciplinary Journal, 5(2), 107–124. https://doi.org/10.1080/10705519809540095

Papanastasiou, E. C. (2005). Factor structure of the ‘Attitudes toward Research” scale. Statistics Education Research Journal, 4(1), 16–26. https://doi.org/10.52041/serj.v4i1.523

Papanastasiou, E. C. (2014). Revised-Attitudes Toward Research scale (R-ATR); A first look at its psychometric properties. Journal of Research in Education, 24(2), 146–159.

Pek, J., & MacCallum, R. C. (2011). Sensitivity analysis in structural equation models: Cases and their influence. Multivariate Behavioral Research, 46(2), 202–228. https://doi.org/10.1080/00273171.2011.561068

Pitts, S. C., West, S. G., & Tein, J.-Y. (1996). Longitudinal measurement models in evaluation research: Examining stability and change. Evaluation and Program Planning, 19(4), 333–350. https://doi.org/10.1016/S0149-7189(96)00027-4

Putnick, D. L., & Bornstein, M. H. (2016). Measurement invariance conventions and reporting: The state of the art and future directions for psychological research. Developmental Review, 41, 71–90. https://doi.org/10.1016/j.dr.2016.06.004

Rhemtulla, M., Brosseau-Liard, P. É., & Savalei, V. (2012). When can categorical variables be treated as continuous? A comparison of robust continuous and categorical SEM estimation methods under suboptimal conditions. Psychological Methods, 17(3), 354–373. https://doi.org/10.1037/a0029315

Rubin, A., & Parrish, D. E. (2007). Challenges to the future of evidence-based practice in social work education. Journal of Social Work Education, 43(3), 405–428. https://doi.org/10.5175/JSWE.2007.200600612

Rubin, A., & Parrish, D. E. (2010). Development and validation of the Evidence-Based Practice Process Assessment Scale: Preliminary findings. Research on Social Work Practice, 20(6), 629–640. https://doi.org/10.1177/1049731508329420

Satorra, A., & Bentler, P. M. (2010). Ensuring positiveness of the scaled difference chi-square test statistic. Psychometrika, 75(2), 243–248. https://doi.org/10.1007/s11336-009-9135-y

Secret, M., Rompf, E. L., & Ford, J. (2003). Undergraduate research courses: A closer look reveals complex social work student attitudes. Journal of Social Work Education, 39(3), 411–422.

Sekaquaptewa, D. (2011). Discounting their own success: A case for the role of implicit stereotypic attribution bias in women’s STEM outcomes. Psychological Inquiry, 22(4), 291–295. https://doi.org/10.1080/1047840X.2011.624979

Spring, B., Craighead, W. E., & Hitchcock, K. (2009). Evidence-based practice in psychology. In I. B. Weiner (Ed.), Corsini’s encyclopedia of psychology (4th ed., pp. 603–607). Wiley.

Thyer, B. A. (2015). Preparing current and future practitioners to integrate research in real practice settings. Research on Social Work Practice, 25(4), 463–472.

Published

2026-04-01

How to Cite

Washburn, M., Carr, L. C., Mauldin, R., Xu, W., & Awua, J. (2026). Exploring MSW students’ attitudes toward research: What predicts them and do they change over time? . The Journal of Practice Teaching and Learning, 23(3), 1–24. https://doi.org/10.1921/jpts20572622
Received 2025-11-17
Accepted 2026-02-11
Published 2026-04-01