Sometimes, you would like to know if a certain variable X in your life affects another variable, so you could use it for your advantage. Perhaps, you are curious if meditating in the evening helps to minimize stress on the following day. Or if that supplement you have been taking really helps you loose weight faster. The problem with turning to scientific studies and online reviews is that what worked for others may not necessarily work for you. The best way to find out if the cause-effect relationship between X and Y exists for you is to conduct your own personal, Quantified-Self experiment.
The first step, would be to formulate the research hypothesis. What is it exactly that you are trying to achieve? It should be precise and to the point, something like this: “if I do X then there will be change in Y”. If I meditate in the evening, my stress levels will be lower on the following day. If I take supplement Z, then my weight loss rate will increase”.
The second step is operationalization and selection of proper instruments and measurement procedures. Will your measurement rely on subjective tools (e.g., psychological questionnaires), more objective (e.g., electronic or balance scale, apps that measure reaction or pulse, gadgets that measure sleep quality, etc.), or both? If you rely on subjective measures, do some research to see if there are any established and scientifically sound questionnaires already exist out there.
You will also need to establish a timeframe. Unlike regular experiments which involve multiple participants, self-experiments have only one subject – you. Hence, in order to accumulate enough data points, self-experiments may need to last at least several days. Ultimately, it all depends on how quickly Y changes: for instance, you may capture changes in mood and sleep quality sooner than the changes in weight and body fat.
Finally, you need to choose a design for your experiment. Here are the most common experimental designs:
The simplest of all, this design is also considered to be the “weakest” when it comes to capturing causality. The A and B are experimental phases. Phase A is a “baseline”, during which you measure Y variable under normal conditions. For instance, you spend a week or two measuring every day your weight, sleep quality, stress levels, etc. Phase B is a “treatment” phase, during which you introduce your variable X: you meditate every evening, take weight loss supplement, etc, and continue measuring Y. At the end, you compare levels of Y during both phases: did you lose more weight during the phase B? were your stress levels lower during the phase B? Based on the findings, you make conclusions about the effectiveness of the treatment.
This design is considered to be more advanced, as it let’s you capture the changes in Y before and after the treatment. For instance, you spend the first week going to bed normally, the second week meditating before you to go sleep, and on the third week you going back to regular regimen. Looking at the changes in your stress levels across the three weeks (if there are any) helps you to conclude if the meditation works and how long does its effects last.
This design may be useful if you want to see if the intensity of treatment B is associated with intensity of the outcome. For instance, whether spending more time meditating will help you to reduce stress even more. So during the fourth phase, you may want to meditate more than once a day, or increase time of your evening meditation twofold. You then compare the changes in stress levels following both treatment phases to make the conclusion. In another version of A-B-A-B design, in either of the treatment phases, the treatment is replaced with placebo (e.g., similarly looking harmless pills instead of weight supplement; of course, during both treatment phases you don’t know which supplement you are taking). The longer versions of this design, A-B-A-B-A-B and A-B1-A-B2-A-B3 (where B1, B2 and B3 are different versions of treatment, e.g., different types of meditation) are also used commonly.
Now that you have chosen the instruments and design for your experiment, you can develop a protocol: detailed instructions on how and when to administer treatments and take measurements. In 90% of the cases, success of experiment depends on sticking to the protocol: dedication and consistency are the key. And remember: negative results are results, too. At least you will know that that super-duper X everyone is swearing by does not work for your Y. Which means that the search for that perfect X of your own continues!