or fatigue. The impact of order effects may be smaller in long-term longitudinal studies or by counterbalancing using a crossover design. Counterbalancing In this technique, two groups each perform the same tasks or experience the same conditions, but in reverse order. With two tasks or conditions, four groups are formed. Counterbalancing attempts to take account of two important sources of systematic variation in this type of design: practice and boredom effects. Both might otherwise lead to different performance of participants due to familiarity with or tiredness to the treatments. Limitations It may not be possible for each participant to be in all conditions of the experiment (i.e. time constraints, location of experiment, etc.). Severely diseased subjects tend to drop out of longitudinal studies, potentially biasing the results. In these cases mixed effects models would be preferable as they can deal with missing values. Mean regression may affect conditions with significant repetitions. Maturation may affect studies that extend over time. Events outside the experiment may change the response between repetitions. Repeated measures ANOVA Repeated measures analysis of variance (rANOVA) is a commonly used statistical approach to repeated measure designs. With such designs, the repeated-measure factor (the qualitative independent variable) is the within-subjects factor, while the dependent quantitative variable on which each participant is measured is the dependent variable. Partitioning of error One of the greatest advantages to rANOVA, as is the case with repeated measures designs in general, is the ability to partition out variability due to individual differences. Consider the general structure of the F-statistic: F = MSTreatment / MSError = (SSTreatment/dfTreatment)/(SSError/dfError) In a between-subjects design there is an element of variance due to individual difference that is combined with the treatment and error terms: SSTotal = SSTreatment + SSError dfTotal = n − 1 In a repeated measures design it is possible to partition subject variability from the treatment and error terms. In such a case, variability can be broken down into between-treatments variability (or within-subjects effects, excluding individual differences) and within-treatments variability. The within-treatments variability can be further partitioned into between-subjects variability (individual differences) and error (excluding the individual differences): SSTotal = SSTreatment (excluding individual difference) + SSSubjects + SSError dfTotal = dfTreatment (within subjects) + dfbetween subjects + dferror = (k − 1) + (n − 1) + ((n − k)(n − 1)) In reference to the general structure of the F-statistic, it is clear that by