Applied researchers faced with limited resources can utilize planned missing designs by incorporating missing data into their research design to collect more and higher quality data compared to a conventional experimental design. Two-method measurement planned missing designs (TMM-PMD) are a type of planned missing design whereby researchers utilize both costly measures with high validity (gold standard measures) and cheap measures with lower validity (efficient measures) to measure a construct. To date, no researchers have applied these TMM-PMDs to growth models. This paper consequently explores the utility of TMM-PMDs for growth modeling studies. Using simulated data for a three-occasion second-order growth model, I examined the power, bias, RMSE, and coverage of key parameter estimates. I manipulated the overall sample size, amount of missing data for gold standard measurements, number/ratio of gold standard to efficient indicators, and the response bias loadings. My results showed minimal effects of these conditions on parameter coverage and relative bias even when I assigned a large amount of missing data and a small sample size. There were clear implications with regard to power and RMSE; power decreased while RMSE increased with lower overall sample sizes, increased missing data, fewer indicators of the construct, and higher response bias loadings. Generally, if researchers are primarily interested in estimating the mean structure of some growth process, they can reliably detect these parameters with smaller sample sizes and large amounts of missing data. If researchers are also interested in the variance/covariance structure, however; more participants and better indicators are needed with smaller amounts of missing data. I then utilized a secondary dataset from project KIDS to examine the performance of the TMM-PMD in growth modeling studies when the data are not as favorable. In this case, the obtained results were noticeably worse. I observed issues with inadmissible situations in every condition. I also observed substantial variability when estimating the slope variance and covariance parameter. The results of these studies will provide guidance to educational researchers who wish to employ TMM-PMDs in studies that measure growth over time. This research also highlights practical issues that researchers may encounter when implementing such designs in growth models. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://bibliotheek.ehb.be:2222/en-US/products/dissertations/individuals.shtml.]