Design of Experiments with Multiple Independent Variables: A Resource Management Perspective on Complete and Reduced Factorial Designs

By Linda M. Collins, John J. Dziak, Runze Li. Psychological methods, 2009

396 citations recorded by the source corpus.

An investigator who plans to conduct experiments with multiple independent variables must decide whether to use a complete or reduced factorial design. This article advocates a resource management perspective on making this decision, in which the investigator seeks a strategic balance between service to scientific objectives and economy. Considerations in making design decisions include whether research questions are framed as main effects or simple effects; whether and which effects are aliased (confounded) in a particular design; the number of experimental conditions that must be implemented in a particular design and the number of experimental subjects the design requires to maintain the desired level of statistical power; and the costs associated with implementing experimental conditions and obtaining experimental subjects. In this article four design options are compared: complete factorial, individual experiments, single factor, and fractional factorial designs. Complete and fractional factorial designs and single factor designs are generally more economical than conducting individual experiments on each factor. Although relatively unfamiliar to behavioral scientists, fractional factorial designs merit serious consideration because of their economy and versatility.

Subfields: Developmental and Educational Psychology, Economics and Econometrics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/98d22de9fccc4c945e7787675003dc88aeec9f8a

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In Developmental and Educational Psychology

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Citations in the corpus

An Overview of Research and Evaluation Designs for Dissemination and Implementation (2017, background): “Fractional designs can make examination of multiple components feasible, even when cluster randomization is necessary (30, 32, 37).”
Mind the theoretical gap: interpreting, using, and developing behavioral theory in HCI research (2013, background): “Some of these designs, such as single case experimental designs [37] and factorial designs [11], might prove to be useful in HCI research as well.”
Evaluating digital health interventions: key questions and approaches (2016, neutral): “This is discussed further in Collins et al.40,45”
When Are Search Completion Suggestions Problematic? (2020, methodology): “To manipulate several factors in order to discern their effect on how system provided suggestions for web search are perceived, we use a 3 (types of prefix samples) x 2 (types of suffix samples) x 2 (types of possible intents samples) factorial-like design [13, 19] to systematically vary the attribu”