My preference would be for a discussion that includes Pearl's DAGs as well as Rubin's potential outcomes framework.

Edit: My take is that Rubin's framework is rooted in a 20th century Fisherian orientation (which is why it's especially popular among statisticians), while Pearl's framework in part reflects new insights on probabilistic graphical models (which is why it's popular among computer scientists). The future, I suspect, will entail both approaches.

This is a great point and I think one at least sociologists will be sympathetic towards. I'm thinking here of Howard Becker's "hierarchy of credibility" principle.

Perhaps include a few sentences on why you're optimistic? I'm optimistic but I (or other readers) could have different reasons.

I initially expected a discussion of mass collaboration in terms of researchers with other researchers. The lone scholar working alone (e.g., Einstein) is replaced with a team of researchers across continents and disciplines (e.g., Large Hadron Collider). However, this kind of mass collaboration may be beyond the scope of your book.

five

show,

respondents'?

An analogue age version of this is the "Delphi Method" developed by RAND in the 1950s. It's has problems and it's about predicting the future, but I see some similarities since often open calls today entail predicting some outcomes. The problem with the "Delphi Method" is that it (a) relies on pre-selected experts, (b) has no clear criterion for what's "best", and (c) presumes consensus-building is truth.

Italicize?

Depends on the style guide, but it seems most style guides suggest writing out numbers 1 to 9. E.g., "There are five general principles..."

To nitpick: you can still get an estimate of a causal effect without randomization (or adjustment), but it's likely to be a lousy estimate (unless particular assumptions are met).

How about controlling?

Consider rewording: "...could lead to uninteresting research, but that's not the case."

Italicize, perhaps?

Do you mean observational big data or observational data in general?

This is a really, really great idea!

Elsewhere you use the spelling "analogs."

E.g., using Google maps and humans to code for "broken windows" in various neighborhoods.

comparisons?

Hawthorne

It'd be incredibly helpful if you could briefly discuss an example of experiments reinforcing each other.

This figure would be clearer to me if the pictures were below the text "Info" and "Info + social."

If you have examples of failure, that may be informative as well.

Or what Merton called the "Matthew effect."

You can specify here why mechanisms are incredibly important. My take is that often experiments have a "black box" approach and that we don't actually understand a causal effect until we understand the mechanisms.

Conversely, understanding the mechanisms helps strengthen the case for a causal effect. The findings in psychology on supposed "psi" effects are weakened because there are no plausible mechanisms. Likewise, we knew smoking caused cancer back in the 1950s because we had a pretty good idea of the mechanism (e.g., tar) from qualitative data and simple observational studies.

Edit: As well, mechanisms could be used to identify causal effects (e.g., Pearl's "front-door" criterion).

Perhaps too strong?

questions

experiments'

Should this figure have some data points or text in the middle of the plot?

And you may condition on a pre-treatment collider variable that induces a back-door path (!).

Also among some social psychologists (e.g., Banaji's work), although still probably not as skeptical as economists.

I think you need to spell this out more clearly here. Will the value of surveys increase because of the decline of traditional landline surveys, so any plausibly reliable survey data will be more valuable? Or will the value increase because of the growth of big data, which can be combined with survey techniques?

increase

person

Remove comma

Consider including a sentence discussing cross-validation for social scientists. I suspect many are not familiar with cross-validation.

third party

Perhaps mention "data fusion"?

Perhaps explain how forecasting is different from prediction? (I view prediction as a more general category than forecasting.)

seems

simpler?

searches

My informal experience is that people find open-ended survey questions (i.e., text responses) more enjoyable to answer than closed-ended survey questions. I have not seen any research on this, however.

I agree, although there's some psychological work suggesting that closed-ended questions are more predictive of human behavior (i.e., quickly answering closed-ended questions is akin to a quasi-implicit bias that affects behavior).

asking questions

how we ask questions

Elsewhere you use the spelling "analog" rather than "analogue."

Was a "perfectly executed probability sampling" ever a realistic option?

The y-axis needs a label.

Remove comma

Remove space

weighting?

I find this phrasing somewhat confusing.

giving

this chapter will be explained

Remove space

Remove comma?

scientists

I like the Blumenstock et al. example, but I think the introduction would show the immense change going on with a parallel example from the analog age. E.g., compare Blau and Duncan's work on the American Occupational Structure, which required specifying hypotheses weeks in advance and entailed slow computation with punch cards.

Cf. Implicit Association Test

appear

Who?

Perhaps mention what prompted the widespread use of probability-based sampling (to parallel the next paragraph, which explains why RDD was used)?

Who?

There's a third approach, too. Causal discovery algorithms (i.e., computational improvements) and large amounts of diverse observational data (i.e., always-on big data systems) are enabling researchers to create and evaluate complex DAGs from observational data.

Edit: There aren't many examples in the social sciences using these algorithms, but I think they have a lot of potential if used judiciously.

Add a space.

Remove this comma, perhaps?

from?

See my previous point about causal discovery algorithms and large volumes of data.

Or a critic might say "what people think are natural experiments are everywhere." Might be worthwhile to mention the criticisms of natural experiments (e.g., Rosenzweig and Wolpin 2000).

Also, I think you can be more forceful in what I think you're claiming -- that we have more opportunities to find plausibly natural experiments in the digital age.

Replace with "the mechanism" or "mechanisms".

I think the figure would be clearer if you numbered the steps in the figure.

posts

is

complete

representative

Check this text formatting. It's off on my computer.

This statement does not seem obvious to me. People's values (i.e., sense of what is important) can differ greatly.

Perhaps provide some context on Jon Kleinberg. E.g., "by the computer scientist Jon Kleinberg in a talk on X at Y."

running?

process

Consider adding a sentence defining the difference between these two kinds of significance.

Remove this comma.

This definition is so common I'm thinking you should place this earlier when you discuss your definition of "big data."

differences

abstruse?

(You mention abstractions in a positive light elsewhere in the book.)

Where would you place statisticians? Are they an audience for your book?

Consider rephrasing. I understand what you mean here, but a perfect urinal is indeed an ugly readymade.

Consider including another example or two to further support your point. (Although it won't strictly be "back to the beginning" if you include other examples.)

I understand why it's a good idea to combine social with data science, but this statement makes it seem like it's a near-inevitability. I'm thinking you could add more material in this section on barriers to combining social with data science, and how we can overcome them.

Consider replacing with "one". E.g., "...in the process of making a transition like the one from photography to cinematography."

Remove "also".

Consider adding: "as well as companies."

Consider incorporating into the previous sentence.

Researchers?

Consider rephrasing.

Search engine personalization presents some ambiguity, however, to the idea that these queries are non-reactive.

researchers

An admirable but flawed attempt at this was Argentina's Project Cybersyn in the early 1970s.

Perhaps you could be more specific here in the example? It will clarify your point more for readers, I think.

Perhaps parenthetically define this phrase?

The period is missing from "etc." In general, I tend to prefer "and so on" or "and so forth" instead of "etc."

It seems that you've already defined digital traces several times earlier, so I would consider removing the phrase "are often called digital traces, and".

I think understand what you mean here, but I'm not sure all readers would understand how surveys entail an interaction with people.

Might be more specific here. E.g., "enough to fully map the wealth distribution in Rwanda."

You use the word "transition" a lot in the first few sentences. I'd consider replacing this word with "change" or "switch" (or something similar).

Do you mean that the principles of analog age social research will inform those for digital age social research?

Another innovation is that physical distance becomes less important. Arthur C. Clarke predicted back in the 1970s that these new forms of communication would render physical travel obsolete. He said that people in the future (i.e., today) would "communicate, not commute."

I generally agree with this view, although there has been some discussion regarding a slowdown in Moore's law. E.g., https://www.technologyreview.com/s/601102/intel-puts-the-brakes-on-moores-law/