How To Use Google Scholar’s Advanced Search Menu For Big Data And Analytics–For Free

How to Use Google ScholarToday’s guest post was written by Carole Levitt, a noted expert on using web-based tools and services to conduct research and obtain information relevant to legal matters. You can learn more about her at the end of the post.


In 2015, Nicole Black, of MyCase, wrote a two-part article, How To Conduct Free Legal Research Using Google Scholar. This article will focus on a few features not noted in those articles, such as how to use the Advanced search menu to narrow and improve your search results to gather big data for the purpose of analytics.

Although Nicole explained where to find the Advanced search menu to search case law, I’ll repeat that here in case you missed it. First, if you want to use the Advanced search menu for case law searching, click into the case law radio button on the Scholar home page. Then, click the down-arrow on the right side of the search box to invoke the Advanced search menu (it’s not labeled, but as you can see in the following illustration, the label appears when you hover over the arrow).

Screen Shot 2016-06-17 at 2.07.24 PM

One of the first things to point out about Scholar is that it began as an articles database, with the case law database added as an afterthought later. So, even after you have clicked into the case law radio button on the Scholar homepage, as shown in the prior illustration, the Google Scholar Advanced search menu is still labeled Find Articles (as shown in the next illustration). Nevertheless, this is the correct place to search case law.

Screen Shot 2016-06-17 at 2.07.34 PM

While the first four Boolean connector and phrase search boxes located on Scholar’s Advanced search menu are the same as’s Advanced Search menu, there are three “field” search boxes and one drop-down list unique to Scholar’s Advanced Search menu. Unfortunately, many searchers don’t use these features because Scholar never bothered labeling them with appropriate case law terminology—they simply left the articles’ database labels on them. For example, the field search box labeled as:

  • Return articles authored by should really be labeled Return cases authored by
  • Return articles published in should really be labeled Return cases published in
  • Return articles dated between should really be labeled Return cases dated between

The where my words occur drop-down list selections are labeled with terminology suited to articles. The anywhere in the article selection should be labeled anywhere in the case while the in the title of the article should be labeled in the title of the case caption.

By failing to use these searches, lawyers are missing out on how to narrow and improve their search results and how to gather big data for the purpose of analytics. Before we discuss how to use field searching, let’s define analytics and big data. Big data simply refers to large volumes of data that have been collected and amassed into databases. The term came out of the business world and often referred to all the customer information a business had collected about their clients, from contact information and financial information to their likes and dislikes. 

But it’s not the amount of data that’s important. It’s what you do with the data that matters. Many businesses used big data for insights into their customers’ buying habits for example and then created better (e.g., targeted) marketing strategies, to entice them into purchase other products for instance. Lawyers began to take note of big data and analytics. While some firms use big data much in the same way as businesses to learn more about their own clients, the examples of big data that we’ll be exploring in this article are the millions of written opinions found in Google Scholar and what you do with the data—that is, how you use court opinions for judge, lawyer, and client analytics.

The same strategies we will discuss can be used at Lexis, Westlaw, Bloomberg, Fastcase, Casemaker, Ravel Law, etc.  Other big data that could be useful for analytics are court dockets and their underlying case documents (found at docket databases created by PACER, various state and local courts, Lexis, Westlaw, Bloomberg, etc.).

Back to Google Scholar and how to conduct field searching to create better, targeted searches useful for analytics. Before you go before a judge, wouldn’t it be useful to learn how many and what types of cases this particular judge has handled and then narrow down to how this judge has ruled on issues similar to your client’s issues in other cases? Also, wouldn’t it be useful to learn which court opinions the judge relied on in those cases? This is what is referred to as gathering data to create analytics.

These analytics could provide you with some insight into the judge’s legal reasoning process. With that knowledge, you might be able to adjust your strategy accordingly (by citing the same or similar cases or by using the same language (and/or reasoning) in your memo, brief, or oral argument. You might learn that the matter might not go your client’s way and decide to settle or you might be assured (or as assured as you can possibly be) that you are on the right track.

So, if you didn’t know that the Return articles authored by field search could actually be used to Return cases authored by, you might simply enter the judge’s name (e.g., Charles Vogel, a California judge), into the all the words Boolean search box and limited your search to California. You would retrieve 476 opinions and it’s possible your results for that name could also be that of a party, an attorney, a witness, or an expert, and so on, many of which would be irrelevant to our analytics.

But, restricting the judge’s name to the Return articles authored by field search box (Remember, we noted that the Return articles authored by field search box should really be labeled Return cases authored by), shown in the next illustration, eliminates all those extraneous and irrelevant results that you would have had to sift through.

Screen Shot 2016-06-17 at 2.07.45 PM

The first bits of analytics you have gathered is that this judge has authored 272 opinions, how many years this judge has been on this particular court, and the types of cases this judge typically has dealt with. Now, let’s enter some search terms that describe our case’s issues, such as the word negligence and the phrase “causal connection” to learn if this judge has handled similar cases. 

Screen Shot 2016-06-17 at 2.07.52 PM

This brings our results down to four—a nice manageable number of cases to review. These results allow us to continue using big data and analytics to learn about the judge’s legal reasoning and learn what cases he relied upon. Note, one problem with this Return articles authored by field search could also return results where Charles Vogel was the concurring or dissenting author, which would alter our analytics.

Before you appear before a certain judge in a case involving a certain opposing lawyer, think about using the Return articles authored by feature to gather data to create combined judge/attorney analytics. For example, you could learn whether this lawyer has appeared before the judge in the past and how the judge ruled. Enter the judge’s last name into the Return articles authored by search box and then enter the lawyer’s first and last name into the with all of the words search box.

You could also add the keyword attorney OR lawyer into the with at least one of the words Boolean search box if you were getting results for random people with that same name who were not the attorney you were searching for. If you were certain that this attorney did not use a middle name or middle initial, you could enter his first and last name into the with the exact phrase search box or if you knew that this attorney always used a middle name (or middle initial), you could enter his first, middle, and last name into the with the exact phrase search box. These tactics will help you narrow your results list to a manageable set.

Remember, it’s what you don’t know that will hurt you, so be sure to use big data and analytics to better prepare for your cases and matters.

More detailed information on how to search Google Scholar and many other free and low cost legal research resources can be found in Carole Levitt’s book, Internet Legal Research on a Budget (2014).

Carole Levitt is President of Internet For Lawyers. She is an acclaimed CLE seminar speaker and best-selling ABA Law Practice Division co-author of seven books. Carole has also co-authored thirteen editions of The Cybersleuth’s Guide to the Internet. Her areas of expertise are: Internet research (investigative, legal, and social media); social media ethics; Google search; and Google cloud apps. Carole was a California attorney, a law librarian, and a Legal Research and Writing Professor at Pepperdine. You can view Carole’s webinars on legal and investigative research here.

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