How are research results translated to public health practice? What is the responsibility of researchers, funding agencies, and journals in facilitating the use of research results in public health programs or policies? We address selected aspects of these questions and announce a new emphasis of the Journal on external validity for appropriate manuscripts.
doi: 10.2105/AJPH.2007.126847
PMID: 18048772
This article has been cited by other articles in PMC.
IMPORTANCE OF EXTERNAL VALIDITY
Over 40 years ago, Campbell and Stanley published their seminal work on experimental and quasi-experimental designs for research, in which they raised issues about threats to internal validity (whether or not observed covariation should be interpreted as a causal relationship) that exist when researchers are not able to randomly assign participants to treatments.1 In that volume and subsequent work, they also raised issues about other types of validity, including2,3:
It has been frequently argued that internal validity is the priority for research.4 However, in an applied discipline, the purpose of which includes working to improve the health of the public, it is also important that external validity be emphasized and strengthened.â For example, it is important to know not only that a program is effective, but that it is likely to be effective in other settings and with other populations.
In an influential 1985 article, âEfficacy and Effectiveness Trials (and Other Phases of Research) in the Development of Health Promotion Programs,â Flay proposes a model that emphasizes internal and external validity at different stages of the research process and that would lead to the translation of research to practice. The two main research levels were âefficacy trialsâ and âeffectiveness trials.â Efficacy trials were to be highly controlled studies that answered the question of whether a proposed intervention would have the desired effects under ideal circumstances. Effectiveness trials were to follow efficacy trials and were to be studies that carried out the proposed intervention in less controlled and more real-life situations. The argument was that a given public health intervention should be successful in both types of trials before it was ready for dissemination to and by public health practitioners.
Efficacy trials were to have high internal validity, and effectiveness trials were to have high external validity. Efficacy trials were more likely to be controlled experiments, such as randomized control trials of public health interventions, that have the virtue of high internal validity but often have the liability of low external validity9 (i.e., the groups, settings, or contexts in which findings would apply). It is axiomatic in social science research that there is an inverse relationship between internal and external validity. A key to internal validity is good measurement and study design, and representative sampling is necessary for inference.9 However, it may be useful to distinguish between inference derived from sample design and our ability to generalize, which is more dependent on judgment.
Historically, researchers have tended to focus on maximizing internal validity, with the idea that it is more important to know if a given public health intervention works under highly controlled conditions than it is to know if it will work among different population groups, organizations, or settings. Similarly, funding organizations and journals have tended to be more concerned with the scientific rigor of intervention studies than with the generalizability of results. The consequence of this emphasis on internal validity has been a lack of attention to and information about external validity, which has contributed to our failure to translate research into public health practice.
For instance, in the area of cancer prevention and control, there is a documented substantial lag between discovery and delivery of effective interventions. Recognition of this lag has been noted for at least 30 years, since the first National Cancer Instituteâconvened cancer control working groups issued reports in the 1970s. More recently, Balas and Boren found that it takes about 17 years to turn 14% of original research to the benefit of patient care. Similarly, the National Research Council concluded that, even when effective interventions have been developed, there often is a gap between scientific knowledge and clinical practice.11 In addition, minorities and underserved communities usually gain access to effective interventions more slowly than do other populations.
Thus, the idea that research would progress from efficacy trials to effectiveness trials to widespread dissemination has not become a reality for a number of reasons, not the least of which is the time and cost involved in this stepwise progress of research to practice. As a result of the failure of this model, practitioners are often unable to determine if a given studyâs findings apply to their local setting, population staffing, or resources. Reviews indicate that reporting on external validity is provided far less often than is reporting on other methodological issues. However, there are several reasons for the lack of information on external validity being an important contributor to the failure to translate research into public health practice. Policy and administrative decision-makers are unable to determine the generalizability or breadth of applicability of research findings. Finally, systematic reviews and meta-analyses are limited in the conclusions that can be drawn when external validity data are not reported.
THE JOURNAL ENDORSES A GREATER EMPHASIS ON EXTERNAL VALIDITY
Although the Journal has long recognized the importance of external validity in articles it has published, the relatively recent CONSORT and TREND reports, as well as the recent emphasis on the RE-AIM model, has strengthened the recognition by the Journal editors and editorial board of the need to formally emphasize external validity and to collect information on appropriate manuscripts that enhances both inference and potential generalizability.,â
Recently, two members of the the Journal editorial board and editors represented the Journal in a meeting with 12 other leading health journals and representatives from the National Institutes of Health, the Centers for Disease Control and Prevention, and the Robert Wood Johnson Foundation. The purpose of the meeting was to encourage and strengthen the reporting of findings on external validity. One of the outcomes of the meeting was that participants agreed that enhancing the quality of reporting on external validity in journal articles warrants higher priority than it has received in public health research publications to date.
The meeting participants identified several characteristics of external validity that should be reported. As with other quality-rating scales and guidelines, not every article would be expected to excel on all of the criteria; rather, authors should report on these issues where appropriate, or state that no information is available. Four categories of external validity information were identified by the meeting participants:
Although we are not intending to add to the burden of authors publishing in the Journal, we believe that many of the articles we publish will benefit by including information on external validity. Most important, we believe that the field of public health and public health practice will benefit considerably from this information.
References
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2. Cook TD, Campbell DT. The design and conduct of quasi experiments and true experiments in field settings. In: Dunnette MD, ed. Handbook of Industrial and Organizational Psychology. Skokie, Ill: Rand McNally; 1976:115â136.
3. Cook TD, Campbell DT. Quasi-Experimentation. Chicago, Ill: Rand McNally; 1979.
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Articles from American Journal of Public Health are provided here courtesy of American Public Health Association
Internal validity is the extent to which a piece of evidence supports a claim about cause and effect, within the context of a particular study. It is one of the most important properties of scientific studies, and is an important concept in reasoning about evidence more generally. Internal validity is determined by how well a study can rule out alternative explanations for its findings (usually, sources of systematic error or 'bias'). It contrasts with external validity, the extent to which results can justify conclusions about other contexts (that is, the extent to which results can be generalized).
Details[edit]
Inferences are said to possess internal validity if a causal relationship between two variables is properly demonstrated.[1][2] A valid causal inference may be made when three criteria are satisfied:
In scientific experimental settings, researchers often change the state of one variable (the independent variable) to see what effect it has on a second variable (the dependent variable).[3] For example, a researcher might manipulate the dosage of a particular drug between different groups of people to see what effect it has on health. In this example, the researcher wants to make a causal inference, namely, that different doses of the drug may be held responsible for observed changes or differences. When the researcher may confidently attribute the observed changes or differences in the dependent variable to the independent variable (that is, when the researcher observes an association between these variables and can rule out other explanations or rival hypotheses), then the causal inference is said to be internally valid.[4]
In many cases, however, the size of effects found in the dependent variable may not just depend on
Rather, a number of variables or circumstances uncontrolled for (or uncontrollable) may lead to additional or alternative explanations (a) for the effects found and/or (b) for the magnitude of the effects found. Internal validity, therefore, is more a matter of degree than of either-or, and that is exactly why research designs other than true experiments may also yield results with a high degree of internal validity.
In order to allow for inferences with a high degree of internal validity, precautions may be taken during the design of the study. As a rule of thumb, conclusions based on direct manipulation of the independent variable allow for greater internal validity than conclusions based on an association observed without manipulation.
When considering only Internal Validity, highly controlled true experimental designs (i.e. with random selection, random assignment to either the control or experimental groups, reliable instruments, reliable manipulation processes, and safeguards against confounding factors) may be the 'gold standard' of scientific research. However, the very methods used to increase internal validity may also limit the generalizability or external validity of the findings. For example, studying the behavior of animals in a zoo may make it easier to draw valid causal inferences within that context, but these inferences may not generalize to the behavior of animals in the wild. In general, a typical experiment in a laboratory, studying a particular process, may leave out many variables that normally strongly affect that process in nature.
Example threats[edit]
For eight of these threats there exists the first letter mnemonicTHIS MESS, which refers to the first letters of Testing (repeated testing), History, Instrument change, Statistical Regression toward the mean, Maturation, Experimental mortality, Selection and Selection Interaction.[5]
Ambiguous temporal precedence[edit]
When it is not known which variable changed first, it can be difficult to determine which variable is the cause and which is the effect.
Confounding[edit]
A major threat to the validity of causal inferences is confounding: Changes in the dependent variable may rather be attributed to variations in a third variable which is related to the manipulated variable. Where spurious relationships cannot be ruled out, rival hypotheses to the original causal inference may be developed.
Selection bias[edit]
Selection bias refers to the problem that, at pre-test, differences between groups exist that may interact with the independent variable and thus be 'responsible' for the observed outcome. Researchers and participants bring to the experiment a myriad of characteristics, some learned and others inherent. For example, sex, weight, hair, eye, and skin color, personality, mental capabilities, and physical abilities, but also attitudes like motivation or willingness to participate.
During the selection step of the research study, if an unequal number of test subjects have similar subject-related variables there is a threat to the internal validity. For example, a researcher created two test groups, the experimental and the control groups. The subjects in both groups are not alike with regard to the independent variable but similar in one or more of the subject-related variables.
Self-selection also has a negative effect on the interpretive power of the dependent variable. This occurs often in online surveys where individuals of specific demographics opt into the test at higher rates than other demographics.
History[edit]
Events outside of the study/experiment or between repeated measures of the dependent variable may affect participants' responses to experimental procedures. Often, these are large-scale events (natural disaster, political change, etc.) that affect participants' attitudes and behaviors such that it becomes impossible to determine whether any change on the dependent measures is due to the independent variable, or the historical event.
Maturation[edit]
Subjects change during the course of the experiment or even between measurements. For example, young children might mature and their ability to concentrate may change as they grow up. Both permanent changes, such as physical growth and temporary ones like fatigue, provide 'natural' alternative explanations; thus, they may change the way a subject would react to the independent variable. So upon completion of the study, the researcher may not be able to determine if the cause of the discrepancy is due to time or the independent variable.
Repeated testing (also referred to as testing effects)[edit]
Repeatedly measuring the participants may lead to bias. Participants may remember the correct answers or may be conditioned to know that they are being tested. Repeatedly taking (the same or similar) intelligence tests usually leads to score gains, but instead of concluding that the underlying skills have changed for good, this threat to Internal Validity provides a good rival hypotheses.
Instrument change (instrumentality)[edit]
The instrument used during the testing process can change the experiment. This also refers to observers being more concentrated or primed, or having unconsciously changed the criteria they use to make judgments. This can also be an issue with self-report measures given at different times. In this case the impact may be mitigated through the use of retrospective pretesting. If any instrumentation changes occur, the internal validity of the main conclusion is affected, as alternative explanations are readily available.
Regression toward the mean[edit]
This type of error occurs when subjects are selected on the basis of extreme scores (one far away from the mean) during a test. For example, when children with the worst reading scores are selected to participate in a reading course, improvements at the end of the course might be due to regression toward the mean and not the course's effectiveness. If the children had been tested again before the course started, they would likely have obtained better scores anyway.Likewise, extreme outliers on individual scores are more likely to be captured in one instance of testing but will likely evolve into a more normal distribution with repeated testing.
Mortality/differential attrition[edit]
This error occurs if inferences are made on the basis of only those participants that have participated from the start to the end. However, participants may have dropped out of the study before completion, and maybe even due to the study or programme or experiment itself. For example, the percentage of group members having quit smoking at post-test was found much higher in a group having received a quit-smoking training program than in the control group. However, in the experimental group only 60% have completed the program. If this attrition is systematically related to any feature of the study, the administration of the independent variable, the instrumentation, or if dropping out leads to relevant bias between groups, a whole class of alternative explanations is possible that account for the observed differences.
Selection-maturation interaction[edit]
This occurs when the subject-related variables, color of hair, skin color, etc., and the time-related variables, age, physical size, etc., interact. If a discrepancy between the two groups occurs between the testing, the discrepancy may be due to the age differences in the age categories.
Diffusion[edit]
If treatment effects spread from treatment groups to control groups, a lack of differences between experimental and control groups may be observed. This does not mean, however, that the independent variable has no effect or that there is no relationship between dependent and independent variable.
Compensatory rivalry/resentful demoralization[edit]
Behavior in the control groups may alter as a result of the study. For example, control group members may work extra hard to see that expected superiority of the experimental group is not demonstrated. Again, this does not mean that the independent variable produced no effect or that there is no relationship between dependent and independent variable. Vice versa, changes in the dependent variable may only be affected due to a demoralized control group, working less hard or motivated, not due to the independent variable.
Experimenter bias[edit]
Experimenter bias occurs when the individuals who are conducting an experiment inadvertently affect the outcome by non-consciously behaving in different ways to members of control and experimental groups. It is possible to eliminate the possibility of experimenter bias through the use of double blind study designs, in which the experimenter is not aware of the condition to which a participant belongs.
See also[edit]References[edit]
External links[edit]
Retrieved from 'https://en.wikipedia.org/w/index.php?title=Internal_validity&oldid=882646102'
External validity is the validity of applying the conclusions of a scientific study outside the context of that study.[1] In other words, it is the extent to which the results of a study can be generalized to and across other situations, people, stimuli, and times.[2] In contrast, internal validity is the validity of conclusions drawn within the context of a particular study. Because general conclusions are almost always a goal in research, external validity is an important property of any study. Mathematical analysis of external validity concerns a determination of whether generalization across heterogeneous populations is feasible, and devising statistical and computational methods that produce valid generalizations.[3]
Threats[edit]
'A threat to external validity is an explanation of how you might be wrong in making a generalization from the findings of a particular study.'[4] In most cases, generalizability is limited when the effect of one factor (i.e. the independent variable) depends on other factors. Therefore, all threats to external validity can be described as statistical interactions.[5] Some examples include:
Note that a study's external validity is limited by its internal validity. If a causal inference made within a study is invalid, then generalizations of that inference to other contexts will also be invalid.
Cook and Campbell[6] made the crucial distinction between generalizing to some population and generalizing across subpopulations defined by different levels of some background factor. Lynch has argued that it is almost never possible to generalize to meaningful populations except as a snapshot of history, but it is possible to test the degree to which the effect of some cause on some dependent variable generalizes across subpopulations that vary in some background factor. That requires a test of whether the treatment effect being investigated is moderated by interactions with one or more background factors.[5][7]
Disarming threats[edit]
Whereas enumerating threats to validity may help researchers avoid unwarranted generalizations, many of those threats can be disarmed, or neutralized in a systematic way, so as to enable a valid generalization. Specifically, experimental findings from one population can be 're-processed', or 're-calibrated' so as to circumvent population differences and produce valid generalizations in a second population, where experiments cannot be performed. Pearl and Bareinboim[3] classified generalization problems into two categories: (1) those that lend themselves to valid re-calibration, and (2) those where external validity is theoretically impossible. Using graph-based calculus,[8] they derived a necessary and sufficient condition for a problem instance to enable a valid generalization, and devised algorithms that automatically produce the needed re-calibration, whenever such exists.[9] This reduces the external validity problem to an exercise in graph theory, and has led some philosophers to conclude that the problem is now solved.[10]
An important variant of the external validity problem deals with selection bias, also known as sampling biasâthat is, bias created when studies are conducted on non-representative samples of the intended population. For example, if a clinical trial is conducted on college students, an investigator may wish to know whether the results generalize to the entire population, where attributes such as age, education, and income differ substantially from those of a typical student. The graph-based method of Bareinboim and Pearl identifies conditions under which sample selection bias can be circumvented and, when these conditions are met, the method constructs an unbiased estimator of the average causal effect in the entire population. The main difference between generalization from improperly sampled studies and generalization across disparate populations lies in the fact that disparities among populations are usually caused by preexisting factors, such as age or ethnicity, whereas selection bias is often caused by post-treatment conditions, for example, patients dropping out of the study, or patients selected by severity of injury. When selection is governed by post-treatment factors, unconventional re-calibration methods are required to ensure bias-free estimation, and these methods are readily obtained from the problem's graph.[11][12]
Examples[edit]
If age is judged to be a major factor causing treatment effect to vary from individual to individual, then age differences between the sampled students and the general population would lead to a biased estimate of the average treatment effect in that population. Such bias can be corrected though by a simple re-weighing procedure: We take the age-specific effect in the student subpopulation and compute its average using the age distribution in the general population. This would give us an unbiased estimate of the average treatment effect in the population. If, on the other hand, the relevant factor that distinguishes the study sample from the general population is in itself affected by the treatment, then a different re-weighing scheme need be invoked. Calling this factor Z, we again average the z-specific effect of X on Y in the experimental sample, but now we weigh it by the 'causal effect' of X on Z. In other words, the new weight is the proportion of units attaining level Z=z had treatment X=x been administered to the entire population. This interventional probability, often written[13]P(Z=z|do(X=x)){displaystyle P(Z=z|do(X=x))}, can sometimes be estimated from observational studies in the general population.
A typical example of this nature occurs when Z is a mediator between the treatment and outcome, For instance, the treatment may be a cholesterol- reducing drug, Z may be cholesterol level, and Y life expectancy. Here, Z is both affected by the treatment and a major factor in determining the outcome, Y. Suppose that subjects selected for the experimental study tend to have higher cholesterol levels than is typical in the general population. To estimate the average effect of the drug on survival in the entire population, we first compute the z-specific treatment effect in the experimental study, and then average it using P(Z=z|do(X=x)){displaystyle P(Z=z|do(X=x))} as a weighting function. The estimate obtained will be bias-free even when Z and Y are confoundedâthat is, when there is an unmeasured common factor that affects both Z and Y.[14]
The precise conditions ensuring the validity of this and other weighting schemes are formulated in Bareinboim and Pearl, 2016[14] and Bareinboim et al., 2014.[12]
External, internal, and ecological validity[edit]
In many studies and research designs, there may be a trade-off between internal validity and external validity: Attempts to increase internal validity may also limit the generalizability of the findings, and vice versa.This situation has led many researchers call for 'ecologically valid' experiments. By that they mean that experimental procedures should resemble 'real-world' conditions. They criticize the lack of ecological validity in many laboratory-based studies with a focus on artificially controlled and constricted environments. Some researchers think external validity and ecological validity are closely related in the sense that causal inferences based on ecologically valid research designs often allow for higher degrees of generalizability than those obtained in an artificially produced lab environment. However, this again relates to the distinction between generalizing to some population (closely related to concerns about ecological validity) and generalizing across subpopulations that differ on some background factor. Some findings produced in ecologically valid research settings may hardly be generalizable, and some findings produced in highly controlled settings may claim near-universal external validity. Thus, external and ecological validity are independentâa study may possess external validity but not ecological validity, and vice versa.
Qualitative research[edit]
Within the qualitative research paradigm, external validity is replaced by the concept of transferability. Transferability is the ability of research results to transfer to situations with similar parameters, populations and characteristics.[15]
In experiments[edit]
It is common for researchers to claim that experiments are by their nature low in external validity. Some claim that many drawbacks can occur when following the experimental method. By the virtue of gaining enough control over the situation so as to randomly assign people to conditions and rule out the effects of extraneous variables, the situation can become somewhat artificial and distant from real life.
There are two kinds of generalizability at issue:
However, both of these considerations pertain to Cook and Campbell's concept of generalizing to some target population rather than the arguably more central task of assessing the generalizability of findings from an experiment across subpopulations that differ from the specific situation studied and people who differ from the respondents studied in some meaningful way.[6]
Critics of experiments suggest that external validity could be improved by use of field settings (or, at a minimum, realistic laboratory settings) and by use of true probability samples of respondents. However, if one's goal is to understand generalizability across subpopulations that differ in situational or personal background factors, these remedies do not have the efficacy in increasing external validity that is commonly ascribed to them. If background factor X treatment interactions exist of which the researcher is unaware (as seems likely), these research practices can mask a substantial lack of external validity. Dipboye and Flanagan (1979), writing about industrial and organizational psychology, note that the evidence is that findings from one field setting and from one lab setting are equally unlikely to generalize to a second field setting.[16] Thus, field studies are not by their nature high in external validity and laboratory studies are not by their nature low in external validity. It depends in both cases whether the particular treatment effect studied would change with changes in background factors that are held constant in that study. If one's study is 'unrealistic' on the level of some background factor that does not interact with the treatments, it has no effect on external validity. It is only if an experiment holds some background factor constant at an unrealistic level and if varying that background factor would have revealed a strong Treatment x Background factor interaction, that external validity is threatened.[5]
Generalizability across situations[edit]
Research in psychology experiments attempted in universities are often criticized for being conducted in artificial situations and that it cannot be generalized to real life.[17] To solve this problem, social psychologists attempt to increase the generalizability of their results by making their studies as realistic as possible. As noted above, this is in the hope of generalizing to some specific population. Realism per se does not help the make statements about whether the results would change if the setting were somehow more realistic, or if study participants were placed in a different realistic setting. If only one setting is tested, it is not possible to make statements about generalizability across settings.[5][7]
However, many authors conflate external validity and realism. There is more than one way that an experiment can be realistic:
This is referred to the extent to which an experiment is similar to real-life situations as the experiment's mundane realism.[17]
It is more important to ensure that a study is high in psychological realismâhow similar the psychological processes triggered in an experiment are to psychological processes that occur in everyday life.[18]
Psychological realism is heightened if people find themselves engrossed in a real event. To accomplish this, researchers sometimes tell the participants a cover storyâa false description of the study's purpose. If however, the experimenters were to tell the participants the purpose of the experiment then such a procedure would be low in psychological realism. In everyday life, no one knows when emergencies are going to occur and people do not have time to plan responses to them. This means that the kinds of psychological processes triggered would differ widely from those of a real emergency, reducing the psychological realism of the study.[2]
People don't always know why they do what they do, or what they do until it happens. Therefore, describing an experimental situation to participants and then asking them to respond normally will produce responses that may not match the behavior of people who are actually in the same situation. We cannot depend on people's predictions about what they would do in a hypothetical situation; we can only find out what people will really do when we construct a situation that triggers the same psychological processes as occur in the real world.
Generalizability across people[edit]
Social psychologists study the way in which people in general are susceptible to social influence. Several experiments have documented an interesting, unexpected example of social influence, whereby the mere knowledge that others were present reduced the likelihood that people helped.
The only way to be certain that the results of an experiment represent the behaviour of a particular population is to ensure that participants are randomly selected from that population. Samples in experiments cannot be randomly selected just as they are in surveys because it is impractical and expensive to select random samples for social psychology experiments. It is difficult enough to convince a random sample of people to agree to answer a few questions over the telephone as part of a political poll, and such polls can cost thousands of dollars to conduct. Moreover, even if one somehow was able to recruit a truly random sample, there can be unobserved heterogeneity in the effects of the experimental treatments.. A treatment can have a positive effect on some subgroups but a negative effect on others. The effects shown in the treatment averages may not generalize to any subgroup.[5][19]
Many researchers address this problem by studying basic psychological processes that make people susceptible to social influence, assuming that these processes are so fundamental that they are universally shared. Some social psychologist processes do vary in different cultures and in those cases, diverse samples of people have to be studied.[20]
Replications[edit]
The ultimate test of an experiment's external validity is replication â conducting the study over again, generally with different subject populations or in different settings. Sims extreme violence mod. Researches will often use different methods, to see if they still get the same results.
When many studies of one problem are conducted, the results can vary. Several studies might find an effect of the number of bystanders on helping behaviour, whereas a few do not. To make sense out of this, there is a statistical technique called meta-analysis that averages the results of two or more studies to see if the effect of an independent variable is reliable. A meta analysis essentially tells us the probability that the findings across the results of many studies are attributable to chance or to the independent variable. Resident evil code veronica pc. If an independent variable is found to have an effect in only one of 20 studies, the meta-analysis will tell you that that one study was an exception and that, on average, the independent variable is not influencing the dependent variable. If an independent variable is having an effect in most of the studies, the meta analysis is likely to tell us that, on average, it does influence the dependent variable.
Lorkhaj. Lorkhan was the last of the litter of Fadomai, and held within his Heart a Great Darkness that would become Namiira. Among the Bretons. To the Bretons, Lorkhan is known as Sheor, or the 'Bad Man'. Within Bretic religion, Lorkhan is the representation of all struggle and strife that can be encountered in the mortal world. Nov 23, 2018 They vanished when Lorkhan's heart was struck by Keening, causing a burst of divine power that tore their souls from their bodies, leaving ash behind. This god (Numidium) was destroyed in later eras, along with the Dwemer souls tied to it.
There can be reliable phenomena that are not limited to the laboratory. For example, increasing the number of bystanders has been found to inhibit helping behaviour with many kinds of people, including children, university students, and future ministers;[20] in Israel;[21] in small towns and large cities in the U.S.;[22] in a variety of settings, such as psychology laboratories, city streets, and subway trains;[23] and with a variety of types of emergencies, such as seizures, potential fires, fights, and accidents,[24] as well as with less serious events, such as having a flat tire.[25] Many of these replications have been conducted in real-life settings where people could not possibly have known that an experiment was being conducted.
Basic dilemma of the social psychologist[edit]
When conducting experiments in psychology, some believe that there is always a trade-off between internal and external validityâ
Some researchers believe that a good way to increase external validity is by conducting field experiments. In a field experiment, people's behavior is studied outside the laboratory, in its natural setting. A field experiment is identical in design to a laboratory experiment, except that it is conducted in a real-life setting. The participants in a field experiment are unaware that the events they experience are in fact an experiment. Some claim that the external validity of such an experiment is high because it is taking place in the real world, with real people who are more diverse than a typical university student sample. However, as real-world settings differ dramatically, findings in one real-world setting may or may not generalize to another real-world setting.[16]
Neither internal nor external validity are captured in a single experiment. Social psychologists opt first for internal validity, conducting laboratory experiments in which people are randomly assigned to different conditions and all extraneous variables are controlled. Other social psychologists prefer external validity to control, conducting most of their research in field studies, and many do both. Taken together, both types of studies meet the requirements of the perfect experiment. Through replication, researchers can study a given research question with maximal internal and external validity.[26]
See also[edit]Notes[edit]
Retrieved from 'https://en.wikipedia.org/w/index.php?title=External_validity&oldid=883713768'
doi: 10.2105/AJPH.2007.126847
PMID: 18048772
This article has been cited by other articles in PMC.
IMPORTANCE OF EXTERNAL VALIDITY
Over 40 years ago, Campbell and Stanley published their seminal work on experimental and quasi-experimental designs for research, in which they raised issues about threats to internal validity (whether or not observed covariation should be interpreted as a causal relationship) that exist when researchers are not able to randomly assign participants to treatments.1 In that volume and subsequent work, they also raised issues about other types of validity, including2,3:
It has been frequently argued that internal validity is the priority for research.4 However, in an applied discipline, the purpose of which includes working to improve the health of the public, it is also important that external validity be emphasized and strengthened.â For example, it is important to know not only that a program is effective, but that it is likely to be effective in other settings and with other populations.
In an influential 1985 article, âEfficacy and Effectiveness Trials (and Other Phases of Research) in the Development of Health Promotion Programs,â Flay proposes a model that emphasizes internal and external validity at different stages of the research process and that would lead to the translation of research to practice. The two main research levels were âefficacy trialsâ and âeffectiveness trials.â Efficacy trials were to be highly controlled studies that answered the question of whether a proposed intervention would have the desired effects under ideal circumstances. Effectiveness trials were to follow efficacy trials and were to be studies that carried out the proposed intervention in less controlled and more real-life situations. The argument was that a given public health intervention should be successful in both types of trials before it was ready for dissemination to and by public health practitioners.
Efficacy trials were to have high internal validity, and effectiveness trials were to have high external validity. Efficacy trials were more likely to be controlled experiments, such as randomized control trials of public health interventions, that have the virtue of high internal validity but often have the liability of low external validity9 (i.e., the groups, settings, or contexts in which findings would apply). It is axiomatic in social science research that there is an inverse relationship between internal and external validity. A key to internal validity is good measurement and study design, and representative sampling is necessary for inference.9 However, it may be useful to distinguish between inference derived from sample design and our ability to generalize, which is more dependent on judgment.
Historically, researchers have tended to focus on maximizing internal validity, with the idea that it is more important to know if a given public health intervention works under highly controlled conditions than it is to know if it will work among different population groups, organizations, or settings. Similarly, funding organizations and journals have tended to be more concerned with the scientific rigor of intervention studies than with the generalizability of results. The consequence of this emphasis on internal validity has been a lack of attention to and information about external validity, which has contributed to our failure to translate research into public health practice.
For instance, in the area of cancer prevention and control, there is a documented substantial lag between discovery and delivery of effective interventions. Recognition of this lag has been noted for at least 30 years, since the first National Cancer Instituteâconvened cancer control working groups issued reports in the 1970s. More recently, Balas and Boren found that it takes about 17 years to turn 14% of original research to the benefit of patient care. Similarly, the National Research Council concluded that, even when effective interventions have been developed, there often is a gap between scientific knowledge and clinical practice.11 In addition, minorities and underserved communities usually gain access to effective interventions more slowly than do other populations.
Thus, the idea that research would progress from efficacy trials to effectiveness trials to widespread dissemination has not become a reality for a number of reasons, not the least of which is the time and cost involved in this stepwise progress of research to practice. As a result of the failure of this model, practitioners are often unable to determine if a given studyâs findings apply to their local setting, population staffing, or resources. Reviews indicate that reporting on external validity is provided far less often than is reporting on other methodological issues. However, there are several reasons for the lack of information on external validity being an important contributor to the failure to translate research into public health practice. Policy and administrative decision-makers are unable to determine the generalizability or breadth of applicability of research findings. Finally, systematic reviews and meta-analyses are limited in the conclusions that can be drawn when external validity data are not reported.
THE JOURNAL ENDORSES A GREATER EMPHASIS ON EXTERNAL VALIDITY
Although the Journal has long recognized the importance of external validity in articles it has published, the relatively recent CONSORT and TREND reports, as well as the recent emphasis on the RE-AIM model, has strengthened the recognition by the Journal editors and editorial board of the need to formally emphasize external validity and to collect information on appropriate manuscripts that enhances both inference and potential generalizability.,â
Recently, two members of the the Journal editorial board and editors represented the Journal in a meeting with 12 other leading health journals and representatives from the National Institutes of Health, the Centers for Disease Control and Prevention, and the Robert Wood Johnson Foundation. The purpose of the meeting was to encourage and strengthen the reporting of findings on external validity. One of the outcomes of the meeting was that participants agreed that enhancing the quality of reporting on external validity in journal articles warrants higher priority than it has received in public health research publications to date.
The meeting participants identified several characteristics of external validity that should be reported. As with other quality-rating scales and guidelines, not every article would be expected to excel on all of the criteria; rather, authors should report on these issues where appropriate, or state that no information is available. Four categories of external validity information were identified by the meeting participants:
Although we are not intending to add to the burden of authors publishing in the Journal, we believe that many of the articles we publish will benefit by including information on external validity. Most important, we believe that the field of public health and public health practice will benefit considerably from this information.
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