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Internal Validity
Internal validity refers to the accuracy of statements made about the causal relationship between two variables, namely, the manipulated (treatment or independent) variable and the measured variable (dependent). Internal validity claims are not based on the labels a researcher attaches to variables or how they are described but, rather, to the procedures and operations used to conduct a research study, including the choice of design and measurement of variables. Consequently, internal validity is relevant to the topic of research methods. In the next three sections, the procedures that support causal inferences are introduced, the threats to internal validity are outlined, and methods to follow to increase the internal validity of a research investigation are described.
Causal Relationships between Variables
When two variables are correlated or found to ...
- Descriptive Statistics
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- Hypothesis Testing
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- Decision Rule
- Hypothesis
- Nondirectional Hypotheses
- Nonsignificance
- Null Hypothesis
- One-Tailed Test
- Power
- Power Analysis
- Significance Level, Concept of
- Significance Level, Interpretation and Construction
- Significance, Statistical
- Two-Tailed Test
- Type I Error
- Type II Error
- Type III Error
- Important Publications
- “Coefficient Alpha and the Internal Structure of Tests”
- “Convergent and Discriminant Validation by the Multitrait–Multimethod Matrix”
- “Meta-Analysis of Psychotherapy Outcome Studies”
- “On the Theory of Scales of Measurement”
- “Probable Error of a Mean, The”
- “Psychometric Experiments”
- “Sequential Tests of Statistical Hypotheses”
- “Technique for the Measurement of Attitudes, A”
- “Validity”
- Aptitudes and Instructional Methods
- Doctrine of Chances, The
- Logic of Scientific Discovery, The
- Nonparametric Statistics for the Behavioral Sciences
- Probabilistic Models for Some Intelligence and Attainment Tests
- Statistical Power Analysis for the Behavioral Sciences
- Teoria Statistica Delle Classi e Calcolo Delle Probabilità
- Inferential Statistics
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- R2
- Association, Measures of
- Coefficient of Concordance
- Coefficient of Variation
- Coefficients of Correlation, Alienation, and Determination
- Confidence Intervals
- Margin of Error
- Nonparametric Statistics
- Odds Ratio
- Parameters
- Parametric Statistics
- Partial Correlation
- Pearson Product-Moment Correlation Coefficient
- Polychoric Correlation Coefficient
- Randomization Tests
- Regression Coefficient
- Semipartial Correlation Coefficient
- Spearman Rank Order Correlation
- Standard Error of Estimate
- Standard Error of the Mean
- Student's t Test
- Unbiased Estimator
- Weights
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- Mathematical Concepts
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- Research Design Concepts
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- Cause and Effect
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- Research
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- Last Observation Carried Forward
- Planning Research
- Primary Data Source
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- Research Hypothesis
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- Secondary Data Source
- Standardization
- Statistical Control
- Type III Error
- Wave
- Research Validity Issues
- Bias
- Critical Thinking
- Ecological Validity
- Experimenter Expectancy Effect
- External Validity
- File Drawer Problem
- Hawthorne Effect
- Heisenberg Effect
- Internal Validity
- John Henry Effect
- Mortality
- Multiple Treatment Interference
- Multivalued Treatment Effects
- Nonclassical Experimenter Effects
- Order Effects
- Placebo Effect
- Pretest Sensitization
- Random Assignment
- Reactive Arrangements
- Regression to the Mean
- Selection
- Sequence Effects
- Threats to Validity
- Validity of Research Conclusions
- Volunteer Bias
- White Noise
- Sampling
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- Convenience Sampling
- Demographics
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- Random Sampling
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- Sample
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- Sampling and Retention of Underrepresented Groups
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- Stratified Sampling
- Systematic Sampling
- Scaling
- Software Applications
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- Autocorrelation
- Biased Estimator
- Cohen's Kappa
- Collinearity
- Correlation
- Criterion Problem
- Critical Difference
- Data Mining
- Data Snooping
- Degrees of Freedom
- Directional Hypothesis
- Disturbance Terms
- Error Rates
- Expected Value
- Fixed-Effects Model
- Inclusion Criteria
- Influence Statistics
- Influential Data Points
- Intraclass Correlation
- Latent Variable
- Likelihood Ratio Statistic
- Loglinear Models
- Main Effects
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- Method Variance
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- Multilevel Modeling
- Odds
- Omega Squared
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- Outlier
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- Pooled Variance
- Precision
- Quality Effects Model
- Random-Effects Models
- Regression Artifacts
- Regression Discontinuity
- Residuals
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- Robust
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- Rosenthal Effect
- Serial Correlation
- Shrinkage
- Simple Main Effects
- Simpson's Paradox
- Sums of Squares
- Statistical Procedures
- Accuracy in Parameter Estimation
- Analysis of Covariance (ANCOVA)
- Analysis of Variance (ANOVA)
- Barycentric Discriminant Analysis
- Bivariate Regression
- Bonferroni Procedure
- Bootstrapping
- Canonical Correlation Analysis
- Categorical Data Analysis
- Confirmatory Factor Analysis
- Contrast Analysis
- Descriptive Discriminant Analysis
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- Dummy Coding
- Effect Coding
- Estimation
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- Greenhouse–Geisser Correction
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- Missing Data, Imputation of
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- Pairwise Comparisons
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- Fisher's Least Significant Difference Test
- Friedman Test
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- Kolmogorov-Smirnov Test
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- Mann–Whitney U Test
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- McNemar's Test
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- Newman–Keuls Test and Tukey Test
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- Sign Test
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- Welch's t Test
- Wilcoxon Rank Sum Test
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- Bayes's Theorem
- Central Limit Theorem
- Classical Test Theory
- Correspondence Principle
- Critical Theory
- Falsifiability
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- Generalizability Theory
- Grounded Theory
- Item Response Theory
- Occam's Razor
- Paradigm
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- Probability, Laws of
- Theory
- Theory of Attitude Measurement
- Weber–Fechner Law
- Types of Variables
- Validity of Scores
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