Welcome back after a bit of silence on my end!![welcome[1]](https://discoveringyourinnerscientist.files.wordpress.com/2017/03/welcome1.gif?w=163&h=119)
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.
Critical thinking: Check out free full-text, quasi-experiment Gough et al., (2017). Tweet for Behavior Change: Using Social Media for the Dissemination of Public Health Messages.
- What makes this a quasi-experimental design? [Hint: Does it have a control group? Were subjects randomly assigned to groups? Are both randomization & control group missing?]
- What might have caused the change in behavior, instead of the tweets?
- What contribution do you think the study makes to improving practice?
For more information on studies with non-randomized control groups see “Of Mice & Cheese” or comment below. Let’s talk!
KEY POINTS:
no, the response tells us only how the patient remembers it.
see 
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Is pain experience as diverse as our populations? This week I came across an interesting meta-analysis.
(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.
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.]
library using reference above. It is available electronically pre-publication. Also check out my 
numbers. Another problem will occur if the reliability and validity of the self-report questionnaire is not established. (Reliability is consistency in measurement and validity is the accuracy of measuring what it purports to measure.) Additionally, self-reports typically provide only a)ordinal level data, such as on a 1-to-5 scale, b) nominal data, such as on a yes/no scale, or c) qualitative descriptions in words without categories or numbers. (Ordinal data=scores are in order with some numbers higher than others, and nominal data = categories. Statistical calculations are limited for both and not possible for qualitative data unless the researcher counts themes or words that recur.)
An example of a self-report measure that we regard as a gold standard for clinical and research data = 0-10 pain scale score. An example of a self-report measure that might be useful but less preferred is a self-assessment of knowledge (e.g., How strong on a 1-5 scale is your knowledge of arterial blood gas interpretation?) The use of it for knowledge can be okay as long as everyone understands that it is perceived level of knowledge.
at 
The 2 basic types of design are 1) experimental & 2) non-experimental. In experimental, the researcher does something to the subjects and measures the effects of that something. In non-experimental, the research merely observes and describes what is happening without doing anything to change it.
Discovering Your Inner Scientist 