
- •Validity and reliability
- •What is Reliability?
- •What is Validity?
- •Conclusion
- •What is External Validity?
- •Psychology and External Validity The Battle Lines are Drawn
- •Randomization in External Validity and Internal Validity
- •Work Cited
- •What is Internal Validity?
- •Internal Validity vs Construct Validity
- •How to Maintain High Confidence in Internal Validity?
- •Temporal Precedence
- •Establishing Causality through a Process of Elimination
- •Internal Validity - the Final Word
- •How is Content Validity Measured?
- •An Example of Low Content Validity
- •Face Validity - Some Examples
- •If Face Validity is so Weak, Why is it Used?
- •Bibliography
- •What is Construct Validity?
- •How to Measure Construct Variability?
- •Threats to Construct Validity
- •Hypothesis Guessing
- •Evaluation Apprehension
- •Researcher Expectancies and Bias
- •Poor Construct Definition
- •Construct Confounding
- •Interaction of Different Treatments
- •Unreliable Scores
- •Mono-Operation Bias
- •Mono-Method Bias
- •Don't Panic
- •Bibliography
- •Criterion Validity
- •Content Validity
- •Construct Validity
- •Tradition and Test Validity
- •Which Measure of Test Validity Should I Use?
- •Works Cited
- •An Example of Criterion Validity in Action
- •Criterion Validity in Real Life - The Million Dollar Question
- •Coca-Cola - The Cost of Neglecting Criterion Validity
- •Concurrent Validity - a Question of Timing
- •An Example of Concurrent Validity
- •The Weaknesses of Concurrent Validity
- •Bibliography
- •Predictive Validity and University Selection
- •Weaknesses of Predictive Validity
- •Reliability and Science
- •Reliability and Cold Fusion
- •Reliability and Statistics
- •The Definition of Reliability Vs. Validity
- •The Definition of Reliability - An Example
- •Testing Reliability for Social Sciences and Education
- •Test - Retest Method
- •Internal Consistency
- •Reliability - One of the Foundations of Science
- •Test-Retest Reliability and the Ravages of Time
- •Inter-rater Reliability
- •Interrater Reliability and the Olympics
- •An Example From Experience
- •Qualitative Assessments and Interrater Reliability
- •Guidelines and Experience
- •Bibliography
- •Internal Consistency Reliability
- •Split-Halves Test
- •Kuder-Richardson Test
- •Cronbach's Alpha Test
- •Summary
- •Instrument Reliability
- •Instruments in Research
- •Test of Stability
- •Test of Equivalence
- •Test of Internal Consistency
- •Reproducibility vs. Repeatability
- •The Process of Replicating Research
- •Reproducibility and Generalization - a Cautious Approach
- •Reproducibility is not Essential
- •Reproducibility - An Impossible Ideal?
- •Reproducibility and Specificity - a Geological Example
- •Reproducibility and Archaeology - The Absurdity of Creationism
- •Bibliography
- •Type I Error
- •Type II Error
- •Hypothesis Testing
- •Reason for Errors
- •Type I Error - Type II Error
- •How Does This Translate to Science Type I Error
- •Type II Error
- •Replication
- •Type III Errors
- •Conclusion
- •Examples of the Null Hypothesis
- •Significance Tests
- •Perceived Problems With the Null
- •Development of the Null
Conclusion
If you have constructed your experiment to contain validity and reliability then the scientific community is more likely to accept your findings.
Eliminating other potential causal relationships, by using controls and duplicate samples, is the best way to ensure that your results stand up to rigorous questioning.
External Validity
External validity is one the most difficult of the validity types to achieve, and is at the foundation of every good experimental design.
Top of Form
Bottom of Form
Many scientific disciplines, especially the social sciences, face a long battle to prove that their findings represent the wider population in real world situations.
The main criteria of external validity is the process of generalization, and whether results obtained from a small sample group, often in laboratory surroundings, can be extended to make predictions about the entire population.
The reality is that if a research program has poor external validity, the results will not be taken seriously, so any research design must justify sampling and selection methods.
What is External Validity?
In 1966, Campbell and Stanley proposed the commonly accepted definition of external validity.
“External validity asks the question of generalizability: To what populations, settings, treatment variables and measurement variables can this effect be generalized?”
External validity is usually split into two distinct types, population validity and ecological validity, and they are both essential elements in judging the strength of an experimental design.
Psychology and External Validity The Battle Lines are Drawn
External validity often causes a little friction between clinical psychologists and research psychologists.
Clinical psychologists often believe that research psychologists spend all of their time in laboratories, testing mice and humans in conditions that bear little resemblance to the outside world. They claim that the data produced has no external validity, and does not take into account the sheer complexity and individuality of the human mind.
Before we are flamed by irate research psychologists, the truth lies somewhere between the two extremes! Research psychologists find out trends and generate sweeping generalizations that predict the behavior of groups. Clinical psychologists end up picking up the pieces, and study the individuals who lie outside the predictions, hence the animosity.
In most cases, research psychology has a very high population validity, because researchers take meticulously randomly select groups and use large sample sizes, allowing meaningful statistical analysis.
However, the artificial nature of research psychology means thatecological validity is usually low.
Clinical psychologists, on the other hand, often use focused case studies, which cause minimum disruption to the subject and have strong ecological validity. However, the small sample sizes mean that the population validity is often low.
Ideally, using both approaches provides useful generalizations, over time!