Kyle McCarthy, Ph.D.
Research Analyst
Since joining HZA in July 2016, Dr. McCarthy has been an integral member of the Title IV-E Waiver evaluation team for Arkansas DCFS. He developed code in SQL and SPSS to select comparison groups using propensity score matching as part of the outcome evaluation. Given statewide implementation of the Waiver initiatives, it has been necessary to select children and families served prior to implementation of the various initiatives to compare the successes of the Waiver initiatives to those served previously with similar characteristics and case circumstances. Dr. McCarthy has also developed code in SQL to measure the outcomes of the Waiver initiatives, analyzing the data to identify the extent to which children with particular characteristics benefit more than others, trending the results over time. He is performing similar analyses for HZA’s evaluation of West Virginia’s Title IV-E Waiver grant award.
Prior to coming aboard at HZA, Dr. McCarthy served as a Research Assistant at the University of Kentucky. There, he developed the MaNGA Stellar Library target selection algorithm in Python which selected the 7,000 highest priority targets from a table of millions of stars. He merged several large data tables into one cohesive data set, and increased the efficiency of the target selection algorithm using a chaining mesh technique. Preceding that, while he was a graduate student at the University of Kentucky, Dr. McCarthy created Python software to extract and cleanse raw data from stellar observatories, and wrote SQL code to access and manipulate large sets of stellar data. He determined statistical offsets in chemical abundance between stars with and without planets using Python statistical packaging; implemented a modified bootstrap method to determine uncertainties in fundamental stellar parameters; and determined rotational velocities of stars using an x2 statistical analysis. In addition to his work at the University of Kentucky, Dr. McCarthy created empirical temperature measures for stars using IDL data analysis tools while serving as a Research Assistant at Georgia State University.
Dr.McCarthy received a B.S. Cum Laude in Physics from Georgia State University in 2010. In 2013, he received his M.A. in Physics from the University of Kentucky, and he went on to earn his Ph.D. in Physics from the University of Kentucky in 2015.
Needs Assessment
From Maine Shared Community Health Needs Assessment Proposal – November 2017
Kyle McCarthy, Ph.D., Data Analysis Lead: Kyle McCarthy is the lead member of the firm’s data analysis team, guiding the outcome evaluation components for child protection program evaluations in Arkansas, Maine and West Virginia, among other projects. Using data from the three states’ child welfare case management systems, he developed code in SQL and SPSS to select comparison groups using propensity score matching as part of the outcome evaluation. Dr. McCarthy has also developed code in SQL to measure the outcomes of the Waiver initiatives, analyzing the data to identify the extent to which children with particular characteristics benefit more than others, trending the results over time. In addition to this work, he is conducting the quantitative analyses for an evaluation of a multi-state predictive analytics project funded by Casey Family Services. He has worked extensively with public health datasets such as MaineCare and Prescription Monitoring Program data.
Program Evaluation
From Kansas Program Evaluation Services Pre-Qualifier Proposal – January 2018
Kyle McCarthy
Dr. Kyle McCarthy is the lead member of the firm’s data analysis team, guiding the outcome evaluation components for evaluations of child protection programs in both Arkansas and West Virginia. He is also participating in benchmark reporting of Indiana’s MIECHV programs.
As the Project Manager for the evaluation of Arkansas’s five Title IV-E Waiver initiatives, plus that state’s Diligent Recruitment grant, Dr. McCarthy meets regularly with state agency staff to review the continued implementation of the initiatives as well as the results of the process and outcome evaluation components.Using data from the state’s case management system, he developed code in SQL and SPSS to select comparison groups using propensity score matching to compare the outcomes of children and families who received treatment services. Given statewide implementation of the Waiver initiatives, it has been necessary to select children and families served prior to implementation of the various initiatives to compare the successes of the Waiver initiatives to those served previously with similar characteristics and case circumstances. Mr. McCarthy has also developed code in SQL to measure the outcomes of the Waiver initiatives, analyzing the data to identify the extent to which children with particular characteristics benefit more than others, trending the results over time. He is doing similar analysis for the outcome evaluations of West Virginia’s and Maine’s Title IV-E Waiver initiatives.
Dr. McCarthy is participating in HZA’s initiative to assist Indiana with its federal MIECHV benchmark reporting requirements. Using data from the state’s case management system, he is analyzing data to identify trends and the effect that different client characteristics have on families’ ability to achieve success for their children. He is working closely with the firm’s programming staff to provide Indiana with an online management dashboard.
Prior to coming aboard at HZA, Dr. McCarthy served as a Research Assistant at the University of Kentucky. There, he developed the MaNGA Stellar Library target selection algorithm in Python which selected the 7,000 highest priority targets from a table of millions of stars. He merged several large data tables into one cohesive data set, and increased the efficiency of the target selection algorithm using a chaining mesh technique. Preceding that, while he was a graduate student at the University of Kentucky, Dr. McCarthy created Python software to extract and cleanse raw data from stellar observatories, and wrote SQL code to access and manipulate large sets of stellar data. He determined statistical offsets in chemical abundance between stars with and without planets using Python statistical packaging, implemented a modified bootstrap method to determine uncertainties in fundamental stellar parameters and determined rotational velocities of stars using an x2 statistical analysis. Dr. McCarthy was awarded a doctoral degree in Physics by the University of Kentucky in Lexington.
From Minnesota EIDBI Evaluation Proposal – December 2017
Kyle McCarthy, Ph.D., Data Analyst (HZA)leads the outcome evaluation components and analyses for many of HZA’s projects, including child welfare evaluations in Arkansas, Maine and West Virginia. He is expert in analyzing administrative datasets such as Medicaid and for using inferential statistics to draw conclusions about the causes of social problems. Using data from three states’ case management systems, he developed code in SQL and SPSS to select comparison groups using propensity score matching and to measure outcomes. Dr. McCarthy’s code identifies the extent to which children with particular characteristics benefit more than others, trending the results over time. Dr. McCarthy earned a Ph.D. in Physics in 2015 from the University of Kentucky.
From Vermont Involuntary Medication Longitudinal Study (RFI) Proposal – November 2017
Kyle McCarthy, Ph.D. Quantitative Lead
Kyle McCarthy is an integral member of HZA’s evaluation team, taking the lead in conducting longitudinal analyses for three state’s Title IV-E Waiver initiatives, including Maine where the focus is on treating drug affected parents whose children have been removed from the home or at risk of being removed. He developed code in SQL and SPSS to select comparison groups using propensity score matching as part of the outcome evaluations. Given statewide implementation of the Waiver initiatives, it has been necessary to select children and families served prior to implementation of the various initiatives to compare the successes of the Waiver initiatives to those served previously with similar characteristics and case circumstances. Dr. McCarthy has also developed code in SQL to measure the prospective outcomes of each state’s Waiver initiatives, analyzing the case management and risk assessment data to identify the extent to which children with particular characteristics benefit more than others, trending the results over time.
For a study HZA is conducting for the Michigan Legislative Council to measure the workload and financial costs to the State and its counties of proposed legislation to move 17 year olds from the adult corrections system to the juvenile justice system, Dr. McCarthy is matching youth between the ages of 14 and 17 to the corrections system. The data are being used to identify what would have happened to 17 year olds had they been involved in the justice system as a juvenile offender.
Dr. McCarthy received a B.S. Cum Laude in Physics from Georgia State University in 2010. In 2013, he received his M.A. in Physics from the University of Kentucky, and he went on to earn his Ph.D. in Physics from the University of Kentucky in 2015.
From Indiana Home Visiting Benchmark Reporting Proposal – June 2017
Kyle McCarthy, Ph.D. is an integral member of the firm’s data analysis team, taking the lead in the outcome evaluation components for evaluations of child protection programs in Arkansas, Maine and West Virginia. Dr. McCarthy serves as the Project Manager for the evaluation of Arkansas’s six Title IV-E Waiver initiatives, meeting regularly with state agency staff to review the continued implementation of the initiatives as well as the results of the process and outcome evaluation components. Using data from the three states’ case management systems, he developed code in SQL and SPSS to select comparison groups using propensity score matching as part of the outcome evaluation. Given statewide implementation of the Waiver initiatives, it has been necessary to select children and families served prior to implementation of the various initiatives to compare the successes of the Waiver initiatives to those served previously with similar characteristics and case circumstances. Mr. McCarthy has also developed code in SQL to measure the outcomes of the Waiver initiatives, analyzing the data to identify the extent to which children with particular characteristics benefit more than others, trending the results over time.
Dr. McCarthy is also leading the data analysis team responsible for measuring resource need for Maine’s child welfare program. Using data collected from a random moment survey and time study, in which caseworkers reported the activities they complete for a sample of cases and the time it takes to complete those activities, two types of time-related data are being measured. The first is the amount of time staff have in the average month to work on cases and the second is the average amount of time it takes to handle a case which satisfies policy requirements. It is those figures, along with caseload volume, that will be used to measure the number of staff needed to handle Maine’s child welfare cases in a quality manner.
Finally, Dr. McCarthy is participating in HZA’s evaluation of the Eckerd Rapid Safety Feedback (ERSF) Model in Maine and Connecticut in conjunction with Casey Family Programs and Eckerd. The ERSF Model, an approach that utilizes measurement of a baseline of serious risk factors with real-time quality assurance, uses historical data from an agency’s case management system. Most recently the data analysis team developed baseline measurements for Maine of the degree to which the modeling was predictive of future repeat maltreatment and/or removal from the home to ensure children’s safety.
Kyle McCarthy works primarily on child welfare projects, including the Arkansas, West Virginia, and Maine IV-E Waiver projects. Although each project is unique, Waivers are federally funded demonstrations which provide states with an opportunity to test innovative practices aimed at preventing children’s removal from the home and improve safety, permanency, and well-being.
From Arkansas Human Service Consulting PreQualifier Proposal (Data Analytics Component) – December 2016
Kyle McCarthy, Research Analyst
Kyle McCarthy is an integral member of the firm’s Title IV-E Waiver evaluation teams, taking the lead in the outcome evaluation components for both Arkansas and West Virginia. For the evaluation being completed for Arkansas’s Waiver six initiatives, Mr. McCarthy serves as Project Manager, meeting regularly with program leads to review the continued implementation of the initiatives as well as the results of the process and outcome evaluation components.
Using data from the two states’ case management systems, he developed code in SQL and SPSS to select comparison groups using propensity score matching as part of the outcome evaluation. Given statewide implementation of the Waiver initiatives, it has been necessary to select children and families served prior to implementation of the various initiatives to compare the successes of the Waiver initiatives to those served previously with similar characteristics and case circumstances. Mr. McCarthy has also developed code in SQL to measure the outcomes of the Waiver initiatives, analyzing the data to identify the extent to which children with particular characteristics benefit more than others, trending the results over time.
Mr. McCarthy is leading the data analysis team responsible for measuring resource need for Maine’s child welfare program. Using data collected from a random moment survey and time study, in which caseworkers reported the activities they complete for a sample of cases and the time it takes to complete those activities, two types of time-related data are being measured. The first is the amount of time staff have in the average month to work on cases and the second is the average amount of time it takes to handle a case which satisfies policy requirements. It is those figures, along with caseload volume, that will be used to measure the number of staff needed to handle Maine’s child welfare cases in a quality manner.
Prior to coming aboard at HZA, Mr. McCarthy served as a Research Assistant at the University of Kentucky. There, he developed the MaNGA Stellar Library target selection algorithm in Python which selected the 7,000 highest priority targets from a table of millions of stars. He merged several large data tables into one cohesive data set, and increased the efficiency of the target selection algorithm using a chaining mesh technique. Preceding that, while he was a graduate student at the University of Kentucky, Mr. McCarthy created Python software to extract and cleanse raw data from stellar observatories, and wrote SQL code to access and manipulate large sets of stellar data. He determined statistical offsets in chemical abundance between stars with and without planets using Python statistical packaging; implemented a modified bootstrap method to determine uncertainties in fundamental stellar parameters; and determined rotational velocities of stars using an x2 statistical analysis. In addition to his work at the University of Kentucky, Mr. McCarthy created empirical temperature measures for stars using IDL data analysis tools while serving as a Research Assistant at Georgia State University.
From SAMHSA IDIQ Proposal – December 2016
Junior Statistician, Survey Methodologist:Kyle McCarthy, Ph.D.
Since joining HZA in July 2016, Dr. McCarthy has assumed the direction of HZA’s Title IV-E Waiver evaluation team for the Arkansas Division of Children and Family Services. He uses SQL and SPSS to select comparison groups using propensity score matching as part of the outcome evaluation. Dr. McCarthy has also used SQL to measure the outcomes of the Waiver initiatives, analyzing the data to identify the extent to which children with particular characteristics benefit more than others, trending the results over time. He is performing similar analyses for HZA’s evaluation of West Virginia’s Title IV-E Waiver grant award and for two projects in Maine.
Dr. McCarthy previously served as a Research Assistant at the University of Kentucky. There, he developed the MaNGA Stellar Library target selection algorithm in Python which selected the 7,000 highest priority targets from a table of millions of stars. He merged several large data tables into one cohesive data set, and increased the efficiency of the target selection algorithm using a chaining mesh technique. Preceding that, while he was a graduate student at the University of Kentucky, Dr. McCarthy created Python software to extract and cleanse raw data from stellar observatories, and wrote SQL code to access and manipulate large sets of stellar data. He determined statistical offsets in chemical abundance between stars with and without planets using Python statistical packaging; implemented a modified bootstrap method to determine uncertainties in fundamental stellar parameters; and determined rotational velocities of stars using an x2 statistical analysis. In addition to his work at the University of Kentucky, Dr. McCarthy created empirical temperature measures for stars using IDL data analysis tools while serving as a Research Assistant at Georgia State University.
Dr. McCarthy received a B.S. Cum Laude in Physics from Georgia State University in 2010. In 2013, he received his M.A. in Physics from the University of Kentucky, and he went on to earn his Ph.D. in Physics from the University of Kentucky in 2015.