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Panel Design
A panel design is used when researchers sample a group, or panel, of participants and then measure some variable or variables of interest at more than one point in time from this sample. Ordinarily, the same people who are measured at Time 1 are measured at Time 2, and so on. The successive measures are commonly referred to as waves. For example, a three-wave panel study would measure the same sample of participants on three separate occasions. The amount of time in between measurements is known as the interwave interval. The use of multiple measures on the same variable(s) over time allows for an assessment of longitudinal changes or stability on the variables of interest.
Perhaps the simplest and most common version of the panel design ...
- Bayesian Statistics
- Descriptive Statistics
- Central Tendency, Measures of
- Cohen’s d Statistic
- Cohen’s f Statistic
- Correspondence Analysis
- Descriptive Statistics
- Effect Size, Measures of
- Eta-Squared
- Factor Loadings
- Mean
- Median
- Mode
- Partial Eta-Squared
- Range
- Relative Measures of Dispersion
- Standard Deviation
- Statistic
- Trimmed Mean
- Variability, Measure of
- Variance
- Distributions
- z Distribution
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- Kurtosis
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- Winsorize
- Graphical Displays of Data
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- Data Visualization
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- Visual Analysis
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- Hypothesis Testing
- p Value
- Alternative Hypotheses
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- Significance, Statistical
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- “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”
- “Structural Holes: The Social Structure of Competition”
- “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
- Social Network Analysis Methodsand Applications
- Statistical Power Analysis for the Behavioral Sciences
- Strength of Weak Ties
- Structural Equivalence of Individuals in Social Networks
- Teoria Statistica Delle Classi e Calcolo Delle Probabilità
- Inferential Statistics
- Association, Measures of
- Coefficient of Concordance
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- Coefficients of Alienation and Determination
- Confidence Intervals
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- Mortality
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- Reciprocity
- Replication
- Research
- Research Design Principles
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- Action Research
- Adaptive Designs in Clinical Trials
- Alternating Treatments Design
- Applied Research
- Balanced Incomplete Block Design
- Basket Trials Design
- Behavior Analysis Design
- Block Design
- Blockmodeling
- Case-Only Design
- Causal-Comparative Design
- Changing Criterion Design
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- Completely Randomized Design
- Confirmatory Research
- Crossover Design
- Cross-Sectional Design
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- Experimental Design
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- Factorial Design
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- Meta-Analysis
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- Monte Carlo Simulation
- Multiple Baseline Single Case Experimental Design
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- Observational Research
- Panel Design
- Partially Randomized Preference Trial Design
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- Propensity Score Matching
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- Quantitative Research
- Quasi-Experimental Design
- Randomized Block Design
- Repeated Measures Design
- Response Surface Design
- Retrospective Study
- Sequential Design
- Single-Blind Study
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- Split-Plot Factorial Design
- Stepped-Wedge Design
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- True Experimental Design
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- Wennberg Design
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- Inference: Deductive and Inductive
- Last Observation Carried Forward
- Masking
- Multisite Research Studies
- Operationalizing
- Primary Data Source
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- Sample Size
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- Social Network Analysis
- Alters
- Connectivity
- Core-Periphery Structure
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- Name Generator
- Network Boundaries
- Network Composition
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- Degrees of Freedom
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- Error Rates
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- Hedges’ g
- Heterogeneity
- Inclusion Criteria
- Influence Statistics
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- Intraclass Correlation
- Latent Change Score
- Latent Variable
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- Likelihood Ratio Statistic
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- Main Effects
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- Multiplicity Problem
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- Residuals
- Restriction of Range
- Robust
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- Root Mean Square Error
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- Statistical Procedures
- F Test
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- Accuracy in Parameter Estimation
- Analysis of Covariance
- Analysis of Variance
- Bartlett’s Test
- Barycentric Discriminant Analysis
- Behrens–Fisher t’ Statistic
- Bivariate Regression
- Bonferroni Procedure
- Bootstrapping
- Canonical Correlation Analysis
- Categorical Data Analysis
- Chi-Square Test
- Cluster Analysis
- Confirmatory Factor Analysis
- Contingency Table Analysis
- Contrast Analysis
- Descriptive Discriminant Analysis
- Diagnostic Classification Modeling
- Dummy Coding
- Duncan’s Multiple Range Test
- Dunnett’s Test
- Effect Coding
- Estimation
- Exploratory Factor Analysis
- Fisher’s Least Significant Difference Test
- Friedman Test
- Greenhouse–Geisser Correction
- Hidden Markov Model
- Hierarchical Linear Modeling
- Holm’s Sequential Bonferroni Procedure
- Jackknife
- Kolmogorov–Smirnov Test
- Kruskal–Wallis Test
- Latent Class Analysis
- Latent Growth Modeling
- Latent Profile Analysis
- Least Squares, Methods of
- Logistic Regression
- Mann–Whitney U Test
- Mauchly Test
- Maximum Likelihood Estimation
- McNemar’s Test
- Mean Comparisons
- Missing Data, Imputation of
- Multidimensional Scaling
- Multiple Comparison Tests
- Multiple Comparisons With Modeling Techniques
- Multiple Regression
- Multivariate Analysis of Variance
- Newman–Keuls Test
- Omnibus Tests
- Pairwise Comparisons
- Path Analysis
- Post Hoc Analysis
- Post Hoc Comparisons
- Predictive Discriminant Analysis
- Principal Components Analysis
- Propensity Score Analysis
- Scheffé Test
- Sequential Analysis
- Sign Test
- Stepwise Model Selection
- Stepwise Regression
- Structural Equation Modeling
- Survival Analysis
- Trend Analysis
- Tukey’s Honestly Significant Difference
- Welch’s t Test
- Wilcoxon Rank Sum Test
- Yates’s Correction
- Statistical Tests
- F Test
- t Test, Independent Samples
- t Test, One Sample
- t Test, Paired Samples
- z Test
- Bartlett’s Test
- Behrens–Fisher t’ Statistic
- Chi-Square Test
- Cochran–Armitage Test for Trend
- Duncan’s Multiple Range Test
- Dunnett’s Test
- Fisher’s Least Significant Difference Test
- Friedman Test
- Hosmer-Lemeshow Test
- Kolmogorov–Smirnov Test
- Kruskal–Wallis Test
- Mann–Whitney U Test
- Mauchly Test
- McNemar’s Test
- Multiple Comparison Tests
- Newman–Keuls Test
- Omnibus Tests
- Scheffé Test
- Sign Test
- Tukey’s Honestly Significant Difference
- Welch’s t Test
- Wilcoxon Rank Sum Test
- Structural Equation Modeling
- Theories, Laws, and Principles
- Central Limit Theorem
- Classical Test Theory
- Correspondence Principle
- Critical Theory
- Diffusion of Innovation Theory
- Falsifiability
- Game Theory
- Gauss–Markov Theorem
- Generalizability Theory
- Graph Theory
- Grounded Theory
- Item Response Theory
- Likelihood Principle
- Machine Learning
- Models
- Neural Networks
- Occam’s Razor
- Paradigm
- Positivism
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- Probability, Laws of
- Social Capital Theory
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- Structural Paradigm
- Theory
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- Toulmin Method
- Weber–Fechner Law
- Types of Variables
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