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Categorizing Continuous Data
Researchers sometimes wish to report or analyze a continuous variable by recoding a range of values into a few categories, such as taking all the scores from a test and categorizing them as high or low. This can be a valuable way to improve the interpretation of data but is often not recommended for statistical analysis, where the most precise measurements enable the most robust statistical tests to be deployed. This entry first discusses continuous and categorical variables and then provides an example to illustrate the use of transformed categorical variables.
Continuous Variables
Quantitative research uses numbers to represent the values of a variable, for instance age. For example, age is a variable because it can vary from one participant to the next within a sample. Many ...
- Bayesian Statistics
- Descriptive Statistics
- Central Tendency, Measures of
- Cohen’s d Statistic
- Cohen’s f Statistic
- Correspondence Analysis
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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
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- Accuracy in Parameter Estimation
- Analysis of Covariance
- Analysis of Variance
- Bartlett’s Test
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- 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
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- 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
- Postmodernism
- Probability, Laws of
- Social Capital Theory
- Social Support Theory
- Structural Paradigm
- Theory
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- Toulmin Method
- Weber–Fechner Law
- Types of Variables
- Validity of Scores
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