2.4 Experimental validity (internal and external)
3 min read•august 7, 2024
Experimental validity is crucial for ensuring research results are trustworthy and useful. focuses on establishing cause-effect relationships, while determines if findings apply to other situations. Both are essential for drawing meaningful conclusions from experiments.
Researchers must balance internal and external validity. High internal validity often means more controlled conditions, potentially limiting generalizability. Conversely, studies with high external validity may sacrifice some control, making causal inferences harder. Understanding these trade-offs is key to good experimental design.
Internal Validity
Construct Validity and Statistical Conclusion Validity
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- Construct validity assesses whether a study measures what it intends to measure
- Ensures the of variables accurately represents the theoretical constructs being investigated
- Threats to construct validity include poor operationalization of variables, confounding variables, and participant reactivity ()
- refers to the accuracy of inferences made about the relationship between the independent and dependent variables
- Assesses whether the study has sufficient statistical power to detect an effect if one exists
- Threats to statistical conclusion validity include low statistical power, violated assumptions of statistical tests, and unreliable measures (inconsistent or inaccurate measurement)
Threats to Internal Validity
- Internal validity is the extent to which a study establishes a causal relationship between the independent and dependent variables
- Ensures that changes in the dependent variable are caused by the independent variable and not extraneous factors
- High internal validity allows researchers to make strong causal claims about the relationship between variables
- Threats to internal validity include:
- History: Events outside the study that affect the dependent variable (changes in government policies during a long-term study)
- Maturation: Natural changes in participants over time (aging, learning, fatigue)
- Testing: The act of measuring participants affects their behavior or responses (practice effects, sensitization to the measures)
- Instrumentation: Changes in the measurement tools or procedures during the study (switching from paper to online surveys)
- : Systematic differences between groups prior to the study (non-, self-selection)
- Attrition: Participants dropping out of the study, leading to differences between groups (more motivated participants remaining)
External Validity
Generalizability and Ecological Validity
- External validity refers to the extent to which the results of a study can be generalized to other populations, settings, or times
- Assesses whether the findings are applicable beyond the specific sample and context of the study
- High external validity allows researchers to make broader claims about the generalizability of their results
- Generalizability is the degree to which the results of a study can be applied to other populations or contexts
- Depends on the representativeness of the sample and the similarity of the study context to other settings (college students vs. general population)
- Threats to generalizability include sampling bias, unique characteristics of the sample, and highly controlled laboratory settings
- refers to the degree to which the study conditions and measures resemble real-life situations
- Assesses whether the findings can be generalized to naturalistic settings and everyday experiences
- Threats to ecological validity include artificial laboratory settings, contrived tasks, and lack of environmental cues (studying memory in a lab vs. real-world memory demands)
Key Terms to Review (16)
Confounding Variable: A confounding variable is an extraneous factor that correlates with both the independent and dependent variables in an experiment, potentially leading to misleading conclusions about the relationship between them. This variable can create a false impression of causation, impacting the clarity and accuracy of the experimental results. Identifying and controlling for confounding variables is crucial for ensuring that findings accurately reflect the intended effects of the independent variable.
Control Group: A control group is a baseline group in an experiment that does not receive the experimental treatment or intervention, allowing researchers to compare it with the experimental group that does receive the treatment. This comparison helps to isolate the effects of the treatment and determine its effectiveness while accounting for other variables.
Ecological Validity: Ecological validity refers to the extent to which the findings of a study can be generalized to real-world settings. It emphasizes how well the conditions and materials used in research reflect the complexities and contexts of everyday life, making it essential for ensuring that experimental results are applicable beyond the controlled environment of the study.
Effect Size: Effect size is a quantitative measure that reflects the magnitude of a treatment effect or the strength of a relationship between variables in a study. It helps in understanding the practical significance of research findings beyond just statistical significance, offering insights into the size of differences or relationships observed.
External Validity: External validity refers to the extent to which research findings can be generalized to, or have relevance for, settings, people, times, and measures beyond the specific conditions of the study. This concept connects research results to real-world applications, making it essential in evaluating how applicable findings are to broader populations and situations.
Hawthorne Effect: The Hawthorne Effect refers to the phenomenon where individuals modify their behavior in response to being observed or studied. This effect highlights how the act of observation can influence participants' actions and potentially skew research results, creating bias and impacting the validity of experimental findings.
Internal Validity: Internal validity refers to the degree to which an experiment accurately establishes a causal relationship between the independent and dependent variables, free from the influence of confounding factors. High internal validity ensures that the observed effects in an experiment are genuinely due to the manipulation of the independent variable rather than other extraneous variables. This concept is crucial in designing experiments that can reliably test hypotheses and draw valid conclusions.
Measurement reliability: Measurement reliability refers to the consistency and stability of a measurement instrument, indicating that it produces the same results under similar conditions over time. This concept is crucial because reliable measurements ensure that data is trustworthy and that conclusions drawn from that data are valid, thereby reinforcing both internal and external validity in experimental research.
Operationalization: Operationalization is the process of defining and measuring concepts in a way that allows them to be tested or analyzed quantitatively or qualitatively. This process is crucial as it translates abstract concepts into specific, measurable variables, making it possible to assess relationships and effects in research. When operationalization is done correctly, it enhances the validity and reliability of research findings, helping researchers draw meaningful conclusions from their studies.
Population Generalizability: Population generalizability refers to the extent to which findings from a study can be applied to a broader population beyond the specific sample used in the research. This concept is crucial for understanding how representative a study's results are, ensuring that conclusions drawn from an experiment can be extended to similar groups or settings.
Quasi-experimental design: Quasi-experimental design refers to research methodologies that aim to evaluate interventions or treatments without random assignment of participants to control or experimental groups. This approach is often used when random assignment is impractical or unethical, allowing researchers to study the effects of an intervention in real-world settings while still attempting to control for confounding variables. Quasi-experimental designs can provide valuable insights, but they often come with trade-offs in terms of internal validity compared to true experiments.
Random Assignment: Random assignment is a technique used in experimental research to ensure that participants are allocated to different groups or conditions in a way that is not influenced by any biases or pre-existing differences. This process helps to create equivalent groups, enhancing the credibility of the experiment's conclusions by minimizing confounding variables.
Randomized controlled trial: A randomized controlled trial (RCT) is a scientific experiment that aims to evaluate the effectiveness of an intervention by randomly assigning participants to either a treatment group or a control group. This design helps minimize bias and confounding variables, enhancing the internal validity of the findings. RCTs are also crucial for making generalizations to broader populations, linking them to external validity.
Selection Bias: Selection bias occurs when the participants included in a study are not representative of the larger population from which they are drawn, leading to results that cannot be generalized. This bias can significantly impact research findings by skewing results toward a certain outcome, making it difficult to draw valid conclusions about relationships between variables.
Statistical Conclusion Validity: Statistical conclusion validity refers to the extent to which conclusions drawn from statistical analyses are valid and accurately reflect the relationships present in the data. It emphasizes the importance of appropriate statistical techniques and sufficient data to ensure that the findings are not only reliable but also meaningful. A strong statistical conclusion validity helps establish whether the observed effects in a study can be attributed to the experimental manipulation rather than other factors.
Statistical Significance: Statistical significance is a measure that helps determine whether the results of a study are likely to be genuine and not due to random chance. It is typically assessed using a p-value, which indicates the probability of observing the results if the null hypothesis is true. Understanding statistical significance is crucial in evaluating experimental outcomes, drawing conclusions from data, and ensuring that findings are robust and meaningful.