Amanda W. Joyce
Murray State University

Psychological research methods can be a dreaded course for students and instructors alike.  Students report negative emotions from and perceptions about about research, they struggle to see the relevance of research-related material, and they are concerned about the complexity of the research process, all of which can negatively impact their understanding of the course content (Balloo, 2019; Murtonen et al., 2008; Rancer et al., 2013).  Similarly, instructors broadly report concerns about student tardiness, dishonesty, inattention to material, and lack of preparation (Fazily et al., 2018; Lashley & de Meneses, 2001) which could be exacerbated in challenging courses like research methods. 

Thus, innovative techniques are needed to improve student and instructor experiences in research methods.  Frequently, this innovation comes in the form of applied, active learning that is directly relevant to student experiences—characteristics which have long been touted as beneficial for student learning (Ball & Pelco, 2006; Etengoff, 2023).  In fact, a recent study drawing upon interviews of experienced research methods instructors heavily emphasized the benefits of allowing students to apply what they learned, particularly through hands-on research experiences (Lewthwaite & Nind, 2016).

Involving students in hands-on research experiences, however, can present still more challenges.  Individual student projects can lead to a heavy grading burden for instructors, and partnered or group projects can be fraught with interpersonal complaints and social loafing.  The purpose of this essay is to explore an option for whole-class collaborative data collection that still allows students individually to propose, analyze, write about, and present data on a project of their own personal choosing.  The collaborative data collection process encourages accountability and teamwork.

The Project

Pedagogical Context

At my university, psychological research methods and statistics are taught in a combined three-course sequence, with the third course focusing on hands-on data collection in what is generally the students’ first research project.  Enrollment for this third course is typically 15 students, all Psychology majors.  The learning objectives for this course require successfully navigating the research process (e.g., “Generate an original research question,” “Conduct a research study in accord with APA’s ethical principles,” etc.).  Thus, the learning objectives of the course, as well as the teaching technique I propose here, encourage students to navigate the research process, from idea generation to final presentation. 

The Research Project: What Works for Me

            I have personally had great luck with an approach to teaching research methods that intermixes individual and group work while leading students through their first ever quantitative research project.  I have found it to increase individual accountability and teamwork while reducing many of the headaches associated with individual or paired data collection.  I provide a brief overview of the project below.  I am also happy to share course resources with interested readers.

Students’ experience with hands-on active learning through research occurs through a semester-long research project that occurs in three main phases: (1) individual idea generation, (2) group questionnaire and database creation, and (3) individual data analyses and presentation.

Individual Idea Generation

            Students begin the semester by individually generating research questions.  Research shows that students have better learning experiences when they work on projects that are personally meaningful (Andresen et al., 2020), and I have found this to be true in my classes as well.  We spend several class periods discussing the contents of a strong research hypothesis that would be testable under the constraints of semester-long project collected on students at their university.  For instance, we discuss how longitudinal hypotheses or hypotheses about overly-specific populations who we are unlikely to recruit on campus (i.e., the elderly; fraternity members who have been diagnosed with schizophrenia) would not be appropriate.  I also limit students to correlational (as opposed to experimental) research designs, which work best within our collaborative data collection process that emphasizes surveys as the primary data collection method.  During the first week of classes, students submit a list of five research questions that they are interested in exploring, which means that they are generating ideas before they have had the benefit of all class discussions on the topic, but generally one or two of their ideas are appropriate, and I am able to guide them toward those ideas. 

Then (week 3) students submit a final research question for approval before they dive into their topic of interest.  A librarian visits the course to teach students about how to use library resources to find peer-reviewed journal articles on their topics of interest, and students use this information to find five or more articles (week 5), which they summarize and later synthesize into an introduction section for their research paper (week 7).  

Group Questionnaire and Database Creation

            Students then gather measures relevant to their individual research hypotheses.  They often overlap with their peers in their topics of interest, meaning that there is overlap, too, in the measures that they may choose.  For instance, one student may be interested in anxiety and sleep quality, while another is interested in fraternity and sorority membership and anxiety, and yet another is interested in sleep quality and religiosity.  I encourage students with overlapping topics to work together to find common measures so as to reduce their burden in working with said measures, and I find that they are happy to take this opportunity for reduced workload.  When students happen to not have variables in common with their peers, I encourage them to use brief measures, such as short-form versions of scales rather than full scales, so as to reduce participant burden.

Students submit their measures (week 6) and, after I have reviewed each of them, we spend a class period gathering each measure into a class-wide shared Google Doc that will later become the questionnaire packet that participants receive.  Combining the measures into a single document during class ensures that everyone has the ability to closely supervise the process and catch any potential errors, like missing items or typographical errors, particularly in overlapping measures, for which several students are very closely monitoring.

Throughout the semester, students learn about the ethical aspects of research, and they have been working through ethical certification (CITI Training).  Thus, as soon as measures are gathered, we are ready to submit our project, as a single application, to our institutional review board (IRB) for approval.  I submit the application on students’ behalf, but I include the measures and hypotheses that they have provided to me, and we spend one class discussing the contents and importance of the IRB application and process.

In the one to two weeks (usually weeks 7 and 8) needed for IRB approval, the class prepares for data collection.  First, we learn about the data collection process and how to write about it.  Students learn departmental policies for data collection, including how to reserve rooms, how to use our participant management system (SONA), and more, and they write drafts of methods sections for their final paper. 

When students begin collecting data (usually week 8 or 9), they host research sessions individually, but they collect on the full research packet that was approved by the IRB.  In other words, even though students collect data individually, they collect data relevant to everyone.  This means that they have the ability to share research materials, that they can cover each other’s research sessions in case of emergency, and that they feel a personal accountability to the group to do good research.  It also means that they can have a large sample size, typically 100 or more students drawn from our department’s research participant pool.  I emphasize throughout the semester how we are a team working toward a common goal, and I find that students will often organically support one another in ways that I haven’t anticipated, such as offering up suggestions about where to find free or cheap printing for research materials.

Similarly, we crowdsource data management.  We spend several class periods building a shared class database in Google Sheets.  Students are responsible for creating a key for their individual measures so that everyone knows how data should be entered for all measures.  Again, in a combination of individual and group efforts, each student is responsible for entering all data that they collect, meaning that they are helping to support not only their own research interests but also their peers’.  This shared data entry strategy is another way in which I find students embracing the collaborative nature of this type of work—many will offer to cover data entry for another student when they know the other student is overwhelmed with their participant workload.

Individual Analyses and Presentation

When students finish data collection (week 11 or 12), we can begin the data analysis process.  Students are reminded as a group how to run the most common analyses (calculating a scale score from Likert data, determining participant demographics, running a reliability analysis, correlations, and t-tests).  Then there are several in-class workdays during which students can practice these analyses on their own data.  Each student is responsible for analyzing data relevant to their own research hypothesis.  I float around the computer lab to provide support to students with questions, but as there is only one of me, they find additional support in their classmates.  Students often answer one another’s questions and double-check analyses.  This is easily the most rewarding part of the semester, hearing students teaching and encouraging one another, and cheering when they see statistically significant results.

Following analyses, students are responsible for sharing their results in a final research paper.  They previously submitted a draft of an introduction (week 7) and method section (week 9).  The initial method draft was written at a time when they did not know their participants’ characteristics, so in that draft, they left placeholders for these numbers.  Thus, one of their first tasks after data analyses is to write a new draft of their methods section with these placeholders replaced with actual data.  They submit this alongside their results section (week 12) with a discussion section to follow roughly two weeks later.  While writing generally can’t be completed fully in class, students have several in-class writing days so that they can consult with the instructor and their peers when questions arise. 

Students then learn about data presentation and create a draft poster to be submitted during the last week of class. Again, because students are working on individual research hypotheses, each of these paper and poster drafts are individual, but students have the benefit of receiving feedback from peers and the instructor on drafts at all stages, meaning that final projects are often in phenomenal shape.

Students submit their finished products early during finals week, and then individually present their research to the class during the final examination period.  This is another very encouraging part of the semester, as students learn more about their peers’ projects and offer encouragement for their hard work.  Furthermore, because the work was approved by the IRB, students are in a very good position to later take their research projects to other venues, such as on-campus undergraduate research conferences and/or regional professional conferences, to share their findings with a broader audience.

The Outcome

            The structure of the class research project, intermixing group and individual components is, admittedly overwhelming sometimes, particularly if an individual student must miss class frequently, such as in cases of student athletes.  In those cases, the students’ lack of attendance has the potential to harbor everyone’s progress on the collaborative project, so a fair amount of instructor foresight and flexibility is necessary in order to accommodate those absences and ensure that the project can still move forward.  That said, I have found the collaboration to be worthwhile.  Grades, attendance, and course evaluations have increased since I began collaborative data collection, as have student accountability and teamwork.  As students move in and out of group and individual efforts, they see the ways in which they efforts impact themselves and others, and they embrace the process of working toward a common goal.

            More than that, students recognize the ways in which collaboration has allowed them to more effectively manage their time so that they are not duplicating efforts.  For instance, by pooling their data collection, they avoid saturating the research pool and have access to many more participants than they would if they had collected data individually.  Similarly, from the instructor perspective, students’ collaboration allows me to more efficiently work with them (for instance, allowing me to work with one IRB application instead of 15), so that I can free up time to provide more detailed feedback on drafts throughout the semester, which also benefits the students.

Teamwork makes dreamwork.  Gone are the days of spending countless office hours listening to students complain about how their research partner isn’t doing their fair share of the work.  Gone, too, are the days of trying to grade results sections based on data collected from 7 participants.  Instead, I see students working together and holding themselves to a high standard, and I see their efforts resulting in extraordinary outcomes.  I hope that others can find relief and excitement in a similar approach.

References

Andresen, L., Boud, D., & Cohen, R. (2020). Experience-based learning. In Understanding adult education and training (pp. 225-239). Routledge.

Ball, C. T., & Pelco, L. E. (2006). Teaching research methods to undergraduate psychology students using an active cooperative learning approach. International Journal of Teaching and Learning in Higher Education, 17(2), 147-154.

Balloo, K. (2019). Students’ difficulties during research methods training acting as potential barriers to their development of scientific thinking. Redefining scientific thinking for higher education: Higher-order thinking, evidence-based reasoning and research skills, 107-137. https://doi.org/10.1007/978-3-030-24215-2_5

Etengoff, C. (2023). Reframing psychological research methods courses as tools for social justice education. Teaching of Psychology, 50(2), 184-190. https://doi.org/10.1177/00986283221097404

Fazli, A., Imani, E., & Abedini, S. (2018). Faculty members' experience of student ethical problems: A qualitative research with a phenomenological approach. Electronic Journal of General Medicine, 15(3). https://doi.org/10.29333/ejgm/84952

Lashley, F. R., & de Meneses, M. (2001). Student civility in nursing programs: A national survey. Journal of Professional Nursing, 17(2), 81-86. https://doi.org/10.1053/jpnu.2001.22271

Lewthwaite, S., & Nind, M. (2016). Teaching research methods in the social sciences: Expert perspectives on pedagogy and practice. British Journal of Educational Studies, 64(4), 413-430. https://doi.org/10.1080/00071005.2016.1197882

Murtonen, M., Olkinuora, E., Tynjälä, P., & Lehtinen, E. (2008). “Do I need research skills in working life?”: University students’ motivation and difficulties in quantitative methods courses. Higher Education, 56, 599-612. https://doi.org/10.1007/s10734-008-9113-9

Rancer, A. S., Durbin, J. M., & Lin, Y. (2013). Teaching communication research methods: Student perceptions of topic difficulty, topic understanding, and their relationship with math anxiety. Communication Research Reports, 30(3), 242-251. https://doi.org/10.1080/08824096.2013.806259