Difference-in-Difference (DiD) and Interrupted Time Series (ITS) analyses are strong quasi-experimental alternatives to randomized controlled trials. Researchers use DID and ITS with real-world data to examine the real-world effects of interventions, which helps inform practices and policies. Counseling researchers can utilize them to assess the impact of specific counseling interventions and policy changes. We discuss the statistical basis of DID and ITS and demonstrate modeling methods and interpretation of model estimates and study findings.