-Scores can decrease over time due to fatigue, or increase due to learning. 
-In taste tests, a dry wine may get a higher rank if it was preceded by a dryer wine and a lower rank if preceded by a sweeter wine. 
THEREFORE: Order effects can interfere with the analysis’ ability to correctly estimate the effect of the treatment itself.

Example of order effects

1. Randomization
2. Allowing time between treatments
3. Counterbalancing the order of treatments among others. 
4. ALTERNATIVE: use independent groups design if applicable

Methods to reduce drawbacks

A carryover effect is a specific type of order effect that occurs when a participant's behavior or response in one experimental condition is directly influenced by having been tested in a prior condition. When these effects are not the primary focus of the research, they can become problematic confounding variables, making it difficult to determine whether changes in the dependent variable are due to the independent variable or merely the lingering impact of previous conditions.

Carryover Effect

Counterbalancing is a methodological solution to the problem of order effects in within-subjects designs. It involves systematically testing different participants in different sequences of conditions. By varying the order, counterbalancing ensures that the sequence of conditions does not become a confounding variable, allowing researchers to either neutralize order effects across the study or detect them by analyzing the data separately for each specific order.

Counterbalancing

In the context of within-subjects experimental designs, what term describes the phenomenon where participants' responses are systematically altered by the sequence in which they experience the experimental conditions?

An order effect is a primary disadvantage of within-subjects experimental designs. It occurs when participants' responses in various experimental conditions are systematically affected by the order in which they were exposed to those conditions. When order effects are not the direct focus of the research, they can act as problematic confounding variables that obscure the true effect of the independent variable.

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Study that involves multiple measures of the same variable taken on the same or matched subjects either under different conditions or over two or more time periods.
Example: repeated measurements are collected in a longitudinal study in which change over time is assessed.

Repeated Measures Designs

A within-subjects experiment is an experimental design strategy where every participant is exposed to all levels of the independent variable. This approach contrasts with between-subjects designs by testing the exact same individuals across every experimental condition instead of assigning them to just one.

Within-Subjects Experiment

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (Vol. 112, pp. 3-7). New York: springer.

An Introduction to Statistical Learning with Applications in R

https://kpu.pressbooks.pub/psychmethods4e/

KPU Research Methods in Psychology - 4th American Edition

Repeated measures design reduces the effect of variability because the same subjects are used throughout the experiment. This allows the researcher to make powerful statistical conclusions with a relatively small set of subjects.

Why use repeated measures design?

1. More statistical power
2. Fewer subjects
3. Quicker and cheaper
4. Assess an effect over time

Benefits

Picture of a common crossover repeated measures design

Order Effect

A notable disadvantage of within-subjects experiments is that experiencing all experimental conditions makes it much easier for participants to guess the researcher's hypothesis. This awareness can inadvertently change their behavior; they might judge situations in a way they think is expected of them, or they might try to act similarly across conditions in an unnatural effort to be 'fair'.

Hypothesis Guessing in Within-Subjects Designs

A simultaneous within-subjects design is an approach where, instead of testing participants in one distinct condition at a time, researchers present stimuli from multiple conditions in an interspersed or mixed sequence. Participants make multiple responses to the varying conditions throughout a single session, and the researcher then calculates each participant's mean response or overall performance for each distinct condition.

Simultaneous Within-Subjects Design

When deciding between experimental approaches, researchers generally prioritize the within-subjects design if practical. A good rule of thumb is to opt for a within-subjects experiment—utilizing proper counterbalancing—if there is sufficient time to test each participant in all conditions and if there are no serious concerns about confounding carryover effects. If these conditions cannot be met, researchers should consider a between-subjects design instead.

Rule of Thumb for Experimental Design Choice

Within-subjects experiments offer distinct benefits by naturally controlling for extraneous participant variables. Because the same individuals are tested in every condition, researchers can reduce statistical noise, making it easier to detect any true effect of the independent variable upon the dependent variable. Consequently, these designs also require fewer overall participants than between-subjects experiments to detect an effect of the same size.

Advantages of Within-Subjects Experiments

Using one type of experimental design does not preclude researchers from using the other type. There is no reason a researcher cannot utilize both a between-subjects design and a within-subjects design to answer the same overall research question across a series of different studies. In fact, professional researchers frequently adopt this type of mixed methods approach to gain a more comprehensive understanding.

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