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Gaining from discretization of continuous data: The correspondence analysis biplot approach
1Fordham University, Bronx, NY, USA. sekim@fordham.edu.
Behavior Research Methods
|November 9, 2018
Summary
Discretizing continuous data, like Wechsler scores, reveals interactive relationships with categories such as gender and race. This method highlights distinct group differences in correlational patterns, offering new insights beyond traditional statistical approaches.
Area of Science:
- Psychometrics
- Quantitative Psychology
- Measurement Theory
Background:
- Continuous data (ratio/interval scales) are typically analyzed assuming linearity and normality using Pearson correlation.
- Traditional statistical methods (t-tests, ANOVA) and factor analysis assume continuous, normally distributed data for studying group differences.
- Discretizing continuous data is often seen as a loss of measurement precision, but it can reveal interactive relationships with categorical variables.
Purpose of the Study:
- To explore the utility of data discretization in uncovering interactive relationships between continuous psychological scores and categorical variables (gender, race).
- To examine gender and racial/ethnic group differences in the correlational patterns of discretized Wechsler intelligence and memory scores.
- To enhance the interpretation of category associations using correlation coefficients.
Main Methods:
- Discretization of Wechsler intelligence and memory scores.
- Estimation of category associations among discretized scores.
- Application of correlation coefficients to interpret category associations.
- Analysis of interactive relationships by gender and race.
Main Results:
- Discretization enabled the examination of interactive relationships between psychological scores and demographic categories.
- Distinct gender-based differences were observed in the correlational patterns of the discretized scores.
- Significant racial/ethnic group differences were identified in the correlational patterns.
- The study successfully demonstrated the value of discretization for revealing nuanced group differences.
Conclusions:
- Discretization of continuous psychological data offers a valuable analytical approach to uncover interactive relationships with categorical variables.
- This method provides insights into gender and racial/ethnic group differences that may be obscured by traditional continuous data analyses.
- The findings support the use of discretization as a complementary technique in psychological research for a more comprehensive understanding of group variations.
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