Experimental Design: Procedures for the Behavioral Sciences
Abstract
Chapter 1. Research Strategies and the Control of Nuisance Variables Chapter 2. Experimental Designs: an Overview Chapter 3. Fundamental Assumptions in Analysis of Variance Chapter 4. Completely Randomized Design Chapter 5. Multiple Comparison Tests Chapter 6. Trend Analysis Chapter 7. General Linear Model Approach to ANOVA Chapter 8. Randomized Block Designs Chapter 9. Completely Randomized Factorial Design with Two Treatments Chapter 10. Completely Randomized Factorial Design with Three or More Treatments and Randomized Block Factorial Design Chapter 11. Hierarchical Designs Chapter 12. Split-Plot Factorial Design: Design with Group-Treatment Confounding Chapter 13. Analysis of Covariance Chapter 14. Latin Square and Related Designs Chapter 15. Confounded Factorial Designs: Designs with Group-Interaction Confounding Chapter 16. Fractional Factorial Designs: Designs with Treatment-Interaction Confounding
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Cross-sectional studies cannot infer causality.
“As cross sectional studies cannot infer causality, the study designs of the included studies are a limitation.66”
From Domain-Specific Physical Activity and Mental Health: A Meta-analysis (2017) · cites this paper as [66]
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- Authors
- Kirk, R.
- Corresponding author
- Kirk, Roger
- Venue
- Sage eBooks
- Publisher
- SAGE Publications, Inc.
- Published
- 2013
- OpenAlex
- W2134654456
- Cited by
- 8,539
- References
- 0
- Citations by year
- 2026: 23 · 2025: 40 · 2024: 50 · 2023: 48 · 2022: 64 · 2021: 64 · 2020: 78 · 2019: 65 · 2018: 94 · 2017: 98 · 2016: 105 · 2015: 83 · 2014: 107 · 2013: 127 · 2012: 107
- FWCI
- 44.24
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Sage eBooks (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Optimal Experimental Design Methods
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Keywords
- experimental design, behavioral science, nuisance parameters, analysis of variance, completely randomized design, multiple comparison tests, trend analysis, general linear model, randomized block design, factorial design, hierarchical design, split-plot design, covariance analysis, Latin square design, fractional factorial design
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