- We admitted we were powerless over our results. That our status-seeking was unmanageable.
- Came to believe that the power of truth-seeking would restore us to sanity.
- Made a decision to turn our research and our careers over to the power of truth-seeking as we understood it.
- Made a searching and fearless moral inventory of our scientific conduct.
- Admitted to ourselves and to another trusted scientist and in public the exact nature of our questionable research practices (QRPs).
- Were entirely ready to let pursuit of truth remove our QRPs.
- Humbly asked truth-seeking to replace our hacking and bias.
- Made a list of all hacked results and became willing to update or retract them all.
- Made such updates and retractions, ensuring that in doing so all innocent co-authors reputations were not harmed.
- Continued to take academic-status-seeking inventory and when we engaged in QRPs promptly admitted it.
- Sought through logic and self-reflection to improve our conscious pursuit of truth, as we understood truth-seeking, praying only for the least biased research practices possible.
- Having had a scientific awakening as a result of these steps, we tried to carry this message to academic-status-seekers, and practice these principles in all our affairs.
A major segment of social science uses formal models and quantitative methods to explain social phenomena. That means they use mathematical symbols to define how they think the world works, and then find data and test it. For example, researchers want to explain social outcomes, like committing a crime, changing jobs, moving or having children. Why do some people do these things and not others? Researchers then speculate that other things cause these outcomes like getting married, having a job or losing a job, how much money a person makes, how old they are and all kinds of other stuff.
Next, researchers change their theoretical ideas into a formal model. This means an equation. Before you stop reading, maybe pause to consider that an equation is just a theory expressed with symbols instead of standard language. For example,
Y = X
and this might represent moving homes (“Y“) is caused by getting married (“X“). Another way of saying this is, Y depends on X or moving depends on getting married.
But seriously, getting married does not always cause moving. Sometimes it does. The correct claim is that moving is a function of getting married. Function means that marriage probably increases the likelihood of moving by some amount. Therefore, researchers add a modifier to X, like the letter b, so if b was 0.3 getting married would increase the likelihood of moving by 30%. People also move when they don’t get married, so researchers add a constant a to account for the likelihood of a person moving for any other reason. So if a was 0.3 then the average person would have a 30% chance of moving at any point.
Y = a + bX
Now if a and bX could be used to perfectly predict whether a person moves or not. The researcher would have made a monumental achievement here. Instead what happens is the researcher goes and observes moving and marriage behaviors. Tries to do this with a random sample of the population of a society. Then applies the above equation to the sample data. Does the equation fit perfectly to the data? No. Never. Researchers must admit that their theories come with uncertainty. Maybe moving depends on the type of home the person always lives in, maybe it depends on whether a couple can afford to move or if it is even a couple or a single parent. There are nearly unlimited things that could cause moving. Also, getting a perfectly random sample, mistakes when observing or coding data, etc. leads to uncertainty in the results. This means the formal equation has to have e, an error term.
Y = a + bX + e
Researchers spend their time trying to reduce what is in the error term. So they are basically tasked with the job of reducing uncertainty. If scientists wants to fly a spaceship to mars they have to reduce all possible forms of uncertainty in the take off, flight path, landing, electronics and so forth. But a social scientist who wants to predict how many people will move, where they will move or which people will move, cannot reduce uncertainty that much. Sure, they can explain moving with job changes, family members getting sick, weather, hobbies, wars, and so many observable things. But they will never fully explain moving. What I’m really saying here is that social scientists will never be able to perfectly explain human behaviors. Its like this:
Why is it so hard? If we could just measure all the things that people are doing and thinking we could predict precisely what humans will do next, right? Maybe so, maybe not; but irrelevant because we cannot measure everything about humans. Even if we could measure the precise actions of the network of neurons in one human brain numbering greater than all the stars in the universe, we probably couldn’t measure love. But something that we might refer to as love seems to be a major force in guiding human actions. Both the effect of love:
“Love affects more than our thinking and our behavior toward those we love. It transforms our entire life”
– Thomas Merton
And the lack of love:
“Intense spiritual and emotional lack in our lives is the perfect breeding ground for material greed and overconsumption.”
– bell hooks(both quotes from hooks 2000, pp. 187 & 105)
These suggest love is a prime mover of human decisions and behaviors. But we don’t know what it is exactly and we have made no attempts, that I am aware of, in any large population surveys to measure it. So,
When we add love as an X variable to our formal model we cannot test anything, because we have not developed instruments to measure it. So it remains in the void of all that variance in Y that we simply cannot explain. Maybe we should start focus some of our effort on that.
hooks, bell. 2000. All About Love: New Visions. William Morrow and Company, New York, NY. ISBN 0-688-16844-2.