Observational studies: In observational studies, the researcher collects the data by merely observing and recording those observations. By going through these observations researcher established in the association. Further, we could divide retrospective studies where researcher uses the past data. Whereas prospector studies are those where researcher collect the data throughout the process. Example could be “study the effects of smoking on lungs of women”
Now in experimental studies, we as a researcher subjects are exposed towards an experiment knowingly that they are part of research and by repeatedly experimenting we collect the data from that experiments and define associations between the results and the conditions through which subject has been studied.
In a random experiment, researcher controls the assignment of treatments to experimental units using a chance mechanism(like the flip of the coin or computer’s random number generator).
If you ask me. I had a strong inclination that the random things do not exist… it’s the cause and effect which plays the important role. It’s only confounding variables which are very important to understand before one concludes any results. The only reason we all try to avoid the confounding variables as they are more random in nature hence study of them is difficult and is always questionable.
In short, we could say that observational study is more inclined towards finding the correlations between the explanatory variable and the response variable. There is experimental studies are more focused on finding out the cause and effect relationship the studies are sometimes referred as causal inferences.
Examples of study types:
- Find 200 women age 40 who do not currently smoke.
- Randomly assign 100 of the 200 women to the smoking treatment and the other 100 to the no smoking treatment.
- Those in the smoking group smoke a pack a day for 10 years while those in the control group remain smoke-free for 10 years.
- Measure lung capacity for each of the 200 women.
Analyze, interpret, and draw conclusions from data
- Find 200 women age 40 of which 50% have been smoking a pack a day for 10 years while the other 50% have been smoke-free for 10 years.
- Measure lung capacity for each of the 200 women.
- Analyze, interpret, and draw conclusions from data.
- Suppose there is a gene that causes smoking to appear to be a very pleasurable experience.
- Suppose the same gene also causes emphysema, lung cancer, throat cancer, etc.
- People who have the gene will be more likely to smoke than people who do not have the gene.
- People who have the gene will be more likely to get emphysema, lung cancer, throat cancer, etc.
- So is it really smoking that causes health problems? Maybe it is just the gene?
A confounding variable is related both to group membership and to the outcome of interest. Or we could say couponing variable is pitiable that affects the explanatory variable and the response variable. Its presence makes it hard to establish the outcome as being a direct consequence of group membership. Here also we could say people who smoke are not even concerned about health in general which could affect their living and eating standards.
But as I mentioned earlier it’s very important to study the confounding variables and yet it costs a lot for the researcher to add more variables in the study. And also it becomes difficult to assess the effects properly if many variables are considered in the study.
Types of observational studies
the case-control study: study originally developed in epidemiology, in which two existing groups differing in outcome are identified and compared on the basis of some supposed causal attribute.
Cross-sectional study: Involves data collection from a population, or a representative subset, at one specific point in time. Like we census after every 10 years, beginning in 1871.
Longitudinal study: correlation research study that involves repeated observations of the same variables over long periods of time. These are particularly or often used in psychology to study developmental trends across the life span, and in sociology to study life events throughout lifetimes or generations. Don’t confuse it with the cross-sectional study as in longitudinal studies we observe same subjects/elements whereas in the cross-sectional study involves elements which could be different from the original study.
Cohort study or panel study: a form of longitudinal study where a group of patients is closely monitored over a span of time.
Ecological study: Researcher looks for associations between the occurrence of disease and exposure to known or suspected causes. In ecological studies, the unit of observation is the population or community. Disease rates and exposures are measured in each of a series of populations and their relationship is examined. Often the information about disease and exposure is abstracted from published statistics and therefore does not require expensive or time-consuming data collection. The populations compared may be defined in various ways.
Often we conclude that the observational studies are only for a descriptive study. These are conducted to find the correlation of the events. Sometimes correlation is having a causal effect and sometimes it’s only a correlation, not a cause.
Like we could say girls who thin eat breakfast based on an observational study. So we could conclude that there is large possibility that many of the girls eat breakfast in a group of slim girl.
Here breakfast is not the cause of slimness in the girls but it’s only representing health consciousness of the girls.
If we want to conclude that “eating breakfast makes girl slim” then we need experimental studies to understand the cause and effect relationship between explanatory variable(breakfast) and response variable (slimness). Also important is to under the confounding variables like education level, health awareness and income groups of the subjects.
Now let’s move ahead to the experimental studies as this is what a researcher will be focusing to find the exact nature of cause and effect and make casual inferences for the business.
In the case of experimental studies we have some standards defined which could also be represented as principles of experimental design:
1. Control: Compare the treatment of interest to a control group.
2. Randomize: randomly assign subjects to treatments.
3. Replicate: collect a sufficiently large sample, or replicate the entire study
4. Block: block for variables known or suspected to affect the outcome
Let’s see how control and block work together while designing an experiment investigating whether energy gels help you run faster:
Treatment: energy gel
Control: no energy gel
Assumptions: Energy gels might affect pro and amateur athletes differently
block for pro status:
Divide the sample to pro and amateur
Randomly assign pro and amateur athletes to treatment and control groups
Pro and amateur athletes are equally represented in both groups
An explanatory variable is the energy gel whereas blocking variable is the nature/characteristics of the athletes (pro & armature). Blocking is like stratifying: blocking during random assignment or stratifying during random sampling.
Random assignment: “Assignment of subjects to different treatments, interventions, or conditions according to chance rather than systematically ( e.g., as dictated by the standard or usual response to their condition, history, or prognosis, or according to demographic characteristics). Random assignment of subjects to conditions is an essential element of experimental research because it makes more likely the probability that differences observed between subject groups are the result of the experimental intervention.” (Penslar and Porter, 2001 ). This enhances the results in determining the casual nature of the experimental study.
Random selection: A form of sampling where a representative group of research participants is selected from a larger group by chance. This enhances the test results to be more generalized.
To avoid the bias in the experimental studies we use techniques like:
Blinding: where the subject is not aware of the group they are in.
Double blinding: the difference is even the tester and subject is not aware of the true nature of the experiments. Blind testing is used wherever items are to be compared without influences from testers’ preferences or expectations.
In medical studies, we often hear terminologies like placebo and placebo effect. Fake treatment (placebo), often used as the control group for medical studies. Sometimes even given fake treatment subjects show changes. In the medical test, subjects are even sugar pills yet improve could be seen in the health.