Scientists have systematic ways of gathering, organizing, and evaluating research studies to get solid answers about how to improve health and wellness.
These organizational tools or strategies are referred to as methodological quality control or evaluative techniques.
They play an important role in ensuring that only high-quality studies make it into the literature and that researchers do not publish false information due to lack of rigor.
When used properly, these evaluation strategies can increase the reliability and trustworthiness of scientific findings.
However, even well-intentioned scientists may not be using some of them consistently or at all which can influence the conclusions they reach.
That is why it is very important to evaluate study designs and data collection methods for accuracy and effectiveness.
It is also crucial to use credible sources when looking up studies so you know what questions were asked and how accurate the results were.
Systematic reviews and meta-analyses are two types of research that go beyond simply studying one study’s findings.
By going back to the drawing board and testing the validity of the underlying studies, we gain more knowledge about the topic being investigated.
This article will talk more about both of these types of evaluations and how professional writers can incorporate them into our work.
Disclaimer: The content in this article should not be construed as medical advice nor is it intended to replace doctor/patient relationships. For any healthcare issues, please consult with a qualified physician or nurse.
The term research design refers to how you organize your study, what variables you include in your experiment, and how you test your hypothesis. These are all important steps in conducting an empirical investigation.
The two major types of research designs are qualitative and quantitative. A qualitative research design is one that focuses on exploring themes, ideas, and perceptions through questions and responses. This is typically done via interviews or surveys with no set structure.
A quantitative research design uses structured questions with clearly defined answers to investigate hypotheses. There are many ways to do this, but the most common method is called controlled experimentation.
With experimental studies, researchers use conditions as controls to see how changes affect outcomes. For example, they may test whether having more vegetables at lunch makes students feel better by studying longer than if students don’t have any veggies.
There are also comparison groups, where one group receives a specific treatment and their results are compared to another group that does not. In these cases, both groups must have similar things going on outside of the intervention for the effect to be accounted for.
A key part of scientific research is having enough data to make conclusions about the topic under investigation. This is called statistical significance!
The amount of sample size needed for meaningful results varies depending on the area of science being studied. For studies looking at how well different diets work, a large number of participants are necessary to determine if one diet is clearly better than another.
For studies that compare two treatments (for example, a new drug against a placebo), you need to have an adequate amount of patients to be able to give clear answers. If you only have a few patients, it may not be feasible to draw any significant findings.
A confounding factor is an aspect of your study or experiment that may influence the results to such a degree that it can distort or even negate the findings you were looking for.
Common confounding factors include what you are studying, how you are conducting your research, and what you are measuring as part of your experiments.
For example, if you wanted to see whether eating chocolate helps reduce stress, then being hungry before testing would be an important placebo control because someone who has just eaten might feel less stressed than someone who is not.
In order to determine whether chocolate really does have calming effects, you would need to test this under both fed and fasting conditions.
The research environment
There are many different types of scientific studies, with each type having their own set criteria for inclusion. These include randomized controlled trials (RCTs), systematic reviews or meta-analyses, cohort studies, case control studies, qualitative interviews, surveys, etc.
The importance of considering how study design can influence results has been well documented. For example, an RCT is considered the “gold standard” way to evaluate new treatments because they eliminate bias due to confounding factors.
However, not all treatment settings have access to such rigorous evaluations. That’s why there is another major category of scientific evidence called nonrandomized experimental studies.
These studies compare groups that receive different interventions and look at whether one was more effective than the other!
But just like with randomization, it is important to consider potential biases when evaluating effectiveness. For instance, participants who received the intervention may have already decided they wanted it, so they might be biased towards showing positive results.
The academic research process goes beyond just gathering information, there are several other steps that must be done to make your study strong. These include organizing the information you gathered into studies, analyzing the findings of each study, and drawing conclusions based on these analyses.
There are many ways to organize scientific studies, such as by topic or field, but one of the most common methods is systematic review. A systematic review is an analysis of all the available evidence on a given topic, with insights drawn from how well past reviews performed and what worked and didn’t work in earlier stages of research.
What makes this method unique is its focus on consistency. When doing systematic reviews, researchers will look at how well other similar studies have been conducted before, and if those past studies produced the same results. By emphasizing internal consistency over external validity (how well the study applies to the population it aims to help), systematic reviews can offer more rigorous answers than individual studies alone.
That said, individual studies still play an important role in making informed decisions. It may be that no systematic reviews exist for some topics, so we must do our own research and analyze whether past studies fit our needs.
A large amount of research does not agree with your hypothesis. This is called “disconfirming” or “antihypothesis” evidence. When you find disconfirming evidence, it can make you question whether your theory is correct.
Hypotheses have to be testable. If there are no tests for your theory, then people will never prove that your theory wrong!
If someone hypothesizes that apples taste good because they are sweet, they cannot prove that this wrong unless they come up with a way to make them taste bad. Likewise, if someone believes that chocolate tastes better due to its cocoa content, they cannot prove that this idea is false without coming up with something else that makes it worse!
When researchers look into a topic, they use systematic methods to determine how strong an effect size (how much an element affects the outcome) has. They compare these sizes across different studies to get a sense of how important each one is.
A major cause of research studies with mixed results is what’s known as publication bias. This happens when researchers only publish studies that show their hypothesis or theory is correct, to either boost their success rate or avoid failing altogether!
By not publishing studies that may disprove your theories, you end up with an incomplete picture of how things are- which makes it more difficult to determine if your theories are wrong or right.
This also helps perpetuate internal debates within academia about whether your approach is worth investing time in or not. It can even influence funding decisions, as institutions want to see consistent results from studies they invest in.
Another way publication bias can affect your findings is by skewing general trends towards our pre-existing beliefs. For example, if most studies agree that doing x will help people, then studying why people do y, instead of doing both at once, will skew data because we assume y must be better than x.
Skewed numbers don’t give us a true representation of effectiveness.
Interpretation of results
After reading through an abstract, you will want to read the full article or study to determine how much it contributes to your research writing. You should be careful about relying too heavily on only the summary information!
If there is not enough context for the reader to understand the significance of the findings, then they are not very helpful. The writer must include enough information so that someone can interpret the findings on their own.
You do not need to reference the same studies every time you write a new paragraph, but making a note here may help you backtrack and find the needed info later.
There are many ways to organize scientific studies. Some good strategies are to make a list, use topic-and-bullet point headers, and use subheadings to emphasize important points.
Some tips when organizing studies are to use short, simple sentences, avoid using jargon, and draw clear conclusions.