Quality Vs. Quantity: Assessing Behavior Change over Time

Beyond the typical design factors that impact a study's power (e.g., participant sample size), planning longitudinal research involves additional considerations such as assessment frequency and participant retention. Because this type of research relies so strongly on individual commitment, investigators must be judicious in determining how much information is necessary to study the phenomena in question; collecting too little information will render the data less useful, but requiring excessive participant investment will likely lower participation rates. We conducted a simulation study to empirically examine statistical power and the trade-off between assessment quality (as a function of instrument length) and assessment frequency across a number of sample sizes with intermittently missing data or attrition. Results indicated that reductions in power resulting from shorter, less reliable measurements can be at least somewhat offset by increasing assessment frequency. Because study planning involves a number of factors competing for finite resources, equations were derived to find the balance points between pairs of design characteristics affecting statistical power. These equations allow researchers to calculate the amount that a particular design factor (e.g., assessment frequency) would need to increase to result in the same improvement in power as increasing an alternative factor (e.g., measurement reliability. Applications for the equations are discussed.