12.4 Limitations and generalizability of experimental results
3 min read•august 7, 2024
Experiments are powerful tools for understanding cause and effect, but they have limits. We'll explore how to assess the validity of experimental results and figure out if they apply to real-world situations.
Researchers use various strategies to boost validity, like and . We'll also look at how to extend findings beyond the lab through and careful consideration of to broader populations.
Types of Validity
Experimental Design Validity
Top images from around the web for Experimental Design Validity
Educational Research and Reviews - internal validity: a must in research designs View original
Is this image relevant?
Chapter 5 Research Design – Research Methods for the Social Sciences View original
Is this image relevant?
Frontiers | Best Practices for Developing and Validating Scales for Health, Social, and ... View original
Is this image relevant?
Educational Research and Reviews - internal validity: a must in research designs View original
Is this image relevant?
Chapter 5 Research Design – Research Methods for the Social Sciences View original
Is this image relevant?
1 of 3
Top images from around the web for Experimental Design Validity
Educational Research and Reviews - internal validity: a must in research designs View original
Is this image relevant?
Chapter 5 Research Design – Research Methods for the Social Sciences View original
Is this image relevant?
Frontiers | Best Practices for Developing and Validating Scales for Health, Social, and ... View original
Is this image relevant?
Educational Research and Reviews - internal validity: a must in research designs View original
Is this image relevant?
Chapter 5 Research Design – Research Methods for the Social Sciences View original
Is this image relevant?
1 of 3
assesses how well the results of a study can be generalized to other situations, settings, or populations beyond the specific context of the original experiment
evaluates the extent to which a study's design and execution allow for accurate conclusions about cause-and-effect relationships between the independent and dependent variables, minimizing the influence of confounding factors
assesses how well the operationalization of variables in a study accurately represents the theoretical constructs being investigated (intelligence tests measuring actual intelligence)
refers to the degree to which the findings of a study can be generalized to real-world settings and situations, reflecting the naturalness and authenticity of the experimental conditions (lab settings vs. real-world environments)
Factors Affecting Validity
Biases and Confounding Variables
occurs when the sample selected for a study is not representative of the target population, leading to skewed results and limited generalizability (convenience sampling, self-selection bias)
are extraneous factors that are not controlled for in an experiment and can influence the relationship between the independent and dependent variables, making it difficult to establish causality (age, socioeconomic status)
Threats to validity can arise from various sources, such as history effects (external events occurring during the study), (natural changes in participants over time), (familiarity with the measures), and (extreme scores moving closer to the average in subsequent measurements)
Strategies for Enhancing Validity
Randomization involves randomly assigning participants to different treatment conditions to minimize the impact of confounding variables and ensure that any differences observed are due to the manipulation of the independent variable
, such as single-blind (participants are unaware of their assigned condition) and double-blind (both participants and researchers are unaware), help reduce bias and expectancy effects that could influence the study's outcomes
Control groups serve as a baseline for comparison, allowing researchers to isolate the effects of the independent variable by comparing the experimental group to a group that does not receive the intervention or manipulation (placebo group)
the order of conditions or tasks helps control for order effects and fatigue, ensuring that the sequence of presentation does not systematically influence the results (alternating the order of tasks across participants)
Extending Experimental Results
Replication and Generalizability
Replication involves conducting the same study multiple times, either by the original researchers or by independent teams, to assess the reliability and robustness of the findings across different samples and contexts (direct replication, conceptual replication)
Generalizability refers to the extent to which the results of a study can be applied to broader populations, settings, or situations beyond the specific sample and context of the original experiment
To enhance generalizability, researchers can use techniques (stratified sampling, random sampling) to ensure that the sample closely mirrors the characteristics of the target population
Conducting experiments in diverse settings and with different populations helps establish the external validity of the findings and their applicability to real-world contexts (cross-cultural studies, field experiments)
Population Inference and Limitations
involves using statistical techniques to draw conclusions about the larger population based on the results obtained from a sample
Researchers use , such as hypothesis testing and confidence intervals, to determine the likelihood that the observed effects in the sample are representative of the population (p-values, effect sizes)
However, it is crucial to recognize the limitations of population inference, as the sample may not perfectly represent the population, and there may be unique characteristics or contextual factors that limit the generalizability of the findings
Researchers should be cautious when making broad generalizations and should clearly communicate the boundaries and constraints of their conclusions based on the specific sample and methodology used in the study