historical validity in research

Chapter 16. Archival and Historical Research

Introduction.

The British sociologist John Goldthorpe ( 2000 ) once remarked, “Any sociologist who is concerned with a theory that can be tested in the present should so test it, in the first place; for it is, in all probability, in this way that it can be tested most rigorously” ( 33 ). Testing can be done through either qualitative or quantitative methods or some mixture of the two. But sometimes a theory cannot be tested in the present at all. What happens when the persons or phenomena we are interested in happened in the past? It’s hardly possible to interview the people involved in abolishing the slave trade, for example. Does this mean social scientists have no role to play in understanding past phenomena? Not at all. People leave traces behind, and although these traces may not be exactly as we would like them to be had we ordered them (as, in a way, we do when we construct an interview guide or a survey with the questions we want answered), they are nevertheless full of potential for exploration and analysis. For examining traces left by persons, we turn to archival methods, the subject of this chapter.

Things happening in the past are not the only reason we turn to archival methods. Sometimes, the people we are interested in are inaccessible to us for other reasons. For example, we are probably not going to be able to sit down and ask Mark Zuckerberg, Bill Gates, and Jeff Bezos a long list of questions about what it is like to be wealthy. And it is even more unlikely that we can get into the boardrooms of Facebook (Meta), Microsoft, or Amazon to watch how corporate decisions are made. But these men and these companies still leave traces, through public records, media reportage, and public meeting minutes. We can use archival methods here too. They might not be quite as good as face-to-face interviews with billionaires or deep ethnographies of corporate culture, but they are nevertheless valid forms of research with much to tell us.

This chapter introduces archival methods of data collection. We begin by exploring in more detail why and when archival methods should be employed and with what limitations. We then discuss the importance of special collections and archives as potential gold mines for social science research. We will explain how to access these places, for what purposes, and how to begin to make sense of what you find.

Disciplinary Segue: Why Social Scientists Don’t Leave Archives to the Historians

One might suppose that only historians look at the past and that historical archives are no place for social scientists. Goldthorpe ( 2000 ) even suggested this. But it would be a mistake to leave historical analyses entirely to historians because historians “typically do not understand our [social science] intellectual and organizational projects.…Social scientists must learn to use the materials that historians have staked out traditionally as their own” ( Hill 1993:4 ). The key difference for our purposes between history and social science is how each discipline understands the goals of its work and how to understand social life. Historians are (mostly) committed to an idiographic approach, where each case is explored to understand itself (this is the “idios” part, where ιδιοs is Ancient Greek for single self). [1] As an example of an idiographic approach, a historian might study the events of January 6, 2021, to understand how a violent mob attempted to stop the electoral count. This might mean tracing motivations back to beliefs in fanciful conspiracies, measuring the impact of Donald Trump’s rhetoric on the violence, or any number of interesting facts and circumstances about that day and what led up to it. But the focus would remain on understanding this case itself. In contrast, social scientists are (mostly) committed to nomothetic research, in which generalizations about the social world are made to understand large-scale social patterns. [2] Whether this generalization is statistical, as quantitative research produces (e.g., we can predict this outcome in other cases and places based on measurable relationships among variables), or theoretical, as qualitative research produces (e.g., we can expect to find similar patterns between conspiratorial belief and action), the point of (most) social science research is to explain the world in such a way that we can possibly expect (if not outright predict) what will happen or be believed in a different place and time . Social scientists are engaged in this “scientific” project of prediction (loosely understood), while historians are (usually) not. It is for this reason that social scientists should not leave archival research to the historians!

When to Use Archival Materials

As mentioned above, sometimes the people we want to hear from or observe are simply not available to us. This may be because they are no longer living or because they are unwilling or unable to be part of a research study, as in the case of elites (e.g., CEOs of Fortune 500 companies, political leaders and other public figures, the very wealthy). In both cases, you might wonder about the ethics of studying people who have not given written consent to be studied. But using archival and historical sources as your research data is not the same thing as studying persons (“human subjects”). When we use archival and historical sources, we are examining the traces that people and institutions have left. Institutional review boards (IRBs) do not have jurisdiction in this area, although we still want to consider the ethics of our research and try to respect privacy and confidentiality when warranted.

In addition to using archival and historical sources when people are inaccessible, there are other reasons we might want to collect this data. First, we may want to explore the generalized discourse about a phenomenon. [3] For example, perhaps we want to understand the historical context of the 2016 US presidential election, so we think it is important to go back in time and collect data that will more vividly paint a picture of how people at the time were evaluating and experiencing the election. We might use archives to collect data about what people were saying about the third presidential debate in 2016 between candidates Hillary Clinton and Donald Trump. There are many ways we could go about doing that. We could sample local and national newspapers and collect op-eds and letters to the editor about the debate. Perhaps we can get Twitter feeds #thirddebate , or perhaps some librarian in 2016 collected oral histories of people’s reactions the day after. Unlike previous person-focused qualitative research strategies, where we carefully create a research design that allows us to construct data through questioning and observing, we will spend our time tracking down data and finding out what possibly exists.

A second (or third) reason to employ these archival and historical sources is that we are interested in the historical “record” as the phenomenon itself. We want to know what was written down by Acme Company in letters to its shareholders from 1945 to 1960 about its Acme Pocket Sled (which had the unfortunate habit of accelerating and hurling its bearers off cliffs). [4] Our interest here is not in any particular human subject but in the record left by the company. If we were forced to employ interviews or observational methods to get this record, we could interview current and former employees of Acme or shareholders who received letters from the company, but all of this would actually be second best because what the employees and shareholders remember would probably be nowhere near as accurate as what the records reflect. I once did a study of the development of US political party platforms over the course of the nineteenth century, using a huge volume I randomly found in the library ( Hurst 2010b ). The volume recorded each party’s platform by election year, so I could trace how parties talked about and included “class” and “class inequality” in their platforms. This allowed me to show how third parties pushed the two major parties toward some recognition of labor rights over time. There was obviously no way to get at this information through interviews or observations.

Finally, archival and historical sources are often used to supplement other qualitative data collection as a form of verification through triangulation. Perhaps you interviewed several Starbucks employees in 2021 about their experiences working for the company, particularly how the company responded to labor organizing attempts. You might also search official Starbucks company records to compare and contrast the official line with the experience of workers. Alternatively, you could collect media coverage of local organizing campaigns that might include quotes or statements from Starbucks representatives. The best and most convincing qualitative researchers often employ archival and historical material in this way. In addition to providing verification through triangulation, supplementing your data with these sources can deepen contextualization. I encourage you to think about what possible archival and historical sources could strengthen any interview or observational-based study you are designing. [5]

How to Find Archival and Historical Sources

People and institutions leave traces in a variety of ways. This section documents some of those ways with the hope that the possibilities listed here will inspire you to explore further.

It might help to distinguish between public and private sources. Many public archives have dedicated web addresses so you can search them from anywhere. More on those below. Private individuals are more likely to have donated personal information to particular archives, perhaps the archival center associated with the college they attended. Famous and not-so-famous people’s diaries and letters are often searchable in particular university archives. Each former US president has his (!) own dedicated national archive. Towns and cities often house interesting historical records in their public libraries. Archivists and librarians at special archives have often done monumental work creating and curating collections of various kinds. Oregon State University’s Special Collections and Archives Research Center (SCARC) is no exception. In addition to a ton of material related to the history of the university, including private diaries of students, financial aid records, and photographs of carpentry classes from the nineteenth century, the librarians have documented the experiences of LGBTQ people within OSU and Corvallis, the history of hops and brewing in the Northwest, and the history of natural resources in the Pacific Northwest, especially around agriculture and forestry.

It can be overwhelming to think about where to start. Being strategic about your use of archival and historical material is often a large part of an effective research plan. Here are some options for kinds of materials to explore:

Public archives include the following:

• Commercial media accounts . These are anything written, drawn, or recorded that is produced for a general audience, including newspapers, books, magazines, television program transcripts, drawn comics, and so on.
• Where to find these: special collections, online newspaper/magazine databases, collected publications [6]
• Examples: Time Magazine Vault is completely free and covers everything the magazine published from 1923 to today; Harper’s Magazine archives go back to 1859; Internet Archive’s Ebony collection is a wealth of historically important images and stories about African American life in the twentieth century and covers the magazine from 1945 to 2015.
• Actuarial and military records . These include birth and death records, records of marriages and divorces, applications for insurance and credit, military service records, and cemeteries (gravestones).
• Where to find these: state archives/state vital records offices, US Census / government agencies, US National Archives
• Examples: USAgov/genealogy will help you walk through the ordering of various vital records related to ancestry; US Census 1950 includes information on household size and occupation for all persons living in the US in 1950; [7] your local historical cemetery will have lots of information recorded on gravestones of possible historical use, as the case where deaths are clustered around a particular point in time or where military service is involved.
• Official and quasi-official documentary records . These include organization meeting minutes, reports to shareholders, interoffice memos, company emails, company newsletters, and so on.
• Where to find these: Historical records are often donated to a special collection or are even included in an official online database. More recent records may have been “leaked” to the public, as in the case of the Democratic National Committee’s emails in 2016 or the Panama (2016) and Pandora (2021) Papers leaks. The National Archives are also a great source for official documentary records of the US and its various organizations and branches (e.g., Supreme Court, US Patent Office).
• Examples: The Forest History Society’s Weyerhauser Collection holds correspondences, director and executive files, branch and region files, advertising materials, oral histories, scrapbooks, publications, photographs, and audio/visual items documenting the activities of the Pacific Northwest timber company from its inception in 1864 through to 2010; the National Archive’s Lewis and Clark documents include presidential correspondences and a list of “presents” received from Native Americans.
• Governmental and legislative documentary records
• Where to find these: National Archives, state archives, Library of Congress, governmental agency records (often available in public libraries)
• Example: Records of the Supreme Court of the United States are housed in the National Archives and include scrapbooks from 1880 to 1935 on microfilm, sound recordings, and case files going back to 1792.

Private archives include the following:

• Autobiographies and memoirs . These might have been published, but they are just as likely to have been written for oneself or one’s family, with no intention of publication. Some of these have been digitized, but others will require an actual visit to the site to see the physical object itself.
• Where to find these: if not published, special collections and archives
• Example: John Adger McCrary graduated from Clemson University in 1898, where he received a degree in mechanical and electrical engineering. After graduation, he was stationed at the Washington Navy Yard as senior mechanical engineer. He donated a 1939 unpublished memoir regarding the early days of Clemson College, which includes a description of the first dormitory being built by convict labor.
• Diaries and letters . These are probably not intended for publication; rather, they are contemporaneous private accounts and correspondences. Some of these have been digitized, but others will require an actual visit to the site to see the physical object itself.
• Where to find these: special collections and archives, Library of Congress for notable persons’ diaries and letters
• Examples: Abraham Lincoln’s Papers housed in the Library of Congress; Diary of Ella Mae Cloake , an OSU student, from 1941 to 1944, documenting her daily activities as a high school and college student in Oregon during World War II, located in OSU Special Collections and Archives
• Home movies, videos, photographs of various kinds . These include drawings and sketches, recordings of places seen and visited, scrapbooks, and other ephemera. People leave traces in various forms, so it is best not to confine yourself solely to what has been written.
• Where to find these: special collections and archives, Library of Congress, Smithsonian
• Example: The McMenamins Brewery Collection at OSU SCARC includes digitized brew sheets, digital images, brochures, coasters, decals, event programs, flyers, newspaper clippings, tap handles, posters, labels, a wooden cask, and a six-pack of Hammerhead beer.
• Oral histories . Oral histories are recorded and often transcribed interviews of various persons for purposes of historic documentation. To the untrained eye, they appear to be qualitative “interviews,” but they are in fact specifically excluded from IRB jurisdiction because their purpose is documentation, not research.
• Where to find these: special collections and archives; Smithsonian
• Examples: Many archivists and librarians are involved in the collecting of such oral histories, often with a particular theme in mind or to strengthen a particular collection. For example, OSU’s SCARC has an Oregon Multicultural Archive, which includes oral histories that document the experiences and perspectives of people of color in Oregon. The Smithsonian is another great resource on a wide variety of historical events and persons.

How to Find Special Collections and Archives

Although much material has been “digitized” and is thus searchable online, the vast majority of private archival material, including ephemera like scrapbooks and beer coasters, is only available “on site.” Qualitative researchers who employ archival and historical sources must often travel to special collections to find the material they are interested in. Often, the material they want has never really been looked at by another researcher. It may belong to a general catalog entry (such as “Student Scrapbooks, 1930–1950”). For official records at the city or county level, travel to the records office or local public library is often required to access the desired material. You will want to consider what kinds of material are available and what kinds of access are required for that material in your research plan.

The good news is that, even if much material has not been digitized, there are general searchable databases for most archives. If you have a particular topic of interest, you can run a general web search and include the topic and “archives” or “special collection.” The more public and well known the entity, the more likely you will find digitally available material or special collections dedicated to the person or phenomenon. Or you might find an archive housed one thousand miles away that is happy to work with you on a visit. Some researchers become very familiar with a particular collection or database and tend to rely on that in their research. As you gain experience with historical documents, you will find it easier to narrow down your searches. One great place to start, though, is your college or university archives. And the librarians who work there will be more than happy to help answer your questions about both the particular collections housed there and how to do archival research in general.

What to Do with All That Content

Once you have found a collection or body of material, what do you do with that? Analyzing content will be discussed in some detail in chapter 17, but for now, let’s think about what can be made of this kind of material and what cannot. As Goldthorpe ( 2000 ) suggested, using historical material or traces left by people is sometimes second best to actually talking to people or observing them in action. We have to be very clear about recognizing the limitations of what we find in the archives.

First, not everything produced manages to survive the ravages of time. Without digitization, historical records are vulnerable to a host of destroyers. Some vital records get destroyed when the local registry burns down, for example. Some memoirs or diaries are destroyed from mildew while sitting in a box in the basement. Photographs get torn up. Boxes of records get accidentally thrown in the garbage. We call this the historical-trace problem. What we have in front of us is thus probably not the entire record of whatever it is we are looking for.

Second, what gets collected is itself often related to who has power and who is perceived as being worthy of recording and collection. This is why projects like OSU’s multicultural archives are so important, as librarians intervene to ensure that it is not only the stories (diaries, papers) of the powerful that are found in the archives. If one were to read all the newspaper editorials from the nineteenth century, one would learn a lot about particular White men’s thoughts on current events but very little from women or people of color or working-class people. This is the power problem of archives, and we need to be aware of it, especially when we are using historical material to build a context of what a time or place was like. What it was like for whom always needs to be properly addressed.

Third, there are issues related to truth telling and audience. There are no at-the-moment credibility checks on the materials you find in archives. Although we think people tend to write honestly in their personal journals, we don’t actually know if this is the case—what about the person who expected to be famous and writes for an imagined posterity? There could be significant gaps and even falsehoods in such an account. People can lie to themselves too, which is something qualitative researchers know well (and partly the reason ethnographers favor observation over interviews). Despite the absence of credibility checks, historical documents sometimes appear more honest simply by having survived for so long. It is important to remember that they are prone to all the same problems as contemporaneously collected data. A diary by a planter in South Carolina in the 1840s is no more and often less truthful to the facts than an interview would have been had it been possible. Newspapers and magazines have always targeted particular audiences—a fact we understand about our own media (e.g., Fox News is hardly “fair and balanced” toward Democrats) but something we are prone to overlook when reading historic media stories.

Whenever using archival or historical sources, then, it is important to clearly identify and state the limitations of their use and any intended audience. In the case of diaries of Southern planters from the 1840s, “This is the story we get told from the point of view of relatively elite White men whose work was collected and safeguarded (and not destroyed) for posterity.” Or in the case of a Harper’s Magazine story from the 1950s, “This is an understanding of Eisenhower politics by a liberal magazine read by a relatively well-educated and affluent audience.”

Collecting the data for an archival-based study is just the beginning. Once you have downloaded all the advertisements from Men’s Health or compiled all the tweets put out on January 6 or scanned all the photographs of the childcare center in the 1950s, you will need to start “analyzing” it. What does that analysis entail? That is the subject of our next several chapters.

Further Readings

Baker, Alan R. H. 1997. “‘The Dead Don’t Answer Questionnaires’: Researching and Writing Historical Geography.” Journal of Geography in Higher Education 21(2):231–243. Among other things, this article discusses the problems associated with making geographical interpretations from historical sources.

Benzecry, Claudio, Andrew Deener, and Armando Lara-Millán. 2020. “Archival Work as Qualitative Sociology.” Qualitative Sociology 43:297–303. An editorial foreword to an issue of Qualitative Sociology dedicated to archival research briefly describing included articles (many of which you may want to read). Distinguishes the “heroic moment of data accumulation” from the “ascetic and sober exercise of distancing oneself from the data, analyzing it, and communicating the meaning to others.” For advanced students only.

Bloch, Marc. 1954. The Historian’s Craft . Manchester: Manchester University Press. A classic midcentury statement of what history is and does from a research perspective. Bloch’s particular understanding and approach to history has resonance for social science too.

Fones-Wolf, Elizabeth A. 1994. Selling Free Enterprise: The Business Assault on Labor and Liberalism, 1945–60 . Urbana: University of Illinois Press.* Using corporate records, published advertisements, and congressional testimony (among other sources), Fones-Wolf builds an impressive account of a coordinated corporate campaign against labor unions and working people in the postwar years.

Hill, Michael R. 1993. Archival Strategies and Techniques . Thousand Oaks, CA: SAGE. Guidebook to archival research. For advanced students.

Moore, Niamh, Andrea Salter, Liz Stanley, and Maria Tamboukou. 2017. The Archive Project: Archival Research in the Social Sciences . London: Routledge. An advanced collection of essays on various methodological ideas and debates in archival research.

Stoler, Ann Laura. 2009. Along the Archival Grain: Epistemic Anxieties and Colonial Common Sense . Princeton, NJ: Princeton University Press.* A difficult but rewarding read for advanced students. Using archives in Indonesia, Stoler explores the history of colonialism and the making of racialized classes while also proposing and demonstrating innovative archival methodologies.

Wilder, Craig Stevens. 2014. Ebony and Ivory: Race, Slavery, and the Troubled History of America’s Universities . London: Bloomsbury.* Although perhaps more history than social science, this is a great example of using university archival data to tell a story about national development, racism, and the role of universities.

• This is where the word idiot comes from as well; in Ancient Greece, failing to participate in collective democracy making was seen as “idiotic”—or, put another way, selfish. ↵
• This word also comes from Greek roots, although it was created recently (we often rummage around in Ancient Greek and Latin when we come up with new concepts!). In Greek, nomos (νομος) means “law.” The use here makes much of the generation of laws or regularities about the social world in the sense of Newton’s “law” of gravity. ↵
• If this is your interest, see also chapter 17, “Content Analysis”! ↵
• For those of you too young to remember, this was a standard plot of Looney Tunes cartoons featuring Wile E. Coyote ( Frazier 1990 ). ↵
• Note that this would be an example of strength through multiple methods rather than strength through mixed methods (chapter 15). The former deepens the contextualization, while the latter increases the overall validity of the findings. ↵
• Such as that volume of party platforms I stumbled across in the library! ↵
• US Census material becomes available to the public seventy years after collection; Census data from the 1950s recently became available for the very first time. ↵

A form of social science research that generally follows the scientific method as established in the natural sciences.  In contrast to idiographic research , the nomothetic researcher looks for general patterns and “laws” of human behavior and social relationships.  Once discovered, these patterns and laws will be expected to be widely applicable.  Quantitative social science research is nomothetic because it seeks to generalize findings from samples to larger populations.  Most qualitative social science research is also nomothetic, although generalizability is here understood to be theoretical in nature rather than statistical .  Some qualitative researchers, however, espouse the idiographic research paradigm instead.

An administrative body established to protect the rights and welfare of human research subjects recruited to participate in research activities conducted under the auspices of the institution with which it is affiliated. The IRB is charged with the responsibility of reviewing all research involving human participants. The IRB is concerned with protecting the welfare, rights, and privacy of human subjects. The IRB has the authority to approve, disapprove, monitor, and require modifications in all research activities that fall within its jurisdiction as specified by both the federal regulations and institutional policy.

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Internal Validity vs. External Validity in Research

What they tell us about the meaningfulness and trustworthiness of research

Verywell / Bailey Mariner

• Internal Validity
• External Validity

How do you determine whether a psychology study is trustworthy and meaningful? Two characteristics that can help you assess research findings are internal and external validity.

• Internal validity measures how well a study is conducted (its structure) and how accurately its results reflect the studied group.
• External validity relates to how applicable the findings are in the real world.

These two concepts help researchers gauge if the results of a research study are trustworthy and meaningful.

Conclusions are warranted

Controls extraneous variables

Eliminates alternative explanations

Focus on accuracy and strong research methods

Findings can be generalized

Outcomes apply to practical situations

Results apply to the world at large

Results can be translated into another context

What Is Internal Validity in Research?

Internal validity is the extent to which a research study establishes a trustworthy cause-and-effect relationship. This type of validity depends largely on the study's procedures and how rigorously it is performed.

Internal validity is important because once established, it makes it possible to eliminate alternative explanations for a finding. If you implement a smoking cessation program, for instance, internal validity ensures that any improvement in the subjects is due to the treatment administered and not something else.

Internal validity is not a "yes or no" concept. Instead, we consider how confident we can be with study findings based on whether the research avoids traps that may make those findings questionable. The less chance there is for "confounding," the higher the internal validity and the more confident we can be.

Confounding refers to uncontrollable variables that come into play and can confuse the outcome of a study, making us unsure of whether we can trust that we have identified the cause-and-effect relationship.

In short, you can only be confident that a study is internally valid if you can rule out alternative explanations for the findings. Three criteria are required to assume cause and effect in a research study:

• The cause preceded the effect in terms of time.
• The cause and effect vary together.
• There are no other likely explanations for the relationship observed.

Factors That Improve Internal Validity

To ensure the internal validity of a study, you want to consider aspects of the research design that will increase the likelihood that you can reject alternative hypotheses. Many factors can improve internal validity in research, including:

• Blinding : Participants—and sometimes researchers—are unaware of what intervention they are receiving (such as using a placebo on some subjects in a medication study) to avoid having this knowledge bias their perceptions and behaviors, thus impacting the study's outcome
• Experimental manipulation : Manipulating an independent variable in a study (for instance, giving smokers a cessation program) instead of just observing an association without conducting any intervention (examining the relationship between exercise and smoking behavior)
• Random selection : Choosing participants at random or in a manner in which they are representative of the population that you wish to study
• Randomization or random assignment : Randomly assigning participants to treatment and control groups, ensuring that there is no systematic bias between the research groups
• Strict study protocol : Following specific procedures during the study so as not to introduce any unintended effects; for example, doing things differently with one group of study participants than you do with another group

Internal Validity Threats

Just as there are many ways to ensure internal validity, a list of potential threats should be considered when planning a study.

• Attrition : Participants dropping out or leaving a study, which means that the results are based on a biased sample of only the people who did not choose to leave (and possibly who all have something in common, such as higher motivation)
• Confounding : A situation in which changes in an outcome variable can be thought to have resulted from some type of outside variable not measured or manipulated in the study
• Diffusion : This refers to the results of one group transferring to another through the groups interacting and talking with or observing one another; this can also lead to another issue called resentful demoralization, in which a control group tries less hard because they feel resentful over the group that they are in
• Experimenter bias : An experimenter behaving in a different way with different groups in a study, which can impact the results (and is eliminated through blinding)
• Historical events : May influence the outcome of studies that occur over a period of time, such as a change in the political leader or a natural disaster that occurs, influencing how study participants feel and act
• Instrumentation : This involves "priming" participants in a study in certain ways with the measures used, causing them to react in a way that is different than they would have otherwise reacted
• Maturation : The impact of time as a variable in a study; for example, if a study takes place over a period of time in which it is possible that participants naturally change in some way (i.e., they grew older or became tired), it may be impossible to rule out whether effects seen in the study were simply due to the impact of time
• Statistical regression : The natural effect of participants at extreme ends of a measure falling in a certain direction due to the passage of time rather than being a direct effect of an intervention
• Testing : Repeatedly testing participants using the same measures influences outcomes; for example, if you give someone the same test three times, it is likely that they will do better as they learn the test or become used to the testing process, causing them to answer differently

What Is External Validity in Research?

External validity refers to how well the outcome of a research study can be expected to apply to other settings. This is important because, if external validity is established, it means that the findings can be generalizable to similar individuals or populations.

External validity affirmatively answers the question: Do the findings apply to similar people, settings, situations, and time periods?

Population validity and ecological validity are two types of external validity. Population validity refers to whether you can generalize the research outcomes to other populations or groups. Ecological validity refers to whether a study's findings can be generalized to additional situations or settings.

Another term called transferability refers to whether results transfer to situations with similar characteristics. Transferability relates to external validity and refers to a qualitative research design.

Factors That Improve External Validity

If you want to improve the external validity of your study, there are many ways to achieve this goal. Factors that can enhance external validity include:

• Field experiments : Conducting a study outside the laboratory, in a natural setting
• Inclusion and exclusion criteria : Setting criteria as to who can be involved in the research, ensuring that the population being studied is clearly defined
• Psychological realism : Making sure participants experience the events of the study as being real by telling them a "cover story," or a different story about the aim of the study so they don't behave differently than they would in real life based on knowing what to expect or knowing the study's goal
• Replication : Conducting the study again with different samples or in different settings to see if you get the same results; when many studies have been conducted on the same topic, a meta-analysis can also be used to determine if the effect of an independent variable can be replicated, therefore making it more reliable
• Reprocessing or calibration : Using statistical methods to adjust for external validity issues, such as reweighting groups if a study had uneven groups for a particular characteristic (such as age)

External Validity Threats

External validity is threatened when a study does not take into account the interaction of variables in the real world. Threats to external validity include:

• Pre- and post-test effects : When the pre- or post-test is in some way related to the effect seen in the study, such that the cause-and-effect relationship disappears without these added tests
• Sample features : When some feature of the sample used was responsible for the effect (or partially responsible), leading to limited generalizability of the findings
• Selection bias : Also considered a threat to internal validity, selection bias describes differences between groups in a study that may relate to the independent variable—like motivation or willingness to take part in the study, or specific demographics of individuals being more likely to take part in an online survey
• Situational factors : Factors such as the time of day of the study, its location, noise, researcher characteristics, and the number of measures used may affect the generalizability of findings

While rigorous research methods can ensure internal validity, external validity may be limited by these methods.

Internal Validity vs. External Validity

Internal validity and external validity are two research concepts that share a few similarities while also having several differences.

Similarities

One of the similarities between internal validity and external validity is that both factors should be considered when designing a study. This is because both have implications in terms of whether the results of a study have meaning.

Both internal validity and external validity are not "either/or" concepts. Therefore, you always need to decide to what degree a study performs in terms of each type of validity.

Each of these concepts is also typically reported in research articles published in scholarly journals . This is so that other researchers can evaluate the study and make decisions about whether the results are useful and valid.

Differences

The essential difference between internal validity and external validity is that internal validity refers to the structure of a study (and its variables) while external validity refers to the universality of the results. But there are further differences between the two as well.

For instance, internal validity focuses on showing a difference that is due to the independent variable alone. Conversely, external validity results can be translated to the world at large.

Internal validity and external validity aren't mutually exclusive. You can have a study with good internal validity but be overall irrelevant to the real world. You could also conduct a field study that is highly relevant to the real world but doesn't have trustworthy results in terms of knowing what variables caused the outcomes.

Examples of Validity

Perhaps the best way to understand internal validity and external validity is with examples.

Internal Validity Example

An example of a study with good internal validity would be if a researcher hypothesizes that using a particular mindfulness app will reduce negative mood. To test this hypothesis, the researcher randomly assigns a sample of participants to one of two groups: those who will use the app over a defined period and those who engage in a control task.

The researcher ensures that there is no systematic bias in how participants are assigned to the groups. They do this by blinding the research assistants so they don't know which groups the subjects are in during the experiment.

A strict study protocol is also used to outline the procedures of the study. Potential confounding variables are measured along with mood , such as the participants' socioeconomic status, gender, age, and other factors. If participants drop out of the study, their characteristics are examined to make sure there is no systematic bias in terms of who stays in.

External Validity Example

An example of a study with good external validity would be if, in the above example, the participants used the mindfulness app at home rather than in the laboratory. This shows that results appear in a real-world setting.

To further ensure external validity, the researcher clearly defines the population of interest and chooses a representative sample . They might also replicate the study's results using different technological devices.

Setting up an experiment so that it has both sound internal validity and external validity involves being mindful from the start about factors that can influence each aspect of your research.

It's best to spend extra time designing a structurally sound study that has far-reaching implications rather than to quickly rush through the design phase only to discover problems later on. Only when both internal validity and external validity are high can strong conclusions be made about your results.

Andrade C. Internal, external, and ecological validity in research design, conduct, and evaluation .  Indian J Psychol Med . 2018;40(5):498-499. doi:10.4103/IJPSYM.IJPSYM_334_18

San Jose State University. Internal and external validity .

Kemper CJ. Internal validity . In: Zeigler-Hill V, Shackelford TK, eds. Encyclopedia of Personality and Individual Differences . Springer International Publishing; 2017:1-3. doi:10.1007/978-3-319-28099-8_1316-1

Patino CM, Ferreira JC. Internal and external validity: can you apply research study results to your patients?   J Bras Pneumol . 2018;44(3):183. doi:10.1590/S1806-37562018000000164

Matthay EC, Glymour MM. A graphical catalog of threats to validity: Linking social science with epidemiology .  Epidemiology . 2020;31(3):376-384. doi:10.1097/EDE.0000000000001161

Amico KR. Percent total attrition: a poor metric for study rigor in hosted intervention designs .  Am J Public Health . 2009;99(9):1567-1575. doi:10.2105/AJPH.2008.134767

Kemper CJ. External validity . In: Zeigler-Hill V, Shackelford TK, eds. Encyclopedia of Personality and Individual Differences . Springer International Publishing; 2017:1-4. doi:10.1007/978-3-319-28099-8_1303-1

Desjardins E, Kurtz J, Kranke N, Lindeza A, Richter SH. Beyond standardization: improving external validity and reproducibility in experimental evolution . BioScience. 2021;71(5):543-552. doi:10.1093/biosci/biab008

Drude NI, Martinez Gamboa L, Danziger M, Dirnagl U, Toelch U. Improving preclinical studies through replications .  Elife . 2021;10:e62101. doi:10.7554/eLife.62101

Michael RS. Threats to internal & external validity: Y520 strategies for educational inquiry .

Pahus L, Burgel PR, Roche N, Paillasseur JL, Chanez P. Randomized controlled trials of pharmacological treatments to prevent COPD exacerbations: applicability to real-life patients . BMC Pulm Med . 2019;19(1):127. doi:10.1186/s12890-019-0882-y

By Arlin Cuncic, MA Arlin Cuncic, MA, is the author of The Anxiety Workbook and founder of the website About Social Anxiety. She has a Master's degree in clinical psychology.

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28 Reliability and Validity: History, Notions, Methods, and Discussion

• Published: June 2016
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Chapter 28 describes the evolution of the two most important concepts in psychometrics and for psychological and educational testing: reliability and validity. Between the publication of the first psychological tests and the most recent developments, the scientific, professional, and ethical requirements demanded by testing have largely evolved. Also the scientific disciplines of psychology and education and the practice based on these disciplines are no longer the same as early in their history. Psychometric models have changed, theories have changed, and the problems and requirements made by psychological and educational practice have changed. It does therefore not surprise that the notions of reliability and validity have also evolved. The aim of this chapter is to offer a historical and conceptual view of both these notions, to discuss some approaches in the investigation of reliability and validity, and to formulate some considerations on the way the two notions have evolved.

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Reliability and Validity: History, Notions, Methods, Discussion

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Reliability vs. Validity in Research | Difference, Types and Examples

Published on July 3, 2019 by Fiona Middleton . Revised on June 22, 2023.

Reliability and validity are concepts used to evaluate the quality of research. They indicate how well a method , technique. or test measures something. Reliability is about the consistency of a measure, and validity is about the accuracy of a measure.opt

It’s important to consider reliability and validity when you are creating your research design , planning your methods, and writing up your results, especially in quantitative research . Failing to do so can lead to several types of research bias and seriously affect your work.

Table of contents

Understanding reliability vs validity, how are reliability and validity assessed, how to ensure validity and reliability in your research, where to write about reliability and validity in a thesis, other interesting articles.

Reliability and validity are closely related, but they mean different things. A measurement can be reliable without being valid. However, if a measurement is valid, it is usually also reliable.

What is reliability?

Reliability refers to how consistently a method measures something. If the same result can be consistently achieved by using the same methods under the same circumstances, the measurement is considered reliable.

What is validity?

Validity refers to how accurately a method measures what it is intended to measure. If research has high validity, that means it produces results that correspond to real properties, characteristics, and variations in the physical or social world.

High reliability is one indicator that a measurement is valid. If a method is not reliable, it probably isn’t valid.

If the thermometer shows different temperatures each time, even though you have carefully controlled conditions to ensure the sample’s temperature stays the same, the thermometer is probably malfunctioning, and therefore its measurements are not valid.

However, reliability on its own is not enough to ensure validity. Even if a test is reliable, it may not accurately reflect the real situation.

Validity is harder to assess than reliability, but it is even more important. To obtain useful results, the methods you use to collect data must be valid: the research must be measuring what it claims to measure. This ensures that your discussion of the data and the conclusions you draw are also valid.

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Reliability can be estimated by comparing different versions of the same measurement. Validity is harder to assess, but it can be estimated by comparing the results to other relevant data or theory. Methods of estimating reliability and validity are usually split up into different types.

Types of reliability

Different types of reliability can be estimated through various statistical methods.

Types of validity

The validity of a measurement can be estimated based on three main types of evidence. Each type can be evaluated through expert judgement or statistical methods.

To assess the validity of a cause-and-effect relationship, you also need to consider internal validity (the design of the experiment ) and external validity (the generalizability of the results).

The reliability and validity of your results depends on creating a strong research design , choosing appropriate methods and samples, and conducting the research carefully and consistently.

Ensuring validity

If you use scores or ratings to measure variations in something (such as psychological traits, levels of ability or physical properties), it’s important that your results reflect the real variations as accurately as possible. Validity should be considered in the very earliest stages of your research, when you decide how you will collect your data.

• Choose appropriate methods of measurement

Ensure that your method and measurement technique are high quality and targeted to measure exactly what you want to know. They should be thoroughly researched and based on existing knowledge.

For example, to collect data on a personality trait, you could use a standardized questionnaire that is considered reliable and valid. If you develop your own questionnaire, it should be based on established theory or findings of previous studies, and the questions should be carefully and precisely worded.

• Use appropriate sampling methods to select your subjects

To produce valid and generalizable results, clearly define the population you are researching (e.g., people from a specific age range, geographical location, or profession).  Ensure that you have enough participants and that they are representative of the population. Failing to do so can lead to sampling bias and selection bias .

Ensuring reliability

Reliability should be considered throughout the data collection process. When you use a tool or technique to collect data, it’s important that the results are precise, stable, and reproducible .

• Apply your methods consistently

Plan your method carefully to make sure you carry out the same steps in the same way for each measurement. This is especially important if multiple researchers are involved.

For example, if you are conducting interviews or observations , clearly define how specific behaviors or responses will be counted, and make sure questions are phrased the same way each time. Failing to do so can lead to errors such as omitted variable bias or information bias .

• Standardize the conditions of your research

When you collect your data, keep the circumstances as consistent as possible to reduce the influence of external factors that might create variation in the results.

For example, in an experimental setup, make sure all participants are given the same information and tested under the same conditions, preferably in a properly randomized setting. Failing to do so can lead to a placebo effect , Hawthorne effect , or other demand characteristics . If participants can guess the aims or objectives of a study, they may attempt to act in more socially desirable ways.

It’s appropriate to discuss reliability and validity in various sections of your thesis or dissertation or research paper . Showing that you have taken them into account in planning your research and interpreting the results makes your work more credible and trustworthy.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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Internal and external validity: can you apply research study results to your patients?

Cecilia maria patino.

1 . Methods in Epidemiologic, Clinical, and Operations Research-MECOR-program, American Thoracic Society/Asociación Latinoamericana del Tórax, Montevideo, Uruguay.

2 . Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.

Juliana Carvalho Ferreira

3 . Divisão de Pneumologia, Instituto do Coração, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo (SP) Brasil.

CLINICAL SCENARIO

In a multicenter study in France, investigators conducted a randomized controlled trial to test the effect of prone vs. supine positioning ventilation on mortality among patients with early, severe ARDS. They showed that prolonged prone-positioning ventilation decreased 28-day mortality [hazard ratio (HR) = 0.39; 95% CI: 0.25-0.63]. 1

STUDY VALIDITY

The validity of a research study refers to how well the results among the study participants represent true findings among similar individuals outside the study. This concept of validity applies to all types of clinical studies, including those about prevalence, associations, interventions, and diagnosis. The validity of a research study includes two domains: internal and external validity.

Internal validity is defined as the extent to which the observed results represent the truth in the population we are studying and, thus, are not due to methodological errors. In our example, if the authors can support that the study has internal validity, they can conclude that prone positioning reduces mortality among patients with severe ARDS. The internal validity of a study can be threatened by many factors, including errors in measurement or in the selection of participants in the study, and researchers should think about and avoid these errors.

Once the internal validity of the study is established, the researcher can proceed to make a judgment regarding its external validity by asking whether the study results apply to similar patients in a different setting or not ( Figure 1 ). In the example, we would want to evaluate if the results of the clinical trial apply to ARDS patients in other ICUs. If the patients have early, severe ARDS, probably yes, but the study results may not apply to patients with mild ARDS . External validity refers to the extent to which the results of a study are generalizable to patients in our daily practice, especially for the population that the sample is thought to represent.

Lack of internal validity implies that the results of the study deviate from the truth, and, therefore, we cannot draw any conclusions; hence, if the results of a trial are not internally valid, external validity is irrelevant. 2 Lack of external validity implies that the results of the trial may not apply to patients who differ from the study population and, consequently, could lead to low adoption of the treatment tested in the trial by other clinicians.

INCREASING VALIDITY OF RESEARCH STUDIES

To increase internal validity, investigators should ensure careful study planning and adequate quality control and implementation strategies-including adequate recruitment strategies, data collection, data analysis, and sample size. External validity can be increased by using broad inclusion criteria that result in a study population that more closely resembles real-life patients, and, in the case of clinical trials, by choosing interventions that are feasible to apply. 2

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Reliability and Validity – Definitions, Types & Examples

Published by Alvin Nicolas at August 16th, 2021 , Revised On October 26, 2023

A researcher must test the collected data before making any conclusion. Every  research design  needs to be concerned with reliability and validity to measure the quality of the research.

What is Reliability?

Reliability refers to the consistency of the measurement. Reliability shows how trustworthy is the score of the test. If the collected data shows the same results after being tested using various methods and sample groups, the information is reliable. If your method has reliability, the results will be valid.

Example: If you weigh yourself on a weighing scale throughout the day, you’ll get the same results. These are considered reliable results obtained through repeated measures.

Example: If a teacher conducts the same math test of students and repeats it next week with the same questions. If she gets the same score, then the reliability of the test is high.

What is the Validity?

Validity refers to the accuracy of the measurement. Validity shows how a specific test is suitable for a particular situation. If the results are accurate according to the researcher’s situation, explanation, and prediction, then the research is valid. 

If the method of measuring is accurate, then it’ll produce accurate results. If a method is reliable, then it’s valid. In contrast, if a method is not reliable, it’s not valid. 

Example:  Your weighing scale shows different results each time you weigh yourself within a day even after handling it carefully, and weighing before and after meals. Your weighing machine might be malfunctioning. It means your method had low reliability. Hence you are getting inaccurate or inconsistent results that are not valid.

Example:  Suppose a questionnaire is distributed among a group of people to check the quality of a skincare product and repeated the same questionnaire with many groups. If you get the same response from various participants, it means the validity of the questionnaire and product is high as it has high reliability.

Most of the time, validity is difficult to measure even though the process of measurement is reliable. It isn’t easy to interpret the real situation.

Example:  If the weighing scale shows the same result, let’s say 70 kg each time, even if your actual weight is 55 kg, then it means the weighing scale is malfunctioning. However, it was showing consistent results, but it cannot be considered as reliable. It means the method has low reliability.

Internal Vs. External Validity

One of the key features of randomised designs is that they have significantly high internal and external validity.

Internal validity  is the ability to draw a causal link between your treatment and the dependent variable of interest. It means the observed changes should be due to the experiment conducted, and any external factor should not influence the  variables .

Example: age, level, height, and grade.

External validity  is the ability to identify and generalise your study outcomes to the population at large. The relationship between the study’s situation and the situations outside the study is considered external validity.

Also, read about Inductive vs Deductive reasoning in this article.

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Threats to Interval Validity

Threats of External Validity

How to Assess Reliability and Validity?

Reliability can be measured by comparing the consistency of the procedure and its results. There are various methods to measure validity and reliability. Reliability can be measured through  various statistical methods  depending on the types of validity, as explained below:

Types of Reliability

Types of Validity

As we discussed above, the reliability of the measurement alone cannot determine its validity. Validity is difficult to be measured even if the method is reliable. The following type of tests is conducted for measuring validity. 

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How to Increase Reliability?

• Use an appropriate questionnaire to measure the competency level.
• Ensure a consistent environment for participants
• Make the participants familiar with the criteria of assessment.
• Train the participants appropriately.
• Analyse the research items regularly to avoid poor performance.

How to Increase Validity?

Ensuring Validity is also not an easy job. A proper functioning method to ensure validity is given below:

• The reactivity should be minimised at the first concern.
• The Hawthorne effect should be reduced.
• The respondents should be motivated.
• The intervals between the pre-test and post-test should not be lengthy.
• Dropout rates should be avoided.
• The inter-rater reliability should be ensured.
• Control and experimental groups should be matched with each other.

How to Implement Reliability and Validity in your Thesis?

According to the experts, it is helpful if to implement the concept of reliability and Validity. Especially, in the thesis and the dissertation, these concepts are adopted much. The method for implementation given below:

Segments	Explanation
	All the planning about reliability and validity will be discussed here, including the chosen samples and size and the techniques used to measure reliability and validity.
	Please talk about the level of reliability and validity of your results and their influence on values.
	Discuss the contribution of other researchers to improve reliability and validity. Frequently Asked Questions What is reliability and validity in research. Reliability in research refers to the consistency and stability of measurements or findings. Validity relates to the accuracy and truthfulness of results, measuring what the study intends to. Both are crucial for trustworthy and credible research outcomes. What is validity? Validity in research refers to the extent to which a study accurately measures what it intends to measure. It ensures that the results are truly representative of the phenomena under investigation. Without validity, research findings may be irrelevant, misleading, or incorrect, limiting their applicability and credibility. What is reliability? Reliability in research refers to the consistency and stability of measurements over time. If a study is reliable, repeating the experiment or test under the same conditions should produce similar results. Without reliability, findings become unpredictable and lack dependability, potentially undermining the study’s credibility and generalisability. What is reliability in psychology? In psychology, reliability refers to the consistency of a measurement tool or test. A reliable psychological assessment produces stable and consistent results across different times, situations, or raters. It ensures that an instrument’s scores are not due to random error, making the findings dependable and reproducible in similar conditions. What is test retest reliability? Test-retest reliability assesses the consistency of measurements taken by a test over time. It involves administering the same test to the same participants at two different points in time and comparing the results. A high correlation between the scores indicates that the test produces stable and consistent results over time. How to improve reliability of an experiment? Standardise procedures and instructions. Use consistent and precise measurement tools. Train observers or raters to reduce subjective judgments. Increase sample size to reduce random errors. Conduct pilot studies to refine methods. Repeat measurements or use multiple methods. Address potential sources of variability. What is the difference between reliability and validity? Reliability refers to the consistency and repeatability of measurements, ensuring results are stable over time. Validity indicates how well an instrument measures what it’s intended to measure, ensuring accuracy and relevance. While a test can be reliable without being valid, a valid test must inherently be reliable. Both are essential for credible research. Are interviews reliable and valid? Interviews can be both reliable and valid, but they are susceptible to biases. The reliability and validity depend on the design, structure, and execution of the interview. Structured interviews with standardised questions improve reliability. Validity is enhanced when questions accurately capture the intended construct and when interviewer biases are minimised. Are IQ tests valid and reliable? IQ tests are generally considered reliable, producing consistent scores over time. Their validity, however, is a subject of debate. While they effectively measure certain cognitive skills, whether they capture the entirety of “intelligence” or predict success in all life areas is contested. Cultural bias and over-reliance on tests are also concerns. Are questionnaires reliable and valid? Questionnaires can be both reliable and valid if well-designed. Reliability is achieved when they produce consistent results over time or across similar populations. Validity is ensured when questions accurately measure the intended construct. However, factors like poorly phrased questions, respondent bias, and lack of standardisation can compromise their reliability and validity. You May Also Like In historical research, a researcher collects and analyse the data, and explain the events that occurred in the past to test the truthfulness of observations. A survey includes questions relevant to the research topic. The participants are selected, and the questionnaire is distributed to collect the data. Inductive and deductive reasoning takes into account assumptions and incidents. Here is all you need to know about inductive vs deductive reasoning. USEFUL LINKS LEARNING RESOURCES COMPANY DETAILS How It Works Privacy Policy Home » Validity – Types, Examples and Guide Validity – Types, Examples and Guide Table of Contents Validity is a fundamental concept in research, referring to the extent to which a test, measurement, or study accurately reflects or assesses the specific concept that the researcher is attempting to measure. Ensuring validity is crucial as it determines the trustworthiness and credibility of the research findings. Research Validity Research validity pertains to the accuracy and truthfulness of the research. It examines whether the research truly measures what it claims to measure. Without validity, research results can be misleading or erroneous, leading to incorrect conclusions and potentially flawed applications. How to Ensure Validity in Research Ensuring validity in research involves several strategies: Clear Operational Definitions : Define variables clearly and precisely. Use of Reliable Instruments : Employ measurement tools that have been tested for reliability. Pilot Testing : Conduct preliminary studies to refine the research design and instruments. Triangulation : Use multiple methods or sources to cross-verify results. Control Variables : Control extraneous variables that might influence the outcomes. Types of Validity Validity is categorized into several types, each addressing different aspects of measurement accuracy. Internal Validity Internal validity refers to the degree to which the results of a study can be attributed to the treatments or interventions rather than other factors. It is about ensuring that the study is free from confounding variables that could affect the outcome. External Validity External validity concerns the extent to which the research findings can be generalized to other settings, populations, or times. High external validity means the results are applicable beyond the specific context of the study. Construct Validity Construct validity evaluates whether a test or instrument measures the theoretical construct it is intended to measure. It involves ensuring that the test is truly assessing the concept it claims to represent. Content Validity Content validity examines whether a test covers the entire range of the concept being measured. It ensures that the test items represent all facets of the concept. Criterion Validity Criterion validity assesses how well one measure predicts an outcome based on another measure. It is divided into two types: Predictive Validity : How well a test predicts future performance. Concurrent Validity : How well a test correlates with a currently existing measure. Face Validity Face validity refers to the extent to which a test appears to measure what it is supposed to measure, based on superficial inspection. While it is the least scientific measure of validity, it is important for ensuring that stakeholders believe in the test’s relevance. Importance of Validity Validity is crucial because it directly affects the credibility of research findings. Valid results ensure that conclusions drawn from research are accurate and can be trusted. This, in turn, influences the decisions and policies based on the research. Examples of Validity Internal Validity : A randomized controlled trial (RCT) where the random assignment of participants helps eliminate biases. External Validity : A study on educational interventions that can be applied to different schools across various regions. Construct Validity : A psychological test that accurately measures depression levels. Content Validity : An exam that covers all topics taught in a course. Criterion Validity : A job performance test that predicts future job success. Where to Write About Validity in A Thesis In a thesis, the methodology section should include discussions about validity. Here, you explain how you ensured the validity of your research instruments and design. Additionally, you may discuss validity in the results section, interpreting how the validity of your measurements affects your findings. Applications of Validity Validity has wide applications across various fields: Education : Ensuring assessments accurately measure student learning. Psychology : Developing tests that correctly diagnose mental health conditions. Market Research : Creating surveys that accurately capture consumer preferences. Limitations of Validity While ensuring validity is essential, it has its limitations: Complexity : Achieving high validity can be complex and resource-intensive. Context-Specific : Some validity types may not be universally applicable across all contexts. Subjectivity : Certain types of validity, like face validity, involve subjective judgments. By understanding and addressing these aspects of validity, researchers can enhance the quality and impact of their studies, leading to more reliable and actionable results. About the author Muhammad Hassan Researcher, Academic Writer, Web developer You may also like Test-Retest Reliability – Methods, Formula and... Split-Half Reliability – Methods, Examples and... Internal Consistency Reliability – Methods... Criterion Validity – Methods, Examples and... Parallel Forms Reliability – Methods, Example... Reliability Vs Validity What Is Internal Validity In Research? Charlotte Nickerson Research Assistant at Harvard University Undergraduate at Harvard University Charlotte Nickerson is a student at Harvard University obsessed with the intersection of mental health, productivity, and design. Learn about our Editorial Process Saul McLeod, PhD Editor-in-Chief for Simply Psychology BSc (Hons) Psychology, MRes, PhD, University of Manchester Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology. Olivia Guy-Evans, MSc Associate Editor for Simply Psychology BSc (Hons) Psychology, MSc Psychology of Education Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors. On This Page: Internal validity refers to whether the design and conduct of a study are able to support that a causal relationship exists between the independent and dependent variables . It ensures that no other variables except the independent variable caused the observed effect on the dependent variable. Conducting research that has strong internal and external validity requires thoughtful planning and design from the outset. Rather than hastening through the design process, it’s wise to invest sufficient time in structuring a study that is methodologically robust and widely applicable.  By carefully considering factors that can compromise internal and external validity during the design phase, one can avoid having to remedy issues later.  Research that exhibits both high internal and external validity permits drawing forceful conclusions about the findings. Though it may require more initial effort, ensuring studies have sound internal and external validity is necessary for producing meaningful and influential research. For example, if you implement a smoking cessation program and see improvement among participants, high internal validity means you can be confident this is due to the program itself rather than other influences.  Internal validity is not black-and-white – it’s about the level of confidence we can have in results based on how well the study controls for variables that could undermine the findings.  The more a study avoids potential “confounding factors,” the higher its internal validity and the more faith we can place in the cause-effect relationship it uncovers.  For the general public, internal validity is important because it means a given study’s results and takeaways can be trusted and applied. Threats to Internal Validity Confounding variables. Confounding variables are extraneous factors that influence the dependent variables in an experiment, causing a misleading association and making it difficult to isolate the true effect of the independent variable.  They threaten internal validity because they provide alternative explanations for study results, making it unclear if changes in the dependent variable are really due to manipulation of the independent variable or due to the confounding variable. A failure to control extraneous variables undermines the ability of researchers to create causal inferences logically. Unfortunately, however, confounding variables are difficult to control outside of laboratory settings. Nonetheless, Campbell (1957) identified several confounding variables that can threaten internal validity.  Participant Factors Participant reaction biases threaten internal validity because participants may act differently when they know they are being observed. These biases include participant expectancies, participant reactance, and evaluation apprehension. Participant expectancies occur when a participant, consciously or unconsciously, attempts to behave in a way that the experimenter expects them to. The overly cooperative participant may often base their behavior on factors such as study setting and directions.  Participant expectancies may also occur during a participant screening process. For example, a participant hoping to participate in a study about depression may exaggerate their symptoms on a screening questionnaire to appear more eligible for the study. Participant reactance occurs when participants intentionally try to act in a way counter to the experimenter’s hypothesis. For example, if studying the effects of daylight exposure on sleep habits, a participant may intentionally sleep at exactly the same time, regardless of whether or not they are exposed to daylight. Intentional uncooperativeness could result from a desire for autonomy or independence (Brehm, 1966). Evaluation apprehension happens when a desire to appear consistent with social or group beliefs affects participant responses. This response style can polarize responses and lead to inappropriate conclusions. For instance, participants asked about their opinions on a political issue in a group may feel pressure to conform to the responses of other group members.  Broadly, researchers can reduce these biases by guaranteeing participant anonymity, using cover stories, unobtrusive observations, and indirect measures. Sampling bias Sampling bias occurs when the process of selecting participants for a research study results in key differences between groups that could skew the results. This threatens internal validity because it introduces systematic error in the comparisons between an experimental group and a control group. For example, let’s say a study is testing a new math tutoring program and students are randomly assigned to either participate in the program (experiment group) or continue with normal instruction (control group). However, the researcher unknowingly samples students for the experiment group from advanced math classes, while the control group is sampled from regular math classes. In this case, a sampling bias is introduced because the students in the experiment group may have higher math abilities or motivation levels to begin with compared to the control group. Any positive effects observed from the tutoring program could simply be due to these pre-existing differences rather than being an actual result of the program itself. According to Campbell (1957), attrition, otherwise known as experimental mortality,  refers to a differential loss of study participants in experimental and control groups.  This can threaten internal validity if the rate of attrition differs significantly between the experimental and control groups. For example, imagine a clinical trial testing the effectiveness of a new therapy for depression. Participants are randomly assigned to either receive the therapy (experimental group) or no therapy (control group) for 8 weeks. Over the course of the study, a number of participants from both groups drop out and are lost to follow-up. However, twice as many participants dropped out from the control group compared to the experimental group. This differential attrition introduces bias because the participants remaining in each condition are no longer equivalent – the experimental group now contains more of its original participants compared to the smaller subset remaining in the control group. Any observed differences in depression levels by the end of the study could be due to this systematic imbalance rather than being an actual effect of the therapy. Experimenter bias Experimenter bias refers to when a researcher’s expectations, perceptions, or motivations influence the outcome of an experiment in unconscious ways. This threatens internal validity because it provides an alternative explanation for results besides the independent variable being tested. For example, a psychologist is conducting an experiment on the effects of praise on child task performance. The psychologist hypothesizes that praising children will improve their task performance. During the experiment, she unconsciously provided more encouragement and positive body language when interacting with the praise group versus the neutral group. Consequently, the praise group shows better task performance. However, it is unclear whether this is truly due to the predictive praise or inadvertent experimenter bias, where children picked up on the researcher’s subtle supportive cues. This demonstrates how a researcher’s cognitive bias can unknowingly impact participant responses and behavior in a way that distorts the causal relationship between variables. History encompasses specific events that a study participant experiences during the course of an experiment that is not part of the experiment itself.  Specifically, it threatens the internal validity of experiments that take place over longer periods of time. For example, imagine a 12-month clinical trial testing a new psychotherapy for reducing anxiety. Participants are randomly assigned to receive either the new therapy or an existing therapy. However, 8 months into the trial, the COVID-19 pandemic begins. This external event increases anxiety levels for people everywhere. By the end of the trial, anxiety levels are reassessed. The new therapy group shows greater reductions in anxiety compared to the existing therapy group. However, it is unclear whether this difference is truly due to the new therapy’s effectiveness or the confounding variable of COVID-19 raising anxiety in the control group. Perhaps anxiety would have decreased similarly in both groups if not for the pandemic. This demonstrates how history can introduce confounds and alternative explanations that undermine internal validity. Instrumentation  Instrumentation refers to the ability of experimental instruments to provide consistent results throughout the course of a study.  Instrumentation threats occur when there are changes in the calibration or administration of the tools, surveys, or measures used to collect data over the course of a study. This can introduce systematic measurement error and provide an alternative explanation for any observed differences aside from the independent variable. For example, a researcher using a battery-powered device to measure blood pressure in an experiment intended to investigate the effectiveness of a drug in reducing hypertension may find that the battery’s progressive decay may result in these readings appearing lower on a post-test than on the pre-tests. Instrumentation is not limited to electronic or mechanical instruments. For example, a newly-hired researcher asked to rate the mental health status of participants over the course of a month may, with experience, be able to rate participants more accurately in the post-test than during the pre-test (Flannelly et al., 2018). Diffusion of information between participants The diffusion of information and treatments between patients can call internal validity into question. The latter case describes a situation in which research participants adopt a different intervention than the one they were assigned because they believe the different interventions to be more effective.  For example, a control participant in a weight-loss study who learns that those in the treatment group are losing more weight than them may adopt the treatment group’s intervention.  Differential diffusion of information can also occur when participants are given different instructions or instructions that can be misinterpreted by those conducting the study. For instance, participants asked to take a medication biweekly may take it twice a week or once every two weeks (Flannelly et al., 2018; Campbell, 1957). Maturation  Maturation encompasses any biological changes related to age, or otherwise that occur with the passage of time. This can include becoming hungry, tired, or fatigued, wound healing, recovering from surgery, and disease progression.  Maturation threatens internal validity because natural changes over time can provide an alternative explanation for study results rather than the independent variable itself.  For example, in a year-long study of a new reading program for children, students may show reading gains over the course of the year. However, some of that improvement could simply be due to neural development and growing reading skills expected with age.  The effects of maturation can also take effect over studies that have a short duration — for example, children given a repetitive computer task may lose focus within an hour, resulting in worsened performance (Flannelly et al., 2018). Testing refers to when participants taking a test or assessment can perform better simply from having experienced it before. Familiarity with the test can influence results rather than any intervention or independent variable being studied. For example, let’s say a researcher is testing a new method for improving memory in older adults. Participants take a memory assessment before and after completing the new memory training program. However, participants may show memory improvements in the post-test partly just because it was their second time taking the exact same test. Their prior experience with the questions and format benefits their scores. This demonstrates how repeated testing on the same measures can threaten internal validity. It provides an alternative explanation that improvements were due to practice effects rather than being an actual result of the intervention. How can we prevent threats to internal validity? Some methods for increasing the internal validity of an experiment include: Random allocation Random allocation is a technique that chooses individuals for treatment groups without regard to researchers’ will or patient condition and preference. This increases internal validity by reducing experimenter and selection bias (Kim & Shin, 2014). Random Selection Randomly selecting participants helps prevent systematic differences between groups that could provide alternative explanations. It ensures any pre-existing factors are evenly distributed by chance, strengthening the ability to attribute results to the independent variable rather than confounds. Blinding  (also called masking) refers to keeping trial participants, healthcare providers, and data collectors unaware of the assigned intervention so as not to be influenced by knowledge. This minimizes bias in instrumentation, drop-out rates (attrition), and participant bias. Control Groups Control groups are groups for whom an experimental condition is not applied. These show whether or not there is a clear difference in outcome related to the application of the independent variable. The use of a control group in combination with randomized allocation constitutes a randomized control trial, which scholars consider to be a “gold standard” for psychological research (Kim & Shin, 2014). Study protocol Study protocols are pre-defined plans that detail all aspects of a study: experimental design, methodology, data collection and analysis procedures, and so on. This helps to ensure consistency throughout the study, reducing the effects of instrumentation and differential diffusion of information on internal validity (Kim & Shin, 2014). Allocation concealment In a research study comparing two treatments, participants must be randomly assigned so that neither the researchers nor participants know which treatment they will get ahead of time.  This process of hiding the upcoming assignment is called allocation concealment. It’s crucial because if researchers or participants know or influence which treatment someone will receive, it ruins the randomness. For example, if a researcher believes one treatment is better, they may steer sicker participants toward it rather than assigning them fairly by chance.  Proper allocation concealment prevents this by keeping upcoming assignments hidden, ensuring unbiased random group assignments. Internal Validity Example What is the difference between internal and external validity. Validity refers to how accurately a test measures what it claims to. Internal validity is a statement of causality and non-interference by extraneous factors, while external validity is a statement of an experiment’s generalizability to different situations or groups. Why is internal validity more critical than external validity in a true experiment? Internal validity concerns the robustness of an experiment in itself. An experiment with external but not internal validity cannot be used to conclude causality. Thus, it is generally unreliable for making any scientific inferences. On the contrary, an experiment that has only internal validity can be used, at least, to draw causal relationships in a narrow context. American Psychological Association. Internal Validity. American Psychological Association Dictionary. Blasco-Fontecilla, H., Delgado-Gomez, D., Legido-Gil, T., De Leon, J., Perez-Rodriguez, M. M., & Baca-Garcia, E. (2012). Can the Holmes-Rahe Social Readjustment Rating Scale (SRRS) be used as a suicide risk scale? An exploratory study. 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The American Counseling Association Encyclopedia , 292-294. Validation and the Uniqueness of Historical Events First Online: 10 April 2019 Cite this chapter Josef Köstlbauer 10   Part of the book series: Simulation Foundations, Methods and Applications ((SFMA)) 2814 Accesses Historians have been slow to include computer simulations into their discipline’s methodological apparatus. This chapter details the challenges faced when trying to employ simulations for historical research. Central to this is the idiographic character of historical research, which leads to problems regarding computer simulations and validation. Historians are concerned with the unique, with distinct historical processes, whose ultimate result is known. They do not formulate general laws or rely on deductive-nomological approaches. But this should not keep historians from exploring the potentials of computer simulations to the full extent: Big-data projects may help to dissolve the nomothetic-idiographic divide, microhistorical research may profit from simulations for contextualization or to compensate for fragmentary sources. In all cases, validation has the potential to make historians reflect more on evaluative assumptions, and on the ways, they pose questions and explain processes. This is a preview of subscription content, log in via an institution to check access. Access this chapter Subscribe and save. Get 10 units per month Download Article/Chapter or eBook 1 Unit = 1 Article or 1 Chapter Cancel anytime Available as PDF Read on any device Instant download Own it forever Available as EPUB and PDF Durable hardcover edition Dispatched in 3 to 5 business days Free shipping worldwide - see info Tax calculation will be finalised at checkout Purchases are for personal use only Institutional subscriptions Similar content being viewed by others Do Historians Study the Mechanisms of History? A Sketch Into the Archives Theorizing with Archives: Contingency, Mistakes, and Plausible Alternatives Franz Leopold von Ranke (1795–886) is considered one of the founding figures of modern historiography. He coined the famous phrase regarding the task of historiography, “zu zeigen wie es eigentlich gewesen ist” (“To show what actually was”). For a detailed analysis and several examples see Guldi and Armitage ( 2014 , pp. 88–116, 151), Porsdam ( 2011 , pp. 2–7). Baudrillard, J. (1994). 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Download references Author information Authors and affiliations. History Department, University of Bremen, Bremen, Germany Josef Köstlbauer You can also search for this author in PubMed   Google Scholar Corresponding author Correspondence to Josef Köstlbauer . Editor information Editors and affiliations. University of Bern, Bern, Switzerland Claus Beisbart Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany Nicole J. Saam Rights and permissions Reprints and permissions Copyright information © 2019 Springer Nature Switzerland AG About this chapter Köstlbauer, J. (2019). Validation and the Uniqueness of Historical Events. In: Beisbart, C., Saam, N. (eds) Computer Simulation Validation. Simulation Foundations, Methods and Applications. 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Provided by the Springer Nature SharedIt content-sharing initiative Publish with us Policies and ethics Find a journal Track your research Cookies & Privacy GETTING STARTED Introduction FUNDAMENTALS Acknowledgements Research questions & hypotheses Concepts, constructs & variables Research limitations Getting started Sampling Strategy Research Quality Research Ethics Data Analysis Internal validity As a concept, internal validity is important because we want to be able to say that the conclusions we made in our dissertation accurately reflect what we were studying. For example, if we conclude that exercise reduces heart disease , we want to make sure that we can say this with as much certainty as possible, confident in the knowledge that what we studied, and not other factors, explains our results. Internal validity is something that can affect dissertations that are guided by a quantitative , qualitative or mixed methods research design [see the section of Research Designs if you are unsure which research design your dissertation follows]. However, if your dissertation was guided by a qualitative research design , the idea of internal validity is often referred to as dependability , and whilst similar to internal validity, is not the same. In quantitative research designs , the level of internal validity will be affected by (a) the type of quantitative research design you adopted (i.e., descriptive , experimental , quasi-experimental or relationship-based research design), and (b) potential threats to internal validity that may have influenced your results. In this article, we (a) explain what internal validity is, and (b) discuss and provide examples of the various threats to internal validity. What is internal validity? Broadly speaking, there are four types of quantitative research design: descriptive , experimental , quasi-experimental and relationship-based research designs [see the articles, Research Designs , to learn more]. Experimental research designs, also known as intervention studies , provide the greatest warrant (i.e., support) for knowledge claims (e.g., all sheep are not black, exercise reduces heart disease, etc.) because they can make the claim that X , the independent variable , causes Y , the dependent variable . By using the word causes , we mean that the independent variable (X) leads to a change in the dependent variable (Y) [see the article, Types of variables , if you are unsure about the different between independent and dependent variables]. For example, if an experimental study found that students that turn up to seminars in addition to lectures get better marks than those student that only turn up to lectures, we may argue that seminar attendance (i.e., the independent variable ) increases (i.e., causes an increase in) exam performance (i.e., the dependent variable ). This idea that X causes Y is important because internal validity is about being able to justify that X actually caused Y. We highlight the word actually because there are many different reasons that can make it difficult to known whether X causes Y. We may think that X causes Y; in other words, we may assume that X causes Y. But we cannot say with certainty that this take place. This reflects the fact that there are many threats to internal validity that can undermine our results, which are discussed in the next section [see the section: Threats to internal validity ]. It also reflects the fact that there are different types of quantitative research design (i.e., descriptive , experimental , quasi-experimental and relationship-based research designs ), which can make us more or less confident that our conclusions are internally valid. Threats to internal validity Dissertations can suffer from a wide range of potential threats to internal validity , which have been discussed extensively in the literature (e.g., Campbell, 1963, 1969; Campbell & Stanley, 1963; Cook & Campbell, 1979). In this section, 14 of the main threats to internal validity that you may face in your research are discussed with associated examples. These include history effects , maturation , testing effects , instrumentation , statistical regression , selection biases , experimental mortality , causal time order , diffusion (or imitation) of treatments , compensation , compensatory rivalry , demoralization , experimenter effects and subject effects . In the sections that follow, each of these threats to internal validity are explained with accompanying examples. THREAT TO INTERNAL VALIDITY: History effects THREAT TO INTERNAL VALIDITY: Maturation THREAT TO INTERNAL VALIDITY: Testing effects THREAT TO INTERNAL VALIDITY: Instrumentation THREAT TO INTERNAL VALIDITY: Statistical regression THREAT TO INTERNAL VALIDITY: Selection biases THREAT TO INTERNAL VALIDITY: Experimental mortality THREAT TO INTERNAL VALIDITY: Causal time order THREAT TO INTERNAL VALIDITY: Diffusion (or imitation) of treatments THREAT TO INTERNAL VALIDITY: Compensatory rivalry THREAT TO INTERNAL VALIDITY: Demoralization THREAT TO INTERNAL VALIDITY: Compensation THREAT TO INTERNAL VALIDITY: Experimenter effects THREAT TO INTERNAL VALIDITY: Subject effects History effects and internal validity History effects refer to events that happen in the environment that change the conditions of a study, affecting its outcome . Such a history event can happen before the start of an experiment, or between the pre-test and post-test. To affect the outcome of an experiment in a way that threatens its internal validity, a history effect must (a) change the scores on the independent and dependent variables, and (b) change the scores of one group more than another (e.g., increase the scores of the treatment group compared with the control group or a second treatment group). If you are unsure about some of these core aspects of experimental designs, you may want to first read the article: Experimental research designs . To understand more about history effects , consider their following characteristics: the timing of history effects , the length of a study and the magnitude of history effects . Each of these characteristics is examined in turn: Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. View all journals Explore content About the journal Publish with us Sign up for alerts Published: 22 August 2024 One hundred years of EEG for brain and behaviour research Faisal Mushtaq   ORCID: orcid.org/0000-0001-7881-1127 1 , 2 , Dominik Welke   ORCID: orcid.org/0000-0002-5529-1998 1 , Anne Gallagher 3 , 4 , 5 , Yuri G. Pavlov   ORCID: orcid.org/0000-0002-3896-5145 6 , Layla Kouara 1 , Jorge Bosch-Bayard   ORCID: orcid.org/0000-0001-7168-5551 7 , Jasper J. F. van den Bosch   ORCID: orcid.org/0000-0001-9326-2090 1 , Mahnaz Arvaneh   ORCID: orcid.org/0000-0002-5124-3497 8 , Amy R. Bland   ORCID: orcid.org/0000-0002-7170-1491 9 , Maximilien Chaumon   ORCID: orcid.org/0000-0001-9664-8861 10 , Cornelius Borck 11 , Xun He   ORCID: orcid.org/0000-0002-0558-0433 12 , Steven J. Luck   ORCID: orcid.org/0000-0002-3725-1474 13 , Maro G. Machizawa   ORCID: orcid.org/0000-0002-5727-1421 14 , 15 , Cyril Pernet   ORCID: orcid.org/0000-0003-4010-4632 16 , Aina Puce   ORCID: orcid.org/0000-0002-0716-4185 17 , Sidney J. Segalowitz   ORCID: orcid.org/0000-0003-4684-9846 18 , Christine Rogers 19 , 20 , Muhammad Awais   ORCID: orcid.org/0000-0001-6421-9245 21 , Claudio Babiloni 22 , 23 , Neil W. Bailey 24 , Sylvain Baillet   ORCID: orcid.org/0000-0002-6762-5713 19 , Robert C. A. Bendall   ORCID: orcid.org/0000-0001-9177-7007 25 , Daniel Brady   ORCID: orcid.org/0000-0003-4671-0676 26 , Maria L. Bringas-Vega 27 , Niko A. Busch   ORCID: orcid.org/0000-0003-4837-0345 28 , Ana Calzada-Reyes 29 , Armand Chatard   ORCID: orcid.org/0000-0003-4823-2903 30 , 31 , Peter E. Clayson   ORCID: orcid.org/0000-0003-4437-6598 32 , Michael X. Cohen   ORCID: orcid.org/0000-0002-1879-3593 33 , 34 , Jonathan Cole 35 , 36 , Martin Constant   ORCID: orcid.org/0000-0001-9574-0674 37 , Alexandra Corneyllie 38 , Damien Coyle   ORCID: orcid.org/0000-0002-4739-1040 39 , 40 , Damian Cruse   ORCID: orcid.org/0000-0003-4363-5981 41 , Ioannis Delis   ORCID: orcid.org/0000-0001-8940-5036 42 , Arnaud Delorme 43 , 44 , Damien Fair   ORCID: orcid.org/0000-0001-8602-393X 45 , Tiago H. Falk   ORCID: orcid.org/0000-0002-5739-2514 46 , Matthias Gamer   ORCID: orcid.org/0000-0002-9676-9038 47 , Giorgio Ganis   ORCID: orcid.org/0000-0001-6175-2618 48 , Kilian Gloy 49 , Samantha Gregory   ORCID: orcid.org/0000-0002-2601-2873 25 , Cameron D. Hassall   ORCID: orcid.org/0000-0001-9283-2813 50 , Katherine E. Hiley 1 , Richard B. Ivry   ORCID: orcid.org/0000-0003-4728-5130 51 , Karim Jerbi   ORCID: orcid.org/0000-0002-3790-9651 3 , 5 , 52 , Michael Jenkins 53 , Jakob Kaiser 54 , Andreas Keil   ORCID: orcid.org/0000-0002-4064-1924 55 , Robert T. Knight   ORCID: orcid.org/0000-0001-8686-1685 51 , Silvia Kochen 56 , Boris Kotchoubey 6 , Olave E. Krigolson 57 , Nicolas Langer   ORCID: orcid.org/0000-0002-6038-9471 58 , Heinrich R. Liesefeld   ORCID: orcid.org/0000-0003-4551-8607 59 , Sarah Lippé 3 , 4 , 5 , Raquel E. London   ORCID: orcid.org/0000-0003-1678-2556 60 , Annmarie MacNamara 61 , Scott Makeig 43 , Welber Marinovic   ORCID: orcid.org/0000-0002-2472-7955 62 , Eduardo Martínez-Montes   ORCID: orcid.org/0000-0002-5852-964X 29 , Aleya A. Marzuki 63 , Ryan K. Mathew   ORCID: orcid.org/0000-0002-2609-9876 64 , 65 , Christoph Michel 66 , 67 , José d. R. Millán   ORCID: orcid.org/0000-0001-5819-1522 68 , 69 , Mark Mon-Williams 1 , Lilia Morales-Chacón   ORCID: orcid.org/0000-0003-0205-0733 27 , 70 , Richard Naar   ORCID: orcid.org/0000-0003-3309-8254 71 , Gustav Nilsonne   ORCID: orcid.org/0000-0001-5273-0150 72 , Guiomar Niso   ORCID: orcid.org/0000-0001-5872-8924 73 , Erika Nyhus 74 , Robert Oostenveld   ORCID: orcid.org/0000-0002-1974-1293 72 , 75 , Katharina Paul 76 , Walter Paulus 77 , 78 , Daniela M. Pfabigan   ORCID: orcid.org/0000-0002-4043-1695 79 , Gilles Pourtois   ORCID: orcid.org/0000-0002-5109-2801 80 , Stefan Rampp 81 , 82 , 83 , Manuel Rausch   ORCID: orcid.org/0000-0002-5805-5544 84 , 85 , Kay Robbins   ORCID: orcid.org/0000-0002-7147-5797 86 , Paolo M. Rossini   ORCID: orcid.org/0000-0003-2665-534X 87 , Manuela Ruzzoli   ORCID: orcid.org/0000-0002-1719-7140 88 , 89 , Barbara Schmidt   ORCID: orcid.org/0000-0002-6828-5546 90 , Magdalena Senderecka   ORCID: orcid.org/0000-0002-2098-5180 91 , Narayanan Srinivasan   ORCID: orcid.org/0000-0001-5342-0381 92 , Yannik Stegmann   ORCID: orcid.org/0000-0002-0933-8492 47 , Paul M. Thompson 93 , Mitchell Valdes-Sosa 29 , Melle J. W. van der Molen   ORCID: orcid.org/0000-0003-0431-7630 94 , Domenica Veniero   ORCID: orcid.org/0000-0001-8235-7270 95 , Edelyn Verona 32 , Bradley Voytek   ORCID: orcid.org/0000-0003-1640-2525 96 , Dezhong Yao   ORCID: orcid.org/0000-0002-8042-879X 27 , 97 , Alan C. 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Download references Acknowledgements This work was supported by the UK Research and Innovation Biotechnology and Biological Sciences Research Council (BB/X008428/1), the National Institute for Health and Care Research (NIHR) Leeds Biomedical Research Centre (NIHR203331) and the German Research Foundation (PA 4005/1-1) and is the result of a partnership between the #EEGManyLabs ( https://www.eegmanylabs.org ) project, EEGNet (Brain Canada Foundation no. 4940) and the Global Brain Consortium ( https://globalbrainconsortium.org/ ). The latter is funded by grant Y0301902610100201 of the University of Electronic Sciences and Technology of China, STI 2030-major projects grant number: 2022ZD0208500 and the Chengdu Science and Technology Bureau Program grant number: 2022GH02-00042- HZ. We thank the organizations, societies and researchers who supported the dissemination of the survey (a full list is available in ref. 11 ) and all of our participants. Author information Authors and affiliations. School of Psychology, University of Leeds, Leeds, UK Faisal Mushtaq, Dominik Welke, Layla Kouara, Jasper J. F. van den Bosch, Katherine E. Hiley & Mark Mon-Williams NIHR Leeds Biomedical Research Centre, Leeds, UK Faisal Mushtaq Psychology Department, Université de Montréal, Montreal, Quebec, Canada Anne Gallagher, Karim Jerbi & Sarah Lippé Sainte-Justine University Hospital Research Center, Montreal, Quebec, Canada Anne Gallagher & Sarah Lippé UNIQUE Centre, Montreal, Quebec, Canada Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany Yuri G. Pavlov & Boris Kotchoubey Facultad de Psicología, Universidad Autonoma de Madrid, Madrid, Spain Jorge Bosch-Bayard Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, UK Mahnaz Arvaneh Department of Psychology, Health and Education, Manchester Metropolitan University, Manchester, UK Amy R. Bland Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, APHP, Hôpital de la Pitié Salpêtrière, Paris, France Maximilien Chaumon Institute of Medical History and Science Research, Universität zu Lübeck, Lübeck, Germany Cornelius Borck Department of Psychology, Bournemouth University, Bournemouth, UK Center for Mind and Brain, UC Davis, Davis, CA, USA Steven J. Luck Xiberlinc Inc., Tokyo, Japan Maro G. Machizawa Tokyo Medical and Dental University, Tokyo, Japan Neurology and Neurobiology Research Unit, Copenhagen University Hospital, Copenhagen, Denmark Cyril Pernet Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA Department of Psychology, Brock University, St. Catharines, Ontario, Canada Sidney J. Segalowitz Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada Christine Rogers, Sylvain Baillet & Alan C. Evans McGill Centre for Integrative Neuroscience, McGill University, Montreal, Quebec, Canada Christine Rogers & Alan C. Evans School of Computing Sciences, University of East Anglia, Norwich, UK Muhammad Awais Department of Physiology and Pharmacology “Vittorio Erspamer”, Sapienza University of Rome, Rome, Italy Claudio Babiloni Hospital San Raffaele Cassino, Cassino, Frosinone, Italy School of Medicine and Psychology, The Australian National University, Canberra, Australian Capital Territory, Australia Neil W. Bailey School of Health and Society, University of Salford, Salford, UK Robert C. A. Bendall & Samantha Gregory School of Computer Science, University of Sheffield, Sheffield, UK Daniel Brady The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China Maria L. Bringas-Vega, Lilia Morales-Chacón, Dezhong Yao & Pedro Valdes-Sosa Institute of Psychology, University of Münster, Münster, Germany Niko A. Busch Cuban Center for Neuroscience, Havana, Cuba Ana Calzada-Reyes, Eduardo Martínez-Montes, Mitchell Valdes-Sosa & Pedro Valdes-Sosa Université de Poitiers, Poitiers, France Armand Chatard Centre National de la Recherche Scientifique, Poitiers, France Department of Psychology, University of South Florida, Tampa, FL, USA Peter E. Clayson & Edelyn Verona Sincxpress Education https://sincxpress.com/ Michael X. Cohen Donders Institute, Nijmegen, The Netherlands University Hospitals Dorset NHS Foundation Trust, Bournemouth, UK Jonathan Cole University of Bournemouth, Bournemouth, UK Faculté de Psychologie et des Sciences de l’Education, Université de Genève, Geneva, Switzerland Martin Constant Lyon Neurosience Research Center, Lyon, France Alexandra Corneyllie The Bath Institute for the Augmented Human, University of Bath, Bath, UK Damien Coyle School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, UK School of Psychology, University of Birmingham, Birmingham, UK Damian Cruse School of Biomedical Sciences, University of Leeds, Leeds, UK Ioannis Delis Swartz Center for Computational Neuroscience, UC San Diego, La Jolla, CA, USA Arnaud Delorme & Scott Makeig Centre de Recherche Cerveau et Cognition, Toulouse III University, Toulouse, France Arnaud Delorme Institute of Child Development, University of Minnesota, Minneapolis, MN, USA Damien Fair INRS-EMT, University de Quebec, Montreal, Quebec, Canada Tiago H. Falk Department of Psychology, University of Würzburg, Würzburg, Germany Matthias Gamer & Yannik Stegmann School of Psychology, University of Plymouth, Plymouth, UK Giorgio Ganis Institute of Psychology, University of Bremen, Bremen, Germany Kilian Gloy Department of Psychology, MacEwan University, Edmonton, Alberta, Canada Cameron D. Hassall Department of Psychology, UC Berkeley, Berkeley, CA, USA Richard B. Ivry & Robert T. Knight Mila, Montreal, Quebec, Canada Karim Jerbi School of Medical and Life Sciences, Sunway University, Kuala Lumpur, Malaysia Michael Jenkins Department of Psychology, Ludwig Maximilian University Munich, Munich, Germany Jakob Kaiser Department of Psychology, University of Florida, Tampa, Florida, USA Andreas Keil Studies in Neuroscience and Complex Systems (ENyS), CONICET, Buenos Aires, Argentina Silvia Kochen Centre for Biomedical Research, University of Victoria, Victoria, British Columbia, Canada Olave E. Krigolson Department of Psychology, University of Zurich, Methods of Plasticity Research, Zurich, Switzerland Nicolas Langer Department of Psychology, University of Bremen, Bremen, Germany Heinrich R. Liesefeld Department of Experimental Psychology, Ghent University, Ghent, Belgium Raquel E. London Institute for Neuroscience, Texas A&M University, College Station, TX, USA Annmarie MacNamara School of Psychology, Curtin University, Perth, Western Australia, Australia Welber Marinovic Department for Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany Aleya A. Marzuki School of Medicine, University of Leeds, Leeds, UK Ryan K. Mathew Department of Neurosurgery, Leeds Teaching Hospitals NHS Trust, Leeds, UK Department of Clinical Neurosciences, University of Geneva, Geneva, Switzerland Christoph Michel Department of Basic Neurosciences, University of Geneva, Geneva, Switzerland Department of Electrical & Computer Engineering, The University of Texas at Austin, Austin, TX, USA José d. R. Millán Department of Neurology, The University of Texas at Austin, Austin, TX, USA Departament of Pharmacology, Toxicology and Therapeutic Chemistry, Faculty of Pharmacy and Food Science, Barcelona University, Barcelona, Spain Lilia Morales-Chacón Institute of Psychology, University of Tartu, Tartu, Estonia Richard Naar Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden Gustav Nilsonne & Robert Oostenveld Cajal Institute, CSIC, Madrid, Spain Guiomar Niso Department of Psychology and Program in Neuroscience, Bowdoin College, Brunswick, ME, USA Erika Nyhus Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands Robert Oostenveld Faculty of Social Sciences, University Hamburg, Hamburg, Germany Katharina Paul Department of Neurology, Ludwig-Maximilians University Munich, Munich, Germany Walter Paulus University Medical Center Göttingen, Göttingen, Germany Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway Daniela M. Pfabigan Faculty of Psychology and Educational Sciences, Ghent University, Ghent, Belgium Gilles Pourtois Department of Neurosurgery, University Hospital Erlangen, Erlangen, Germany Stefan Rampp Department of Neurosurgery, University Hospital Halle (Saale), Halle, Germany Department of Neuroradiology, University Hospital Erlangen, Erlangen, Germany Faculty Society and Economics, Rhine-Waal University of Applied Sciences, Kleve, Germany Manuel Rausch Faculty of Philosophy and Education, Catholic University of Eichstätt-Ingolstadt, Eichstätt, Germany Department of Computer Science, The University of Texas at San Antonio, San Antonio, TX, USA Kay Robbins Department of Neuroscience & Neurorehabilitation, IRCCS San Raffaele Roma, Rome, Italy Paolo M. Rossini Basque Center on Cognition Brain & Language, San Sebastian, Spain Manuela Ruzzoli Basque Foundation for Science, Bilbao, Spain Institute of Psychosocial Medicine, Psychotherapy and Psychooncology, Jena University Hospital, Jena, Germany Barbara Schmidt Centre for Cognitive Science, Jagiellonian University, Kraków, Poland Magdalena Senderecka Department of Cognitive Science, Indian Institute of Technology Kanpur, Kanpur, India Narayanan Srinivasan Keck School of Medicine, University of Southern California, Los Angeles, CA, USA Paul M. Thompson Institute of Psychology, Leiden University, Leiden, The Netherlands Melle J. W. van der Molen School of Psychology, University of Nottingham, Nottingham, UK Domenica Veniero Department of Cognitive Science, UC San Diego, San Diego, CA, USA Bradley Voytek Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China Dezhong Yao You can also search for this author in PubMed   Google Scholar Corresponding authors Correspondence to Faisal Mushtaq or Pedro Valdes-Sosa . Ethics declarations Competing interests. M.G.M. is CEO of Xiberlinc Inc., a neurotechnology company. D. Coyle is founder and CEO of NeuroCONCISE Ltd, a wearable EEG company. R.K.M. is a shareholder in RBM Healthcare Ltd and an advisory shareholder for Opto Biosystems Ltd, a neurotechnology company. The remaining authors declare no competing interests. Rights and permissions Reprints and permissions About this article Cite this article. Mushtaq, F., Welke, D., Gallagher, A. et al. One hundred years of EEG for brain and behaviour research. Nat Hum Behav 8 , 1437–1443 (2024). https://doi.org/10.1038/s41562-024-01941-5 Download citation Published : 22 August 2024 Issue Date : August 2024 DOI : https://doi.org/10.1038/s41562-024-01941-5 Share this article Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative Quick links Explore articles by subject Guide to authors Editorial policies Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily. 181 IMAGES Validity and Reliability in Research- Types and Differences 2024 PPT School essay: Components of valid research Validity and Reliability in Research Validity In Research 9 Types of Validity in Research (2024) COMMENTS Validity In Psychology Research: Types & Examples In psychology research, validity refers to the extent to which a test or measurement tool accurately measures what it's intended to measure. It ensures that the research findings are genuine and not due to extraneous factors. Validity can be categorized into different types, including construct validity (measuring the intended abstract trait), internal validity (ensuring causal conclusions ... Chapter 16. Archival and Historical Research Chapter 16. Archival and Historical Research Introduction. The British sociologist John Goldthorpe once remarked, "Any sociologist who is concerned with a theory that can be tested in the present should so test it, in the first place; for it is, in all probability, in this way that it can be tested most rigorously" ().Testing can be done through either qualitative or quantitative methods ... Internal Validity vs. External Validity in Research Differences. The essential difference between internal validity and external validity is that internal validity refers to the structure of a study (and its variables) while external validity refers to the universality of the results. But there are further differences between the two as well. For instance, internal validity focuses on showing a ... Reliability and Validity: History, Notions, Methods, and Discussion For both concepts we will (1) briefly discuss the history, (2) present the notions in line with how they are formulated in the Standards, (3) describe some research tools, and (4) formulate some considerations regarding the present-day conception of reliability and validity. Internal Validity Several threats can undermine internal validity and compromise the validity of research findings. Here are some common threats to internal validity: History. Events or circumstances that occur during the course of a study and affect the outcome, making it difficult to attribute the results solely to the treatment or intervention being studied. Social Psychological Theory as History: Outlining the Critical From this perspective, theories should be committed to deep interdisciplinarity and historical validity claims—understanding individual and group experiences as part of historically contingent forces. Theories also should be critical, containing an awareness of the researcher as implicated in the social process and committed to actively ... Internal, External, and Ecological Validity in Research Design, Conduct Internal validity examines whether the study design, conduct, and analysis answer the research questions without bias. External validity examines whether the study findings can be generalized to other contexts. Ecological validity examines, specifically, whether the study findings can be generalized to real-life settings; thus ecological ... Validating Psychological Constructs: Historical, Philosophical, and Explores the history and development of construct validity theory (CVT) in relation to the broader domain of psychological measurement; Critically examines CVT in a broader context; Brings together historical, philosophical, and pragmatic dimensions of CVT in a single work; Includes supplementary material: sn.pub/extras Internal Validity in Research Internal validity makes the conclusions of a causal relationship credible and trustworthy. Without high internal validity, an experiment cannot demonstrate a causal link between two variables. Research example. You want to test the hypothesis that drinking a cup of coffee improves memory. You schedule an equal number of college-aged ... Reliability and Validity: History, Notions, Methods, Discussion After a brief historical review focusing mainly on construct validity, the current state of validity theory will be summarized, with an emphasis on the role of arguments in validation. Historical method Historical method is the collection of techniques and guidelines that historians use to research and write histories of the past. Secondary sources, primary sources and material evidence such as that derived from archaeology may all be drawn on, and the historian's skill lies in identifying these sources, evaluating their relative authority, and combining their testimony appropriately in order ... Reliability vs. Validity in Research Reliability is about the consistency of a measure, and validity is about the accuracy of a measure.opt. It's important to consider reliability and validity when you are creating your research design, planning your methods, and writing up your results, especially in quantitative research. Failing to do so can lead to several types of research ... Construct Validity: Advances in Theory and Methodology An Historical Overview of Validation Efforts in Clinical Psychology. ... and confirmatory factor analysis (CFA) in the analysis of MTMM matrices. A major advantage of CFA in construct validity research is the possibility of directly comparing alternative models of relationships among constructs, a critical component of theory testing ... Cognitive Validity Evidence for Validating Assessments of Historical By Kadriye Ercikan , Juliette Lyons-Thomas , Lindsay Gibson. Book New Directions in Assessing Historical Thinking. Edition 1st Edition. First Published 2015. Imprint Routledge. Pages 15. eBook ISBN 9781315779539. Share. Previous Chapter Next Chapter. External Validity External Validity. Definition: External validity refers to the extent to which the results of a study can be generalized or applied to a larger population, settings, or conditions beyond the specific context of the study. It is a measure of how well the findings of a study can be considered representative of the real world. 1.3: Threats to Internal Validity and Different Control Techniques Figure 1.3.1 1.3. 1: Common Threats to Internal Validity. Any event that occurs while the experiment is in progress might be an alternation; using a control group mitigates this concern. If groups lost participants (e.g., due to dropping out of the experiment) they may not be equivalent. Internal and external validity: can you apply research study results to The validity of a research study includes two domains: internal and external validity. Internal validity is defined as the extent to which the observed results represent the truth in the population we are studying and, thus, are not due to methodological errors. In our example, if the authors can support that the study has internal validity ... Reliability and Validity Reliability refers to the consistency of the measurement. Reliability shows how trustworthy is the score of the test. If the collected data shows the same results after being tested using various methods and sample groups, the information is reliable. If your method has reliability, the results will be valid. Example: If you weigh yourself on a ... Validity Examples of Validity. Internal Validity: A randomized controlled trial (RCT) where the random assignment of participants helps eliminate biases. External Validity: A study on educational interventions that can be applied to different schools across various regions. Construct Validity: A psychological test that accurately measures depression levels. What Is Internal Validity In Research? Internal validity is crucial for being able to draw credible conclusions from research. It allows researchers to rule out alternative explanations for study findings besides the factor being tested. For example, if you implement a smoking cessation program and see improvement among participants, high internal validity means you can be confident ... Validation and the Uniqueness of Historical Events Historians have been slow to include computer simulations into their discipline's methodological apparatus. This chapter details the challenges faced when trying to employ simulations for historical research. Central to this is the idiographic character of historical research, which leads to problems regarding computer simulations and validation. Internal validity Threats to internal validity. Dissertations can suffer from a wide range of potential threats to internal validity, which have been discussed extensively in the literature (e.g., Campbell, 1963, 1969; Campbell & Stanley, 1963; Cook & Campbell, 1979).In this section, 14 of the main threats to internal validity that you may face in your research are discussed with associated examples. Threats to Internal Validity I: History, Instrumentation & Subject When conducting research to show the effectiveness of a treatment, threats to internal validity can weaken the experiment's conclusions. Understand three of these threats -- history, subject ... One hundred years of EEG for brain and behaviour research Validity, which will be established by ensuring our work is robust, reliable and replicable, and as reproducible as possible, in both basic research and clinical settings (2) Evidence for test validation: A guide for practitioners. Background: Validity is a core topic in educational and psychological assessment. Although there are many available resources describing the concept of validity, sources of validity evidence, and suggestions about how to obtain validity evidence; there is little guidance providing specific instructions for planning and carrying out validation studies. assignment essay homework paper phd report resume review speech thesis