Study Notes on Internal Validity in Experiments
Internal Validity in Experiments
Internal validity refers to the degree to which one can conclusively determine that the independent variable caused changes in the dependent variable in an experiment.
Reflects the ability to draw conclusions about causal relationships based on experimental results.
High internal validity occurs when:
Alternative explanations, confounds, and uncontrolled influences are eliminated.
The results can reliably be attributed to the experimental manipulation rather than extraneous factors.
Criteria for High Internal Validity
To infer a cause-effect relationship between the independent and dependent variables, three criteria must be present:
Covariation
The independent and dependent variables must change together. For instance, if caffeine (the independent variable) increases alertness (the dependent variable), the change in alertness should correlate directly with caffeine consumption.
If a control or placebo condition is applied (e.g., blocking effects of caffeine), alertness should not change.
Temporal Precedence
The cause must precede the effect in time. For example, caffeine must be consumed before measuring alertness.
Elimination of Alternative Explanations
Other potential causes for the observed effects must be ruled out, such as the possibility that better sleep could explain increased alertness.
Researchers must control for possible confounding factors (e.g., ensuring participants get sufficient sleep before the study).
Experiments designed to meet these three criteria generally exhibit high internal validity, while poorly designed experiments may have compromised internal validity due to the presence of alternative explanations.
Confounding Variables
Definition: Variables that systematically change along with the independent variable, providing alternative explanations for changes in the dependent variable.
Example: Comparing morning and afternoon classes without accounting for time-of-day effects, where more alertness in the morning may confuse results if the intervention is only applied to the morning group.
Solutions to Control Confounding Variables:
Experimental Control: Conduct experiments at the same time of the day.
Statistical Control: Use covariate analysis (e.g., including age as a parameter in analysis).
Utilize ANCOVA (Analysis of Covariance) to adjust for confounding variables in the experimental design.
Threats to Internal Validity
There are multiple potential threats to internal validity:
Maturation Effects
Changes in participants due to the passage of time that can influence results (especially in developmental studies). For instance, children may improve in language skills naturally over time, potentially affecting test results.
Effects can be permanent (e.g., aging) or temporary (e.g., fatigue).
Control Strategy: Use a control group to compare changes over time, thereby isolating the effect of the independent variable.
Repeat Testing Effects
Improvements in performance due to familiarity with the testing procedure rather than the experimental manipulation. For example, in mirror tracing tasks, individuals generally improve due to practice with the task rather than the independent variable.
Control Strategy: Employ control groups for comparison, use different forms of the same test to minimize familiarity effects, or employ independent groups designs to measure the dependent variable only once.
Recognize that performance may sometimes decrease or remain unchanged due to factors like stress or fatigue.
Sequence Effects
The order of conditions affects performance, distinct from mere repetition effects. For instance, completing a difficult task first can influence performance on subsequently easier tasks.
Control Strategy: Use counterbalancing (either full or Latin square designs) to evenly distribute condition orders across participants or randomly assign condition orders.
Other strategies may include spacing conditions apart or providing practice trials.
Regression to the Mean
This occurs when extreme scores tend to return closer to the mean upon retesting, influenced by measurement errors or the unreliability of the measurement instrument.
For example: Selecting high-performing athletes based on a solitary, exceptional performance would likely yield less extreme scores upon subsequent tests.
Control Strategies: Use control groups to assess changes in extreme-scored individuals, random allocation to groups, and ensure reliable measurement instruments to minimize regression effects.
Conclusion
Understanding and controlling for these threats to internal validity is critical for ensuring that the causal inferences drawn from experimental research are reliable and valid. High internal validity allows researchers to confidently attribute observed effects to their independent variable without the interference of confounding factors or threats that may misrepresent the results.
Full Counterbalancing
Full counterbalancing involves creating all possible orders of conditions for an experiment. Each participant experiences all conditions but in a different order.
This method ensures that each condition appears equally across the sequence, helping to control for artifacts related to the order of presentation that might skew results.
For example, if an experiment has three conditions (A, B, C), the full counterbalancing approach would utilize every possible order:
A, B, C
A, C, B
B, A, C
B, C, A
C, A, B
C, B, A
Latin Square Design
The Latin square is a specific type of counterbalancing that ensures each condition appears in each position (order) exactly once, reducing the number of orders needed compared to full counterbalancing.
This design is especially useful when there are many conditions, making full counterbalancing impractical due to the exponential increase in combinations.
For example, with three conditions (A, B, C):
Condition orders could be:
1: A, B, C
2: B, C, A
3: C, A, B
In a Latin square of order 3, each condition appears once in each position (first, second, third) across participants while maintaining a balanced representation of conditions.
Both methods aim to mitigate the impact of sequence effects, ensuring that results are more reliable and valid by controlling for the influence of order on performance.