Quasi-experiments are a lot of work, yet don’t have the same scientific powerto show cause and effect, as do randomized controlled trials (RCTs).An RCT would provide better support for any hypothesis that X causes Y. [As a quick review of what quasi-experimental versus RCT studies are, see “Of Mice & Cheese” and/or “Out of Control (Groups).”]
So why do quasi-experimental studies at all? Why not always do RCTs when we are testing cause and effect? Here are 3 reasons:
#1 Sometimes ETHICALLY the researcher canNOT randomly assign subjects to a control and an experimental group. If the researcher wants to compare health outcomes of smokers with non-smokers, the researcher cannot assign some people to smoke and others not to smoke! Why? Because we already know that smoking has significant harmful effects. (Of course, in a dictatorship, by using the police a researcher could assign them to smoke or not smoke, but I don’t think we wanna go there.)
#2 Sometimes PHYSICALLY the researcher canNOT randomly assign subjects to control & experimental groups. If the researcher wants to compare health outcomes of
individuals from different countries, it is physically impossible to assign country of origin.
#3 Sometimes FINANCIALLY the researcher canNOT afford to assign subjects randomly to control & experimental groups. It costs $ & time to get a list of subjects and then assign them to control & experimental groups using random numbers table or drawing names from a hat.
Thus, researchers sometimes are left with little alternative, but to do a quasi-experiment as the next best thing to an RCT, then discuss its limitations in research reports.
Critical Thinking: You read a research study in which a researcher recruits the 1st 100 patients on a surgical ward January-March quarter as a control group. Then the researcher recruits the 2nd 100 patients on that same surgical ward April-June for the experimental group. With the experimental group, the staff uses a new, standardized pain script for better pain communications. Then the pain communication outcomes of each group are compared statistically.
Is this a quasi-experiment or a randomized controlled trial (RCT)?
What factors (variables) might be the same among control & experimental groups in this study?
What factors (variables) might be different between control & experimental groups that might affect study outcomes?
How could you design an ethical & possible RCT that would overcome the problems with this study?
Why might you choose to do the study the same way that this researcher did?
In the last “Quasi-wha??” blogpost, I described 1 type of experimental design: Quasi-experimental. To review… In quasi-experimental designs, the researcher manipulates some variable, but either 1) doesn’t randomly assign subjects to a control and experimental group OR 2) doesn’t have a control group at all.
For example, the researcher may introduce pet therapy on unit #1 and avoid pet therapy on unit #2 and then afterwards compare the anxiety levels of patients on the 2 units. That study has a control group (unit #2), but because patients weren’t (& probably couldn’t be) randomly assigned to the units, this would be a quasi-experimental study. The control group in this pet therapy case is what researchers call a “non-equivalent control group.” Non-equivalent means the groups are different in ways that might affect study results! [Note: For review of what constitutes a true experimental study see first part of “Quasi-wha??” blogpost.]
Herein lies a weak link in the cause-and-effect chain. Quasi- designs are NOT as strong as true experimental designs because something other than our treatment (in this case pet therapy) may have created any difference in outcomes (e.g., anxiety levels). Why? Here’s your answer.
In an experimental study, randomly assigning subjects to a
control and a separate experimental group means that all the little, variable weirdities of all subjects are equally distributed to each group. Each group is the same mix of different types of people. This means we can assume that both groups are the exact same type of
people in regard to things that may influence study outcomes, such as attitudes, values, preferences, beliefs, anxiety level, psychology, physiology and so on.
In contrast, in the quasi-experimental pet therapy example above, there is probably something that caused a certain type of person to be on unit #1 and a different type to be on unit #2. Maybe it was their diagnosis, their doctor, their type of surgery, or other. Thus, we cannot assume that people in unit #1 and unit #2 groups are the same before pet therapy, and so any differences between them after pet therapy might have already existed.
So why do quasi-experimental studies at all?? There are great reasons! Stay tuned for next blogpost.
the participants are randomly assigned to groups, &
one group is a control group that gets a placebo or some inert treatment so that outcomes in that group can be compared to the group(s) that did get the treatment.
Non-experimental design in which the researcher doesn’t manipulate anything, but just observes & records what is going on. Some of these are descriptive, correlational, case, or cohort study designs for example.
One particularly interesting “experimental” design is one in which 1 or 2 of the experimental design ideal requirements as listed above are missing. These are called quasi-experimental designs.
In a quasi experimental design
The researcher manipulates some variable, but….
Either the participants are NOT randomly assigned to groups
&/OR there is no control group.
A quasi-experimental design is not as strong as a true experiment in showing that the manipulated variable X causes changes in the outcome variable Y. For example, a true experimental study with manipulation, randomization, and a control group would create much stronger evidence that hospital therapy dogs really reduced patient pain and anxiety. We would not be as confident in the results of a quasi-experimental design examining the exact same thing. In the next blog, we’ll examine why.
Critical thinking: Go to PubMed & use search terms “experiment AND nurse” (without the quotation marks). Open an interesting abstract and look for the 3 elements of a classic experimental design. Now look for “quasi experiment AND nurse” (without the quotation marks.) See what element is missing!
If you haven’t done a scientific research study before or don’t have a PhD, then realize that your project will go much more smoothly if you consult with a PhD or someone with experience.
You bring the great clinical ideas, & the experienced researcher will bring research design expertise. The design is the overall research plan for getting and analyzing the data to answer your question or to find out how well your new ideas work. That person will know the technical things you need to plan into your study in order to make the study ‘sparkle’ and to get approval from human subjects review committees. The person doesn’t have to be an expert on your topic. You fill that role, or soon will!
If you have access to a librarian who is good at helping you look for current literature, s/he is one of your Best friends in getting a project done.
Searching for on-target literature from the millions of publications out there takes some special skills. Of course you can learn these on your own, but how much nicer to talk with a librarian about the key ideas in your project and allow them to use their special skills to help you. As an experience researcher, I can tell you that good librarians are worth their weight in gold! Librarians can help you find what others have learned about your topic already, and then you can build on that knowledge. [note: check out Finding the Needles in the Haystacks: Evidence Hunting Efficiently & Effectively for more]
Because it’s your first foray into research, you might want to stick with a descriptive study.
What does that mean? It means that you will collect data about what the current situation is. For example, you might measure the average days to return of bowels sounds on your unit, OR the number of minutes it takes to do some task, OR the interruptions of patient sleep during the night. This will help you to establish whether or not there really is a problem to be solved. Descriptive studies are much simpler to conduct and analyze than experimental studies in which you measure something, make an improvement, and then see if the improvement improved things. For example, you would measure sleep interruptions, institute a quiet time, and then measure sleep interruptions again to see if there were fewer. [check out “What it is.” – a primer on descriptive studies for more]
Pick a topic you are really jazzed about!
Every researcher from time to time can feel ‘bogged down’ or bored with what they are doing, & one of the best protections against that is making sure you think the topic is super-interesting in the first place. If you get a little bored or stuck later don’t be surprised; it just means you’re pretty normal. Those stuck times might even feel like “hitting the wall” in a long race, and once you get past it things get better. Remind yourself why you loved the topic in the first place. Talk to your PhD friend or a mentor for encouragement. Take a little break. Read something really interesting about your topic.
While not every step of the research study process will make you want to jump up, sing, and dance, the process as a whole is really rewarding and great fun. You will be empowered by new learning—not just about your topic, but about how to do research!
In a prior blog (Direct speaking about INdirect outcomes: HCAHPS as a measurement*), I argued that HCAHPS questions were indirect measures of outcomes. Indirect measures are weaker than direct measures because they are influenced by tons of variables that have nothing to do with the outcome of interest. But wait!! There’s more! HCAPS can sometimes be a DIRECT measure; it all depends on what you want to know.
(If you know this, then you are way ahead of many when it comes to measuring outcomes accurately!!)
If your research question is what do patients remember about hospitalization then HCAHPS is a DIRECT measure of what patients remember.
However if your research question is what did hospital staff actually do then HCHAPS is an INDIRECT* measure of what staff did.
Example:HCAHPS question #16 is, “Before giving you any new medicine, how often did hospital staff tell you what the medicine was for?” Whether the patient answers yes or no, the response tells us only how the patient remembers it.
Why is this important?
Because if you want to know whether or not RNs actually taughtinpatients about their medications, then for the most direct & accuratemeasure you will have to observe RNs .
However, if you want to know whether patients remember RNs teachingthem about discharge medications, then HCAHPS question #16 is one of the most direct & accurate measure of what they remember.
Is pain experience as diverse as our populations? This week I came across an interesting meta-analysis.
A meta-analysis (MA) is one of the strongest types of evidence there is. Some place it at the top; others, 2nd after evidence-based clinical practice guidelines. (For more on strength of evidence, click here.)
MA is not merely a review of literature, but is a statistical integration of studies on the same topic. MA that is based on integration of randomized controlled trials (RCTs) or experimental studies is the strongest type of MA. MA based on descriptive or non-experimental studies is a little less strong, because it just describes things as they seem to be; & it cannot show that one thing causes another.
Main findings?“AAs [African Americans], Asians, and Hispanics had higher pain sensitivity compared to NHWs [non-Hispanic Whites], particularly lower pain tolerance, higher pain ratings, and greater temporal summation of pain.” (https://www.ncbi.nlm.nih.gov/pubmed/27682208) (Temporal summation is the increase in subjective pain ratings as a pain stimulus is repeated again and again.)
Critical thinking:Given that this is a well-done meta-analysis and that the pain was created by researchers in each study, how should this change your practice? Or should it? How can you use the findings with your patients? Should each patient be treated as a completely unique individual? Or what are the pros & cons of using this MA to give us a starting point with groups of patients? [To dialogue about this, comment below.]
For more info?Request the full Kim et al. article via interlibrary loan from your med center or school library using reference above. It is available electronically pre-publication. Also check out my blog on strength of different types of evidence.