Data is a critical aspect of maintaining the integrity of research. Systematic data collection allows for the measurement of the interactions between variables of interest that enable individuals to answer research questions, test hypotheses, and evaluate outcomes (Research Integrity, 2022). Such data collection enables effective research to help individuals make informed decisions, improve the lives of others, find solutions to policy problems, and enable effective provision of resources. Data is also essential to estimating patterns in homelessness and combating its growth (U.S. Department of Housing and Urban Development, 2022). By measuring the extent to which homelessness has been exacerbated, analysts and researchers can propose informed and effective solutions to ending homelessness by providing services to those most vulnerable. The Homeless Management Information System (HMIS) is a database that allows organizations to compile client-level, system-wide information on the services they provide to people at risk of homelessness and individuals experiencing homelessness (Los Angeles Homeless Services Authority, 2022). The Point-In-Time Count, another major data source, occurs on a single night, or several successive nights, and aims to estimate the number of individuals experiencing homelessness in a community that includes sheltered and unsheltered populations (National Alliance to End Homelessness, 2012). Data from HMIS and the Point-In-Time Count allow policymakers to strategically allocate funds and advocate on demographics, trends, and causes of homelessness, as well as the availability and usage of services among the populations experiencing homelessness (U.S. Department of Housing and Urban Development, 2022).
The Importance of Data Integrity in the Homeless Services Sector
Homelessness Policy Research Institute