Types of Data

There exists a fundamental distinction between two types of data: qualitative and quantitative. The way we typically define them, we call data 'quantitative' if it is in numerical form and 'qualitative' if it is not. Notice that qualitative data could be much more than just words or text. Photographs, videos, sound recordings and so on, can be considered qualitative data. The distinction between qualitative and quantitative data might have some utility, but most people draw too hard a distinction, and that can lead to all sorts of confusion. In some areas of social research, the qualitative-quantitative distinction has led to protracted arguments with the proponents of each arguing the superiority of their kind of data over the other. The quantitative types argue that their data is 'hard', 'rigorous', 'credible', and 'scientific'. The qualitative proponents counter that their data is 'sensitive', 'nuanced', 'detailed', and 'contextual'. For many of us in social research, this kind of polarized debate has become less than productive. And, it obscures the fact that qualitative and quantitative data are intimately related to each other. All quantitative data is based upon qualitative judgments; and all qualitative data can be described and manipulated numerically. For instance, think about a very common quantitative measure in social research-a self esteem scale. The researchers who develop such instruments had to make countless judgments in constructing them: how to define self esteem; how to distinguish it from other related concepts; how to word potential scale items; how to make sure the items would be understandable to the intended respondents; what kinds of contexts it could be used in; what kinds of cultural and language constraints might be present; and on and on. The researcher who decides to use such a scale in their study has to make another set of judgments: how well does the scale measure the intended concept; how reliable or consistent is it; how appropriate is it for the research context and intended respondents; and on and on. Believe it or not, even the respondents make many judgments when filling out such a scale: what is meant by various terms and phrases; why is the researcher giving this scale to them; how much energy and effort do they want to expend to complete it, and so on. Even the consumers and readers of the research will make lots of judgments about the self esteem measure and its appropriateness in that …

Eg: Business Horizons conducted a comprehensive survey of 800 CEOs who run the country's largest global corporations.Some of the variables measured are given below.Classify them as quantitative or qualitative.

Random Samples from a finite population
Random Sample: All elements of the population have the same chance of being chosen in the sample.
• Random sampling reduces bias in analysis.

Eg:
In the baseball salaries data, we took a sample of the 100 topearning players.This is clearly not random, and gives a very inflated picture of the average salary.
µ = 4,509,878 = 19,371,913 x • In a sample with replacement, an element of the population may be selected more than once.
Eg: Shuffle a deck of cards, pick one at random, put it back, repeat 20 times.The Ace of Spades may be selected several times.
• In a sample without replacement, an element can be selected at most once.
Eg: Shuffle cards, pick 20 cards at random.Ace of Spades can't appear more than once in the sample.
Eg: In the baseball salaries example, the sample was taken without replacement, since a player cannot appear in the sample more than once.

Surveys
Survey: A set of questions about beliefs, attitudes, behaviors and other characteristics posed to individuals/organizations.
Eg: Cellphone survey on voting preferences for an upcoming election.
Non-random sample: An influencer asks for followers to send in their opinions.Results could be extremely biased.(Why?) Another non-random sample: We pick the first 250 names listed in alphabetical order.(Why isn't this random?) Random sample with replacement: Computer dials cellphone numbers at random, using computer-simulated random numbers.Some cellphones may be dialed more than once.
(Is this method better than picking numbers at random from an alphabetized list?) If a control is not used, patients will often get better simply because they believe the treatment should work.This is called the placebo effect, and is extremely powerful.
Eg: Does Hydroxychloroquine help for Covid-19?What about a sugar pill with a Hydroxychloroquine label?(Maybe just as effective!) Studies show that placebos are 55-60% as effective as medications like aspirin and codeine for treating pain.
In an experimental study, the experimenter assigns treatments to participants.This is often done at random, and in a double blind fashion, so that neither the experimenter nor the participant knows which treatment was given.
In an observational study, the experimenter does not make the assignment of treatments to the participants.This makes it difficult to make cause-and-effect conclusions, since the effect of the treatment on the response may be confounded by other hidden factors.
Experimental studies are preferable.
Clinical Trials.Before a drug can be approved for use by the general public, the Food and Drug Administration requires a randomized clinical trial, to be funded by the pharmaceutical company.Ideally, these are multi-year, double blind studies, with careful attention to sample selection.Covid-19: much faster!
Eg: It was difficult to prove that smoking causes lung cancer in humans since it is unethical (and impossible) to perform a controlled experiment on the long-term effects of smoking on humans.The available data are mostly observational.
An early (incorrect) theory on why throat cancer rates are higher in smokers than in non-smokers was proposed by R.A. Fisher, inventor of the F-test, and an avid pipe smoker.Fisher said that people who get throat cancer try pipe smoking to soothe the discomfort.In other words, throat cancer causes pipe smoking (!!).
From the observational data alone, it's hard to prove that Fisher has reversed the cause and the effect.