Quantitative research seeks to prove something through experimentation and statistics. Once you’ve determined that what you have here is an original piece of quantitative research and you’ve already considered the basic questions here, you’re ready for the specific questions:
1. How many groups are compared? Did the authors show that the groups are statistically similar? Look for a table showing things like basic demographic information comparing the groups. Some studies will only have one group. In this case, the authors may be comparing the group at two different times (like before and after a treatment) or they may be comparing the study group to the general population using existing data. If the authors are using existing population data as a control, they should demonstrate that the study group is similar to the general population.
2. Did the authors prove correlation or causation? This is a very important distinction to understand.
Correlation just demonstrates that two things tend to happen together. They could be completely unrelated. To use a fictional example, you might find a correlation between mothers with blue eyes and the number of towels used in the labor room, but it does not mean that the blue eyes are the reason for the increased towel use. A correlation can be positive (when the rate of variable A increases, so does the rate of variable B) or negative (when the rate of A increases, the rate of B decreases). You’ll sometimes hear things described as “associated with” – this is generally referring to correlation.
Causation requires statistics and probability to determine if the connection is likely to be because of the variable tested. Researchers must create two groups of participants who are similar in every way except the intervention that they are testing. This can be done by randomizing participants into two groups or using statistical procedures to control for differences.
(This is very much an oversimplification. I’ll be doing a series on statistics later that will explore these concepts further.)
3. Are the tables, charts, and graphs understandable? Do they relate to the conclusions? Could they mislead someone who does not read the text?
4. Is this study population applicable to my practice or situation? Look at the criteria for including (or excluding) the study population. Read it over and see who the study was done with. A study done only with low risk first time moms may not be applicable to a diabetic woman pregnant with her fourth baby. On the flip side, sometimes studies that look at a specific population can provide very useful and helpful information for that specific population. Just make sure you know what the study population is, and recognize that you cannot accurately apply that information to a wider or different group.
5. Are the findings really significant? There is a difference between statistical significance and clinical significance. A student once showed me a study of castor oil induction where the authors reported a significant difference in APGAR scores between the two groups. While the calculated p value was less than .05, the two groups average Apgar scores were 9.78 and 9.71. The babies in BOTH groups had good outcomes – the difference simply didn’t mean much clinically.
6. Is the study size sufficient? In quantitative research, a bigger sample size usually helps. The more you have in the study, you’re better able to find statistical differences. It isn’t just the overall study size, either. Many studies will run analysis of smaller subgroups. So to use another hypothetical example, a study looking at a new drug might have 5,000 women in the study, but if the authors report that “among women who have 6 or more previous pregnancies, the risk is lowered” – you should find out how many women are in that subgroup. If there were only 15 in that subgroup, it might be hard to make a valid conclusion. It is very common to look at subgroups by the number of previous pregnancies, by race, or by other categories.
7. What is being tested, and what is it being compared to? Some studies will have one or more experimental groups and a “control” group as a comparison. This control group will either have no treatment, a placebo treatment, or the current “standard” treatment. Ethically, you cannot test a new cancer drug by giving cancer patients in the control group no treatment, but you can compare a new drug against the current treatment. Make sure that (within the bounds of ethics) the researchers have chosen an appropriate comparison group.
8. What were the outcomes measured? How were they measured? Every study has at least one independent variable – the thing(s) the researchers are trying to learn about. They choose certain things, called outcomes (or dependant variables) to watch for. An example of this would be an epidural study that compares those who have early epidurals with late epidurals. The timing of the epidural would be the independent variable. The outcomes are chosen by the researcher, and could include things like cesarean rate, epidural complications, APGAR scores, etc. The study should clearly outline which outcomes they were interested in and how they were measured.
9. If applicable, did the researchers do a good job of “blinding”? Blinding is the term for keeping from the study participants and staff which group they are in. This is common in drug trials. Sometimes people are helped by simply believing that something will help – the well known “placebo effect”. If the participant does not know which drug they are taking (experimental drug, standard treatment, or “fake” drug with no effects) the researcher can better determine which effects truly are from the drug being tested. The staff is also blinded whenever possible to avoid accidentally or subconsciously biasing the result. Sometimes blinding is simply not possible, but whenever possible, it is a helpful technique.
10. And finally, what does this mean for me? That will vary widely based on your personal situation. As a reader, you may be a nurse, midwife, childbirth educator, doula, doctor, or parent. You may have more than one role. Carefully think about how this may – or may not – apply to you in your various roles.
Remember that not every study is perfect. Finding a minor flaw in a study does not necessarily invalidate the whole study. You as the reader need to remember to be objective and ask yourself if the study does a good enough job of showing what it set out to do. Because of our differing perspectives and biases, it is possible to come to a different conclusion than another reader. Also, each study should be considered in the context of all the other research done on the topic. Right now that seems overwhelming, doesn’t it? Our next type of article, literature reviews, will give you insight on how you can view studies in the context of other research.