9.2 Between-subjects and within-subjects factors
3 min read•august 7, 2024
Repeated measures designs involve multiple observations of the same participants. Between-subjects factors assign participants to different groups, while within-subjects factors expose participants to all conditions. These approaches have unique advantages and considerations for controlling individual differences and .
Mixed designs combine both factor types, allowing researchers to examine complex relationships. Understanding the differences between these approaches is crucial for selecting the most appropriate design for a study, considering research questions, practical constraints, and potential confounds.
Types of Factors
Between-Subjects and Within-Subjects Factors
Top images from around the web for Between-Subjects and Within-Subjects Factors
Chapter 10 Experimental Research – Research Methods for the Social Sciences View original
Is this image relevant?
A Comparison of Within-Subjects and Between-Subjects Designs in Studies with Discrete-Time ... View original
Is this image relevant?
Frontiers | Experiment Design Frameworks for Accelerated Discovery of Targeted Materials Across ... View original
Is this image relevant?
Chapter 10 Experimental Research – Research Methods for the Social Sciences View original
Is this image relevant?
A Comparison of Within-Subjects and Between-Subjects Designs in Studies with Discrete-Time ... View original
Is this image relevant?
1 of 3
Top images from around the web for Between-Subjects and Within-Subjects Factors
Chapter 10 Experimental Research – Research Methods for the Social Sciences View original
Is this image relevant?
A Comparison of Within-Subjects and Between-Subjects Designs in Studies with Discrete-Time ... View original
Is this image relevant?
Frontiers | Experiment Design Frameworks for Accelerated Discovery of Targeted Materials Across ... View original
Is this image relevant?
Chapter 10 Experimental Research – Research Methods for the Social Sciences View original
Is this image relevant?
A Comparison of Within-Subjects and Between-Subjects Designs in Studies with Discrete-Time ... View original
Is this image relevant?
1 of 3
- Between-subjects factors assign each participant to only one level of the factor
- Participants in different groups receive different treatments (drug vs. placebo)
- Reduces the impact of individual differences on the results
- Requires a larger sample size to achieve the same statistical power as within-subjects designs
- Within-subjects factors expose each participant to all levels of the factor
- Participants serve as their own control, reducing the impact of individual differences
- Requires fewer participants to achieve the same statistical power as between-subjects designs
- May introduce order effects or carryover effects that need to be controlled ()
Mixed and Split-Plot Designs
- incorporates both between-subjects and within-subjects factors
- Allows for the examination of both types of effects and their interactions
- Can be more efficient than a purely between-subjects or
- Requires careful consideration of the order of conditions and counterbalancing
- is a type of mixed design where the levels of one factor are assigned to larger groups (plots) and the levels of another factor are assigned within each plot
- Commonly used in agricultural research where large plots of land are divided into subplots (fertilizer type as between-subjects factor, crop variety as within-subjects factor)
- Can reduce the impact of variability between plots on the within-subjects factor
Effects and Interactions
Main Effects and Interaction Effects
- Main effects represent the overall effect of a single factor, averaged across the levels of other factors
- Indicates whether there is a significant difference between the levels of a factor (drug vs. placebo)
- Can be examined for both between-subjects and within-subjects factors
- Interaction effects occur when the effect of one factor depends on the level of another factor
- Indicates that the factors do not operate independently (drug effectiveness may depend on dosage)
- Can provide insights into the complex relationships between variables
- Requires a factorial design with multiple factors to detect
Interpreting and Reporting Effects
- Main effects and interaction effects are typically reported using F-tests and p-values
- A significant suggests that the levels of the factor differ significantly
- A significant suggests that the factors interact with each other
- Effect sizes (eta-squared, partial eta-squared) provide a standardized measure of the magnitude of the effect
- Helps to assess the practical significance of the findings
- Allows for comparisons across studies and meta-analyses
Considerations
Individual Differences and Statistical Power
- Individual differences can introduce variability into the data, reducing the ability to detect true effects
- Within-subjects designs help to control for individual differences by having each participant serve as their own control
- Between-subjects designs require larger sample sizes to account for individual differences
- Statistical power is the probability of detecting a true effect when it exists
- Depends on the sample size, , and chosen significance level (alpha)
- Within-subjects designs typically have higher statistical power than between-subjects designs due to the reduction in individual differences
- Researchers should conduct power analyses to determine the appropriate sample size for their study
Choosing the Appropriate Design
- The choice between a between-subjects, within-subjects, or mixed design depends on several factors:
- Research question and hypotheses
- Nature of the variables and manipulations
- Practical constraints (time, resources, sample availability)
- Potential confounds and sources of bias (order effects, carryover effects)
- Researchers should carefully consider the trade-offs between different designs and choose the one that best addresses their research goals while minimizing potential confounds
- Between-subjects designs are often preferred when order effects or carryover effects are a concern
- Within-subjects designs are preferred when individual differences are a major concern or when sample sizes are limited
- Mixed designs offer a compromise between the two, allowing for the examination of both between-subjects and within-subjects effects
Key Terms to Review (22)
ANOVA: ANOVA, or Analysis of Variance, is a statistical method used to test differences between two or more group means. This technique helps determine if at least one of the group means is significantly different from the others, making it a powerful tool in experimental design for comparing multiple treatments or conditions.
Between-subjects design: Between-subjects design is a type of experimental setup where different participants are assigned to separate groups, each exposed to a different level of the independent variable. This method helps to minimize the risk of carryover effects that can occur in repeated measures, making it crucial for establishing clear cause-and-effect relationships while maintaining the integrity of the scientific method and experimentation.
Control Group: A control group is a baseline group in an experiment that does not receive the experimental treatment or intervention, allowing researchers to compare it with the experimental group that does receive the treatment. This comparison helps to isolate the effects of the treatment and determine its effectiveness while accounting for other variables.
Counterbalancing: Counterbalancing is a technique used in experimental design to control for potential confounding variables by systematically varying the order of conditions for participants. This helps to ensure that any effects observed in an experiment can be attributed to the independent variable rather than the order in which conditions were presented. It's particularly crucial in repeated measures designs where participants are exposed to multiple conditions.
Dependent Variable: The dependent variable is the outcome or response that researchers measure in an experiment, which is affected by the independent variable. It plays a crucial role in determining the effects of various treatments or conditions, making it essential for drawing conclusions from experimental data.
Effect Size: Effect size is a quantitative measure that reflects the magnitude of a treatment effect or the strength of a relationship between variables in a study. It helps in understanding the practical significance of research findings beyond just statistical significance, offering insights into the size of differences or relationships observed.
Experimental Group: An experimental group is a set of subjects or participants that receive the treatment or intervention being tested in a scientific experiment. This group is essential in determining the effect of the independent variable, as its results are compared to those of a control group, which does not receive the treatment. The distinction between the experimental group and other groups helps researchers assess causality and the impact of specific variables within the research design.
Experimenter bias: Experimenter bias refers to the unconscious tendency of researchers to influence the results of their studies based on their expectations or beliefs about the outcomes. This bias can affect various stages of research, from data collection to interpretation, potentially skewing findings and reducing the reliability of conclusions drawn from the study. Understanding this bias is essential for maintaining objectivity in the scientific method, ensuring that experimental designs effectively minimize its impact and enhance the validity of results.
External Validity: External validity refers to the extent to which research findings can be generalized to, or have relevance for, settings, people, times, and measures beyond the specific conditions of the study. This concept connects research results to real-world applications, making it essential in evaluating how applicable findings are to broader populations and situations.
Independent Variable: An independent variable is a factor or condition that is manipulated or controlled by the researcher in an experiment to observe its effect on a dependent variable. It serves as the primary element in establishing cause-and-effect relationships within research, influencing the outcomes of various experimental designs and analyses.
Interaction effect: An interaction effect occurs when the relationship between one independent variable and a dependent variable changes depending on the level of another independent variable. This concept highlights how different factors can work together to produce unique outcomes, demonstrating that the combined influence of multiple variables may not simply be additive, but can actually modify each other's effects in significant ways.
Internal Validity: Internal validity refers to the degree to which an experiment accurately establishes a causal relationship between the independent and dependent variables, free from the influence of confounding factors. High internal validity ensures that the observed effects in an experiment are genuinely due to the manipulation of the independent variable rather than other extraneous variables. This concept is crucial in designing experiments that can reliably test hypotheses and draw valid conclusions.
Main Effect: A main effect refers to the direct influence of an independent variable on a dependent variable in an experimental design. This concept is crucial in understanding how different levels of a factor affect outcomes, separate from any interaction effects that may occur between factors. Recognizing main effects helps researchers interpret the results of complex experiments and evaluate the significance of individual variables in various designs.
Measurement scale: A measurement scale is a system used to assign numbers or labels to various attributes of objects or events according to specific rules. This scale categorizes data into different types, influencing how statistical analyses are conducted and the conclusions that can be drawn. Understanding the type of measurement scale employed in a study is crucial, as it determines the level of information conveyed and the appropriate statistical techniques that can be applied.
Mixed design: A mixed design is a research approach that combines both between-subjects and within-subjects factors, allowing researchers to utilize the strengths of both experimental designs. This approach enables the investigation of interactions between different types of variables, providing a more comprehensive understanding of how these factors influence outcomes. By incorporating both designs, researchers can control for individual differences while also examining the effects of treatment across multiple groups.
Operational Definition: An operational definition specifies how a concept or variable is measured or identified in a particular study, providing clear and measurable criteria. This term is crucial because it ensures that researchers can replicate experiments and understand the context in which variables operate. It connects closely to types of variables in experiments, as well as the design involving between-subjects and within-subjects factors, since the way variables are operationally defined can influence how they are manipulated or measured across different groups.
Random Assignment: Random assignment is a technique used in experimental research to ensure that participants are allocated to different groups or conditions in a way that is not influenced by any biases or pre-existing differences. This process helps to create equivalent groups, enhancing the credibility of the experiment's conclusions by minimizing confounding variables.
Selection Bias: Selection bias occurs when the participants included in a study are not representative of the larger population from which they are drawn, leading to results that cannot be generalized. This bias can significantly impact research findings by skewing results toward a certain outcome, making it difficult to draw valid conclusions about relationships between variables.
Split-plot design: A split-plot design is a type of experimental design that involves multiple levels of experimental units, allowing researchers to study two or more factors simultaneously while accounting for the variability at different levels. This design is particularly useful when some factors are harder or more costly to manipulate than others, making it necessary to control these factors at a larger scale while manipulating others at a finer scale. This flexibility connects it to various principles and applications in experimental research.
Statistical Power: Statistical power is the probability that a statistical test will correctly reject a false null hypothesis, which means detecting an effect if there is one. Understanding statistical power is crucial for designing experiments as it helps researchers determine the likelihood of finding significant results, influences the choice of sample sizes, and informs about the effectiveness of different experimental designs.
T-test: A t-test is a statistical test used to determine if there is a significant difference between the means of two groups. This test helps researchers understand whether the differences observed in sample data are likely to be present in the larger population. The t-test connects to important concepts like statistical power, effect size, and the type of experimental design employed, which can influence the test's outcome and interpretation.
Within-subjects design: Within-subjects design is an experimental setup where the same participants are exposed to all conditions of the experiment, allowing for comparisons across different treatment levels. This design is crucial because it controls for participant variability, enhances statistical power, and often requires fewer participants, making it a practical choice for researchers.