thesis selection criteria

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How to Conduct a Literature Review (Health Sciences and Beyond)

What is a Literature Review?
Developing a Research Question

Selection Criteria

Inclusion criteria, exclusion criteria.

Database Search
Documenting Your Search
Organize Key Findings
Reference Management

You may want to think about criteria that will be used to select articles for your literature review based on your research question. These are commonly known as inclusion criteria and exclusion criteria . Be aware that you may introduce bias into the final review if these are not used thoughtfully.

Inclusion criteria are the elements of an article that must be present in order for it to be eligible for inclusion in a literature review. Some examples are:

Included studies must have compared certain treatments
Included studies must be experimental
Included studies must have been published in the last 5 years

Exclusion criteria are the elements of an article that disqualify the study from inclusion in a literature review. Some examples are:

Study used an observational design
Study used a qualitative methodology
Study was published more than 5 years ago
Study was published in a language other than English
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Last Updated: Mar 15, 2024 12:22 PM
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The Thesis Process

The thesis is an opportunity to work independently on a research project of your own design and contribute to the scholarly literature in your field. You emerge from the thesis process with a solid understanding of how original research is executed and how to best communicate research results. Many students have gone on to publish their research in academic or professional journals.

To ensure affordability, the per-credit tuition rate for the 8-credit thesis is the same as our regular course tuition. There are no additional fees (regular per-credit graduate tuition x 8 credits).

Below are the steps that you need to follow to fulfill the thesis requirement. Please know that through each step, you will receive guidance and mentorship.

1. Determine Your Thesis Topic and Tentative Question

When you have completed between 24 and 32 credits, you work with your assigned research advisor to narrow down your academic interests to a relevant and manageable thesis topic. Log in to MyDCE , then ALB/ALM Community to schedule an appointment with your assigned research advisor via the Degree Candidate Portal.

Thesis Topic Selection

We’ve put together this guide to help frame your thinking about thesis topic selection.

Every effort is made to support your research interests that are grounded in your ALM course work, but faculty guidance is not available for all possible projects. Therefore, revision or a change of thesis topic may be necessary.

The point about topic selection is particularly pertinent to scientific research that is dependent upon laboratory space, project funding, and access to private databases. It is also critical for our candidates in ALM, liberal arts fields (English, government, history, international relations, psychology, etc.) who are required to have Harvard faculty direct their thesis projects. Review Harvard’s course catalog online ( my.harvard.edu ) to be sure that there are faculty teaching courses related to your thesis topic. If not, you’ll need to choose an alternative topic.
Your topic choice must be a new area of research for you. Thesis work represents thoughtful engagement in new academic scholarship. You cannot re-purpose prior research. If you want to draw or expand upon your own previous scholarship for a small portion of your thesis, you need to obtain the explicit permission of your research advisor and cite the work in both the proposal and thesis. Violations of this policy will be referred to the Administrative Board.

2. Prepare Prework for the Crafting the Thesis Proposal (CTP) Course or Tutorial

The next step in the process is to prepare and submit Prework in order to gain registration approval for the Crafting the Thesis Proposal (CTP) tutorial or course. The Prework process ensures that you have done enough prior reading and thinking about your thesis topic to benefit from the CTP.

The CTP provides an essential onramp to the thesis, mapping critical issues of research design, such as scope, relevance to the field, prior scholarly debate, methodology, and perhaps, metrics for evaluating impact as well as bench-marking. The CTP identifies and works through potential hurdles to successful thesis completion, allowing the thesis project to get off to a good start.

In addition to preparing, submitting, and having your Prework approved, to be eligible for the CTP, you need to be in good standing, have completed a minimum of 32 degree-applicable credits, including the statistics/research methods requirement (if pertinent to your field). You also need to have completed Engaging in Scholarly Conversation (if pertinent to your field). If you were admitted after 9/1/2023 Engaging in Scholarly Conversation (A and B) is required, if admitted before 9/1/2023 this series is encouraged.

Advising Note for Biology, Biotechnology, and Bioengineering and Nanotechnology Candidates : Thesis projects in these fields are designed to support ongoing scientific research happening in Harvard University, other academic institutions, or life science industry labs and usually these are done under the direction of a principal investigator (PI). Hence, you need to have a thesis director approved by your research advisor prior to submitting CTP prework. Your CTP prework is then framed by the lab’s research. Schedule an appointment with your research advisor a few months in advance of the CTP prework deadlines in order to discuss potential research projects and thesis director assignment.

CTP Prework is sent to our central email box: [email protected] between the following firm deadlines:

April 1 and June 1 for fall CTP
September 1 and November 1 for spring CTP.
August 1 and October 1 for the three-week January session (ALM sustainability candidates only)
International students who need a student visa to attend Harvard Summer School should submit their prework on January 1, so they can register for the CTP on March 1 and submit timely I-20 paperwork. See international students guidelines for more information.

Your research advisor will provide feedback on your prework submission to gain CTP registration approval. If your prework is not approved after 3 submissions, your research advisor cannot approve your CTP registration. If not approved, you’ll need to take additional time for further revisions, and submit new prework during the next CTP prework submission time period for the following term (if your five-year degree completion deadline allows).

3. Register and Successfully Complete the Crafting the Thesis Proposal Tutorial or Course

Once CTP prework is approved, you register for the Crafting the Thesis Proposal (CTP) course or tutorial as you would any other course. The goal of the CTP is to produce a complete, well-written draft of a proposal containing all of the sections required by your research advisor. Creating an academically strong thesis proposal sets the foundation for a high-quality thesis and helps garner the attention of a well-respected thesis director. The proposal is normally between 15 to 25 pages in length.

The CTP tutorial is not a course in the traditional sense. You work independently on your proposal with your research advisor by submitting multiple proposal drafts and scheduling individual appointments. You need to make self-directed progress on the proposal without special prompting from the research advisor. You receive a final grade of SAT or UNSAT (failing grade).

The CTP for sustainability is a three-week course in the traditional sense and you receive a letter grade, and it must be B- or higher to receive degree credit for the course.

You are expected to incorporate all of your research advisor’s feedback and be fully committed to producing an academically strong proposal leading to a thesis worthy of a Harvard degree. If you are unable to take advice from your research advisor, follow directions, or produce an acceptable proposal, you will not pass the CTP.

Successful CTP completion also includes a check on the proper use of sources according to our academic integrity guidelines. Violations of our academic integrity policy will be referred to the Administrative Board.

Maximum of two attempts . If you don’t pass that CTP, you’ll have — if your five-year, degree-completion date allows — just one more attempt to complete the CTP before being required to withdraw from the program. If you fail the CTP just once and have no more time to complete the degree, your candidacy will automatically expire. Please note that a WD grade counts as an attempt.

If by not passing the CTP you fall into poor academic standing, you will need to take additional degree-applicable courses to return to good standing before enrolling in the CTP for your second and final time, only if your five-year, degree-completion date allows. If you have no more time on your five-year clock, you will be required to withdraw.

Human Subjects

If your thesis, regardless of field, will involve the use of human subjects (e.g., interviews, surveys, observations), you will need to have your research vetted by the Committee on the Use of Human Subjects (CUHS) of Harvard University. Please review the IRB LIFECYCLE GUIDE located on the CUHS website. Your research advisor will help you prepare a draft copy of the project protocol form that you will need to send to CUHS. The vetting process needs to be started during the CTP tutorial, before a thesis director has been assigned.

4. Thesis Director Assignment and Thesis Registration

We expect you to be registered in thesis soon after CTP completion or within 3 months — no later. You cannot delay. It is critical that once a research project has been approved through the CTP process, the project must commence in a timely fashion to ensure the academic integrity of the thesis process.

Once you (1) successfully complete the CTP and (2) have your proposal officially approved by your research advisor (RA), you move to the thesis director assignment phase. Successful completion of the CTP is not the same as having an officially approved proposal. These are two distinct steps.

If you are a life science student (e.g., biology), your thesis director was identified prior to the CTP, and now you need the thesis director to approve the proposal.

The research advisor places you with a thesis director. Do not approach faculty to ask about directing your thesis. You may suggest names of any potential thesis directors to your research advisor, who will contact them, if they are eligible/available to direct your thesis, after you have an approved thesis proposal.

When a thesis director has been identified or the thesis proposal has been fully vetted by the preassigned life science thesis director, you will receive a letter of authorization from the Assistant Dean of Academic Programs officially approving your thesis work and providing you with instructions on how to register for the eight-credit Master’s Thesis. The letter will also have a tentative graduation date as well as four mandatory thesis submission dates (see Thesis Timetable below).

Continuous Registration Tip: If you want to maintain continued registration from CTP to thesis, you should meet with your RA prior to prework to settle on a workable topic, submit well-documented prework, work diligently throughout the CTP to produce a high-quality proposal that is ready to be matched with a thesis director as soon as the CTP is complete.

Good academic standing. You must be good academic standing to register for the thesis. If not, you’ll need to complete additional courses to bring your GPA up to the 3.0 minimum prior to registration.

Thesis Timetable

The thesis is a 9 to 12 month project that begins after the Crafting the Thesis Proposal (CTP); when your research advisor has approved your proposal and identified a Thesis Director.

The date for the appointment of your Thesis Director determines the graduation cycle that will be automatically assigned to you:

Once registered in the thesis, we will do a 3-month check-in with you and your thesis director to ensure progress is being made. If your thesis director reports little to no progress, the Dean of Academic Programs reserves the right to issue a thesis not complete (TNC) grade (see Thesis Grading below).

As you can see above, you do not submit your thesis all at once at the end, but in four phases: (1) complete draft to TA, (2) final draft to RA for format review and academic integrity check, (3) format approved draft submitted to TA for grading, and (4) upload your 100% complete graded thesis to ETDs.

Due dates for all phases for your assigned graduation cycle cannot be missed. You must submit materials by the date indicated by 5 PM EST (even if the date falls on a weekend). If you are late, you will not be able to graduate during your assigned cycle.

If you need additional time to complete your thesis after the date it is due to the Thesis Director (phase 1), you need to formally request an extension (which needs to be approved by your Director) by emailing that petition to: [email protected] . The maximum allotted time to write your thesis, including any granted extensions of time is 12 months.

Timing Tip: If you want to graduate in May, you should complete the CTP in the fall term two years prior or, if a sustainability student, in the January session one year prior. For example, to graduate in May 2025:

Complete the CTP in fall 2023 (or in January 2024, if a sustainability student)
Be assigned a thesis director (TD) in March/April 2024
Begin the 9-12 month thesis project with TD
Submit a complete draft of your thesis to your TD by February 1, 2025
Follow through with all other submission deadlines (April 1, April 15 and May 1 — see table above)
Graduate in May 2025

5. Conduct Thesis Research

When registered in the thesis, you work diligently and independently, following the advice of your thesis director, in a consistent, regular manner equivalent to full-time academic work to complete the research by your required timeline.

You are required to produce at least 50 pages of text (not including front matter and appendices). Chapter topics (e.g., introduction, background, methods, findings, conclusion) vary by field.

6. Format Review — Required of all Harvard Graduate Students and Part of Your Graduation Requirements

All ALM thesis projects must written in Microsoft Word and follow a specific Harvard University format. A properly formatted thesis is an explicit degree requirement; you cannot graduate without it.

Your research advisor will complete the format review prior to submitting your thesis to your director for final grading according to the Thesis Timetable (see above).

You must use our Microsoft Word ALM Thesis Template or Microsoft ALM Thesis Template Creative Writing (just for creative writing degree candidates). It has all the mandatory thesis formatting built in. Besides saving you a considerable amount of time as you write your thesis, the preprogrammed form ensures that your submitted thesis meets the mandatory style guidelines for margins, font, title page, table of contents, and chapter headings. If you use the template, format review should go smoothly, if not, a delayed graduation is highly likely.

Format review also includes a check on the proper use of sources according to our academic integrity guidelines. Violations of our academic integrity policy will be referred directly to the Administrative Board.

7. Mandatory Thesis Archiving — Required of all Harvard Graduate Students and Part of Your Graduation Requirements

Once your thesis is finalized, meaning that the required grade has been earned and all edits have been completed, you must upload your thesis to Harvard University’s electronic thesis and dissertation submission system (ETDs). Uploading your thesis ETDs is an explicit degree requirement; you cannot graduate without completing this step.

The thesis project will be sent to several downstream systems:

Your work will be preserved using Harvard’s digital repository DASH (Digital Access to Scholarship at Harvard).
Metadata about your work will be sent to HOLLIS (the Harvard Library catalog).
Your work will be preserved in Harvard Library’s DRS2 (digital preservation repository).

By submitting work through ETDs @ Harvard you will be signing the Harvard Author Agreement. This license does not constrain your rights to publish your work subsequently. You retain all intellectual property rights.

For more information on Harvard’s open access initiatives, we recommend you view the Director of the Office of Scholarly Communication (OSC), Peter Suber’s brief introduction .

Thesis Grading

You need to earn a grade of B- or higher in the thesis. All standard course letter grades are available to your thesis director. If you fail to complete substantial work on the thesis, you will earn a grade of TNC (thesis not complete). If you have already earned two withdrawal grades, the TNC grade will count as a zero in your cumulative GPA.

If you earn a grade below B-, you will need to petition the Administrative Board for permission to attempt the thesis for a second and final time. The petition process is only available if you are in good academic standing and your five-year, degree-completion deadline allows for more time. Your candidacy will automatically expire if you do not successfully complete the thesis by your required deadline.

If approved for a second attempt, you may be required to develop a new proposal on a different topic by re-enrolling in the CTP and being assigned a different thesis director. Tuition for the second attempt is calculated at the current year’s rate.

If by not passing the thesis you fall into poor academic standing, you’ll need to take additional degree-applicable courses to return to good standing before re-engaging with the thesis process for the second and final time. This is only an option if your five-year, degree-completion deadline allows for more time.

The Board only reviews cases in which extenuating circumstances prevented the successful completion of the thesis.

Harvard Division of Continuing Education

The Division of Continuing Education (DCE) at Harvard University is dedicated to bringing rigorous academics and innovative teaching capabilities to those seeking to improve their lives through education. We make Harvard education accessible to lifelong learners from high school to retirement.

Think of yourself as a member of a jury, listening to a lawyer who is presenting an opening argument. You'll want to know very soon whether the lawyer believes the accused to be guilty or not guilty, and how the lawyer plans to convince you. Readers of academic essays are like jury members: before they have read too far, they want to know what the essay argues as well as how the writer plans to make the argument. After reading your thesis statement, the reader should think, "This essay is going to try to convince me of something. I'm not convinced yet, but I'm interested to see how I might be."

An effective thesis cannot be answered with a simple "yes" or "no." A thesis is not a topic; nor is it a fact; nor is it an opinion. "Reasons for the fall of communism" is a topic. "Communism collapsed in Eastern Europe" is a fact known by educated people. "The fall of communism is the best thing that ever happened in Europe" is an opinion. (Superlatives like "the best" almost always lead to trouble. It's impossible to weigh every "thing" that ever happened in Europe. And what about the fall of Hitler? Couldn't that be "the best thing"?)

A good thesis has two parts. It should tell what you plan to argue, and it should "telegraph" how you plan to argue—that is, what particular support for your claim is going where in your essay.

Steps in Constructing a Thesis

First, analyze your primary sources. Look for tension, interest, ambiguity, controversy, and/or complication. Does the author contradict himself or herself? Is a point made and later reversed? What are the deeper implications of the author's argument? Figuring out the why to one or more of these questions, or to related questions, will put you on the path to developing a working thesis. (Without the why, you probably have only come up with an observation—that there are, for instance, many different metaphors in such-and-such a poem—which is not a thesis.)

Once you have a working thesis, write it down. There is nothing as frustrating as hitting on a great idea for a thesis, then forgetting it when you lose concentration. And by writing down your thesis you will be forced to think of it clearly, logically, and concisely. You probably will not be able to write out a final-draft version of your thesis the first time you try, but you'll get yourself on the right track by writing down what you have.

Keep your thesis prominent in your introduction. A good, standard place for your thesis statement is at the end of an introductory paragraph, especially in shorter (5-15 page) essays. Readers are used to finding theses there, so they automatically pay more attention when they read the last sentence of your introduction. Although this is not required in all academic essays, it is a good rule of thumb.

Anticipate the counterarguments. Once you have a working thesis, you should think about what might be said against it. This will help you to refine your thesis, and it will also make you think of the arguments that you'll need to refute later on in your essay. (Every argument has a counterargument. If yours doesn't, then it's not an argument—it may be a fact, or an opinion, but it is not an argument.)

This statement is on its way to being a thesis. However, it is too easy to imagine possible counterarguments. For example, a political observer might believe that Dukakis lost because he suffered from a "soft-on-crime" image. If you complicate your thesis by anticipating the counterargument, you'll strengthen your argument, as shown in the sentence below.

Some Caveats and Some Examples

A thesis is never a question. Readers of academic essays expect to have questions discussed, explored, or even answered. A question ("Why did communism collapse in Eastern Europe?") is not an argument, and without an argument, a thesis is dead in the water.

A thesis is never a list. "For political, economic, social and cultural reasons, communism collapsed in Eastern Europe" does a good job of "telegraphing" the reader what to expect in the essay—a section about political reasons, a section about economic reasons, a section about social reasons, and a section about cultural reasons. However, political, economic, social and cultural reasons are pretty much the only possible reasons why communism could collapse. This sentence lacks tension and doesn't advance an argument. Everyone knows that politics, economics, and culture are important.

A thesis should never be vague, combative or confrontational. An ineffective thesis would be, "Communism collapsed in Eastern Europe because communism is evil." This is hard to argue (evil from whose perspective? what does evil mean?) and it is likely to mark you as moralistic and judgmental rather than rational and thorough. It also may spark a defensive reaction from readers sympathetic to communism. If readers strongly disagree with you right off the bat, they may stop reading.

An effective thesis has a definable, arguable claim. "While cultural forces contributed to the collapse of communism in Eastern Europe, the disintegration of economies played the key role in driving its decline" is an effective thesis sentence that "telegraphs," so that the reader expects the essay to have a section about cultural forces and another about the disintegration of economies. This thesis makes a definite, arguable claim: that the disintegration of economies played a more important role than cultural forces in defeating communism in Eastern Europe. The reader would react to this statement by thinking, "Perhaps what the author says is true, but I am not convinced. I want to read further to see how the author argues this claim."

A thesis should be as clear and specific as possible. Avoid overused, general terms and abstractions. For example, "Communism collapsed in Eastern Europe because of the ruling elite's inability to address the economic concerns of the people" is more powerful than "Communism collapsed due to societal discontent."

Choosing a Research Paper Topic

Topic Selection & Thesis Formation
Sources for Researching Topic Selection
Preemption Checking
Citation (Plagiarism) Mistakes to Avoid
Submitting for Publication
Additional Resources

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Choosing a Paper Topic

This guide is intended to assist law students at the University of St. Thomas School of Law with topic selection for Upper Level papers, Law Journal write-on competition, publishable papers for law reviews, and other research papers. This guide will provide guidance on choosing a topic, forming a thesis, preemption checking, and plagiarism/citation mistakes to avoid. This guide will also provide lists of sources for researching topic selection, available both in print and online through subscription databases and the free web.

For information on scholarly writing, please see our guide on Writing Resources for Law Students .

Identifying a Problem/Choosing a Claim

Think back to cases you have read for class that left an important question unresolved or that the reasoning is unpersuasive.
Try to recall a class discussion that intrigued you but did not yield a well-settled answer.
Read the comments, notes, and questions sections in your casebooks from class (or look at ones available in the library).
Read recent Supreme Court decisions in fields that interest you, and see whether they leave open major issues or create new ambiguities or uncertainties.
Check topical highlight databases for summaries of recent noteworthy cases.
Read legal blogs that specialize in the field you are interested in writing about – bloggers often post about interesting new cases that pose unresolved problems.
Cultivate ideas through research – READ articles pertinent to your subject.

Topicality/Originality

Take challenging position on controversial issues.
Apply intelligent analysis to existing cases and commentary.
Strive to achieve original conclusion.
Select a topic that is the result of recent technology or shift in public policy.
Is it a “hot” topic? – need to move fast.

What to Avoid:

Writing an article that shows there is a problem but does not give any suggested solution(s).
If you got your topic from a particular case, don’t focus on the case, focus on the problem.
Single-state articles. Instead, frame your article as a general piece that discusses all the laws in this family/issue.
Articles that just explain what the law is.
Responses to other people’s works. This will limit your readership. If your piece is stimulated by disagreement with another work, come up with your own claims and prove it. Cite the other work, but don’t let it be the main claim.

Examples of Topic Types that Work

1. Resolving a Jurisdictional Conflict

Paper that identifies an unresolved area of law, evaluates conflicting lines of authority, and identifies and argues for the better rule.
in the U.S. Courts of Appeal,
between state courts of intermediate appeal,
between state and federal courts, and
between the U.S. Supreme Court and statutory laws of individual states.
Topics in comparative law – especially good with secondary law reviews (i.e. Minnesota Journal of International Law ).
Requires timeliness – paper must be published before the central issue is resolved – check to see if an appeal has been filed or whether the issue is included in pending legislation.
For jurisdictional splits: Topic #106 (Courts) – key numbers 90-98
Court Circuit /5 split & da(aft 1/2007)
Search on the introductory signal “Compare” in law review database
ALLFEDS – sy,di(split conflict /s circuit authority) & da(aft 2007)
SCT-PETITION: “employment discrimination” & split /s circuit authority
For state law – MN-CS: co(low) & “first impression”
Add terms to narrow it down to an area of law (i.e. A.D.A.) or use a topical database
Petitions that are denied may be a better source
U.S. Law Week – search circuit /5 split
Can also set up alerts on Westlaw & LexisNexis
Topic 170B (Federal Courts) & Key #452 (Certiorari) & HE(conflict)

2. New Facts, Old Laws: Old Facts, New Laws

Apply an existing law to a new factual backdrop (i.e. technology issues).
Apply a new law to existing facts for new results.
TIMELINESS is essential here – race to publish.
Search for phrase “first impression” and limit to current year in case database
Federal district courts
Administrative agency opinions
Issue question matter /s “first impression” novel & da(aft 3/2008)
interesting or intriguing or open /s issue or question or topic /p “beyond the scope” or “another day”
“beyond the scope” /s note article comment /s court circuit & da(aft 2/2008)
Take an issue of first impression in one district and apply to it the law of a circuit that has not yet considered the issue.
Check whether the case presenting the issue has been appealed – briefs may be available.

Narrowing Your Topic & Developing a Thesis

First do some preliminary research on your topic – you will need to narrow your topic and develop a thesis.
Example – topic may be the rights of voluntarily committed mental patients in a particular state. You would want to narrow this topic and focus on something like the source of those rights. You would then need to develop your thesis, for example, determining that the appropriate source of such rights is the common law, not the federal or state constitution.
Not narrowing a topic is one of the most frequent problems seen in student papers.
Explore your subject to find an unresolved issue or an inadequate solution – can you break the subject down into parts?
Be a critical reader – ask questions.
Look at argument type (precedent, interpretive, institutional etc.).
Take a problem-solving approach.
Examine the broader context (are there statutes or cases involved that you should read?).
Keep a reading journal – help to keep track of citations and your thoughts.
Test/modify your thesis (use hypotheticals).
Next: Sources for Researching Topic Selection >>
Last Updated: Apr 11, 2024 8:45 AM
URL: https://libguides.stthomas.edu/Choosing_a_Paper_Topic

University of Michigan Library
Research Guides

Systematic Reviews

Selection Process
Work with a Search Expert
Covidence Review Software
Types of Reviews
Evidence in a Systematic Review
Information Sources
Search Strategy
Managing Records

Study Selection Guidance

Typical screening and selection process, systematic review tools.

Data Collection Process
Study Risk of Bias Assessment
Reporting Results
For Search Professionals

The eligibility criteria (also referred to as inclusion and exclusion criteria) for your systematic review should be developed before you begin screening articles. The PRISMA-p Protocols extension and systematic review protocol templates include eligibility criteria among the methods to plan.

Best practices for study selection

Use explicit, pre-defined eligibility criteria to guide screening
Have two independent reviewers select eligible studies to reduce the risk of mistakes and personal biases

Related sections of the Cochrane Handbook

As you develop your protocol and methodology, the following sections of the online version of the Cochrane Handbook for Systematic Reviews of Interventions provide detailed guidance on study selection:

Chapter 3: Defining the criteria for including studies and how the will be grouped for the synthesis
Section 4.6 Selecting Studies of Chapter 4: Searching for and selecting studies

For most evidence synthesis projects, study selection will occur in two phases: title and abstract screening and full text review.

Title and Abstract Screening

Review the title and abstract for each record retrieved to determine whether it should be excluded based on the eligibility criteria. If the relevance is unclear from the title and abstract, it is best to move the record forward for full text review.

During title and abstract screening, reviewers will document their overall decision (for example, include in full text review, exclude from project, or maybe). The title and abstract screening form can also include sub-questions related to the inclusion and exclusion criteria. Responses to these prompts are useful reminders when discussing conflicts.

Full Text Review

Retrieve and review the full text of all records advanced from the title and abstract screening process. Use the pre-defined eligibility criteria to select studies for inclusion in the review.

During full text review, document the overall decision and exclusion reason for each excluded study.

Systematic review tools have been designed to help streamline systematic review processes. The Systematic Review Toolbox catalogs available tools; the entry for each tool provides a description, supported processes, related papers, and basic costs (i.e., free vs. subscription required). Use the filters under "Advanced Search" to view tools that support specific systematic review processes.

Popular tools with screening features include:

DistillerSR (subscription required)
Covidence (subscription required)
Rayyan (free and subscription options available)
Abstrakr (free)

Select the best systematic review tool for your team by considering your team's needs and comparing features, usability, and costs of various tools.

Recent Tool Evaluations

Harrison H, Griffin SJ, Kuhn I, Usher-Smith JA. Software tools to support title and abstract screening for systematic reviews in healthcare: an evaluation. BMC Med Res Methodol . 2020;20(1):7. Published 2020 Jan 13. doi: 10.1186/s12874-020-0897-3
Kohl C, McIntosh EJ, Unger S, Haddaway NR, Kecke S, Schiemann J, Wilhelm R. Online tools supporting the conduct and reporting of systematic reviews and systematic maps: a case study on CADIMA and review of existing tools. Environmental Evidence . 2018 Dec;7(1):1-7. doi: 10.1186/s13750-018-0115-5

Unit 4: Fundamentals of Academic Essay Writing

24 Evidence Selection

Preview Questions:

What types of evidence can you use in your essay?
What are the criteria for determining if a piece of evidence is appropriate to use in your essay?
Why should you strive to use a variety of types of evidence?

Your next step is to locate evidence to support your claims.

Criteria for selecting effective evidence

You must able to understand and explain the evidence easily and clearly.
The evidence should be directly related to your supporting points; it must support your thesis.
A variety of types of evidence can make your writing more credible.

1 Easy to understand

If you find an article, but cannot understand the information in the article, it will be difficult or even impossible to use the evidence. You must therefore make sure you can understand the information you want use so you can paraphrase it clearly. Since most of the evidence you use will be paraphrased (not quoted), it is essential that you select information that you can easily express in your own words.

2 Supports your thesis statement

The evidence must be directly related to your topic and thesis statement. Even if the information is very interesting and easy to paraphrase, if it is not related to your thesis, it could lead to problems with logic or cause confusion in your writing.

3 Types of evidence

Using various types of evidence will show your reader you are familiar with the topic. There are two general categories of evidence, documented and undocumented.

Documented evidence is information for which the writer provides a source (and includes a citation). Documented evidence has therefore, been “documented” or “written down” previously and recorded for the public.

Types of documented evidence may include facts, statistics, expert opinions, examples, or anecdotes that the reader can locate in the original publication based on the citation and reference.

Nearly 98% of UW students bring a laptop or other device to class (Pierce, 2013, p. 1).
The UW-Madison will offer free tuition to transfer students who are the first in their families to attend college (Savidge, 2017, p. 1).
Note: If you are writing an essay about social media use among college students, it would be fairly easy to look at the percent (the number of) students who have Facebook or Instagram accounts. You could include these numbers, along with a citation and reference, in your essay as documented evidence.

Undocumented

Undocumented evidence may include general knowledge that most people accept to be true. What constitutes such shared knowledge may differ depending on the audience, but a good way to think about this is to ask yourself whether or not it is likely that most people will know what you are writing about without having to look up the information.

Teenagers feel pressure to fit in with those around them.
In the United States, cars drive on the right side of the road.
It is a simple fact that the earth travels around the sun; we all know and accept this as a truth, and it’s unnecessary to include a citation.
However, such an assertion could further be supplemented (and strengthened) with documented evidence such as which particular accounts are most common and what percent of students have them. In this way, documented and undocumented evidence can work together.
Another rough guideline is that if you can easily locate the information in five sources, it might be considered undocumented. Check with your instructor if you are unsure whether evidence is undocumented.

4 Types of evidence: Support your writing with a variety of evidence

Look at the types of evidence below and their examples. Which are most appropriate for academic writing?

Key Takeaways

Strive to use documented evidence in your essay.
If you are unsure whether evidence is documented or undocumented, ask your instructor.
Use a variety of types of evidence and perspectives to make your essay more credible.

Knowledge Check: Revisit this exercise on Common Knowledge

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Systematic Reviews: Study selection and appraisal

Types of literature review, methods, & resources
Protocol and registration
Search strategy
Medical Literature Databases to search
Study selection and appraisal
Data Extraction/Coding/Study characteristics/Results
Reporting the quality/risk of bias
Manage citations using RefWorks This link opens in a new window
GW Box file storage for PDF's This link opens in a new window

Study selection: PRISMA Item 9

Inclusion/Exclusion criteria

See http://unimelb.libguides.com/c.php?g=492361&p=3368110

First level screening - title and abstract review

At the initial screening stage read just the title and abstract of the candidate studies and make a decision to include or exclude the study from your review.

For small reviews of a few studies (e.g. <100)

T he research team should agree on the inclusion and exclusion criteria for studies you wish to review and put together a study screening form. To help identify your inclusion/exclusion criteria, r evisit the PICOS of interest you came up with for your search strategy and gain agreement/approval from your colleagues or supervisor . The screening form may look similar to Table 3 of Brown et al (2013) . You may write down your decision to include or exclude an article on an Excel spreadsheet like this one , or if you have a small number of records you may choose to print out one copy for each record, although printing will be impractical for larger numbers of records . S creen each potentially useful article identified in the literature search as follows:

Read the title and abstract (where available) and apply the inclusion/exclusion criteria from the screening form.
Make a decision on whether or not to include the study in the review.
Record the decision and reasons for inclusion/exclusion on the study screening form or spreadsheet . You will summarise the reasons for exclusion on the PRISMA flow diagram - see Study Selection PRISMA item # 17 below.

For large reviews of many studies (e.g. >100) - in case you need to partially automate the screening process

There are three web-based software applications that can help with screening and tracking your selection decisions:

Covidence (GW in 2019 bought a subscription so you can use this tool now). Provides a decision dashboard and annotation tool, and the ability to screen candidate citations you locate in your literature search. Covidence is used by Cochrane review teams as their first level screening tool, the resulting study characteristics and decision data can be exported to RevMan (free for academic use) or Excel.
Abstrackr (free, Beta, open-source). Abstrackr comprises two components; a web-based annotation tool that allows participants in a review to collaboratively screen citations for relevance, and machine learning technologies that semi-automate the screening process. The web-based annotation tool allows project leads to import the citations that are to be screened for a review from either RefMan or Pubmed. Participants can then join the project and begin screening; the tool maintains a digital paper trail of all screening decisions. The machine learning technology permits reviewers to screen roughly half of the set of citations imported for a given review, and then let the software automatically exclude a (hopefully large) portion of the remaining citations; the reviewers will then only need to screen the articles classified as relevant by the software. A recent article evaluating the use of Abstrackr in the systematic review process is Rathbone, J., Hoffmann, T., & Glasziou, P. (2015). Faster title and abstract screening? Evaluating Abstrackr, a semi-automated online screening program for systematic reviewers. Systematic Reviews, 480. doi:10.1186/s13643-015-0067-6
DistillerSR (requires subscription). Enables you to create forms for making screening decisions, and extract data.

Second level screening - full text review

Having excluded candidate studies that did not meet your inclusion/exclusion criteria you should have a smaller number of potentially relevant studies. GW affiliates at GW and Children's National Health System can use Box to store and share the full text PDF's of copyrighted journal articles https://it.gwu.edu/backup-storage-document-management . Read and critically appraise the full text of each study you selected at the first pass screening stage to determine whether you wish to include them in your discussion and analysis. Specifically each study must be evaluated based on the following criteria:

Does this study address a clearly focused question? Did the study use valid methods to address this question? Are the valid results of this study important? Are these valid, important results applicable to my patient or population?

If the answer to any of these questions is “no”, you may wish to read no further and exclude the study, or you may decide to include the study to inform your discussion but not include the results in your analysis.

To help with this process you may wish to download and apply one of the following Critical Appraisal tools:

Study Quality Assessment Tools developed in 2013 by the National Heart Lung & Blood Institute: https://www.nhlbi.nih.gov/health-topics/study-quality-assessment-tools Choose an appraisal tool that matches the type of study you are reviewing from one of the following 6 study types: Controlled Intervention Studies, Systematic Reviews and Meta-Analyses, Observational Cohort and Cross-Sectional Studies, Case-Control Studies, Before-After (Pre-Post) Studies With No Control Group, & Case Series Studies.

Worksheets from the Oxford University Center for Evidence Based Medicine - choose a worksheet that matches the type of study: Systematic Review article Critical Appraisal Sheet Diagnosis study Critical Appraisal Sheet Prognosis study Critical Appraisal Sheet Therapy / Randomized Controlled Trial Critical Appraisal Sheet

Alternatively the CASP: Critical Appraisal Skills Checklists are eight critical appraisal tools designed to be used when reading and evaluating the quality of Systematic Reviews, Randomised Controlled Trials, Cohort Studies, Case Control Studies, Economic Evaluations, Diagnostic Studies, Qualitative studies and Clinical Prediction Rule.

Another alternative set of Critical Appraisal checklists are from the Joanna Briggs Institute (JBI). JBI require you use their critical appraisal checklists if you are conducing a JBI systematic review following the methods described in the JBI Manual for Evidence Synthesis .

Make a decision on whether or not to include the study in your review, and write your decision and reasons for inclusion/exclusion at this second level/full text review stage on the study screening form. You will summarize the reasons for exclusion on the PRISMA flow diagram - see Study Selection PRISMA item # 17 below.

Reporting your screening decisions

In the final report in the methods section the PRISMA checklist Item 9 study selection will be reported as:

How studies were screened e.g. by reading title & abstract, and how they were critically appraised e.g. by applying a standardised appraisal form appropriate for that study type - see above.
What sort of studies were excluded e.g. letters, conference abstracts, etc.
Who reviewed/appraised the studies
What the process was for resolving disagreements e.g. reporting the level of inter-rater agreement, how often arbitration about selection was required, & what efforts were made to resolve disagreement e.g. were original authors contacted

Study selection PRISMA Item 17

Researchers must keep the screening forms to create a summary descriptive flow diagram of study selection.

In the final report in the results section the PRISMA checklist Item 17 study selection should be reported as follows:

Record the number of studies screened, assessed for eligibility and included in the review, with reasons for exclusions both in the text and in form of a PRISMA flow diagram of study selection e.g. similar to Fig 2 of Liberati et al. (2009). Covidence keeps track of your screening decisions and generates a PRISMA flow diagram for you, GW affiliates can register for a Covidence account here . Alternatively there is a PRISMA flow diagram generator at http://www.prisma-statement.org/PRISMAStatement/FlowDiagram
<< Previous: Medical Literature Databases to search
Next: Data Extraction/Coding/Study characteristics/Results >>

Last Updated: Apr 22, 2024 9:18 AM
URL: https://guides.himmelfarb.gwu.edu/systematic_review

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Navigating the Maze of Topic Selection in Research Methodology: Best Practices and Strategies

Welcome, PhD students, to the perplexing yet crucial journey of academic exploration! As you embark on your path towards research excellence, one of the most critical challenges you will encounter is navigating the intricate maze of topic selection in research methodology. As aspiring scholars seek to delve into MBA thesis topics in management, the process of identifying the perfect research subject can be daunting. However, in this comprehensive guide, we shall uncover the best practices and strategies to assist you in your MBA in research topic selection criteria, ensuring your journey is both insightful and rewarding.

Key factors and considerations that influence the selection of a research topic

The selection of a research topic for an MBA thesis in management within the field of research methodology is a crucial decision that requires careful consideration of several key factors. Firstly, it is essential to choose a topic that aligns with your interests, expertise, and career goals, as this will ensure sustained motivation and dedication throughout the research process. Secondly, the topic should be relevant and timely, addressing current issues or gaps in the field of management to contribute meaningful insights to the academic and business communities. Additionally, the feasibility of conducting the research, availability of data, and access to resources and literature must be taken into account. The scope of the research should be realistic and manageable, given the time constraints of an MBA program. Moreover, students must consider the potential impact of their research, aiming to make a practical and valuable contribution to the business world. Lastly, seeking guidance from faculty advisors and conducting a thorough literature review are essential steps to refine and validate the research topic.

At mbathesis, our company is committed to helping students navigate the challenging process of selecting a research topic for their MBA thesis in management. We offer personalized support and expert guidance to assist students in identifying relevant and innovative research areas within the realm of research methodology. Our team of experienced professionals will work closely with students to understand their interests and career aspirations, providing them with a curated list of potential topics to choose from. Through our vast resources and access to the latest academic literature, we ensure that students can explore feasible and timely research avenues. With our assistance, students can confidently embark on their MBA thesis journey, equipped with a well-defined research topic that has the potential to make a valuable impact in the field of management.

Effectively narrowing down options from a vast array of potential research topics

Selecting a compelling research topic for PhD candidates in the realm of business administration requires a nuanced approach. Beyond the generic advice, prospective scholars can delve into their personal experiences and career aspirations, reflecting on industry challenges that resonate with them deeply. Immersing themselves in relevant industry events, discussions, and emerging trends can provide firsthand insight. To craft a distinct niche, candidates should not just skim the literature but critically analyze it, identifying unexplored intersections and unresolved contradictions that align with their expertise. Collaborating with potential advisors and peers can offer valuable perspectives on the viability of research directions, aiding in the formulation of research questions that blend academic rigour with real-world applicability. Balancing passion with pragmatic evaluation, this approach ensures that the chosen research path not only resonates with their MBA pursuits but also advances the field in a tangible and sustainable manner.

Our company, MBAthesis, can be an invaluable resource for PhD researchers in this process. We offer a wide range of services, including personalized consultation with subject matter experts who can help scholars explore potential research avenues aligning with their interests and the current research landscape. Our team can aid in conducting thorough literature reviews and provide guidance to ensure the chosen research topic is viable and well-defined. With mbathesis' assistance, PhD researchers can confidently embark on their academic journey, equipped with a well-crafted research proposal and a clear sense of direction.

Leveraging online databases, academic journals, and other digital resources effectively

Aspiring researchers, particularly in the MBA field, can harness the power of online databases to uncover emerging research trends and pertinent gaps systematically. To begin, they can target databases like ProQuest, JSTOR, and EBSCOhost, renowned for housing a plethora of academic journals, articles, and business-related resources. Starting with specific keywords aligned with their interests—such as "MBA leadership trends" or "business sustainability strategies"—researchers can initiate focused searches. Utilizing advanced search filters within these databases, like refining results by publication date, authorship, and keywords, can further streamline the process.

Next, delving into the abstracts and introductions of relevant articles can provide a snapshot of prevailing academic discussions and ongoing research discourse. By tracing citation networks and identifying frequently cited papers, researchers can pinpoint seminal works and influential research domains. Participating in online academic communities, LinkedIn groups, or specialized forums can facilitate direct interactions with field experts, enriching understanding of emerging trends and potential research gaps.

To illustrate, let's consider ProQuest as an example. Initiating a search using terms like "MBA innovation strategies" and filtering results for the past five years could yield recent and pertinent articles. By reading abstracts and introductions, researchers can grasp the evolving landscape. Exploring citation patterns might reveal critical studies. Engaging in platforms like LinkedIn groups focused on business research can foster dialogues with scholars actively shaping the field. By skillfully amalgamating these tactics and resources, aspiring researchers can sculpt research topics that seamlessly blend with the contemporary MBA landscape, ensuring the relevance and impact of their contributions.

Our company, mbathesis, can significantly assist aspiring researchers in this process. With our comprehensive database of academic resources, including journals, articles, and conference papers, researchers can access a vast repository of up-to-date information. Our advanced search algorithms and filtering options make it easy to find relevant studies quickly. Moreover, mbathesis provides a collaborative platform for researchers to connect with each other, fostering knowledge exchange and networking opportunities within the academic community. Through our services, researchers can efficiently navigate the digital realm of scholarly information, enabling them to identify emerging research trends and gaps, ultimately aiding in the selection of a research topic that aligns with the current academic landscape.

Role of conducting a thorough SWOT analysis in the topic selection process

Conducting a thorough SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis plays a critical role in the topic selection process for research methodology. By systematically evaluating the internal and external factors related to the chosen research subject, researchers can gain valuable insights into its viability and potential impact.

Strengths: Identifying the strengths of the research subject allows researchers to recognize the positive aspects that make it a promising area of investigation. This may include available resources, existing expertise, and supportive infrastructure that can facilitate the research process.

Weaknesses: Assessing the weaknesses helps researchers acknowledge the limitations and challenges associated with the chosen topic. Understanding these limitations early on enables them to plan strategies to overcome potential obstacles and address shortcomings effectively.

Opportunities: Identifying opportunities within the research subject can highlight potential areas for growth and development. Researchers can leverage these opportunities to add novel dimensions to their study, explore uncharted territories, or capitalize on emerging trends.

Threats: Analyzing potential threats allows researchers to be aware of external factors that could hinder their progress or impact the validity of their findings. By addressing these threats proactively, researchers can enhance the reliability and credibility of their research outcomes.

Final Thoughts

In conclusion, the process of topic selection in research methodology can be likened to navigating a complex maze, requiring researchers to adopt best practices and employ effective strategies to find their way to success. Aspiring scholars seeking MBA thesis topics in management must navigate through the vast landscape of academic resources, and our company, mbathesis, serves as the ultimate guide on this scholarly journey. With our comprehensive database, advanced search algorithms, and collaborative platform, mbathesis empowers researchers to identify emerging trends and relevant gaps in their fields, aligning their chosen research subjects with the current academic landscape. Utilizing our SWOT analysis tools, researchers can make well-informed decisions about the viability and potential impact of their chosen MBA in research topic selection criteria, ensuring their academic endeavours thrive and excel. Embrace the possibilities with mbathesis and set yourself on the path to academic excellence.

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The Oxford Handbook of Political Methodology

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28 Case Selection for Case‐Study Analysis: Qualitative and Quantitative Techniques

John Gerring is Professor of Political Science, Boston University.

Published: 02 September 2009
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This article presents some guidance by cataloging nine different techniques for case selection: typical, diverse, extreme, deviant, influential, crucial, pathway, most similar, and most different. It also indicates that if the researcher is starting from a quantitative database, then methods for finding influential outliers can be used. In particular, the article clarifies the general principles that might guide the process of case selection in case-study research. Cases are more or less representative of some broader phenomenon and, on that score, may be considered better or worse subjects for intensive analysis. The article then draws attention to two ambiguities in case-selection strategies in case-study research. The first concerns the admixture of several case-selection strategies. The second concerns the changing status of a case as a study proceeds. Some case studies follow only one strategy of case selection.

Case ‐study analysis focuses on one or several cases that are expected to provide insight into a larger population. This presents the researcher with a formidable problem of case selection: Which cases should she or he choose?

In large‐sample research, the task of case selection is usually handled by some version of randomization. However, in case‐study research the sample is small (by definition) and this makes random sampling problematic, for any given sample may be wildly unrepresentative. Moreover, there is no guarantee that a few cases, chosen randomly, will provide leverage into the research question of interest.

In order to isolate a sample of cases that both reproduces the relevant causal features of a larger universe (representativeness) and provides variation along the dimensions of theoretical interest (causal leverage), case selection for very small samples must employ purposive (nonrandom) selection procedures. Nine such methods are discussed in this chapter, each of which may be identified with a distinct case‐study “type:” typical, diverse, extreme, deviant, influential, crucial, pathway, most‐similar , and most‐different . Table 28.1 summarizes each type, including its general definition, a technique for locating it within a population of potential cases, its uses, and its probable representativeness.

While each of these techniques is normally practiced on one or several cases (the diverse, most‐similar, and most‐different methods require at least two), all may employ additional cases—with the proviso that, at some point, they will no longer offer an opportunity for in‐depth analysis and will thus no longer be “case studies” in the usual sense ( Gerring 2007 , ch. 2 ). It will also be seen that small‐ N case‐selection procedures rest, at least implicitly, upon an analysis of a larger population of potential cases (as does randomization). The case(s) identified for intensive study is chosen from a population and the reasons for this choice hinge upon the way in which it is situated within that population. This is the origin of the terminology—typical, diverse, extreme, et al. It follows that case‐selection procedures in case‐study research may build upon prior cross‐case analysis and that they depend, at the very least, upon certain assumptions about the broader population.

In certain circumstances, the case‐selection procedure may be structured by a quantitative analysis of the larger population. Here, several caveats must be satisfied. First, the inference must pertain to more than a few dozen cases; otherwise, statistical analysis is problematic. Second, relevant data must be available for that population, or a significant sample of that population, on key variables, and the researcher must feel reasonably confident in the accuracy and conceptual validity of these variables. Third, all the standard assumptions of statistical research (e.g. identification, specification, robustness) must be carefully considered, and wherever possible, tested. I shall not dilate further on these familiar issues except to warn the researcher against the unreflective use of statistical techniques. 1 When these requirements are not met, the researcher must employ a qualitative approach to case selection.

The point of this chapter is to elucidate general principles that might guide the process of case selection in case‐study research, building upon earlier work by Harry Eckstein, Arend Lijphart, and others. Sometimes, these principles can be applied in a quantitative framework and sometimes they are limited to a qualitative framework. In either case, the logic of case selection remains quite similar, whether practiced in small‐ N or large‐ N contexts.

Before we begin, a bit of notation is necessary. In this chapter “ N ” refers to cases, not observations. Here, I am concerned primarily with causal inference, rather than inferences that are descriptive or predictive in nature. Thus, all hypotheses involve at least one independent variable ( X ) and one dependent variable ( Y ). For convenience, I shall label the causal factor of special theoretical interest X 1 , and the control variable, or vector of controls (if there are any), X 2 . If the writer is concerned to explain a puzzling outcome, but has no preconceptions about its causes, then the research will be described as Y‐centered . If a researcher is concerned to investigate the effects of a particular cause, with no preconceptions about what these effects might be, the research will be described as X‐centered . If a researcher is concerned to investigate a particular causal relationship, the research will be described as X 1 / Y‐centered , for it connects a particular cause with a particular outcome. 2 X ‐ or Y ‐centered research is exploratory; its purpose is to generate new hypotheses. X 1 / Y‐centered research, by contrast, is confirmatory/disconfirmatory; its purpose is to test an existing hypothesis.

1 Typical Case

In order for a focused case study to provide insight into a broader phenomenon it must be representative of a broader set of cases. It is in this context that one may speak of a typical‐case approach to case selection. The typical case exemplifies what is considered to be a typical set of values, given some general understanding of a phenomenon. By construction, the typical case is also a representative case.

Some typical cases serve an exploratory role. Here, the author chooses a case based upon a set of descriptive characteristics and then probes for causal relationships. Robert and Helen Lynd (1929/1956) selected a single city “to be as representative as possible of contemporary American life.” Specifically, they were looking for a city with

1) a temperate climate; 2) a sufficiently rapid rate of growth to ensure the presence of a plentiful assortment of the growing pains accompanying contemporary social change; 3) an industrial culture with modern, high‐speed machine production; 4) the absence of dominance of the city's industry by a single plant (i.e., not a one‐industry town); 5) a substantial local artistic life to balance its industrial activity …; and 6) the absence of any outstanding peculiarities or acute local problems which would mark the city off from the midchannel sort of American community. ( Lynd and Lynd 1929/1956 , quoted in Yin 2004 , 29–30)

After examining a number of options the Lynds decided that Muncie, Indiana, was more representative than, or at least as representative as, other midsized cities in America, thus qualifying as a typical case.

This is an inductive approach to case selection. Note that typicality may be understood according to the mean, median, or mode on a particular dimension; there may be multiple dimensions (as in the foregoing example); and each may be differently weighted (some dimensions may be more important than others). Where the selection criteria are multidimensional and a large sample of potential cases is in play, some form of factor analysis may be useful in identifying the most‐typical case(s).

However, the more common employment of the typical‐case method involves a causal model of some phenomenon of theoretical interest. Here, the researcher has identified a particular outcome ( Y ), and perhaps a specific X 1 / Y hypothesis, which she wishes to investigate. In order to do so, she looks for a typical example of that causal relationship. Intuitively, one imagines that a case selected according to the mean values of all parameters must be a typical case relative to some causal relationship. However, this is by no means assured.

Suppose that the Lynds were primarily interested in explaining feelings of trust/distrust among members of different social classes (one of the implicit research goals of the Middletown study). This outcome is likely to be affected by many factors, only some of which are included in their six selection criteria. So choosing cases with respect to a causal hypothesis involves, first of all, identifying the relevant parameters. It involves, secondly, the selection of a case that has a “typical” value relative to the overall causal model; it is well explained. Cases with untypical scores on a particular dimension (e.g. very high or very low) may still be typical examples of a causal relationship. Indeed, they may be more typical than cases whose values lie close to the mean. Thus, a descriptive understanding of typicality is quite different from a causal understanding of typicality. Since it is the latter version that is more common, I shall adopt this understanding of typicality in the remainder of the discussion.

From a qualitative perspective, causal typicality involves the selection of a case that conforms to expectations about some general causal relationship. It performs as expected. In a quantitative setting, this notion is measured by the size of a case's residual in a large‐ N cross‐case model. Typical cases lie on or near the regression line; their residuals are small. Insofar as the model is correctly specified, the size of a case's residual (i.e. the number of standard deviations that separate the actual value from the fitted value) provides a helpful clue to how representative that case is likely to be. “Outliers” are unlikely to be representative of the target population.

Of course, just because a case has a low residual does not necessarily mean that it is a representative case (with respect to the causal relationship of interest). Indeed, the issue of case representativeness is an issue that can never be definitively settled. When one refers to a “typical case” one is saying, in effect, that the probability of a case's representativeness is high, relative to other cases. This test of typicality is misleading if the statistical model is mis‐specified. And it provides little insurance against errors that are purely stochastic. A case may lie directly on the regression line but still be, in some important respect, atypical. For example, it might have an odd combination of values; the interaction of variables might be different from other cases; or additional causal mechanisms might be at work. For this reason, it is important to supplement a statistical analysis of cases with evidence drawn from the case in question (the case study itself) and with our deductive knowledge of the world. One should never judge a case solely by its residual. Yet, all other things being equal, a case with a low residual is less likely to be unusual than a case with a high residual, and to this extent the method of case selection outlined here may be a helpful guide to case‐study researchers faced with a large number of potential cases.

By way of conclusion, it should be noted that because the typical case embodies a typical value on some set of causally relevant dimensions, the variance of interest to the researcher must lie within that case. Specifically, the typical case of some phenomenon may be helpful in exploring causal mechanisms and in solving identification problems (e.g. endogeneity between X 1 and Y , an omitted variable that may account for X 1 and Y , or some other spurious causal association). Depending upon the results of the case study, the author may confirm an existing hypothesis, disconfirm that hypothesis, or reframe it in a way that is consistent with the findings of the case study. These are the uses of the typical‐case study.

2 Diverse Cases

A second case‐selection strategy has as its primary objective the achievement of maximum variance along relevant dimensions. I refer to this as a diverse‐case method. For obvious reasons, this method requires the selection of a set of cases—at minimum, two—which are intended to represent the full range of values characterizing X 1 , Y , or some particular X 1 / Y relationship. 3

Where the individual variable of interest is categorical (on/off, red/black/blue, Jewish/Protestant/Catholic), the identification of diversity is readily apparent. The investigator simply chooses one case from each category. For a continuous variable, the choices are not so obvious. However, the researcher usually chooses both extreme values (high and low), and perhaps the mean or median as well. The researcher may also look for break‐points in the distribution that seem to correspond to categorical differences among cases. Or she may follow a theoretical hunch about which threshold values count, i.e. which are likely to produce different values on Y .

Another sort of diverse case takes account of the values of multiple variables (i.e. a vector), rather than a single variable. If these variables are categorical, the identification of causal types rests upon the intersection of each category. Two dichotomous variables produce a matrix with four cells. Three trichotomous variables produce a matrix of eight cells. And so forth. If all variables are deemed relevant to the analysis, the selection of diverse cases mandates the selection of one case drawn from within each cell. Let us say that an outcome is thought to be affected by sex, race (black/white), and marital status. Here, a diverse‐case strategy of case selection would identify one case within each of these intersecting cells—a total of eight cases. Things become slightly more complicated when one or more of the factors is continuous, rather than categorical. Here, the diversity of case values do not fall neatly into cells. Rather, these cells must be created by fiat—e.g. high, medium, low.

It will be seen that where multiple variables are under consideration, the logic of diverse‐case analysis rests upon the logic of typological theorizing—where different combinations of variables are assumed to have effects on an outcome that vary across types ( Elman 2005 ; George and Bennett 2005 , 235; Lazarsfeld and Barton 1951 ). George and Smoke, for example, wish to explore different types of deterrence failure—by “fait accompli,” by “limited probe,” and by “controlled pressure.” Consequently, they wish to find cases that exemplify each type of causal mechanism. 4

Diversity may thus refer to a range of variation on X or Y , or to a particular combination of causal factors (with or without a consideration of the outcome). In each instance, the goal of case selection is to capture the full range of variation along the dimension(s) of interest.

Since diversity can mean many things, its employment in a large‐ N setting is necessarily dependent upon how this key term is defined. If it is understood to pertain only to a single variable ( X 1 or Y ), then the task is fairly simple. A categorical variable mandates the choice of at least one case from each category—two if dichotomous, three if trichotomous, and so forth. A continuous variable suggests the choice of at least one “high” and “low” value, and perhaps one drawn from the mean or median. But other choices might also be justified, according to one's hunch about the underlying causal relationship or according to natural thresholds found in the data, which may be grouped into discrete categories. Single‐variable traits are usually easy to discover in a large‐ N setting through descriptive statistics or through visual inspection of the data.

Where diversity refers to particular combinations of variables, the relevant cross‐ case technique is some version of stratified random sampling (in a probabilistic setting) or Qualitative Comparative Analysis (in a deterministic setting) ( Ragin 2000 ). If the researcher suspects that a causal relationship is affected not only by combinations of factors but also by their sequencing , then the technique of analysis must incorporate temporal elements ( Abbott 2001 ; Abbott and Forrest 1986 ; Abbott and Tsay 2000 ). Thus, the method of identifying causal types rests upon whatever method of identifying causal relationships is employed in the large‐ N sample.

Note that the identification of distinct case types is intended to identify groups of cases that are internally homogeneous (in all respects that might affect the causal relationship of interest). Thus, the choice of cases within each group should not be problematic, and may be accomplished through random sampling or purposive case selection. However, if there is suspected diversity within each category, then measures should be taken to assure that the chosen cases are typical of each category. A case study should not focus on an atypical member of a subgroup.

Indeed, considerations of diversity and typicality often go together. Thus, in a study of globalization and social welfare systems, Duane Swank (2002) first identifies three distinctive groups of welfare states: “universalistic” (social democratic), “corporatist conservative,” and “liberal.” Next, he looks within each group to find the most‐typical cases. He decides that the Nordic countries are more typical of the universalistic model than the Netherlands since the latter has “some characteristics of the occupationally based program structure and a political context of Christian Democratic‐led governments typical of the corporatist conservative nations” ( Swank 2002 , 11; see also Esping‐Andersen 1990 ). Thus, the Nordic countries are chosen as representative cases within the universalistic case type, and are accompanied in the case‐study portion of his analysis by other cases chosen to represent the other welfare state types (corporatist conservative and liberal).

Evidently, when a sample encompasses a full range of variation on relevant parameters one is likely to enhance the representativeness of that sample (relative to some population). This is a distinct advantage. Of course, the inclusion of a full range of variation may distort the actual distribution of cases across this spectrum. If there are more “high” cases than “low” cases in a population and the researcher chooses only one high case and one low case, the resulting sample of two is not perfectly representative. Even so, the diverse‐case method probably has stronger claims to representativeness than any other small‐ N sample (including the standalone typical case). The selection of diverse cases has the additional advantage of introducing variation on the key variables of interest. A set of diverse cases is, by definition, a set of cases that encompasses a range of high and low values on relevant dimensions. There is, therefore, much to recommend this method of case selection. I suspect that these advantages are commonly understood and are applied on an intuitive level by case‐study researchers. However, the lack of a recognizable name—and an explicit methodological defense—has made it difficult for case‐study researchers to utilize this method of case selection, and to do so in an explicit and self‐conscious fashion. Neologism has its uses.

3 Extreme Case

The extreme‐case method selects a case because of its extreme value on an independent ( X 1 ) or dependent ( Y ) variable of interest. Thus, studies of domestic violence may choose to focus on extreme instances of abuse ( Browne 1987 ). Studies of altruism may focus on those rare individuals who risked their lives to help others (e.g. Holocaust resisters) ( Monroe 1996 ). Studies of ethnic politics may focus on the most heterogeneous societies (e.g. Papua New Guinea) in order to better understand the role of ethnicity in a democratic setting ( Reilly 2000–1 ). Studies of industrial policy often focus on the most successful countries (i.e. the NICS) ( Deyo 1987 ). And so forth. 5

Often an extreme case corresponds to a case that is considered to be prototypical or paradigmatic of some phenomena of interest. This is because concepts are often defined by their extremes, i.e. their ideal types. Italian Fascism defines the concept of Fascism, in part, because it offered the most extreme example of that phenomenon. However, the methodological value of this case, and others like it, derives from its extremity (along some dimension of interest), not its theoretical status or its status in the literature on a subject.

The notion of “extreme” may now be defined more precisely. An extreme value is an observation that lies far away from the mean of a given distribution. This may be measured (if there are sufficient observations) by a case's “Z score”—the number of standard deviations between a case and the mean value for that sample. Extreme cases have high Z scores, and for this reason may serve as useful subjects for intensive analysis.

For a continuous variable, the distance from the mean may be in either direction (positive or negative). For a dichotomous variable (present/absent), extremeness may be interpreted as unusual . If most cases are positive along a given dimension, then a negative case constitutes an extreme case. If most cases are negative, then a positive case constitutes an extreme case. It should be clear that researchers are not simply concerned with cases where something “happened,” but also with cases where something did not. It is the rareness of the value that makes a case valuable, in this context, not its positive or negative value. 6 Thus, if one is studying state capacity, a case of state failure is probably more informative than a case of state endurance simply because the former is more unusual. Similarly, if one is interested in incest taboos a culture where the incest taboo is absent or weak is probably more useful than a culture where it is present or strong. Fascism is more important than nonfascism. And so forth. There is a good reason, therefore, why case studies of revolution tend to focus on “revolutionary” cases. Theda Skocpol (1979) had much more to learn from France than from Austro‐Hungary since France was more unusual than Austro‐Hungary within the population of nation states that Skocpol was concerned to explain. The reason is quite simple: There are fewer revolutionary cases than nonrevolutionary cases; thus, the variation that we explore as a clue to causal relationships is encapsulated in these cases, against a background of nonrevolutionary cases.

Note that the extreme‐case method of case selection appears to violate the social science folk wisdom warning us not to “select on the dependent variable.” 7 Selecting cases on the dependent variable is indeed problematic if a number of cases are chosen, all of which lie on one end of a variable's spectrum (they are all positive or negative), and if the researcher then subjects this sample to cross‐case analysis as if it were representative of a population. 8 Results for this sort of analysis would almost assuredly be biased. Moreover, there will be little variation to explain since the values of each case are explicitly constrained.

However, this is not the proper employment of the extreme‐case method. (It is more appropriately labeled an extreme‐ sample method.) The extreme‐case method actually refers back to a larger sample of cases that lie in the background of the analysis and provide a full range of variation as well as a more representative picture of the population. It is a self‐conscious attempt to maximize variance on the dimension of interest, not to minimize it. If this population of cases is well understood— either through the author's own cross‐case analysis, through the work of others, or through common sense—then a researcher may justify the selection of a single case exemplifying an extreme value for within‐case analysis. If not, the researcher may be well advised to follow a diverse‐case method, as discussed above.

By way of conclusion, let us return to the problem of representativeness. It will be seen that an extreme case may be typical or deviant. There is simply no way to tell because the researcher has not yet specified an X 1 / Y causal proposition. Once such a causal proposition has been specified one may then ask whether the case in question is similar to some population of cases in all respects that might affect the X 1 / Y relationship of interest (i.e. unit homogeneous). It is at this point that it becomes possible to say, within the context of a cross‐case statistical model, whether a case lies near to, or far from, the regression line. However, this sort of analysis means that the researcher is no longer pursuing an extreme‐case method. The extreme‐case method is purely exploratory—a way of probing possible causes of Y , or possible effects of X , in an open‐ended fashion. If the researcher has some notion of what additional factors might affect the outcome of interest, or of what relationship the causal factor of interest might have with Y , then she ought to pursue one of the other methods explored in this chapter. This also implies that an extreme‐case method may transform into a different kind of approach as a study evolves; that is, as a more specific hypothesis comes to light. Useful extreme cases at the outset of a study may prove less useful at a later stage of analysis.

4 Deviant Case

The deviant‐case method selects that case(s) which, by reference to some general understanding of a topic (either a specific theory or common sense), demonstrates a surprising value. It is thus the contrary of the typical case. Barbara Geddes (2003) notes the importance of deviant cases in medical science, where researchers are habitually focused on that which is “pathological” (according to standard theory and practice). The New England Journal of Medicine , one of the premier journals of the field, carries a regular feature entitled Case Records of the Massachusetts General Hospital. These articles bear titles like the following: “An 80‐Year‐Old Woman with Sudden Unilateral Blindness” or “A 76‐Year‐Old Man with Fever, Dyspnea, Pulmonary Infiltrates, Pleural Effusions, and Confusion.” 9 Another interesting example drawn from the field of medicine concerns the extensive study now devoted to a small number of persons who seem resistant to the AIDS virus ( Buchbinder and Vittinghoff 1999 ; Haynes, Pantaleo, and Fauci 1996 ). Why are they resistant? What is different about these people? What can we learn about AIDS in other patients by observing people who have built‐in resistance to this disease?

Likewise, in psychology and sociology case studies may be comprised of deviant (in the social sense) persons or groups. In economics, case studies may consist of countries or businesses that overperform (e.g. Botswana; Microsoft) or underperform (e.g. Britain through most of the twentieth century; Sears in recent decades) relative to some set of expectations. In political science, case studies may focus on countries where the welfare state is more developed (e.g. Sweden) or less developed (e.g. the United States) than one would expect, given a set of general expectations about welfare state development. The deviant case is closely linked to the investigation of theoretical anomalies. Indeed, to say deviant is to imply “anomalous.” 10

Note that while extreme cases are judged relative to the mean of a single distribution (the distribution of values along a single variable), deviant cases are judged relative to some general model of causal relations. The deviant‐case method selects cases which, by reference to some (presumably) general relationship, demonstrate a surprising value. They are “deviant” in that they are poorly explained by the multivariate model. The important point is that deviant‐ness can only be assessed relative to the general (quantitative or qualitative) model. This means that the relative deviant‐ness of a case is likely to change whenever the general model is altered. For example, the United States is a deviant welfare state when this outcome is gauged relative to societal wealth. But it is less deviant—and perhaps not deviant at all—when certain additional (political and societal) factors are included in the model, as discussed in the epilogue. Deviance is model dependent. Thus, when discussing the concept of the deviant case it is helpful to ask the following question: Relative to what general model (or set of background factors) is Case A deviant?

Conceptually, we have said that the deviant case is the logical contrary of the typical case. This translates into a directly contrasting statistical measurement. While the typical case is one with a low residual (in some general model of causal relations), a deviant case is one with a high residual. This means, following our previous discussion, that the deviant case is likely to be an un representative case, and in this respect appears to violate the supposition that case‐study samples should seek to reproduce features of a larger population.

However, it must be borne in mind that the primary purpose of a deviant‐case analysis is to probe for new—but as yet unspecified—explanations. (If the purpose is to disprove an extant theory I shall refer to the study as crucial‐case, as discussed below.) The researcher hopes that causal processes identified within the deviant case will illustrate some causal factor that is applicable to other (more or less deviant) cases. This means that a deviant‐case study usually culminates in a general proposition, one that may be applied to other cases in the population. Once this general proposition has been introduced into the overall model, the expectation is that the chosen case will no longer be an outlier. Indeed, the hope is that it will now be typical , as judged by its small residual in the adjusted model. (The exception would be a circumstance in which a case's outcome is deemed to be “accidental,” and therefore inexplicable by any general model.)

This feature of the deviant‐case study should help to resolve questions about its representativeness. Even if it is not possible to measure the new causal factor (and thus to introduce it into a large‐ N cross‐case model), it may still be plausible to assert (based on general knowledge of the phenomenon) that the chosen case is representative of a broader population.

5 Influential Case

Sometimes, the choice of a case is motivated solely by the need to verify the assumptions behind a general model of causal relations. Here, the analyst attempts to provide a rationale for disregarding a problematic case or a set of problematic cases. That is to say, she attempts to show why apparent deviations from the norm are not really deviant, or do not challenge the core of the theory, once the circumstances of the special case or cases are fully understood. A cross‐case analysis may, after all, be marred by several classes of problems including measurement error, specification error, errors in establishing proper boundaries for the inference (the scope of the argument), and stochastic error (fluctuations in the phenomenon under study that are treated as random, given available theoretical resources). If poorly fitting cases can be explained away by reference to these kinds of problems, then the theory of interest is that much stronger. This sort of deviant‐case analysis answers the question, “What about Case A (or cases of type A)? How does that, seemingly disconfirming, case fit the model?”

Because its underlying purpose is different from the usual deviant‐case study, I offer a new term for this method. The influential case is a case that casts doubt upon a theory, and for that reason warrants close inspection. This investigation may reveal, after all, that the theory is validated—perhaps in some slightly altered form. In this guise, the influential case is the “case that proves the rule.” In other instances, the influential‐case analysis may contribute to disconfirming, or reconceptualizing, a theory. The key point is that the value of the case is judged relative to some extant cross‐case model.

A simple version of influential‐case analysis involves the confirmation of a key case's score on some critical dimension. This is essentially a question of measurement. Sometimes cases are poorly explained simply because they are poorly understood. A close examination of a particular context may reveal that an apparently falsifying case has been miscoded. If so, the initial challenge presented by that case to some general theory has been obviated.

However, the more usual employment of the influential‐case method culminates in a substantive reinterpretation of the case—perhaps even of the general model. It is not just a question of measurement. Consider Thomas Ertman's (1997) study of state building in Western Europe, as summarized by Gerardo Munck. This study argues

that the interaction of a) the type of local government during the first period of statebuilding, with b) the timing of increases in geopolitical competition, strongly influences the kind of regime and state that emerge. [Ertman] tests this hypothesis against the historical experience of Europe and finds that most countries fit his predictions. Denmark, however, is a major exception. In Denmark, sustained geopolitical competition began relatively late and local government at the beginning of the statebuilding period was generally participatory, which should have led the country to develop “patrimonial constitutionalism.” But in fact, it developed “bureaucratic absolutism.” Ertman carefully explores the process through which Denmark came to have a bureaucratic absolutist state and finds that Denmark had the early marks of a patrimonial constitutionalist state. However, the country was pushed off this developmental path by the influence of German knights, who entered Denmark and brought with them German institutions of local government. Ertman then traces the causal process through which these imported institutions pushed Denmark to develop bureaucratic absolutism, concluding that this development was caused by a factor well outside his explanatory framework. ( Munck 2004 , 118)

Ertman's overall framework is confirmed insofar as he has been able to show, by an in‐depth discussion of Denmark, that the causal processes stipulated by the general theory hold even in this apparently disconfirming case. Denmark is still deviant, but it is so because of “contingent historical circumstances” that are exogenous to the theory ( Ertman 1997 , 316).

Evidently, the influential‐case analysis is similar to the deviant‐case analysis. Both focus on outliers. However, as we shall see, they focus on different kinds of outliers. Moreover, the animating goals of these two research designs are quite different. The influential‐case study begins with the aim of confirming a general model, while the deviant‐case study has the aim of generating a new hypothesis that modifies an existing general model. The confusion stems from the fact that the same case study may fulfill both objectives—qualifying a general model and, at the same time, confirming its core hypothesis.

Thus, in their study of Roberto Michels's “iron law of oligarchy,” Lipset, Trow, and Coleman (1956) choose to focus on an organization—the International Typographical Union—that appears to violate the central presupposition. The ITU, as noted by one of the authors, has “a long‐term two‐party system with free elections and frequent turnover in office” and is thus anything but oligarchic ( Lipset 1959 , 70). As such, it calls into question Michels's grand generalization about organizational behavior. The authors explain this curious result by the extraordinarily high level of education among the members of this union. Michels's law is shown to be true for most organizations, but not all. It is true, with qualifications. Note that the respecification of the original model (in effect, Lipset, Trow, and Coleman introduce a new control variable or boundary condition) involves the exploration of a new hypothesis. In this instance, therefore, the use of an influential case to confirm an existing theory is quite similar to the use of a deviant case to explore a new theory.

In a quantitative idiom, influential cases are those that, if counterfactually assigned a different value on the dependent variable, would most substantially change the resulting estimates. They may or may not be outliers (high‐residual cases). Two quantitative measures of influence are commonly applied in regression diagnostics ( Belsey, Kuh, and Welsch 2004 ). The first, often referred to as the leverage of a case, derives from what is called the hat matrix . Based solely on each case's scores on the independent variables, the hat matrix tells us how much a change in (or a measurement error on) the dependent variable for that case would affect the overall regression line. The second is Cook's distance , a measure of the extent to which the estimates of all the parameters would change if a given case were omitted from the analysis. Cases with a large leverage or Cook's distance contribute quite a lot to the inferences drawn from a cross‐case analysis. In this sense, such cases are vital for maintaining analytic conclusions. Discovering a significant measurement error on the dependent variable or an important omitted variable for such a case may dramatically revise estimates of the overall relationships. Hence, it may be quite sensible to select influential cases for in‐depth study.

Note that the use of an influential‐case strategy of case selection is limited to instances in which a researcher has reason to be concerned that her results are being driven by one or a few cases. This is most likely to be true in small to moderate‐sized samples. Where N is very large—greater than 1,000, let us say—it is extremely unlikely that a small set of cases (much less an individual case) will play an “influential” role. Of course, there may be influential sets of cases, e.g. countries within a particular continent or cultural region, or persons of Irish extraction. Sets of influential observations are often problematic in a time‐series cross‐section data‐set where each unit (e.g. country) contains multiple observations (through time), and hence may have a strong influence on aggregate results. Still, the general rule is: the larger the sample, the less important individual cases are likely to be and, hence, the less likely a researcher is to use an influential‐case approach to case selection.

6 Crucial Case

Of all the extant methods of case selection perhaps the most storied—and certainly the most controversial—is the crucial‐case method, introduced to the social science world several decades ago by Harry Eckstein. In his seminal essay, Eckstein (1975 , 118) describes the crucial case as one “that must closely fit a theory if one is to have confidence in the theory's validity, or, conversely, must not fit equally well any rule contrary to that proposed.” A case is crucial in a somewhat weaker—but much more common—sense when it is most, or least, likely to fulfill a theoretical prediction. A “most‐likely” case is one that, on all dimensions except the dimension of theoretical interest, is predicted to achieve a certain outcome, and yet does not. It is therefore used to disconfirm a theory. A “least‐likely” case is one that, on all dimensions except the dimension of theoretical interest, is predicted not to achieve a certain outcome, and yet does so. It is therefore used to confirm a theory. In all formulations, the crucial‐case offers a most‐difficult test for an argument, and hence provides what is perhaps the strongest sort of evidence possible in a nonexperimental, single‐case setting.

Since the publication of Eckstein's influential essay, the crucial‐case approach has been claimed in a multitude of studies across several social science disciplines and has come to be recognized as a staple of the case‐study method. 11 Yet the idea of any single case playing a crucial (or “critical”) role is not widely accepted among most methodologists (e.g. Sekhon 2004 ). (Even its progenitor seems to have had doubts.)

Let us begin with the confirmatory (a.k.a. least‐likely) crucial case. The implicit logic of this research design may be summarized as follows. Given a set of facts, we are asked to contemplate the probability that a given theory is true. While the facts matter, to be sure, the effectiveness of this sort of research also rests upon the formal properties of the theory in question. Specifically, the degree to which a theory is amenable to confirmation is contingent upon how many predictions can be derived from the theory and on how “risky” each individual prediction is. In Popper's (1963 , 36) words, “Confirmations should count only if they are the result of risky predictions ; that is to say, if, unenlightened by the theory in question, we should have expected an event which was incompatible with the theory—and event which would have refuted the theory. Every ‘good’ scientific theory is a prohibition; it forbids certain things to happen. The more a theory forbids, the better it is” (see also Popper 1934/1968 ). A risky prediction is therefore one that is highly precise and determinate, and therefore unlikely to be achieved by the product of other causal factors (external to the theory of interest) or through stochastic processes. A theory produces many such predictions if it is fully elaborated, issuing predictions not only on the central outcome of interest but also on specific causal mechanisms, and if it is broad in purview. (The notion of riskiness may also be conceptualized within the Popperian lexicon as degrees of falsifiability .)

These points can also be articulated in Bayesian terms. Colin Howson and Peter Urbach explain: “The degree to which h [a hypothesis] is confirmed by e [a set of evidence] depends … on the extent to which P(eČh) exceeds P (e) , that is, on how much more probable e is relative to the hypothesis and background assumptions than it is relative just to background assumptions.” Again, “confirmation is correlated with how much more probable the evidence is if the hypothesis is true than if it is false” ( Howson and Urlbach 1989 , 86). Thus, the stranger the prediction offered by a theory—relative to what we would normally expect—the greater the degree of confirmation that will be afforded by the evidence. As an intuitive example, Howson and Urbach (1989 , 86) offer the following:

If a soothsayer predicts that you will meet a dark stranger sometime and you do in fact, your faith in his powers of precognition would not be much enhanced: you would probably continue to think his predictions were just the result of guesswork. However, if the prediction also gave the correct number of hairs on the head of that stranger, your previous scepticism would no doubt be severely shaken.

While these Popperian/Bayesian notions 12 are relevant to all empirical research designs, they are especially relevant to case‐study research designs, for in these settings a single case (or, at most, a small number of cases) is required to bear a heavy burden of proof. It should be no surprise, therefore, that Popper's idea of “riskiness” was to be appropriated by case‐study researchers like Harry Eckstein to validate the enterprise of single‐case analysis. (Although Eckstein does not cite Popper the intellectual lineage is clear.) Riskiness, here, is analogous to what is usually referred to as a “most‐ difficult” research design, which in a case‐study research design would be understood as a “least‐likely” case. Note also that the distinction between a “must‐fit” case and a least‐likely case—that, in the event, actually does fit the terms of a theory—is a matter of degree. Cases are more or less crucial for confirming theories. The point is that, in some circumstances, a paucity of empirical evidence may be compensated by the riskiness of the theory.

The crucial‐case research design is, perforce, a highly deductive enterprise; much depends on the quality of the theory under investigation. It follows that the theories most amenable to crucial‐case analysis are those which are lawlike in their precision, degree of elaboration, consistency, and scope. The more a theory attains the status of a causal law, the easier it will be to confirm, or to disconfirm, with a single case. Indeed, risky predictions are common in natural science fields such as physics, which in turn served as the template for the deductive‐nomological (“covering‐law”) model of science that influenced Eckstein and others in the postwar decades (e.g. Hempel 1942 ).

A frequently cited example is the first important empirical demonstration of the theory of relativity, which took the form of a single‐event prediction on the occasion of the May 29, 1919, solar eclipse ( Eckstein 1975 ; Popper 1963 ). Stephen Van Evera (1997 , 66–7) describes the impact of this prediction on the validation of Einstein's theory.

Einstein's theory predicted that gravity would bend the path of light toward a gravity source by a specific amount. Hence it predicted that during a solar eclipse stars near the sun would appear displaced—stars actually behind the sun would appear next to it, and stars lying next to the sun would appear farther from it—and it predicted the amount of apparent displacement. No other theory made these predictions. The passage of this one single‐case‐study test brought the theory wide acceptance because the tested predictions were unique—there was no plausible competing explanation for the predicted result—hence the passed test was very strong.

The strength of this test is the extraordinary fit between the theory and a set of facts found in a single case, and the corresponding lack of fit between all other theories and this set of facts. Einstein offered an explanation of a particular set of anomalous findings that no other existing theory could make sense of. Of course, one must assume that there was no—or limited—measurement error. And one must assume that the phenomenon of interest is largely invariant; light does not bend differently at different times and places (except in ways that can be understood through the theory of relativity). And one must assume, finally, that the theory itself makes sense on other grounds (other than the case of special interest); it is a plausible general theory. If one is willing to accept these a priori assumptions, then the 1919 “case study” provides a very strong confirmation of the theory. It is difficult to imagine a stronger proof of the theory from within an observational (nonexperimental) setting.

In social science settings, by contrast, one does not commonly find single‐case studies offering knockout evidence for a theory. This is, in my view, largely a product of the looseness (the underspecification) of most social science theories. George and Bennett point out that while the thesis of the democratic peace is as close to a “law” as social science has yet seen, it cannot be confirmed (or refuted) by looking at specific causal mechanisms because the causal pathways mandated by the theory are multiple and diverse. Under the circumstances, no single‐case test can offer strong confirmation of the theory ( George and Bennett 2005 , 209).

However, if one adopts a softer version of the crucial‐case method—the least‐likely (most difficult) case—then possibilities abound. Indeed, I suspect that, implicitly , most case‐study work that makes a positive argument focusing on a single case (without a corresponding cross‐case analysis) relies largely on the logic of the least‐ likely case. Rarely is this logic made explicit, except perhaps in a passing phrase or two. Yet the deductive logic of the “risky” prediction is central to the case‐study enterprise. Whether a case study is convincing or not often rests on the reader's evaluation of how strong the evidence for an argument might be, and this in turn—wherever cross‐ case evidence is limited and no manipulated treatment can be devised—rests upon an estimation of the degree of “fit” between a theory and the evidence at hand, as discussed.

Lily Tsai's (2007) investigation of governance at the village level in China employs several in‐depth case studies of villages which are chosen (in part) because of their least‐likely status relative to the theory of interest. Tsai's hypothesis is that villages with greater social solidarity (based on preexisting religious or familial networks) will develop a higher level of social trust and mutual obligation and, as a result, will experience better governance. Crucial cases, therefore, are villages that evidence a high level of social solidarity but which, along other dimensions, would be judged least likely to develop good governance, e.g. they are poor, isolated, and lack democratic institutions or accountability mechanisms from above. “Li Settlement,” in Fujian province, is such a case. The fact that this impoverished village nonetheless boasts an impressive set of infrastructural accomplishments such as paved roads with drainage ditches (a rarity in rural China) suggests that something rather unusual is going on here. Because her case is carefully chosen to eliminate rival explanations, Tsai's conclusions about the special role of social solidarity are difficult to gainsay. How else is one to explain this otherwise anomalous result? This is the strength of the least‐likely case, where all other plausible causal factors for an outcome have been minimized. 13

Jack Levy (2002 , 144) refers to this, evocatively, as a “Sinatra inference:” if it can make it here, it can make it anywhere (see also Khong 1992 , 49; Sagan 1995 , 49; Shafer 1988 , 14–6). Thus, if social solidarity has the hypothesized effect in Li Settlement it should have the same effect in more propitious settings (e.g. where there is greater economic surplus). The same implicit logic informs many case‐study analyses where the intent of the study is to confirm a hypothesis on the basis of a single case.

Another sort of crucial case is employed for the purpose of dis confirming a causal hypothesis. A central Popperian insight is that it is easier to disconfirm an inference than to confirm that same inference. (Indeed, Popper doubted that any inference could be fully confirmed, and for this reason preferred the term “corroborate.”) This is particularly true of case‐study research designs, where evidence is limited to one or several cases. The key proviso is that the theory under investigation must take a consistent (a.k.a. invariant, deterministic) form, even if its predictions are not terrifically precise, well elaborated, or broad.

As it happens, there are a fair number of invariant propositions floating around the social science disciplines (Goertz and Levy forthcoming; Goertz and Starr 2003 ). It used to be argued, for example, that political stability would occur only in countries that are relatively homogeneous, or where existing heterogeneities are mitigated by cross‐cutting cleavages ( Almond 1956 ; Bentley 1908/1967 ; Lipset 1960/1963 ; Truman 1951 ). Arend Lijphart's (1968) study of the Netherlands, a peaceful country with reinforcing social cleavages, is commonly viewed as refuting this theory on the basis of a single in‐depth case analysis. 14

Granted, it may be questioned whether presumed invariant theories are really invariant; perhaps they are better understood as probabilistic. Perhaps, that is, the theory of cross‐cutting cleavages is still true, probabilistically, despite the apparent Dutch exception. Or perhaps the theory is still true, deterministically, within a subset of cases that does not include the Netherlands. (This sort of claim seems unlikely in this particular instance, but it is quite plausible in many others.) Or perhaps the theory is in need of reframing; it is true, deterministically, but applies only to cross‐ cutting ethnic/racial cleavages, not to cleavages that are primarily religious. One can quibble over what it means to “disconfirm” a theory. The point is that the crucial case has, in all these circumstances, provided important updating of a theoretical prior.

Heretofore, I have treated causal factors as dichotomous. Countries have either reinforcing or cross‐cutting cleavages and they have regimes that are either peaceful or conflictual. Evidently, these sorts of parameters are often matters of degree. In this reading of the theory, cases are more or less crucial. Accordingly, the most useful—i.e. most crucial—case for Lijphart's purpose is one that has the most segregated social groups and the most peaceful and democratic track record. In these respects, the Netherlands was a very good choice. Indeed, the degree of disconfirmation offered by this case study is probably greater than the degree of disconfirmation that might have been provided by other cases such as India or Papua New Guinea—countries where social peace has not always been secure. The point is that where variables are continuous rather than dichotomous it is possible to evaluate potential cases in terms of their degree of crucialness .

Note that the crucial‐case method of case‐selection, whether employed in a confirmatory or disconfirmatory mode, cannot be employed in a large‐ N context. This is because an explicit cross‐case model would render the crucial‐case study redundant. Once one identifies the relevant parameters and the scores of all cases on those parameters, one has in effect constructed a cross‐case model that confirms or disconfirms the theory in question. The case study is thenceforth irrelevant, at least as a means of decisive confirmation or disconfirmation. 15 It remains highly relevant as a means of exploring causal mechanisms, of course. Yet, because this objective is quite different from that which is usually associated with the term, I enlist a new term for this technique.

7 Pathway Case

One of the most important functions of case‐study research is the elucidation of causal mechanisms. But which sort of case is most useful for this purpose? Although all case studies presumably shed light on causal mechanisms, not all cases are equally transparent. In situations where a causal hypothesis is clear and has already been confirmed by cross‐case analysis, researchers are well advised to focus on a case where the causal effect of X 1 on Y can be isolated from other potentially confounding factors ( X 2 ). I shall call this a pathway case to indicate its uniquely penetrating insight into causal mechanisms. In contrast to the crucial case, this sort of method is practicable only in circumstances where cross‐case covariational patterns are well studied and where the mechanism linking X 1 and Y remains dim. Because the pathway case builds on prior cross‐case analysis, the problem of case selection must be situated within that sample. There is no standalone pathway case.

The logic of the pathway case is clearest in situations of causal sufficiency—where a causal factor of interest, X 1 , is sufficient by itself (though perhaps not necessary) to account for Y 's value (0 or 1). The other causes of Y , about which we need make no assumptions, are designated as a vector, X 2 .

Note that wherever various causal factors are substitutable for one another, each factor is conceptualized (individually) as sufficient ( Braumoeller 2003 ). Thus, situations of causal equifinality presume causal sufficiency on the part of each factor or set of conjoint factors. An example is provided by the literature on democratization, which stipulates three main avenues of regime change: leadership‐initiated reform, a controlled opening to opposition, or the collapse of an authoritarian regime ( Colomer 1991 ). The case‐study format constrains us to analyze one at a time, so let us limit our scope to the first one—leadership‐initiated reform. So considered, a causal‐pathway case would be one with the following features: (a) democratization, (b) leadership‐initiated reform, (c) no controlled opening to the opposition, (d) no collapse of the previous authoritarian regime, and (e) no other extraneous factors that might affect the process of democratization. In a case of this type, the causal mechanisms by which leadership‐initiated reform may lead to democratization will be easiest to study. Note that it is not necessary to assume that leadership‐initiated reform always leads to democratization; it may or may not be a deterministic cause. But it is necessary to assume that leadership‐initiated reform can sometimes lead to democratization on its own (given certain background features).

Now let us move from these examples to a general‐purpose model. For heuristic purposes, let us presume that all variables in that model are dichotomous (coded as 0 or 1) and that the model is complete (all causes of Y are included). All causal relationships will be coded so as to be positive: X 1 and Y covary as do X 2 and Y . This allows us to visualize a range of possible combinations at a glance.

Recall that the pathway case is always focused, by definition, on a single causal factor, denoted X 1 . (The researcher's focus may shift to other causal factors, but may only focus on one causal factor at a time.) In this scenario, and regardless of how many additional causes of Y there might be (denoted X 2 , a vector of controls), there are only eight relevant case types, as illustrated in Table 28.2 . Identifying these case types is a relatively simple matter, and can be accomplished in a small‐ N sample by the construction of a truth‐table (modeled after Table 28.2 ) or in a large‐ N sample by the use of cross‐tabs.

Notes : X 1 = the variable of theoretical interest. X 2 = a vector of controls (a score of 0 indicates that all control variables have a score of 0, while a score of 1 indicates that all control variables have a score of 1). Y = the outcome of interest. A–H = case types (the N for each case type is indeterminate). G, H = possible pathway cases. Sample size = indeterminate.

Assumptions : (a) all variables can be coded dichotomously (a binary coding of the concept is valid); (b) all independent variables are positively correlated with Y in the general case; ( c ) X 1 is (at least sometimes) a sufficient cause of Y .

Note that the total number of combinations of values depends on the number of control variables, which we have represented with a single vector, X 2 . If this vector consists of a single variable then there are only eight case types. If this vector consists of two variables ( X 2a , X 2b ) then the total number of possible combinations increases from eight (2 3 ) to sixteen (2 4 ). And so forth. However, none of these combinations is relevant for present purposes except those where X 2a and X 2b have the same value (0 or 1). “Mixed” cases are not causal pathway cases, for reasons that should become clear.

The pathway case, following the logic of the crucial case, is one where the causal factor of interest, X 1 , correctly predicts Y while all other possible causes of Y (represented by the vector, X 2 ) make “wrong” predictions. If X 1 is—at least in some circumstances—a sufficient cause of Y , then it is these sorts of cases that should be most useful for tracing causal mechanisms. There are only two such cases in Ta b l e 28.2—G and H. In all other cases, the mechanism running from X 1 to Y would be difficult to discern either because X 1 and Y are not correlated in the usual way (constituting an unusual case, in the terms of our hypothesis) or because other confounding factors ( X 2 ) intrude. In case A, for example, the positive value on Y could be a product of X 1 or X 2 . An in‐depth examination of this case is not likely to be very revealing.

Keep in mind that because the researcher already knows from her cross‐case examination what the general causal relationships are, she knows (prior to the case‐ study investigation) what constitutes a correct or incorrect prediction. In the crucial‐ case method, by contrast, these expectations are deductive rather than empirical. This is what differentiates the two methods. And this is why the causal pathway case is useful principally for elucidating causal mechanisms rather than verifying or falsifying general propositions (which are already more or less apparent from the cross‐case evidence). Of course, we must leave open the possibility that the investigation of causal mechanisms would invalidate a general claim, if that claim is utterly contingent upon a specific set of causal mechanisms and the case study shows that no such mechanisms are present. However, this is rather unlikely in most social science settings. Usually, the result of such a finding will be a reformulation of the causal processes by which X 1 causes Y —or, alternatively, a realization that the case under investigation is aberrant (atypical of the general population of cases).

Sometimes, the research question is framed as a unidirectional cause: one is interested in why 0 becomes 1 (or vice versa) but not in why 1 becomes 0. In our previous example, we asked why democracies fail, not why countries become democratic or authoritarian. So framed, there can be only one type of causal‐pathway case. (Whether regime failure is coded as 0 or 1 is a matter of taste.) Where researchers are interested in bidirectional causality—a movement from 0 to 1 as well as from 1 to 0—there are two possible causal‐pathway cases, G and H. In practice, however, one of these case types is almost always more useful than the other. Thus, it seems reasonable to employ the term “pathway case” in the singular. In order to determine which of these two case types will be more useful for intensive analysis the researcher should look to see whether each case type exhibits desirable features such as: (a) a rare (unusual) value on X 1 or Y (designated “extreme” in our previous discussion), (b) observable temporal variation in X 1 , ( c ) an X 1 / Y relationship that is easier to study (it has more visible features; it is more transparent), or (d) a lower residual (thus indicating a more typical case, within the terms of the general model). Usually, the choice between G and H is intuitively obvious.

Now, let us consider a scenario in which all (or most) variables of concern to the model are continuous, rather than dichotomous. Here, the job of case selection is considerably more complex, for causal “sufficiency” (in the usual sense) cannot be invoked. It is no longer plausible to assume that a given cause can be entirely partitioned, i.e. rival factors eliminated. However, the search for a pathway case may still be viable. What we are looking for in this scenario is a case that satisfies two criteria: (1) it is not an outlier (or at least not an extreme outlier) in the general model and (2) its score on the outcome ( Y ) is strongly influenced by the theoretical variable of interest ( X 1 ), taking all other factors into account ( X 2 ). In this sort of case it should be easiest to “see” the causal mechanisms that lie between X 1 and Y .

Achieving the second desiderata requires a bit of manipulation. In order to determine which (nonoutlier) cases are most strongly affected by X 1 , given all the other parameters in the model, one must compare the size of the residuals for each case in a reduced form model, Y = Constant + X 2 + Res reduced , with the size of the residuals for each case in a full model, Y = Constant + X 2 + X 1 + Res full . The pathway case is that case, or set of cases, which shows the greatest difference between the residual for the reduced‐form model and the full model (ΔResidual). Thus,

Note that the residual for a case must be smaller in the full model than in the reduced‐ form model; otherwise, the addition of the variable of interest ( X 1 ) pulls the case away from the regression line. We want to find a case where the addition of X 1 pushes the case towards the regression line, i.e. it helps to “explain” that case.

As an example, let us suppose that we are interested in exploring the effect of mineral wealth on the prospects for democracy in a society. According to a good deal of work on this subject, countries with a bounty of natural resources—particularly oil—are less likely to democratize (or once having undergone a democratic transition, are more likely to revert to authoritarian rule) ( Barro 1999 ; Humphreys 2005 ; Ross 2001 ). The cross‐country evidence is robust. Yet as is often the case, the causal mechanisms remain rather obscure. In order to better understand this phenomenon it may be worthwhile to exploit the findings of cross‐country regression models in order to identify a country whose regime type (i.e. its democracy “score” on some general index) is strongly affected by its natural‐research wealth, all other things held constant. An analysis of this sort identifies two countries— the United Arab Emirates and Kuwait—with high Δ Residual values and modest residuals in the full model (signifying that these cases are not outliers). Researchers seeking to explore the effect of oil wealth on regime type might do well to focus on these two cases since their patterns of democracy cannot be well explained by other factors—e.g. economic development, religion, European influence, or ethnic fractionalization. The presence of oil wealth in these countries would appear to have a strong independent effect on the prospects for democratization in these cases, an effect that is well modeled by general theory and by the available cross‐case evidence.

To reiterate, the logic of causal “elimination” is much more compelling where variables are dichotomous and where causal sufficiency can be assumed ( X 1 is sufficient by itself, at least in some circumstances, to cause Y ). Where variables are continuous, the strategy of the pathway case is more dubious, for potentially confounding causal factors ( X 2 ) cannot be neatly partitioned. Even so, we have indicated why the selection of a pathway case may be a logical approach to case‐study analysis in many circumstances.

The exceptions may be briefly noted. Sometimes, where all variables in a model are dichotomous, there are no pathway cases, i.e. no cases of type G or H (in Table 28.2 ). This is known as the “empty cell” problem, or a problem of severe causal multicollinearity. The universe of observational data does not always oblige us with cases that allow us to independently test a given hypothesis. Where variables are continuous, the analogous problem is that of a causal variable of interest ( X 1 ) that has only minimal effects on the outcome of interest. That is, its role in the general model is quite minor. In these situations, the only cases that are strongly affected by X 1 —if there are any at all—may be extreme outliers, and these sorts of cases are not properly regarded as providing confirmatory evidence for a proposition, for reasons that are abundantly clear by now.

Finally, it should be clarified that the identification of a causal pathway case does not obviate the utility of exploring other cases. One might, for example, want to compare both sorts of potential pathway cases—G and H—with each other. Many other combinations suggest themselves. However, this sort of multi‐case investigation moves beyond the logic of the causal‐pathway case.

8 Most‐similar Cases

The most‐similar method employs a minimum of two cases. 16 In its purest form, the chosen pair of cases is similar in all respects except the variable(s) of interest. If the study is exploratory (i.e. hypothesis generating), the researcher looks for cases that differ on the outcome of theoretical interest but are similar on various factors that might have contributed to that outcome, as illustrated in Table 28.3 (A) . This is a common form of case selection at the initial stage of research. Often, fruitful analysis begins with an apparent anomaly: two cases are apparently quite similar, and yet demonstrate surprisingly different outcomes. The hope is that intensive study of these cases will reveal one—or at most several—factors that differ across these cases. These differing factors ( X 1 ) are looked upon as putative causes. At this stage, the research may be described by the second diagram in Table 28.3 (B) . Sometimes, a researcher begins with a strong hypothesis, in which case her research design is confirmatory (hypothesis testing) from the get‐go. That is, she strives to identify cases that exhibit different outcomes, different scores on the factor of interest, and similar scores on all other possible causal factors, as illustrated in the second (hypothesis‐testing) diagram in Table 28.3 (B) .

The point is that the purpose of a most‐similar research design, and hence its basic setup, often changes as a researcher moves from an exploratory to a confirmatory mode of analysis. However, regardless of where one begins, the results, when published, look like a hypothesis‐testing research design. Question marks have been removed: (A) becomes (B) in Table 28.3 .

As an example, let us consider Leon Epstein's classic study of party cohesion, which focuses on two “most‐similar” countries, the United States and Canada. Canada has highly disciplined parties whose members vote together on the floor of the House of Commons while the United States has weak, undisciplined parties, whose members often defect on floor votes in Congress. In explaining these divergent outcomes, persistent over many years, Epstein first discusses possible causal factors that are held more or less constant across the two cases. Both the United States and Canada inherited English political cultures, both have large territories and heterogeneous populations, both are federal, and both have fairly loose party structures with strong regional bases and a weak center. These are the “control” variables. Where they differ is in one constitutional feature: Canada is parliamentary while the United States is presidential. And it is this institutional difference that Epstein identifies as the crucial (differentiating) cause. (For further examples of the most‐similar method see Brenner 1976 ; Hamilton 1977 ; Lipset 1968 ; Miguel 2004 ; Moulder 1977 ; Posner 2004 .)

X 1 = the variable of theoretical interest. X 2 = a vector of controls. Y = the outcome of interest.

Several caveats apply to any most‐similar analysis (in addition to the usual set of assumptions applying to all case‐study analysis). First, each causal factor is understood as having an independent and additive effect on the outcome; there are no “interaction” effects. Second, one must code cases dichotomously (high/low, present/absent). This is straightforward if the underlying variables are also dichotomous (e.g. federal/unitary). However, it is often the case that variables of concern in the model are continuous (e.g. party cohesion). In this setting, the researcher must “dichotomize” the scoring of cases so as to simplify the two‐case analysis. (Some flexibility is admissible on the vector of controls ( X 2 ) that are “held constant” across the cases. Nonidentity is tolerable if the deviation runs counter to the predicted hypothesis. For example, Epstein describes both the United States and Canada as having strong regional bases of power, a factor that is probably more significant in recent Canadian history than in recent American history. However, because regional bases of power should lead to weaker parties, rather than stronger parties, this element of nonidentity does not challenge Epstein's conclusions. Indeed, it sets up a most‐difficult research scenario, as discussed above.)

In one respect the requirements for case control are not so stringent. Specifically, it is not usually necessary to measure control variables (at least not with a high degree of precision) in order to control for them. If two countries can be assumed to have similar cultural heritages one needn't worry about constructing variables to measure that heritage. One can simply assert that, whatever they are, they are more or less constant across the two cases. This is similar to the technique employed in a randomized experiment, where the researcher typically does not attempt to measure all the factors that might affect the causal relationship of interest. She assumes, rather, that these unknown factors have been neutralized across the treatment and control groups by randomization or by the choice of a sample that is internally homogeneous.

The most useful statistical tool for identifying cases for in‐depth analysis in a most‐ similar setting is probably some variety of matching strategy—e.g. exact matching, approximate matching, or propensity‐score matching. 17 The product of this procedure is a set of matched cases that can be compared in whatever way the researcher deems appropriate. These are the “most‐similar” cases. Rosenbaum and Silber (2001 , 223) summarize:

Unlike model‐based adjustments, where [individuals] vanish and are replaced by the coefficients of a model, in matching, ostensibly comparable patterns are compared directly, one by one. Modern matching methods involve statistical modeling and combinatorial algorithms, but the end result is a collection of pairs or sets of people who look comparable, at least on average. In matching, people retain their integrity as people, so they can be examined and their stories can be told individually.

Matching, conclude the authors, “facilitates, rather than inhibits, thick description” ( Rosenbaum and Silber 2001 , 223).

In principle, the same matching techniques that have been used successfully in observational studies of medical treatments might also be adapted to the study of nation states, political parties, cities, or indeed any traditional paired cases in the social sciences. Indeed, the current popularity of matching among statisticians—relative, that is, to garden‐variety regression models—rests upon what qualitative researchers would recognize as a “case‐based” approach to causal analysis. If Rosenbaum and Silber are correct, it may be perfectly reasonable to appropriate this large‐ N method of analysis for case‐study purposes.

As with other methods of case selection, the most‐similar method is prone to problems of nonrepresentativeness. If employed in a qualitative fashion (without a systematic cross‐case selection strategy), potential biases in the chosen case must be addressed in a speculative way. If the researcher employs a matching technique of case selection within a large‐ N sample, the problem of potential bias can be addressed by assuring the choice of cases that are not extreme outliers, as judged by their residuals in the full model. Most‐similar cases should also be “typical” cases, though some scope for deviance around the regression line may be acceptable for purposes of finding a good fit among cases.

X 1 = the variable of theoretical interest. X 2a–d = a vector of controls. Y = the outcome of interest.

9 Most‐different Cases

A final case‐selection method is the reverse image of the previous method. Here, variation on independent variables is prized, while variation on the outcome is eschewed. Rather than looking for cases that are most‐similar, one looks for cases that are most‐ different . Specifically, the researcher tries to identify cases where just one independent variable ( X 1 ), as well as the dependent variable ( Y ), covary, while all other plausible factors ( X 2a–d ) show different values. 18

The simplest form of this two‐case comparison is illustrated in Table 28.4 . Cases A and B are deemed “most different,” though they are similar in two essential respects— the causal variable of interest and the outcome.

As an example, I follow Marc Howard's (2003) recent work, which explores the enduring impact of Communism on civil society. 19 Cross‐national surveys show a strong correlation between former Communist regimes and low social capital, controlling for a variety of possible confounders. It is a strong result. Howard wonders why this relationship is so strong and why it persists, and perhaps even strengthens, in countries that are no longer socialist or authoritarian. In order to answer this question, he focuses on two most‐different cases, Russia and East Germany. These two countries were quite different—in all ways other than their Communist experience— prior to the Soviet era, during the Soviet era (since East Germany received substantial subsidies from West Germany), and in the post‐Soviet era, as East Germany was absorbed into West Germany. Yet, they both score near the bottom of various cross‐ national indices intended to measure the prevalence of civic engagement in the current era. Thus, Howard's (2003 , 6–9) case selection procedure meets the requirements of the most‐different research design: Variance is found on all (or most) dimensions aside from the key factor of interest (Communism) and the outcome (civic engagement).

What leverage is brought to the analysis from this approach? Howard's case studies combine evidence drawn from mass surveys and from in‐depth interviews of small, stratified samples of Russians and East Germans. (This is a good illustration, incidentally, of how quantitative and qualitative evidence can be fruitfully combined in the intensive study of several cases.) The product of this analysis is the identification of three causal pathways that, Howard (2003 , 122) claims, help to explain the laggard status of civil society in post‐Communist polities: “the mistrust of communist organizations, the persistence of friendship networks, and the disappointment with post‐communism.” Simply put, Howard (2003 , 145) concludes, “a great number of citizens in Russia and Eastern Germany feel a strong and lingering sense of distrust of any kind of public organization, a general satisfaction with their own personal networks (accompanied by a sense of deteriorating relations within society overall), and disappointment in the developments of post‐communism.”

The strength of this most‐different case analysis is that the results obtained in East Germany and Russia should also apply in other post‐Communist polities (e.g. Lithuania, Poland, Bulgaria, Albania). By choosing a heterogeneous sample, Howard solves the problem of representativeness in his restricted sample. However, this sample is demonstrably not representative across the population of the inference, which is intended to cover all countries of the world.

More problematic is the lack of variation on key causal factors of interest— Communism and its putative causal pathways. For this reason, it is difficult to reach conclusions about the causal status of these factors on the basis of the most‐different analysis alone. It is possible, that is, that the three causal pathways identified by Howard also operate within polities that never experienced Communist rule.

Nor does it seem possible to conclusively eliminate rival hypotheses on the basis of this most‐different analysis. Indeed, this is not Howard's intention. He wishes merely to show that whatever influence on civil society might be attributed to economic, cultural, and other factors does not exhaust this subject.

My considered judgment is that the most‐different research design provides minimal leverage into the problem of why Communist systems appear to suppress civic engagement, years after their disappearance. Fortunately, this is not the only research design employed by Howard in his admirable study. Indeed, the author employs two other small‐ N cross‐case methods, as well as a large‐ N cross‐country statistical analysis. These methods do most of the analytic work. East Germany may be regarded as a causal pathway case (see above). It has all the attributes normally assumed to foster civic engagement (e.g. a growing economy, multiparty competition, civil liberties, a free press, close association with Western European culture and politics), but nonetheless shows little or no improvement on this dimension during the post‐ transition era ( Howard 2003 , 8). It is plausible to attribute this lack of change to its Communist past, as Howard does, in which case East Germany should be a fruitful case for the investigation of causal mechanisms. The contrast between East and West Germany provides a most‐similar analysis since the two polities share virtually everything except a Communist past. This variation is also deftly exploited by Howard.

I do not wish to dismiss the most‐different research method entirely. Surely, Howard's findings are stronger with the intensive analysis of Russia than they would be without. Yet his book would not stand securely on the empirical foundation provided by most‐different analysis alone. If one strips away the pathway‐case (East Germany) and the most‐similar analysis (East/West Germany) there is little left upon which to base an analysis of causal relations (aside from the large‐ N cross‐national analysis). Indeed, most scholars who employ the most‐different method do so in conjunction with other methods. 20 It is rarely, if ever, a standalone method. 21

Generalizing from this discussion of Marc Howard's work, I offer the following summary remarks on the most‐different method of case analysis. (I leave aside issues faced by all case‐study analyses, issues that are explored in Gerring 2007 .)

Let us begin with a methodological obstacle that is faced by both Millean styles of analysis—the necessity of dichotomizing every variable in the analysis. Recall that, as with most‐similar analysis, differences across cases must generally be sizeable enough to be interpretable in an essentially dichotomous fashion (e.g. high/low, present/absent) and similarities must be close enough to be understood as essentially identical (e.g. high/high, present/present). Otherwise the results of a Millean style analysis are not interpretable. The problem of “degrees” is deadly if the variables under consideration are, by nature, continuous (e.g. GDP). This is a particular concern in Howard's analysis, where East Germany scores somewhat higher than Russia in civic engagement; they are both low, but Russia is quite a bit lower. Howard assumes that this divergence is minimal enough to be understood as a difference of degrees rather than of kinds, a judgment that might be questioned. In these respects, most‐different analysis is no more secure—but also no less—than most‐similar analysis.

In one respect, most‐different analysis is superior to most‐similar analysis. If the coding assumptions are sound, the most‐different research design may be quite useful for eliminating necessary causes . Causal factors that do not appear across the chosen cases—e.g. X 2a–d in Table 28.4 —are evidently unnecessary for the production of Y . However, it does not follow that the most‐different method is the best method for eliminating necessary causes. Note that the defining feature of this method is the shared element across cases— X 1 in Table 28.4 . This feature does not help one to eliminate necessary causes. Indeed, if one were focused solely on eliminating necessary causes one would presumably seek out cases that register the same outcomes and have maximum diversity on other attributes. In Table 28.4 , this would be a set of cases that satisfy conditions X 2a–d , but not X 1 . Thus, even the presumed strength of the most‐different analysis is not so strong.

Usually, case‐study analysis is focused on the identification (or clarification) of causal relations, not the elimination of possible causes. In this setting, the most‐ different technique is useful, but only if assumptions of causal uniqueness hold. By “causal uniqueness,” I mean a situation in which a given outcome is the product of only one cause: Y cannot occur except in the presence of X . X is necessary, and in some situations (given certain background conditions) sufficient, to cause Y . 22

Consider the following hypothetical example. Suppose that a new disease, about which little is known, has appeared in Country A. There are hundreds of infected persons across dozens of affected communities in that country. In Country B, located at the other end of the world, several new cases of the disease surface in a single community. In this setting, we can imagine two sorts of Millean analyses. The first examines two similar communities within Country A, one of which has developed the disease and the other of which has not. This is the most‐similar style of case comparison, and focuses accordingly on the identification of a difference between the two cases that might account for variation across the sample. A second approach focuses on communities where the disease has appeared across the two countries and searches for any similarities that might account for these similar outcomes. This is the most‐different research design.

Both are plausible approaches to this particular problem, and we can imagine epidemiologists employing them simultaneously. However, the most‐different design demands stronger assumptions about the underlying factors at work. It supposes that the disease arises from the same cause in any setting. This is often a reasonable operating assumption when one is dealing with natural phenomena, though there are certainly many exceptions. Death, for example, has many causes. For this reason, it would not occur to us to look for most‐different cases of high mortality around the world. In order for the most‐different research design to effectively identify a causal factor at work in a given outcome, the researcher must assume that X 1 —the factor held constant across the diverse cases—is the only possible cause of Y (see Table 28.4 ). This assumption rarely holds in social‐scientific settings. Most outcomes of interest to anthropologists, economists, political scientists, and sociologists have multiple causes. There are many ways to win an election, to build a welfare state, to get into a war, to overthrow a government, or—returning to Marc Howard's work—to build a strong civil society. And it is for this reason that most‐different analysis is rarely applied in social science work and, where applied, is rarely convincing.

If this seems a tad severe, there is a more charitable way of approaching the most‐different method. Arguably, this is not a pure “method” at all but merely a supplement, a way of incorporating diversity in the sub‐sample of cases that provide the unusual outcome of interest. If the unusual outcome is revolutions, one might wish to encompass a wide variety of revolutions in one's analysis. If the unusual outcome is post‐Communist civil society, it seems appropriate to include a diverse set of post‐Communist polities in one's sample of case studies, as Marc Howard does. From this perspective, the most‐different method (so‐called) might be better labeled a diverse‐case method, as explored above.

10 Conclusions

In order to be a case of something broader than itself, the chosen case must be representative (in some respects) of a larger population. Otherwise—if it is purely idiosyncratic (“unique”)—it is uninformative about anything lying outside the borders of the case itself. A study based on a nonrepresentative sample has no (or very little) external validity. To be sure, no phenomenon is purely idiosyncratic; the notion of a unique case is a matter that would be difficult to define. One is concerned, as always, with matters of degree. Cases are more or less representative of some broader phenomenon and, on that score, may be considered better or worse subjects for intensive analysis. (The one exception, as noted, is the influential case.)

Of all the problems besetting case‐study analysis, perhaps the most persistent— and the most persistently bemoaned—is the problem of sample bias ( Achen and Snidal 1989 ; Collier and Mahoney 1996 ; Geddes 1990 ; King, Keohane, and Verba 1994 ; Rohlfing 2004 ; Sekhon 2004 ). Lisa Martin (1992 , 5) finds that the overemphasis of international relations scholars on a few well‐known cases of economic sanctions— most of which failed to elicit any change in the sanctioned country—“has distorted analysts view of the dynamics and characteristics of economic sanctions.” Barbara Geddes (1990) charges that many analyses of industrial policy have focused exclusively on the most successful cases—primarily the East Asian NICs—leading to biased inferences. Anna Breman and Carolyn Shelton (2001) show that case‐study work on the question of structural adjustment is systematically biased insofar as researchers tend to focus on disaster cases—those where structural adjustment is associated with very poor health and human development outcomes. These cases, often located in sub‐Saharan Africa, are by no means representative of the entire population. Consequently, scholarship on the question of structural adjustment is highly skewed in a particular ideological direction (against neoliberalism) (see also Gerring, Thacker, and Moreno 2005) .

These examples might be multiplied many times. Indeed, for many topics the most‐studied cases are acknowledged to be less than representative. It is worth reflecting upon the fact that our knowledge of the world is heavily colored by a few “big” (populous, rich, powerful) countries, and that a good portion of the disciplines of economics, political science, and sociology are built upon scholars' familiarity with the economics, political science, and sociology of one country, the United States. 23 Case‐study work is particularly prone to problems of investigator bias since so much rides on the researcher's selection of one (or a few) cases. Even if the investigator is unbiased, her sample may still be biased simply by virtue of “random” error (which may be understood as measurement error, error in the data‐generation process, or as an underlying causal feature of the universe).

There are only two situations in which a case‐study researcher need not be concerned with the representativeness of her chosen case. The first is the influential case research design, where a case is chosen because of its possible influence on a cross‐case model, and hence is not expected to be representative of a larger sample. The second is the deviant‐case method, where the chosen case is employed to confirm a broader cross‐case argument to which the case stands as an apparent exception. Yet even here the chosen case is expected to be representative of a broader set of cases—those, in particular, that are poorly explained by the extant model.

In all other circumstances, cases must be representative of the population of interest in whatever ways might be relevant to the proposition in question. Note that where a researcher is attempting to disconfirm a deterministic proposition the question of representativeness is perhaps more appropriately understood as a question of classification: Is the chosen case appropriately classified as a member of the designated population? If so, then it is fodder for a disconfirming case study.

If the researcher is attempting to confirm a deterministic proposition, or to make probabilistic arguments about a causal relationship, then the problem of representativeness is of the more usual sort: Is case A unit‐homogeneous relative to other cases in the population? This is not an easy matter to test. However, in a large‐ N context the residual for that case (in whatever model the researcher has greatest confidence in) is a reasonable place to start. Of course, this test is only as good as the model at hand. Any incorrect specifications or incorrect modeling procedures will likely bias the results and give an incorrect assessment of each case's “typicality.” In addition, there is the possibility of stochastic error, errors that cannot be modeled in a general framework. Given the explanatory weight that individual cases are asked to bear in a case‐study analysis, it is wise to consider more than just the residual test of representativeness. Deductive logic and an in‐depth knowledge of the case in question are often more reliable tools than the results of a cross‐case model.

In any case, there is no dispensing with the question. Case studies (with the two exceptions already noted) rest upon an assumed synecdoche: The case should stand for a population. If this is not true, or if there is reason to doubt this assumption, then the utility of the case study is brought severely into question.

Fortunately, there is some safety in numbers. Insofar as case‐study evidence is combined with cross‐case evidence the issue of sample bias is mitigated. Indeed, the suspicion of case‐study work that one finds in the social sciences today is, in my view, a product of a too‐literal interpretation of the case‐study method. A case study tout court is thought to mean a case study tout seul . Insofar as case studies and cross‐case studies can be enlisted within the same investigation (either in the same study or by reference to other studies in the same subfield), problems of representativeness are less worrisome. This is the virtue of cross‐level work, a.k.a. “triangulation.”

11 Ambiguities

Before concluding, I wish to draw attention to two ambiguities in case‐selection strategies in case‐study research. The first concerns the admixture of several case‐ selection strategies. The second concerns the changing status of a case as a study proceeds.

Some case studies follow only one strategy of case selection. They are typical , diverse , extreme , deviant , influential , crucial , pathway , most‐similar , or most‐different research designs, as discussed. However, many case studies mix and match among these case‐selection strategies. Indeed, insofar as all case studies seek representative samples, they are always in search of “typical” cases. Thus, it is common for writers to declare that their case is, for example, both extreme and typical; it has an extreme value on X 1 or Y but is not, in other respects, idiosyncratic. There is not much that one can say about these combinations of strategies except that, where the cases allow for a variety of empirical strategies, there is no reason not to pursue them. And where the same cases can serve several functions at once (without further effort on the researcher's part), there is little cost to a multi‐pronged approach to case analysis.

The second issue that deserves emphasis is the changing status of a case during the course of a researcher's investigation—which may last for years, if not decades. The problem is acute wherever a researcher begins in an exploratory mode and proceeds to hypothesis‐testing (that is, she develops a specific X 1 / Y proposition) or where the operative hypothesis or key control variable changes (a new causal factor is discovered or another outcome becomes the focus of analysis). Things change. And it is the mark of a good researcher to keep her mind open to new evidence and new insights. Too often, methodological discussions give the misleading impression that hypotheses are clear and remain fixed over the course of a study's development. Nothing could be further from the truth. The unofficial transcripts of academia— accessible in informal settings, where researchers let their guards down (particularly if inebriated)—are filled with stories about dead‐ends, unexpected findings, and drastically revised theory chapters. It would be interesting, in this vein, to compare published work with dissertation prospectuses and fellowship applications. I doubt if the correlation between these two stages of research is particularly strong.

Research, after all, is about discovery, not simply the verification or falsification of static hypotheses. That said, it is also true that research on a particular topic should move from hypothesis generating to hypothesis‐testing. This marks the progress of a field, and of a scholar's own work. As a rule, research that begins with an open‐ended ( X ‐ or Y ‐centered) analysis should conclude with a determinate X 1 / Y hypothesis.

The problem is that research strategies that are ideal for exploration are not always ideal for confirmation. The extreme‐case method is inherently exploratory since there is no clear causal hypothesis; the researcher is concerned merely to explore variation on a single dimension ( X or Y ). Other methods can be employed in either an open‐ ended (exploratory) or a hypothesis‐testing (confirmatory/disconfirmatory) mode. The difficulty is that once the researcher has arrived at a determinate hypothesis the originally chosen research design may no longer appear to be so well designed.

This is unfortunate, but inevitable. One cannot construct the perfect research design until (a) one has a specific hypothesis and (b) one is reasonably certain about what one is going to find “out there” in the empirical world. This is particularly true of observational research designs, but it also applies to many experimental research designs: Usually, there is a “good” (informative) finding, and a finding that is less insightful. In short, the perfect case‐study research design is usually apparent only ex post facto .

There are three ways to handle this. One can explain, straightforwardly, that the initial research was undertaken in an exploratory fashion, and therefore not constructed to test the specific hypothesis that is—now—the primary argument. Alternatively, one can try to redesign the study after the new (or revised) hypothesis has been formulated. This may require additional field research or perhaps the integration of additional cases or variables that can be obtained through secondary sources or through consultation of experts. A final approach is to simply jettison, or de‐emphasize, the portion of research that no longer addresses the (revised) key hypothesis. A three‐case study may become a two‐case study, and so forth. Lost time and effort are the costs of this downsizing.

In the event, practical considerations will probably determine which of these three strategies, or combinations of strategies, is to be followed. (They are not mutually exclusive.) The point to remember is that revision of one's cross‐case research design is normal and perhaps to be expected. Not all twists and turns on the meandering trail of truth can be anticipated.

12 Are There Other Methods of Case Selection?

At the outset of this chapter I summarized the task of case selection as a matter of achieving two objectives: representativeness (typicality) and variation (causal leverage). Evidently, there are other objectives as well. For example, one wishes to identify cases that are independent of each other. If chosen cases are affected by each other (sometimes known as Galton's problem or a problem of diffusion), this problem must be corrected before analysis can take place. I have neglected this issue because it is usually apparent to the researcher and, in any case, there are no simple techniques that might be utilized to correct for such biases. (For further discussion of this and other factors impinging upon case selection see Gerring 2001 , 178–81.)

I have also disregarded pragmatic/logistical issues that might affect case selection. Evidently, case selection is often influenced by a researcher's familiarity with the language of a country, a personal entrée into that locale, special access to important data, or funding that covers one archive rather than another. Pragmatic considerations are often—and quite rightly—decisive in the case‐selection process.

A final consideration concerns the theoretical prominence of a particular case within the literature on a subject. Researchers are sometimes obliged to study cases that have received extensive attention in previous studies. These are sometimes referred to as “paradigmatic” cases or “exemplars” ( Flyvbjerg 2004 , 427).

However, neither pragmatic/logistical utility nor theoretical prominence qualifies as a methodological factor in case selection. That is, these features of a case have no bearing on the validity of the findings stemming from a study. As such, it is appropriate to grant these issues a peripheral status in this chapter.

One final caveat must be issued. While it is traditional to distinguish among the tasks of case selection and case analysis, a close look at these processes shows them to be indistinct and overlapping. One cannot choose a case without considering the sort of analysis that it might be subjected to, and vice versa. Thus, the reader should consider choosing cases by employing the nine techniques laid out in this chapter along with any considerations that might be introduced by virtue of a case's quasi‐experimental qualities, a topic taken up elsewhere ( Gerring 2007 , ch. 6 ).

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Kendall, P. L. and Wolf, K. M. 1949/ 1955 . The analysis of deviant cases in communications research. In Communications Research, 1948–1949 , ed. P. F. Lazarsfeld and F. N. Stanton. New York: Harper and Brothers. Reprinted as pp. 167–70 in The Language of Social Research , ed. P. F. Lazarsfeld and M. Rosenberg . New York: Free Press.

Kennedy, C. H. 2005 . Single‐case Designs for Educational Research . Boston: Allyn and Bacon.

Kennedy, P. 2003 . A Guide to Econometrics , 5th edn. Cambridge, Mass.: MIT Press.

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—— 1969 . Consociational democracy. World Politics , 21: 207–25. 10.2307/2009820

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Yin, R. K. 2004 . Case Study Anthology . Thousand Oaks, Calif.: Sage.

Gujarati (2003) ; Kennedy (2003) . Interestingly, the potential of cross‐case statistics in helping to choose cases for in‐depth analysis is recognized in some of the earliest discussions of the case‐study method (e.g. Queen 1928 , 226).

This expands on Mill (1843/1872 , 253), who wrote of scientific enquiry as twofold: “either inquiries into the cause of a given effect or into the effects or properties of a given cause.”

This method has not received much attention on the part of qualitative methodologists; hence, the absence of a generally recognized name. It bears some resemblance to J. S. Mill's Joint Method of Agreement and Difference ( Mill 1843/1872 ), which is to say a mixture of most‐similar and most‐different analysis, as discussed below. Patton (2002 , 234) employs the concept of “maximum variation (heterogeneity) sampling.”

More precisely, George and Smoke (1974 , 534, 522–36, ch. 18 ; see also discussion in Collier and Mahoney 1996 , 78) set out to investigate causal pathways and discovered, through the course of their investigation of many cases, these three causal types. Yet, for our purposes what is important is that the final sample includes at least one representative of each “type.”

For further examples see Collier and Mahoney (1996) ; Geddes (1990) ; Tendler (1997) .

Traditionally, methodologists have conceptualized cases as having “positive” or “negative” values (e.g. Emigh 1997 ; Mahoney and Goertz 2004 ; Ragin 2000 , 60; 2004 , 126).

Geddes (1990) ; King, Keohane, and Verba (1994) . See also discussion in Brady and Collier (2004) ; Collier and Mahoney (1996) ; Rogowski (1995) .

The exception would be a circumstance in which the researcher intends to disprove a deterministic argument ( Dion 1998 ).

Geddes (2003 , 131). For other examples of casework from the annals of medicine see “Clinical reports” in the Lancet , “Case studies” in Canadian Medical Association Journal , and various issues of the Journal of Obstetrics and Gynecology , often devoted to clinical cases (discussed in Jenicek 2001 , 7). For examples from the subfield of comparative politics see Kazancigil (1994) .

For a discussion of the important role of anomalies in the development of scientific theorizing see Elman (2003) ; Lakatos (1978) . For examples of deviant‐case research designs in the social sciences see Amenta (1991) ; Coppedge (2004) ; Eckstein (1975) ; Emigh (1997) ; Kendall and Wolf (1949/1955) .

For examples of the crucial‐case method see Bennett, Lepgold, and Unger (1994) ; Desch (2002) ; Goodin and Smitsman (2000) ; Kemp (1986) ; Reilly and Phillpot (2003) . For general discussion see George and Bennett (2005) ; Levy (2002) ; Stinchcombe (1968 , 24–8).

A third position, which purports to be neither Popperian or Bayesian, has been articulated by Mayo (1996 , ch. 6 ). From this perspective, the same idea is articulated as a matter of “severe tests.”

It should be noted that Tsai's conclusions do not rest solely on this crucial case. Indeed, she employs a broad range of methodological tools, encompassing case‐study and cross‐case methods.

See also the discussion in Eckstein (1975) and Lijphart (1969) . For additional examples of case studies disconfirming general propositions of a deterministic nature see Allen (1965); Lipset, Trow, and Coleman (1956) ; Njolstad (1990) ; Reilly (2000–1) ; and discussion in Dion (1998) ; Rogowski (1995) .

Granted, insofar as case‐study analysis provides a window into causal mechanisms, and causal mechanisms are integral to a given theory, a single case may be enlisted to confirm or disconfirm a proposition. However, if the case study upholds a posited pattern of X/Y covariation, and finds fault only with the stipulated causal mechanism, it would be more accurate to say that the study forces the reformulation of a given theory, rather than its confirmation or disconfirmation. See further discussion in the following section.

Sometimes, the most‐similar method is known as the “method of difference,” after its inventor ( Mill 1843/1872 ). For later treatments see Cohen and Nagel (1934) ; Eggan (1954) ; Gerring (2001 , ch. 9 ); Lijphart (1971 ; 1975) ; Meckstroth (1975) ; Przeworski and Teune (1970) ; Skocpol and Somers (1980) .

For good introductions see Ho et al. (2004) ; Morgan and Harding (2005) ; Rosenbaum (2004) ; Rosenbaum and Silber (2001) . For a discussion of matching procedures in Stata see Abadie et al. (2001) .

The most‐different method is also sometimes referred to as the “method of agreement,” following its inventor, J. S. Mill (1843/1872) . See also De Felice (1986) ; Gerring (2001 , 212–14); Lijphart (1971 ; 1975) ; Meckstroth (1975) ; Przeworski and Teune (1970) ; Skocpol and Somers (1980) . For examples of this method see Collier and Collier (1991/2002) ; Converse and Dupeux (1962) ; Karl (1997) ; Moore (1966) ; Skocpol (1979) ; Yashar (2005 , 23). However, most of these studies are described as combining most‐similar and most‐different methods.

In the following discussion I treat the terms social capital, civil society, and civic engagement interchangeably.

E.g. Collier and Collier (1991/2002) ; Karl (1997) ; Moore (1966) ; Skocpol (1979) ; Yashar (2005 , 23). Karl (1997) , which affects to be a most‐different system analysis (20), is a particularly clear example of this. Her study, focused ostensibly on petro‐states (states with large oil reserves), makes two sorts of inferences. The first concerns the (usually) obstructive role of oil in political and economic development. The second sort of inference concerns variation within the population of petro‐states, showing that some countries (e.g. Norway, Indonesia) manage to avoid the pathologies brought on elsewhere by oil resources. When attempting to explain the constraining role of oil on petro‐states, Karl usually relies on contrasts between petro‐states and nonpetro‐states (e.g. ch. 10 ). Only when attempting to explain differences among petro‐states does she restrict her sample to petro‐states. In my opinion, very little use is made of the most‐different research design.

This was recognized, at least implicitly, by Mill (1843/1872 , 258–9). Skepticism has been echoed by methodologists in the intervening years (e.g. Cohen and Nagel 1934 , 251–6; Gerring 2001 ; Skocpol and Somers 1980 ). Indeed, explicit defenses of the most‐different method are rare (but see De Felice 1986 ).

Another way of stating this is to say that X is a “nontrivial necessary condition” of Y .

Wahlke (1979 , 13) writes of the failings of the “behavioralist” mode of political science analysis: “It rarely aims at generalization; research efforts have been confined essentially to case studies of single political systems, most of them dealing …with the American system.”

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Although there are "steps" to preparing a speech, a more appropriate way of thinking of speech preparation is as a dynamic process. Instead of seeing speech development as a linear process, it is better to see it as a holistic process of creating all components of the speech so they fit together as an effective whole. A puzzle metaphor demonstrates this approach.

As the model illustrates, the core of this dynamic process is the audience analysis See Module VIII, Section 3 , and the speech is built around our understanding of our audience. We then develop the content (selecting the topic, finding the content, and organizing the speech), and prepare the content for presentation (practice the delivery).

Although there is a sense of a linear process, sticking to some sort of artificial step process is not as important as making sure that all the pieces fit together as an effective, unified whole. Although we may have developed one area, as we prepare the whole speech, we may need to revisit earlier parts of the process and alter those to achieve a unified whole.

While there are a variety of ways to organize a speech, the most common structure breaks the speech into four parts:

Introduction
Thesis/preview
Body of the speech

Portions of the Speech

The introduction, ending with the thesis/preview, comprises approximately 10% of the speech. The body of the speech is about 85% of the speech, and the remaining 5% is the conclusion. The percentages should be used as guidelines for the speaker, not as absolutes. The majority of the speaker’s efforts should be focused on relating the core information or arguments the speaker needs to share and the audience is there to hear. Since the body of the speech contains this core information, most of the time should be spent in that area.

Order of development

In developing the speech, novice speakers often make the mistake of starting with the introduction. Since the introduction comes first, it seems logical to start there; however, this is wrong. Creating the thesis is the first step in good speech development. Until we know what the speech is about, we cannot effectively determine an introduction. Just as we cannot introduce a person we do not know, we cannot introduce a topic not yet developed. The most effective order of preparation is:

Thesis. Since the thesis defines what the speech is about and what it is not about, developing it first helps guide the speaker in developing the body, doing research, and staying properly focused.
Body. The body of the speech is the key content the audience is there to hear, so the speaker should spend a substantive amount of time researching, organizing, and fine-tuning this core content.
Introduction. A speech introduction is the most creative part of the process. Since it is intended to pull the audience into the thesis and prepare them for the body, by waiting until after developing the body, the speaker will have a clear sense of what the introduction should do. During research for the body, it is common to come across a quotation, example, or some other idea for the attention getting device of the introduction.
Conclusion. While it is the shortest part of a speech, it is very important as it is the last thing the audience will hear, leaving the audience with their final impression of the speech. This is developed last as there are ways to conclude a speech that are built on how the speaker begins the speech.

While this order of development is important, always remember the “puzzle” metaphor: we have to work to make all the parts fit together, so there can be a lot of revisiting parts to alter or fine tune them. Speech development is a dynamic process in which changing one part of the speech may have a ripple effect, affecting other parts. In the end, a good speaker makes sure that the speech is consistent, coherent, organized, and flows well for the audience.

Topic Selection Criteria

In selecting a topic, one of the most common mistakes novice speakers make to take a sender-based approach See Module I, Section 1 . This is assuming the audience has a strong interest in the same things the speaker feels passionate about. Just because a speaker may be deeply into video gaming does not inherently mean the audience shares that interest. To select a good topic, the speaker needs to be receiver-based See Module I, Section 1 and objectively consider what is most likely to be successful. While the speaker’s interest can certainly serve as a good starting point to identify a general topic, the specific topic and approach to the topic must be carefully considered.

There are four criteria to determine the appropriateness of a topic:

Audience Interest We need to select a topic we think will appeal to the specific audience. This may be a topic we know the audience will have an immediate interest in, or one in which the audience will have an interest once we develop the topic to some degree. Being receiver-based, the speaker must be honest in their assessment of the topic and the audience, careful not to project their own interests on the audience.
Speaker Interest Although audience interest is certainly key, the speaker must also have an interest in the topic. A lack of speaker interest can be deadly. If the speaker is unmotivated to develop and present the speech, the speech usually sounds as if the speaker is bored and does not care. The speaker loses their own sense of desire to do a good job. A good topic is one that has a healthy balance of audience interest and speaker interest.
Occasion Appropriateness We need to consider why the audience is gathered and select a topic that fits the occasion. If the audience is gathered at a business conference, learning new ways of interacting with clients, an informative topic on some aspect of communication skill may be appropriate. For a commencement address, talking about the dire state of the economy may not fit the celebratory nature of the event; the topic should invoke growth, opportunity, and an optimistic future. We want our topic to complement the reason the audience is gathered.
Time Limits The speaker must fit the speech into the given time limits. The speech needs to fill the allotted time, and yet it cannot exceed that given time. It is a core speaker responsibility to treat the audience with respect and to fill those time limits appropriately. Exceeding time limits is simply not an option. If a topic cannot be covered within a given time, the speaker has two options: limit the topic, or get a new topic. As we know from looking at culture, Americans are quite monochronic See Module I, Section 1 . We see time as a resource, like money, to be budgeted and spent wisely. When speeches end on time, we have gotten what we have paid for. If they run a little short, we may feel we got a deal, but if they run quite short, we feel we got cheated. The audience is spending their time on the speaker; give them their money’s worth. On the other end, if the speech runs overtime, the speaker is "stealing" time from the audience, taking our time resource without our permission. Time limits are very important in a monochronic culture.

Narrowing a Topic

Since the speaker needs to fit the speech into the allotted time, we need to move from a broader topic to a narrower, much more specific topic. Finding a specific topic is a process of analysis, selection, and narrowing. The goal of the process is to find a specific topic that fits the same criteria as discussed above: audience interest; speaker interest; occasion appropriateness; and time limits.

A good way to narrow the topic is to start with a broader topic and brainstorm a large list of sub-topics. Using the previous four criteria, narrow the topic to the best fit. If the topic is still too large, repeat the process as often as needed to reach a manageable size topic.

After finding that specific topic, develop the specific speech purpose . The specific speech purpose is the narrow, focused direction the speech will be taking . The function of the specific speech purpose is twofold: to identify what goes in the speech, and to identify what does not go in the speech. The specific speech purpose establishes the parameters of the speech. We use the parameters as guidance as to what to include in the speech and what to keep out of the speech. This is an important consideration. Unless the speaker keeps a tight rein on the development of the speech, the speech can get out of control, suddenly diverting into a different area or expanding beyond the time limit.

For example:

After listening to my speech, the audience will be informed of how to write an effective resume.
After listening to my speech, the audience will be informed of alternative forms of financial aid.
After listening to my speech, the audience will be informed of creative ways of using macaroni and cheese.

For persuasion, the specific speech purposes would be slightly different, reflecting the idea of changing an audience's belief, attitude, or action:

After listening to my speech, the audience will be persuaded to donate blood.
After listening to my speech, the audience will be persuaded to vote for the school referendum.
After listening to my speech, the audience will be persuaded to use a designated driver.

Once the specific speech purpose has been developed, we can easily create the thesis . The thesis is the specific, concise statement of intent for the speech . It is the one, single sentence clearly stating exactly what the speech will be addressing. Converting the specific speech purpose to the thesis is simple:

"After listening to my speech, the audience will be informed of how to write an effective resume." becomes "Today I'll take you through the steps of writing an effective resume."
"After listening to my speech, the audience will be informed of alternative forms of financial aid." becomes "There are several alternate forms of financial aid for you to consider."
"After listening to my speech, the audience will be informed of creative ways of using macaroni and cheese." becomes "I'll show you several creative ways of using macaroni and cheese."
"After listening to my speech, the audience will be persuaded to donate blood." becomes "Today I'll show you why it is important that you donate blood."
"After listening to my speech, the audience will be persuaded to vote for the school referendum." becomes "Voting for the upcoming school referendum is important for the success of our schools."
" After listening to my speech, the audience will be persuaded to use a designated driver." becomes "When you go out partying, you should use a designated driver."

There are several traits of a good speech thesis:

Concise . The thesis is a simple, straightforward sentence clearly telling the audience what the speech is going to be about.
Grammatically simple . There is one subject and one predicate; it is not a compound sentence, nor a compound-complex sentence. The thesis is not a question.
Blatant . A speech thesis is more blunt and obvious than what we might use in writing.
Identifies the parameters of the speech . It tells the audience what the speaker will be doing; which, by definition, also tells the audience what the speaker is not doing.
Consistent with the speaker's overall speech purpose . The wording reflects the proper informative or persuasive tone.

The terms and concepts students should be familiar with from this section include:

Speech Development

Parts of the Speech

Portions of the speech
Order of development
Audience interest
Speaker interest
Occasion appropriateness
Time limits

Specific Speech Purpose The Thesis

Grammatically simple
Identifies parameters
Consistent with purpose

Augmenting Large Language Models with Humor Theory To Understand Puns

This research explores the application of large language models (LLMs) to comprehension of puns. Leveraging the expansive capabilities of LLMs, this study delves into the domain of pun classification by examining it through the prism of two humor theories: the Computational Model of Humor and the Benign Violation theory, which is an extension of the N+V Theory. The computational model posits that for a phrase to qualify as a pun, it must possess both ambiguity and distinctiveness, characterized by a word that can be interpreted in two plausible ways, each interpretation being supported by at least one unique word. On the other hand, the Benign Violation theory posits that puns work by breaching one linguistic rule while conforming to another, thereby creating a "benign violation." By leveraging the capabilities of large language models (LLMs), this research endeavors to scrutinize a curated collection of English language puns. Our aim is to assess the validity and effectiveness of the use of these theoretical frameworks in accurately classifying puns. We undertake controlled experiments on the dataset, selectively removing a condition specific to one theory and then evaluating the puns based on the criteria of the other theory to see how well it classifies the altered inputs. This approach allows us to uncover deeper insights into the processes that facilitate the recognition of puns and to explore the practical implications of applying humor theories. The findings of our experiments, detailed in the subsequent sections, sheds light on how the alteration of specific conditions impacts the ability of the LLMs to accurately classify puns, according to each theory, where each component of the theory does not influence the result to the same extent, thereby contributing to our understanding of humor mechanics through the eyes of LLMs.

Degree Type

Master of Science
Computer and Information Technology

Campus location

West Lafayette

Advisor/Supervisor/Committee Chair

Additional committee member 2, additional committee member 3, usage metrics.

Deep learning
Computational linguistics
Natural language processing

Men's volleyball tourney

🎾 Men's tennis selections announced

🎾 Women's tennis selections announced

⚾️ Math teacher, pitcher, doctoral student: Zach Gitschier's journey

NCAA.com | April 29, 2024

2024 ncaa dii women's golf championship: schedule, results.

The 2024 NCAA DII women's golf championship will start with regionals from May 6-8 and conclude with final site at the Orange County National Golf Center in Orlando, Florida from May 21-25. Selections for the DII women's golf championship were released via a press release on Monday, April 29, right here on NCAA.com .

At the finals, all 18 teams and 8 individuals will complete 54 holes of stroke play. Following 54 holes of stroke play, an individual champion will be declared along with finishers Nos. 2-10 based on cumulative total score and any applicable tiebreakers. The top eight teams after 54 holes of play will be placed into a bracket and seeded based on 54-hole scores. The pairings for the quarterfinals will be Seed No. 1 versus Seed No. 8, Seed No. 2 versus Seed No. 7, Seed No. 3 versus Seed No. 6 and Seed No. 4 versus Seed No. 5, competing in head-to-head medal play (stroke play over 18 holes and low score wins).

The number of teams and individuals from non-qualifying teams at each regionals are as follows:

Click or tap here to view the selections information .

Below you can find complete information for the 2024 championship:

Championships Schedule

Selections revealed in press release

Monday, April 29 | Click here for the full selections announcement

Regionals (May 6-8)

Bartlesville, Oklahoma | Hillcrest Country Club (Rogers State University, host)
Carmel, Indiana | Prairie View Golf Club (University of Indianapolis, host)
Cleveland, Tennessee | Cleveland Country Club (Lee University, host)
Stockton, California | Brookside Golf & Country Club (California Collegiate Athletic Association and Visit Stockton, hosts)

Finals (May 21-25)

Orlando, Florida | Orange County National Golf Center (Rollins College and Greater Orlando Sports Commission, hosts)

Championship history

DBU captured the 2023 DII women's golf championship after defeating Nova Southeastern, 3-2. See the full year-by-year team championship history below.

***Won by tie-breaker

**Tournament format changed to head-to-head medal play

* Tournament shortened to three rounds due to weather

* From 1996-99, Divisions II and III competed in a combined championship.

NCAA Division II women's golf committee announces 2024 championship field

2024 NCAA Division I women's golf regional selections announced

2024 NCAA DI women's golf championships: Schedule, how to watch

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Blueprint is an independent, advertising-supported comparison service focused on helping readers make smarter decisions. We receive compensation from the companies that advertise on Blueprint which may impact how and where products appear on this site. The compensation we receive from advertisers does not influence the recommendations or advice our editorial team provides in our articles or otherwise impact any of the editorial content on Blueprint. Blueprint does not include all companies, products or offers that may be available to you within the market. A list of selected affiliate partners is available here .

S&P (SPX) today: The index is up 7.56% YTD

Farran Powell

“Verified by an expert” means that this article has been thoroughly reviewed and evaluated for accuracy.

Published 10:51 a.m. UTC April 30, 2024

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How is the S&P 500 doing today?

The S&P 500 opened today at 5,103.78. Within 30 minutes of trading, the benchmark index rose by 3.86 points, or 0.08%, to 5,107.64.

Year to date, the benchmark index is up, with a return of 7.56%.

S&P 500 market summary

S&p 500 performance, s&p 500 index (spx).

The S&P 500 (SPX), or Standard & Poor's 500, is a notable stock market index that measures the performance of 500 large companies listed on U.S. stock exchanges. The index was introduced in 1957 and is now one of the most widely followed equity indexes. It is often considered a benchmark for U.S. market performance.

The S&P 500 is a market-capitalization-weighted index, meaning that larger companies account for a bigger portion of the index. It tracks around 80% of total U.S. market capitalization.

The index is updated in real time during market hours, reflecting price changes in its constituent companies as they happen. The well-known index isn’t only a tool for investors and analysts. It also serves as a benchmark for multiple investment products, including mutual funds , exchange-traded funds, options and futures.

"The S&P 500 has become the most widely used benchmark over the past 50 years due to its ability to serve as the best indicator for U.S. markets," said Derek Horstmeyer, finance professor at George Mason University School of Business. "It has surpassed the Dow Jones Industrial Average in popularity due to its more cohesive and extensive construction."

S&P 500 companies

Companies in the S&P 500 hail from all 11 Global Industry Classification Standard sectors, making it a comprehensive representation of the U.S. economy.

Here are the top 10 stocks in the S&P 500 index weighted by market capitalization.

S&P 500 selection criteria

The selection criteria for the S&P 500 index provides a comprehensive overview of the U.S. stock market.

All constituent companies must be U.S.-based. Stocks listed on eligible U.S. exchanges, as well as real estate investment trusts, can make the cut. However, closed-end funds, ETFs, American depositary receipts and other specific types of securities are ineligible for inclusion.
A company must have an unadjusted market capitalization of at least $14.5 billion, and its float-adjusted market cap, meaning the portion of shares available for public trading, must meet at least 50% of this threshold.
Financial viability is another essential criterion; companies must exhibit positive earnings for the most recent quarter as well as the sum of the past four quarters.
Liquidity is also a key factor; the ratio of the annual dollar value traded to float-adjusted market capitalization must be at least 0.75, meaning a substantial portion of the company's publicly available shares are actively traded on the market. This ratio ensures liquidity and that the stock can be easily bought or sold without causing a significant impact on its price.
The stock needs to have an investable weight factor of at least 0.10.
Lastly, the stock must have traded at least 250,000 shares in the six months leading up to the evaluation date, confirming its liquidity and accessibility to investors.

The index is rebalanced quarterly — in March, June, September and December — to ensure it continues to reflect the U.S. equity market accurately.

S&P 500 index history

The history of the S&P 500 index dates back to 1957 when it was introduced by the financial services company Standard & Poor's. But backtested data for the index now goes back as far as Jan. 3, 1928.

The S&P 500 has been modified and expanded over the years. But its core function remains the same. The index provides investors and analysts with a comprehensive overview of the overall U.S. equity market.

The index has weathered various economic cycles, market crashes and bear markets. During the dot-com bubble, the index fell by 10.14%, 13.04% and 23.37% in 2000, 2001 and 2002, respectively. During the Great Recession, the index plunged by 39.23% in 2008.

Despite these drawdowns, the S&P 500's robust historical performance is often used as a benchmark against which other investments are measured.

The S&P 500's constituent companies have also changed to reflect the shifting landscape of the U.S. economy, from a manufacturing-heavy list in the earlier years to a more diversified group today that includes technology, health care and financials as its largest sectors.

Some of the world's largest and most successful companies, such as Apple (AAPL), Microsoft (MSFT), Alphabet (GOOG) and Amazon (AMZN), lead the index, making it a key reference point for investors globally.

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S&P 500 index return history

The following section provides the S&P 500's historical annual total returns over the past decade.

Total returns include not only price appreciation but also reinvested dividends . In contrast, price returns account for only the change in the index's price, excluding dividends.

Focusing on total returns provides a more comprehensive picture of the S&P 500's historical performance in terms of compound growth.

Investing in the S&P 500

Investing in the S&P 500 is a straightforward process thanks to various financial products that track its performance. Different asset managers offer index-linked products that mirror the S&P 500, making it easy for investors to gain exposure to this benchmark index.

S&P 500 index funds

One popular option is investing in mutual funds that aim to replicate the index's performance. These funds pool money from multiple investors to buy the stocks in the S&P 500, essentially mimicking the index's composition and weightings. A popular example is the Vanguard 500 Index Fund Admiral Shares (VFIAX).

S&P 500 ETFs

ETFs are another commonly used instrument for investing in the S&P 500.

S&P 500 ETFs are similar to mutual funds but are traded on stock exchanges throughout the day like individual stocks. While these investment vehicles also track the S&P 500, they usually have lower fees than mutual funds. That makes them a cost-effective way to invest in the index. A popular example is the SPDR S&P 500 ETF Trust (SPY).

For those interested in derivatives, options and futures contracts on the S&P 500 are also available. Options give investors the right, but not the obligation, to buy or sell the index at a predetermined price before a specific expiration date. Futures obligate the investor to buy or sell the index at a predetermined price at a specified time in the future.

Options and futures can be used for hedging risks or speculation but come with additional risks and complexity.

Whether you’re looking for long-term exposure to the U.S. stock market or short-term trading strategies, there are multiple ways to invest in the S&P 500. Each investment vehicle has its own set of characteristics, fees and risk exposures.

What are S&P 500 futures?

S&P 500 futures are among the most traded futures on the U.S. markets. This type of future is a derivative contract that provides an investor with a price based on an expected future value of the index.

These futures were first introduced by the Chicago Mercantile Exchange in 1982. All S&P futures are products of the CME and trade electronically.

Blueprint is an independent publisher and comparison service, not an investment advisor. The information provided is for educational purposes only and we encourage you to seek personalized advice from qualified professionals regarding specific financial decisions. Past performance is not indicative of future results.

Blueprint has an advertiser disclosure policy . The opinions, analyses, reviews or recommendations expressed in this article are those of the Blueprint editorial staff alone. Blueprint adheres to strict editorial integrity standards. The information is accurate as of the publish date, but always check the provider’s website for the most current information.

Tony Dong is a freelance financial writer with bylines in U.S. News and World Report, the NYSE, the Nasdaq, The Motley Fool and Benzinga. He lives in Vancouver, Canada and is an avid watch collector.

Farran Powell is the lead editor of investing at USA TODAY Blueprint. She was previously the assistant managing editor of investing at U.S. News and World Report. Her work has appeared in numerous publications including TheStreet, Mansion Global, CNN, CNN Money, DNAInfo, Yahoo! Finance, MSN Money and the New York Daily News. She holds a BSc from the London School of Economics and an MA from the University of Texas at Austin. You can follow her on Twitter at @farranpowell.

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Opening statements in Trump's historic trial set to begin Monday after tense day of jury selection

Opening statements are set to begin next week in Donald Trump’s historic criminal trial after the final members of the jury were seated Friday, following a dramatic day in which two prospective jurors broke down in tears, an appeals court judge rejected Trump's request for a stay, and a man set himself on fire in front of the courthouse.

“We’re going to have opening statements on Monday morning. This trial is starting,” Judge Juan Merchan said towards the end of the day, after successfully seating the remaining five alternate jurors that were needed.

The case — the first-ever criminal trial of a former president —will be heard by a panel of 12 jurors and a total of six alternates. It's expected to last roughly six weeks.

The five alternates ultimately selected Friday include an unemployed married woman who’s into art and described herself as not political, an audio professional, a contract specialist, a clothing company executive and a construction company project manager. It took four days of jury selection to find the 18 jurors.

Around the same time the judge declared, "we have our full panel" inside the courtroom in the early afternoon, a man set himself on fire outside the courthouse. The NYPD said the man, identified as Max Azzarello of Florida, later died. He appeared to have had pamphlets describing a conspiracy involving cryptocurrency that he threw around before setting himself ablaze, police said.

Later in the afternoon, Trump's attorneys were in a state appeals court trying again to get an emergency stay of the trial. Trump attorney Cliff Robert argued his client could not get a fair trial in Manhattan, which had been Trump's longtime home before moving to Florida after he was elected president in 2016.

Steven Wu of Manhattan District Attorney Alvin Bragg's office countered that "what the last week has shown is that the jury selection has worked."

"We have 18 ordinary New Yorkers who are ready to serve. It would be unfair to them and the public for this to be delayed further," he argued. The judge rejected Trump's stay request a short time later.

The jury selection process Friday was especially intense, some potential jurors breaking down in tears and others saying they were too anxious to serve.

The day began with the judge calling up the 22 remaining potential jurors from the previous pool of 96 to answer questions designed to indicate whether they could be fair and impartial about the divisive real estate mogul and presumptive Republican nominee for president.

The first of those potential jurors was dismissed after she said she didn’t think she could be fair. “I have really, really bad anxiety and people have found out where I am,” she told the judge. A short time later, two other potential jurors were dismissed after each told the judge that upon further reflection, “I don’t think I can be impartial.”

Other potential jurors included a married father who said he listens to a podcast called “Order of Man,” which is described on Apple’s website as discussions about “reclaiming what it means to be a man.” Some past guests of the podcast include people who’ve been outspoken in their support of Trump and were highly critical of the civil fraud case New York Attorney General Letitia James brought against the former president. The man, an audio specialist, was chosen as one of the alternates.

Another potential juror was a married fund manager who said he’d done “get-out-the-vote” work for former Secretary of State Hillary Clinton, Trump’s 2016 presidential opponent. Trump and his attorney Todd Blanche passed notes back and forth while that juror was speaking. He was later dismissed after being asked about a 2020 Facebook post where he apparently called Trump “the devil and a sociopath.”

Trump appeared most interested in jurors whose answers offer ambiguity around their personal political views. When one prospective juror said they were a Fox News viewer, Trump cocked his head, then quickly conferred with his lawyer, Todd Blanche.

Another potential juror was a woman who became emotional as she disclosed she'd served two years in prison on drug-related charges, but said she could be "fair and impartial."

During a morning break, Merchan — who'd chided reporters on Thursday for disclosing too much information about potential jurors — said the woman had shared "very personal things about her life" and was "very brave." “I just wanted to encourage the press to please be kind. Please be kind to this person,” the judge said. He later dismissed her, saying she needed a certificate of release to be qualified for service going forward. On her way out, she cheerfully called out, "Good luck!"

Following that juror's departure, the DA's office began its individual questioning of the jurors. One woman, who'd disclosed that her father is lifelong friends with Trump ally turned critic Chris Christie, broke down in tears when prosecutor Susan Hoffinger asked her an innocuous question about the burden of proof in the case. "I feel so nervous and anxious right now. I’m sorry," she responded, bursting out into tears. "I thought I could do this," she said, adding "I wouldn’t want someone who feels this way to judge my case." She was dismissed.

Hoffinger's questioning was followed by Trump attorney Susan Necheles, who asked a potential juror who'd started their own business how she would assess a witness's credibility. The woman then asked to speak to the judge, saying she was "getting anxiety and self-doubt” from Necheles's line of questioning. She was dismissed.

Necheles later asked another woman — who previously said she was a victim of sexual assault — whether she would hold it against Trump that women outside this case have accused Trump of sexual assault. She said she would not have a problem setting those accusations aside but the judge ultimately excused her, saying, "It’s best to err on the side of caution."

Another man said he has some differences from Trump on his policies but thinks he's “usually awesome.” He was not chosen for the jury.

On his way into court in the morning, Trump again complained the case against him is "unfair," and that the partial gag order preventing him from lashing out at witnesses, prosecutors, court staffers and jurors is not "constitutional." "Everyone else can say whatever they want about me. They can say anything they want. They can continue to make up lies and everything else. They lie. They’re real scum. But you know what? I’m not allowed to speak," he told reporters.

Prosecutors this week asked the judge to fine Trump and hold him in contempt for social media posts that they said violate the gag order. A hearing on the matter is scheduled for Tuesday.

The m a in pa nel of 12 is made up of seven men and five women, including two lawyers, a teacher, a retired wealth manager, a product development manager, a security engineer, a software engineer, a speech therapist and a physical therapist. The foreman — the juror who essentially acts as the leader and spokesperson for the panel — is a married man who works in sales and gets his news from The New York Times, MSNBC and Fox News.

The lone alternate selected Thursday is a woman who works as an asset manager.

Trump vented about the speed of the process in a post on social media shortly after the final jurors were selected, claiming the judge is “‘railroading’ me, at breakneck speed, in order to completely satisfy his ‘friends’.”

Later in the day, Merchan held what's known as a Sandoval hearing . That's a type of hearing designed to let defendants know the scope of questions they could face from prosecutors on cross-examination so they can make informed decisions about whether to take the witness stand in their own defense.

Leaving court on Friday, Trump was asked whether he was still planning to testify and he said he was.

Manhattan District Attorney Alvin Bragg's office disclosed in a court filing that it would like to ask Trump about several items, among them the $464 million civil judgment against him and his company for fraud , the total $88 million verdicts and liability findings for sexual abuse and defamation in lawsuits brought by writer E. Jean Carroll and a number of other adverse court rulings over the past few years.

Trump has denied wrongdoing in all the cases and is appealing the fraud judgment and the Carroll verdicts.

Prosecutors said they want to be able to bring those findings up “to impeach the credibility of the defendant” if he takes the witness stand.

Discussing the findings in the fraud case, prosecutor Matthew Colangelo told the judge it was "hard to think of something that is more squarely in the wheelhouse” for the DA to ask Trump about "than a finding by a judge of persistent and repeated fraud and illegality."

Trump's attorney Emil Bove countered that prosecutors shouldn't be able to breach the topic at all because Trump's appeal is still pending. He made similar arguments over the DA's contention that they should be allowed to ask about a judge's finding that he was untruthful on the witness stand during the fraud trial and had violated a gag order in the case.

“Is it your position that because a case is being appealed or might be appealed, that therefore it can not be used?" Merchan asked the lawyer. "Not necessarily," Bove replied.

The judge said he'd issue his ruling on the dispute on Monday morning.

Trump said last week he “absolutely” plans to testify , but he is under no obligation to do so.

Asked by Necheles at the end of the day who the DA's first witness would be, prosecutor Joshua Steinglass said they wouldn't inform Trump's team of the person's identity until Sunday, given that Trump has been criticizing some witnesses on social media despite the partial gag order in the case. “And if that should be tweeted, that’ll be the last time we provide that courtesy,” Steinglass said.

Merchan called the DA's position "understandable" and told Necheles "I will not compel them to do anything."

Trump has pleaded not guilty to 34 counts of falsifying business records and faces up to four years in prison if he is convicted.

Bragg alleges that Trump falsified records to hide money he was paying his former lawyer Michael Cohen to reimburse him for $130,000 he paid adult film actor Stormy Daniels near the end of the 2016 presidential campaign. Daniels has claimed she had a sexual encounter with Trump in 2006. Trump has denied that he slept with Daniels, but he has acknowledged repaying Cohen.

The DA’s office also alleges that as part of a scheme to boost Trump, National Enquirer publisher American Media Inc. paid $150,000 to model and actor Karen McDougal , who appeared in Playboy magazine and claimed that she had a nine-month affair with Trump before he was elected president “in exchange for her agreement not to speak out about the alleged sexual relationship,” according to a statement of facts filed by Bragg.

Trump has also denied having a sexual relationship with McDougal.

Adam Reiss is a reporter and producer for NBC and MSNBC.

Lisa Rubin is an MSNBC legal correspondent and a former litigator.

Dareh Gregorian is a politics reporter for NBC News.

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