Balanced Design in Research: A Review
Introduction
The concept of balanced design in research has become increasingly important in recent years. Balanced design is a type of experimental design used to compare two or more groups of participants on an outcome measure. It seeks to reduce the effects of extraneous variables by ensuring that the characteristics of the groups are similar in order to make the comparison more meaningful. This review will discuss the various types of balanced designs and their applications in research.
Types of Balanced Designs
The most common types of balanced designs are matched designs, repeated measures designs, and factorial designs.
Matched designs involve matching participants on certain characteristics, such as age, gender, or race, in order to reduce the influence of these factors on the outcome measure. Matched designs are often used in clinical studies, such as those assessing the effectiveness of a new drug or intervention.
In repeated measures designs, the same set of participants is used in each condition, and the outcome measure is measured at multiple points in time. This type of design is useful for assessing the effects of time on an outcome measure.
Factorial designs involve multiple independent variables and multiple dependent variables. This type of design is useful for assessing the effects of different combinations of variables on an outcome measure.
Applications
Balanced designs have been used in a variety of research studies. For example, they have been used to compare the effectiveness of different interventions, to assess the effects of different combinations of variables on an outcome measure, and to evaluate the effects of time on an outcome measure. Balanced designs can also be used to reduce the effects of extraneous variables in experiments.
Conclusion
Balanced designs are an important tool in research that can be used to reduce the effects of extraneous variables and to compare two or more groups of participants. Balanced designs have been used in a variety of research studies, and they can be an effective way to make meaningful comparisons between groups.
References
Berg, S. (2011). Statistics for the behavioral and social sciences: A brief course (5th ed.). Upper Saddle River, NJ: Pearson.
Crow, J. F., & Crow, M. R. (2013). An introduction to experimental design and statistics for biology (3rd ed.). Oxford, UK: Oxford University Press.
Kirk, R. E. (2018). Experimental design: Procedures for the behavioral sciences (4th ed.). Thousand Oaks, CA: Sage.