Difference Between Within-Subject and Between-Subject Effects: The Answer to Ice-Cream is Always Yes

Within-person (or within-subject) effects represent the variability of a particular value for individuals in a sample. You see this commonly examined in repeated measures analysis (such as repeated measures ANOVA, repeated measures ANCOVA, repeated measures MANOVA or MANCOVA…etc). In these instances, a within person effect is a measure of how much an individual in your sample tends to change (or vary) over time. In other words, it is the mean of the change for the average individual case in your sample.

Imagine we collected a score from every person in your town that measured how much they wanted ice cream at the particular moment of data collection (let's say scores could range from 1 to 100, with 100 meaning REALLY WANT ice cream). Further, let's pretend we did this once a day for 5 days. Our within-subject effect would be a measure of how much individuals in our sample tended to change on their wanting of ice cream over the five days.

Each colored line represents individuals' trend line for change over time in liking of ice cream (each person's within-subject effect). The black line represents the average of the individual trend lines in the sample (which is the sample's within-subject effect). 

Each colored line represents individuals' trend line for change over time in liking of ice cream (each person's within-subject effect). The black line represents the average of the individual trend lines in the sample (which is the sample's within-subject effect). 

Between-persons (or between-subjects) effects, by contrast, examine differences between individuals. This can be between groups of cases when the independent variable (IV) is categorical or between individuals when the (IV) is continuous. These type of effects can be observed in either the univariate context or the multivariate context (including repeated measures). Either way, between-subjects effects determine if respondents differ on the dependent variable (DV), depending on their group (males vs. females, young vs. old…etc) or depending on their score on a particular continuous IV. 

For example, let's return to our ice cream anecdote. If we want to test whether respondents are more likely to want ice cream if they score highly on an IQ test, we are testing for between-subjects effects. In this example, we are seeing if differences between persons with different IQs also have correspondingly different scores for "wanting ice cream". If course, the correct answer here is obviously yes.

Townspeople with a higher IQ tended to like ice cream more than those with a comparatively lower IQ, as the blue trend line shows. This is a between-subjects effect - it is comparing "ice cream liking" between people with various levels of intelligence (IQ). 

Townspeople with a higher IQ tended to like ice cream more than those with a comparatively lower IQ, as the blue trend line shows. This is a between-subjects effect - it is comparing "ice cream liking" between people with various levels of intelligence (IQ). 


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Jeremy J. Taylor

Stats Make Me Cry is owned and operated by Jeremy J. Taylor, Ph.D. Jeremy completed his doctoral training in Clinical Psychology at DePaul University and completed his pre-doctoral internship at the Kennedy Krieger Institute, Johns Hopkins School of Medicine. He is currently a Senior Research Associate at the Collaborative for Academic, Social, and Emotional Learning. Although Jeremy's background is in Psychology, he consulted on dissertations for more than 100 students, from 13 countries, and from a variety of disciplines.