It is important to study research methods to determine which method would work best in a particular scenario. Below, we will examine the top five research methods used by criminologists today: survey, longitudinal, meta-analysis, quasi-experimental research, cross-sectional research methods, and the gold standard of research methods: randomized control trial (RCT) method (Kleck, Tark & Bellows, 2006).

Survey Research Method

Survey research is a quantitative and qualitative method with two important characteristics.

Qualitative Research: Involves exploring and understanding human experiences and behaviors by delving into in-depth narratives, like personal stories and interviews.

Quantitative Research: Uses numerical data and statistical analysis to identify patterns and draw conclusions, such as counting and measuring to uncover trends.

First, the variables of interest are measured using self-reports. In essence, survey researchers ask their participants (who are often called respondents) to report directly on their own thoughts, feelings, and behaviors. Second, considerable attention is paid to the issue of sampling. In particular, survey researchers have a strong preference for large random samples because they provide the most accurate estimates of what is true in the population. In fact, survey research may be the only approach in which random sampling is routinely used. Beyond these two characteristics, almost anything goes in survey research. Surveys can be long or short. They can be conducted in person, by telephone, through the mail, or over the Internet. They can be about voting intentions, consumer preferences, social attitudes, health, or anything else that it is possible to ask people about and receive meaningful answers. Although survey data are often analyzed using statistics, there are many questions that lend themselves to more qualitative analysis.

Meta-Analysis Research Method

Meta-analysis is a research method that involves combining data from multiple studies to draw conclusions about a particular research question or topic. The goal of a meta-analysis is to identify consistent patterns or trends across studies, which can provide more reliable and precise estimates of the effects of an intervention or factor than any single study could provide on its own. For example, imagine you’re investigating whether a new study technique improves students’ test scores. Instead of just looking at one study, a meta-analysis would gather data from various research projects on the same topic. By combining results from multiple studies, it helps us make more informed conclusions by considering a broader picture of research findings.

To conduct a meta-analysis, researchers typically begin by identifying a research question and a set of studies that have investigated that question. They then use statistical methods to combine the results of those studies, often weighting each study according to its sample size or other factors. By combining the results of multiple studies, meta-analysis can help to identify consistent findings across studies, as well as identify factors that may explain variability in results across studies.

One example of how a criminologist might use meta-analysis is to examine the effectiveness of a particular tool aimed at reducing crime, such as a community policing program. By conducting a meta-analysis of studies that have investigated the effectiveness of community policing, a criminologist could identify whether the tool consistently leads to reductions in crime across different settings, populations, and study designs.

Meta-analysis helps researchers figure out what factors might affect how well an intervention works. These factors could include how well the plan is put into action, the features of the community it’s used in, or the specific details of the crime issue it’s addressing. Discovering these factors can provide valuable insights into what makes a crime reduction strategy effective. Policymakers and law enforcement can use this information to make better decisions about where to put resources and how to implement strategies for reducing crime in a community.

Quasi-Experimental Research Method

Quasi-experimental research is a method that shares some similarities with experimental research, but it operates under different rules. In a typical experiment, researchers manipulate variables to observe their effects. However, in quasi-experimental research, this manipulation occurs without randomly assigning participants to different groups. This absence of random assignment means that the groups being compared might not be perfectly equivalent, potentially complicating the interpretation of results. While quasi-experimental designs offer more control than mere observational studies, they fall short of the standard set by true experimental designs.

These quasi-experiments are often useful in settings where random assignment is impractical, such as educational environments. Let’s consider a scenario: a researcher wishes to assess the impact of a new teaching method on student performance. Randomly assigning students to different teaching styles isn’t feasible, so the researcher compares students who happen to be in classes where the new method is used with those who aren’t. However, because the students weren’t randomly assigned to these classes, there may be differences between the groups beyond just the teaching method. These differences, like student motivation or prior knowledge, could change the results.

To address this, researchers might use statistical techniques to match up students who are similar in relevant characteristics, like academic ability or socio-economic background, across the different teaching methods. By doing so, they hope to isolate the effect of the teaching method from other factors that could influence student performance.

This approach allows educators and policymakers to make informed decisions about teaching practices without the need for large-scale randomized trials, which can be costly and time-consuming. Nevertheless, it’s crucial to acknowledge the limitations of quasi-experimental designs, as they may introduce biases that could undermine the validity of the findings.

Quasi-experimental research is a middle ground between purely observational studies and full-fledged experiments. While it offers valuable insights, researchers must tread carefully to ensure their conclusions are well-founded.

Cross-Sectional Research Method

A cross-sectional research method is like taking a picture of a moment in time. Instead of studying how things change over time, it captures data from a group of people at one specific moment. The purpose of this type of research is to describe a variable, not measure it (Simkus, 2023). Imagine researchers want to know how students feel about their teachers. They might choose a bunch of students from different schools and ask them to fill out a survey about their thoughts on their teachers, their trust in their teachers, and their experiences with them.

The survey happens just once, maybe over a week or a month. After collecting the responses, the researchers get a snapshot of how students currently view their teachers across different schools. Again, this can only be used to describe these student and teacher relationships, it cannot be used to measure these relationships.

These findings can be useful for schools to understand student perspectives and identify areas for improvement. For instance, if many students express a lack of trust in their teachers, schools might decide to implement programs to build trust or enhance teacher-student relationships.

But remember, this method only gives a glimpse of what’s happening at one point in time (Simkus, 2023). It can’t tell us how student attitudes might change over time. For that, we’d need to use longitudinal research methods, which track changes in attitudes over a longer period.

Randomized Control Trial (RCT) Research Method

Think of a Randomized Controlled Trial (RCT) as a teacher’s experiment to assess the impact of a new teaching method on student performance. In this scenario, students are randomly assigned to two groups: the intervention group, which receives the new teaching method, and the control group, which continues with the traditional teaching approach.

Advantages of this method in the classroom setting include the ability to neutralize any existing biases among the students, enabling analysis of results using established statistical techniques, and providing clear identification of the participating student populations.

However, there are also drawbacks, such as the considerable investment of time and resources required, and the possibility of volunteer biases where students who opt into the study may not represent the entire student body.

Let’s explore an example of how this method might be applied in a student/teacher context, Imagine a study evaluating the effectiveness of a new tutoring program for struggling students. In this study, a group of struggling students would be randomly assigned to either receive the tutoring intervention or continue with standard classroom instruction.

After a set period, both groups would be assessed for outcomes like improved test scores or increased classroom engagement. By comparing the outcomes between the two groups, researchers could determine whether the tutoring program significantly impacted student performance. This student-centered RCT would offer evidence of the tutoring program’s effectiveness. Random assignment helps to mitigate external factors that could influence outcomes, allowing policymakers and educators to make informed decisions about the implementation and expansion of such interventions.

Advantages

• Good randomization will “wash out” any population bias
• Results can be analyzed with established statistical tools
• Populations of participating individuals are clearly identified

Disadvantages

• Expensive in terms of time and money
• Volunteer biases: the population that participates in the study may not be representative of the actual entire population

Licenses and Attributions for Research Methods

Open Content, Original

“Cross-Sectional Research Method” by Roxie Supplee, is licensed under CC BY 4.0.

Open Content, Shared Previously

“Research Methods” by Trudi Radtke and Megan Gonzalez is licensed under CC BY-NC-SA 4.0. Revised by Roxie Supplee, licensed CC BY-NC-SA 4.0, for content.

“Survey Research Method” is adapted from “Overview of Survey Research” by Paul C. Price, Rajiv Jhangiani, & I-Chant A. Chiang in Research Methods in Psychology – 2nd Canadian Edition, licensed under CC BY-NC-SA 4.0, by Trudi Radtke and Megan Gonzalez. Revised by Roxie Supplee, licensed CC BY-NC-SA 4.0, for clarity, reading level, and accuracy.

“Meta Analysis Research Method” is adapted from “Research Designs” by Christie Napa Scollon, Singapore Management University, which is licensed under CC BY-NC-SA 4.0. Modifications by Roxie Supplee, licensed CC BY-NC-SA 4.0, include expanding and revising the content.

“Randomized Control Trial (RCT) Research Method” is adapted from “Randomised controlled trials—the gold standard for effectiveness research” which is in the Public Domain. Modifications by Roxie Supplee, licensed CC BY 4.0, include substantially rewriting and adding new examples.