What Internal Validity Means in Research

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What Internal Validity Means in Research

Research Methods in Psychology · Chapter 10: Internal Validity and Its ThreatsSection 10.1 · lesson 58 of 9411 min · free with a free account

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In this lesson

Establishing that the independent variable - and nothing else - produced the effect.

  • Define internal validity and its role in causal inference
  • State the three conditions a causal claim must satisfy
  • Place internal validity among the four forms of validity
  • Rank common designs by inherent internal validity

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Internal Validity and Its Threats

What Internal Validity Means in Research

A new therapy reduced anxiety. A wage increase changed employment. A teaching method improved comprehension. Each of those sentences makes the most powerful claim in research. A causal one. And each is one of the easiest kinds of claim to get wrong. This chapter is about the property that makes such claims trustworthy. You will define internal validity, and see its role in causal inference. You will state the three conditions any causal claim must satisfy. You will place internal validity among the four forms of validity. And you will rank common research designs by how much of it they start with.

The Central Question

Causal Claims Are the Easiest to Get Wrong

Start with why causal claims are so fragile. When a researcher says X produced a change in Y, they are claiming a great deal. And here is the problem in one sentence. In any real study, the independent variable is never the only thing that varies. Participants differ from one another in ways nobody recorded. Time passes. Instruments get administered. Situations change. A dozen background factors operate simultaneously, all the way through. So the investigator's task is not to observe an effect. It is to isolate the contribution of the independent variable from everything else. That isolation problem is what this entire chapter addresses.

The Definition

Internal Validity

Internal validity is the name for how well a study solves that problem. It is the degree to which a study's design and execution permit a specific conclusion. That the observed change in the dependent variable was caused by the independent variable. Rather than by any other factor. It is the property that lets a researcher say something quite bold. The manipulation, and not something else, produced this effect. A study with high internal validity has ruled out the plausible alternative explanations. Note the word plausible. You do not have to rule out everything imaginable. A study with low internal validity may produce results that look impressive. And mean very little, because too many other variables could have produced the same pattern.

Three Conditions

What a Causal Inference Requires

Three conditions must be satisfied before a causal claim is responsible. They come from a long tradition. The philosophical analysis of John Stuart Mill, and the methodological refinements of Donald Campbell. The first is covariation. X and Y change together. The second is temporal order. X occurs before Y. The third is the absence of alternatives. No other plausible cause explains it. Notice the character of each one. The first is empirical, the second is logical, and the third is the hard one. Internal validity is essentially the question of whether all three have been met. Within this study, with these participants, on this occasion.

Condition One

Covariation Between Cause and Effect

The first condition is empirical, and it is the easiest to check. As the independent variable changes, the dependent variable must also change. In some systematic way, not merely by coincidence. If a tutoring program is supposed to raise test scores, look for the difference. Those who receive tutoring should differ from those who do not. Without covariation there is simply no effect to explain. You would be arguing about the cause of something that did not happen. This condition is normally evaluated through statistical analysis. So it is a matter of statistical-conclusion validity as much as internal validity. The two overlap here, which is worth knowing when you read a critique.

Condition Two

Temporal Precedence of the Cause

The second condition is logical rather than empirical. The cause must occur before the effect. If the supposed cause and the supposed effect occur simultaneously, the claim is in trouble. And if the effect can be shown to have preceded the cause, the claim collapses entirely. Experimental designs satisfy this condition automatically. The researcher manipulates the independent variable, and then measures the dependent variable. The order is built into the procedure, so it cannot be violated. Correlational and cross-sectional designs frequently struggle here. Knowing that depression and unemployment correlate tells you nothing about which came first. And that is not a technicality, because reversing the arrow reverses the policy. Whether unemployment causes depression, or depression causes unemployment. Those two conclusions imply completely different interventions.

Condition Three

Eliminating Alternative Explanations

The third condition is the hardest, and it is the heart of internal validity. Even when X and Y covary, and X precedes Y, you are not finished. Some other variable may be responsible for the apparent effect. Go back to the tutoring program and consider three possibilities. Perhaps the participants who chose the program were already more motivated. Perhaps a national curriculum reform took effect during the study period. Perhaps the test was simply easier the second time it was administered. Each of those is an alternative explanation. And a researcher claiming the tutoring worked must show that none is plausible. That is a demanding burden, and it does not go away with a bigger sample. The remainder of this chapter is essentially a catalog of those alternative explanations. Along with the techniques researchers use to neutralize each of them.

The Family

Four Forms of Validity

Internal validity is one of four interrelated forms of validity. Together they determine the credibility of a research finding. Internal validity asks whether the independent variable caused the change, within this study. Its concern is ruling out confounds, history, maturation, and selection. External validity asks whether findings generalize beyond this sample, setting, and time. Its concern is representativeness of participants, contexts, and operationalizations. Construct validity asks whether the measures and manipulations represent the theoretical constructs. Its concern is operationalization, measurement adequacy, and conceptual fit. And statistical-conclusion validity asks whether inferences about covariation used appropriate procedures. Its concern is power, test selection, assumption checking, and error rates. Distinguishing these clearly helps you diagnose exactly where a study is weak. And what kind of evidence its conclusions can actually support.

The Hierarchy

Why Internal Validity Comes First

These four are not equally important in every study. But internal validity occupies a privileged position, and here is why. If a study fails internal validity, its findings cannot support any causal claim. No matter how representative the sample. No matter how sophisticated the statistical analysis. External validity becomes meaningful only once the internal relationship is established. Think about what generalization actually is. It is extending a finding to new people and settings. Generalizing a finding that was never established generalizes nothing at all. Which is why methodologists describe internal validity as the foundation. The other validity claims are built on top of it, and inherit its weaknesses.

A Frequent Trade-Off

Internal Against External Validity

Internal and external validity often pull in opposite directions. Consider a tightly controlled laboratory experiment. It maximizes internal validity by holding nearly every variable constant. You can be confident that the manipulation caused what you saw. But the artificiality of the setting may limit how far the findings travel. Real behavior does not happen under those conditions. Now consider a naturalistic field study. It captures real-world conditions, and is high in external validity. But the messiness of those same conditions makes alternative explanations hard to rule out. Notice that neither design is better. They are strong in different places. So strong research programs combine designs of different kinds. Rather than relying on any single study to settle a question by itself.

The Difficulty

Why the World Does Not Cooperate

Internal validity is hard to achieve because the world does not cooperate. Participants arrive at a study with histories, expectations, and abilities. None of which the researcher can fully observe. Studies unfold over time, during which participants grow, fatigue, or learn. Measurement instruments may behave differently on second administration. And events outside the study can shift an entire population's response. Each of those realities is a potential alternative explanation for your effect. So think of the observed relationship as the visible tip of a much larger system. Beneath it lie the threats this chapter unpacks systematically. History, maturation, selection, testing, instrumentation, mortality, and regression. Along with the interactions between them. A study with high internal validity is one where the researcher anticipated these. Designed the study to neutralize them, and reported honestly on the ones that remained.

Ranking Designs

Some Designs Start Ahead

Some designs are inherently better at ruling out alternatives than others. At the top sits the randomized controlled experiment. Random assignment equalizes groups on every variable, observed and unobserved. That phrase, and unobserved, is what makes randomization irreplaceable. Next comes the quasi-experiment with matching. It lacks random assignment, but uses statistical or matched controls to approximate equivalence. Then the single-group pre-test post-test design, which is low to moderate. It controls many participant differences, and stays vulnerable to history, maturation, and testing. Then the cross-sectional correlational study, which is low. It cannot establish temporal order, and cannot rule out confounds. And at the bottom, the one-shot case study, which is very low. No comparison group, no baseline, and nearly any factor could explain the outcome. But read that hierarchy carefully, because it ranks potential, not achievement. A poorly executed experiment loses its advantage entirely. Internal validity is a property of the study as conducted, not of the label on its proposal.

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FAQ

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About What Internal Validity Means in Research

What is internal validity in research?
Internal validity is the degree to which a study's design and execution support the conclusion that the independent variable, rather than some other factor, caused the observed change in the dependent variable. A study high in internal validity has ruled out the plausible alternative explanations, not every imaginable one. A study low in internal validity may show impressive results that mean little, because other variables could have produced the same pattern.
What are the three conditions needed to show causation?
A causal claim requires covariation, temporal precedence, and the elimination of alternative explanations. Covariation means the cause and effect change together. Temporal precedence means the cause comes before the effect. Eliminating alternatives means no other plausible variable explains the relationship. These criteria trace back to John Stuart Mill and were refined methodologically by Donald Campbell, and the third is usually the hardest to satisfy.
What is the difference between internal and external validity?
Internal validity asks whether the independent variable caused the change within a particular study, while external validity asks whether the finding generalizes to other people, settings, and times. The two often pull in opposite directions: a tightly controlled lab experiment strengthens internal validity but may be artificial, while a field study is more realistic but makes alternative explanations harder to rule out. Construct and statistical-conclusion validity complete the four main types.
Which research designs have the highest internal validity?
The randomized controlled experiment has the highest potential internal validity, because random assignment balances groups on both observed and unobserved variables. Below it come quasi-experiments with matching or statistical controls, then single-group pretest-posttest designs, then cross-sectional correlational studies, and finally one-shot case studies with no comparison group or baseline. This ranking reflects potential only, since a poorly executed experiment can lose its advantage.

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Research Methods in Psychology (Psychology 111) carries a NCCRS credit recommendation for 3 semester credits. ACE and NCCRS have over 2,100 cooperating colleges that will consider accepting credits; the final decision is with the universities.
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