Research designs that include planned missing data are gaining popularity in applied education research. These methods have traditionally relied on introducing missingness into data collections using the missing completely at random (MCAR) mechanism. This study assesses whether planned missingness can also be implemented when data are instead designed to be purposefully missing based on student performance. A research design with purposefully selected missingness would allow researchers to focus all assessment efforts on a target sample, while still maintaining the statistical power of the full sample. This study introduces the method and demonstrates the performance of the purposeful missingness method within the two-method measurement planned missingness design using a Monte Carlo simulation study. Results demonstrate that the purposeful missingness method can recover parameter estimates in models with as much accuracy as the MCAR method, across multiple conditions.