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 scientific study.
As a rule of thumb, conclusions based on correlations or associations may only allow for lesser degrees of internal validity than conclusions drawn on the basis of direct manipulation of the independent variable.
And, when viewed only from the perspective of Internal Validity, highly controlled true experimental designs i. By contrast, however, the very strategies employed to control these factors may also limit the generalizability or external validity of the findings. Lack of clarity about which variable occurred first may yield confusion about which variable is the cause and which is the effect. A major threat to the validity of causal inferences is confounding: Changes in the dependent variable may rather be attributed to the existence or variations in the degree of a third variable which is related to the manipulated variable.
Where spurious relationships cannot be ruled out, rival hypotheses to the original causal inference hypothesis of the researcher may be developed. 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.
Often, these are large-scale events natural disaster, political change, etc. 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. On the other hand, effectiveness studies attempt to address a different issue: Does the treatment work in the real-life situation?
Interestingly enough, the US drug approval and monitoring processes seem to compartmentalize efficacy and effectiveness.
Rigorous experiments and hard data are required to gain the FDA's approval. But after the drugs are on the market, it takes other agencies to monitor the effectiveness of the drugs. Contrary to the popular belief, FDA has no authority to recall unsafe drugs. Rather, FDA could suggest a voluntarily recall only. Several drugs that had been approved by FDA before were re-called from the market later e.
This discrepancy between the results yielded from lab tests and the real world led to an investigation by the Institute of Medicine IOM. To close the gap between internal and external validity, the IOM committee recommended that the FDA should take proactive steps to monitor the safety of the approved drugs throughout their time on the market Ramsey, Indeed, there is a similar concept to "effectiveness" in educational research: Educational researchers realize that it is impossible for teacher to blocking all interferences by closing the door.
Whether internal validity or external validity is more important has been a controversial topic in the research community. Campbell and Stanley stated that although ideally speaking a good study should be strong in both types of validity, internal validity is indispensable and essential while the question of external validity is never completely answerable. External validity is concerned with whether the same result of a given study can be observed in other situations.
Like inductive inference, this question will never be conclusive. No matter how many new cases concur with the previous finding, it takes just one counter-example to weaken the external validity of the study. In other words, Campbell and Stanley's statement implies that internal validity is more important than external validity.
Cronbach is opposed to this notion. He argued that if a treatment is expected to be relevant to a broader context, the causal inference must go beyond the specific conditions. Tests of significance for this design--although this design may be developed and conducted appropriately, statistical tests of significance are not always used appropriately.
Wrong statistic in common use--many use a t-test by computing two ts, one for the pre-post difference in the experimental group and one for the pre-post difference of the control group. If the experimental t-test is statistically significant as opposed to the control group, the treatment is said to have an effect.
However this does not take into consideration how "close" the t-test may really have been. A better procedure is to run a 2X2 ANOVA repeated measures, testing the pre-post difference as the within-subject factor , the group difference as the between-subject factor , and the interaction effect of both factors.
By using experimental and control groups with and without pretests, both the main effects of testing and the interaction of testing and the treatment are controlled. Therefore generalizability increases and the effect of X is replicated in four different ways.
Statistical tests for this design--a good way to test the results is to rule out the pretest as a "treatment" and treat the posttest scores with a 2X2 analysis of variance design-pretested against unpretested. And can be seen as controlling for testing as main effect and interaction, but unlike this design, it doesn't measure them.
But the measurement of these effects isn't necessary to the central question of whether of not X did have an effect. This design is appropriate for times when pretests are not acceptable. Statistical tests for this design--the most simple form would be the t-test.
However covariance analysis and blocking on subject variables prior grades, test scores, etc. However, some widespread concepts may also contribute other types of threats against internal and external validity. Some researchers downplay the importance of causal inference and assert the worth of understanding.
This understanding includes "what," "how," and "why. If a question "why X happens" is asked and the answer is "Y happens," does it imply that "Y causes X"? If X and Y are correlated only, it does not address the question "why. In fact, a particular explanation does not explain anything. For example, if one askes, "Why Alex Yu behaves in that way," the asnwer could be "because he is Alex Yu. He is a unqiue human being. He has a particular family background and a specific social circle.
Threats to validity include: Selection--groups selected may actually be disparate prior to any treatment.. Mortality--the differences between O 1 and O 2 may be because of the drop-out rate of subjects from a specific experimental group, which would cause the groups to be unequal.. Others--Interaction of selection and maturation and interaction .
We often conduct research in order to determine Threats to Internal & External Validity The controlled or experimental design enables the investigator to control for threats to internal and external validity. Threats to internal validity .
Construct validity is the quality of choices about the particular forms of the independent and dependent variables. These choices will affect the quality of research findings. Threats to construct validity can arise from the choice of treatment (the operationalization of the IV, and the. Threats to validity of Research Design Chong-ho Yu () The books by Campbell and Stanley (), Cook and Campbell (), and Shadish, Cook, and Campbell, () are considered seminal works in the field of experimental design.
Video: Threats to Internal Validity I: History, Instrumentation & Subject Mortality In research, there are many things besides the independent variable that can affect the dependent variable. Before we launch into a discussion of the most common threats to construct validity, let's recall what a threat to validity is. In a research study you are likely to reach a conclusion that your program was a good operationalization of what you wanted and that your measures reflected what you wanted them to reflect.