Analyzing Data in Social Science vs. Engineering: Key Differences to Keep In Mind

A neophyte’s self-reflection

Photo by Matthew Henry on Unsplash

Engineering Data

  1. The unit of analysis is “one conductor”
  2. When you are running the experiment, you are changing voltage across two points of the same conductor
  3. The conductor remains the same throughout the experiment
  4. You change voltage (independent/explanatory/treatment variable)
  5. You observe a change in the flow of current (dependent/outcome variable)
  1. Because this data is experimental, you know the treatment variable (i.e., voltage) and the outcome variable (i.e., current). By treatment variable, we refer to the cause, and by outcome variable, we refer to the effect
  2. Working with data like the Ohm’s law one is a massive privilege — you are not stuck in a situation in which you have to ponder over whether X causes Y, or Y causes X, or both X and Y are caused by Z. Here, you clearly know that the change in voltage is the cause (treatment), and the change in current is the effect (outcome)
  3. You can make a counterfactual claim, such as “Had I not changed the voltage, the current wouldn’t have changed”
  4. Also, you can make a causal claim, such as “A change in voltage across the conductor causes a change in current through the conductor”
  5. There is reasonable homogeneity, i.e., whether you run the experiment in a lab in Bangladesh or the U.S., for the same conductor, you will get the same numbers, and they will fall on a line, ignoring negligible noise introduced by measurement errors, power loss, etc.

Note that in the Ohm’s law data, we already had a law (I=V/R) which we tested using empirical data. Unfortunately, given the enormous complexity of the social world, often, there isn’t a robust law /theory that we can test using empirical data. Sometimes we do the opposite, i.e., using empirical data, we try to understand social laws/propose social theories. To make things even more complicated, these social laws/theories may change across time and/or societies and require frequent updates.

Modeling Like an Engineer, but the Data is Social

  1. Customers who spend more tend to be more satisfied
  2. Monthly spending is positively associated with/related to customer satisfaction

You Need a Theory of How the World Works

Curve Fitting is Extremely Useful, Though

Wrapping Up



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Vivekananda Das

Sharing synthesized ideas on Causal Inference, Data Analysis in R, Stat Literacy, and Wellbeing | Ph.D. candidate @UW-Madison | More: