Postprint manuscript, data files, and complete analytic code for: McAllister, T., Nightingale, C., Moya-Gale, G., Kawamura, A., \& Ramig, L. O. (To appear). Crowdsourced perceptual ratings of voice quality in people with Parkinson’s Disease before and after intensive voice and articulation therapies: Secondary outcome of a randomized controlled trial. Forthcoming in *Journal of Speech, Language, and Hearing Research.* **Purpose**: Limited research has examined the suitability of crowdsourced ratings to measure treatment effects in speakers with Parkinson’s Disease (PD), particularly for constructs such as voice quality. This study obtained measures of reliability and validity for crowdsourced listeners’ ratings of voice quality in speech samples from a published study (Moya-Galé et al., 2022). We also investigated whether aggregated listener ratings would replicate the original study’s findings of treatment effects based on the Acoustic Voice Quality Index (AVQI) measure. **Method**: This study reports on a secondary outcome measure of a randomized controlled trial (RCT) with speakers with dysarthria associated with PD, including two active comparators (LSVT LOUD and LSVT ARTIC), an inactive comparator, and a healthy control group. Speech samples from three time points (pre-treatment, post-treatment, six-month follow-up) were presented in random order for rating as “typical” or “atypical” with respect to voice quality. Untrained listeners were recruited through the Amazon Mechanical Turk crowdsourcing platform until each sample had at least 25 ratings. **Results**: Intrarater reliability for tokens presented repeatedly was substantial (Cohen’s kappa .65-.70) and interrater agreement significantly exceeded chance level. There was a significant correlation of moderate magnitude between the AVQI and the proportion of listeners classifying a given sample as “typical.” Consistent with the original study, we found a significant interaction between group and time point, with the LSVT LOUD group alone showing significantly higher perceptually rated voice quality at post-treatment and follow-up relative to the pre-treatment time point. **Conclusions**: These results suggest that crowdsourcing can be a valid means to evaluate clinical speech samples, even for less familiar constructs such as voice quality. The findings also replicate the results of Moya-Galé et al. (2022) and support their functional relevance by demonstrating that the effects of treatment measured acoustically in that study are perceptually apparent to everyday listeners. *Acknowledgments*: Research reported in this publication was supported by National Institute on Deafness and Other Communication Disorders Awards R01DC001150 (L. Ramig, PI) and R01DC017476 (T. McAllister, PI) and by LSVT Global, Inc. *Conflict of Interest Statement*: Professor Lorraine Olson Ramig is employed as Chief Scientific Officer and has ownership interest in the for-profit company LSVT Global, Inc. She is in full compliance with Federal Statute 42 C.F.R. Part 50, Subpart F (see https://grants.nih.gov/grants/policy/coi/index.htm). She has fully disclosed any conflict of interest and her conflict-of-interest management plan has been approved by the Office of Conflict of Interest and Commitment at the University of Colorado, Boulder.