What is social work research? That is, how does social work contribute to our understanding of the field and the world around us?
Social workers have conducted studies for over 100 years! Since the early days when there was no formal education beyond teaching yourself by reading books and attending trainings, to conducting large-scale surveys that require rigorous statistics, we have been studying about ourselves and the world.
We now know a lot about what works (and doesn’t) in helping people overcome addictions or recover from traumatic experiences like natural disasters or violence. We also learn about effective ways to prevent mental health issues such as depression and suicide.
But before those lessons were learned, there wasn’t much progress made towards solving the most pressing problems facing society. This includes addressing poverty, improving access to quality healthcare, and reducing discrimination due to race, gender, and socioeconomic status.
Until recently, however, there hasn’t been much systematic effort to understand why these things happen and what can be done to fix them. Part of this is because researchers didn’t do an excellent job defining their terms, which has led to confusion and controversy within the profession.
This article will take a closer look at some common misconceptions related to social work research and the nature of scientific inquiry. After you’re done, read more about how social work fits into the bigger picture of academia and professional practice.
Three types of scientific research
There are three main categories or forms that social work research follows, depending on what question you want to ask. These include quantitative studies, qualitative studies, and mixed-methods studies.
A quantitative study asks about one thing and tries to prove its effect by looking at large groups of people. For example, a quantitative study might look at how many students who attended a certain class finished their high school degree as compared to those who did not attend that class.
A qualitative study looks into small groups of individuals to understand their experiences or perceptions of something. A good way to use this knowledge is to then create an intervention or program for the same population.
A mixed-methods study uses both a qualitative approach and a quantitative approach to gain insights into two different variables. This can be done simultaneously within the same study or sequentially so that one factor is tested first and then the other.
Importance of science ernaceisng
When doing social work, it is important to know why things work (or don’t) for this field. Why should anyone take seriously claims that psychology has answers for everything when there are no clear guidelines on how to do your job?
Social workers need to know whether interventions work under empirical conditions, not just because someone else said they would, but also because the evidence proves it! If you’re ever unsure if an intervention will work, run some experiments.
An observational study is one that observes people’s behavior or tracks how individuals are responding to an intervention or treatment. Because they use naturalistic observations, studies of this type are more practical than experimental studies, where participants are exposed to either the intervention or control condition for longer periods of time.
Social workers perform their jobs every day, so there are lots of opportunities to collect data about their practices. For example, researchers could examine whether social work students who attend certain educational programs will have better success in becoming licensed professionals. They could also monitor the effectiveness of different strategies when working with specific types of clients.
Observational studies cannot determine if someone’s employment status was related to personal wellness (for instance, did being married make people feel happier?), but they can be used to assess relationships between external factors and psychological health. By observing patterns over time, we may be able to infer causal effects.
What does it mean to say that social work is scientific? One of the most important things about what makes social work research scientific is the method used to gather knowledge. An experiment is defined as an observation, or gathering information through testing something.
In experimental studies, people are divided into two groups- those who receive the intervention (the test) and those who do not. The participants’ experiences are compared to see if there are significant changes before and after the intervention. By doing this repeatedly under similar conditions, we can generalize how effective the intervention is for the average person.
By having a control group, you can also determine whether anything changed outside of the intervention being tested. This helps rule out external factors like changing medication, new health issues, or lifestyle choices causing the results.
When talking about interventions, there is no one “correct” way to help people- different approaches are needed for different situations. A good researcher will use evidence from both randomized controlled trials (RCTs), which randomly assign individuals to either the intervention or the control condition, and nonrandomized experiments, where individuals choose their treatment.
However, even when using only observational data, there is still value in understanding why some treatments work better than others. When researchers cannot directly intervene with patients, they look at differences between the treatments to find underlying causes of success and failure.
Randomized controlled trials
Randomized clinical trials (RCTs) are an important research design because they allow us to determine if conclusions can be made about cause and effect. By randomly assigning participants to different conditions, we are able to assess whether one group experiences more positive changes than another.
By adding a control condition, you can compare your intervention with what is typically done to see how it works. This helps rule out bias as a reason for success or failure of the treatment.
It also allows us to measure the effects of the intervention without changing anything else – making it essential to ensure that things like medication, diet, and exercise remain stable during the study.
Social work interventions are usually not conducted under a placebo condition, so using an alternative method to evaluate effectiveness is not possible. When doing RCTs for social services, researchers must make sure that those who will receive the service are aware they will be part of a studied group and that there are no differences between the groups outside of the intervention.
Researchers must also find ways to blind individuals receiving the intervention on whether they received the active service or the control activity to prevent participant bias due to difference in attention or perception of the two treatments. Researchers cannot test their own theories while conducting studies either, which limits the validity of results when there are personal biases.
Systematic reviews and meta-analyses are other ways to evaluate the effectiveness of social services.
Correlation vs. causation
What does it mean to say that social work is research-based? It means defining what makes up your field first, and then determining whether there are studies that prove if one thing is helpful or not.
It also means looking at how well these studies match up with real life. For example, most mental health studies focus on medication as the main treatment option. But what happens when someone who has done their best on the medications doesn’t get any better?
That’s when people turn to other treatments like talk therapy or community services. So even though they were studied as part of an antidepressant trial, you can’t necessarily take their results to confirm that antidepressants are definitely effective for everyone.
Similarly, while some studies look into the effectiveness of different types of counseling, it’s important to remember that individuals respond differently to various styles of talking. Some people may benefit more from expressive therapies such as talking about feelings or doing art projects than others.
One of the most important things to consider when doing research is your sample size. What we mean by this is how many people you include in your study. If you are studying how to improve someone’s self-confidence, for example, then your sample size needs to be large enough so that there is an adequate amount of data gathered!
If you only test your theory on 20 individuals, your results may not be representative of the population as a whole. This could lead to wrong conclusions about what works and doesn’t work with improving mental health or confidence.
So how big of a sample do you need? The standard recommendation is at least 50 participants per group (for a total of 100 participants overall). But some studies require much more than that!
Social science researchers use statistical methods to determine whether the differences they find between two groups are due to chance alone or if there really is a difference.
What is statistical significance? Statistical significance refers to whether or not an experiment’s results are due to chance. If we perform an experimental test many times, then what happens depends on the random luck of the situation, but if this pattern continues, then we can say with confidence that the result was because of the treatment or hypothesis being tested.
It is important to remember that even if an effect is significant, that does not mean it is strong. The strength of an effect is determined by how much it changes the outcome for the condition it exists under.
There is no hard and fast rule about what constitutes enough evidence to prove an intervention works, but most experts agree that studies with very large sample sizes and high internal validity (experiments conducted under controlled conditions) provide more reliable information than those with smaller samples or less rigorous methods.
Surveys are typically considered weaker research designs because they do not use structured questionnaires and instead rely on respondents’ self-report, which may be biased either towards or away from making assumptions based on past experiences.
When researchers perform a study, they must test their hypothesis. If the researchers find that the hypothesis is not true, then they will reject it and replace it with another hypothesis.
After they gather all of their data, they need to compare what they know about the study’s hypotheses before collecting the data with what they learned after studying the data. This process is called statistical inference.
Researchers use statistics to determine if there are significant differences between two groups or how much one group differs from another. These tests are used to draw conclusions about the topic under investigation.
When scientists do this, they create an idea of what they call a confidence interval. A confidence interval is an estimate of the size of a parameter such as mean age. The longer the confidence interval, the more certainty we have in the number.
A 95% confidence interval means that we are certain that the parameter lies within this range around the estimated value 1 million times out of 100 times.
That means if we repeat our experiment a hundred times, we would be confident that the result falls within this range 95 percent of the time.
However, remember that just because the results fall into a given area does not mean that the theory is definitely correct! Only when the researcher has a high degree of certainty can she/he say so.