Related Experiment Video
Updated: Jul 4, 2025
06:05
The Participant-Reported Implementation Update and Score PRIUS: A Novel Method for Capturing Implementation-Related Data Over Time
Published on: February 19, 2021
1.3K
How Mixed-Methods Research Can Improve the Policy Relevance of Impact Evaluations
Burt S Barnow1, Sanjay K Pandey1, Qian Eric Luo2
1Trachtenberg School of Public Policy and Public Administration, George Washington University, Washington, DC, USA.
Evaluation Review
|February 1, 2024
Summary
Mixed methods research enhances impact evaluations by improving policy relevance and addressing external validity. Increased use of these approaches can reinforce findings and explain program successes or failures.
Area of Science:
- Social Sciences
- Policy Evaluation
- Research Methodology
Background:
- Impact evaluations are crucial for assessing program effectiveness.
- External validity and generalization are key challenges in research.
- Mixed methods offer potential solutions to these challenges.
Purpose of the Study:
- To explore the utility of mixed methods in enhancing impact evaluations.
- To investigate the application of mixed methods for external validity.
- To document the current use of mixed methods in impact evaluations.
Main Methods:
- Literature review on rationales for mixed methods.
- Systematic documentation of mixed methods in recent impact evaluations.
- Full-text review of selected impact evaluation articles.
- Analysis of exemplar case studies.
Main Results:
- Mixed methods are underutilized in current impact evaluations.
- Mixed methods can reinforce quantitative findings through triangulation.
- Mixed methods help elucidate program impact mechanisms and reasons for failure.
Conclusions:
- Increased adoption of mixed methods can significantly improve impact evaluation value and policy relevance.
- Mixed methods are essential for addressing external validity and generalization issues.
- Further research and application of mixed methods are recommended for robust program evaluation.
Related Concept Videos
01:25Strategies for Assessing and Addressing Confounding
101
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
101
02:01Group Design
8.9K
The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between...
8.9K
01:23Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
128
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
128
01:20Comparing the Survival Analysis of Two or More Groups
188
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
188
01:17Cochran's Q Test
334
Cochran's Q Test is a nonparametric statistical test used to determine if there are potential differences in the outcomes of three or more related groups on a binary (yes/no) or dichotomous outcome. It is essentially an extension of the McNemar Test, which is limited to two related samples - Cochran's Q test can handle three or more related samples, making it more versatile in scenarios where subjects are measured under multiple conditions. The test statistic follows a Chi-Square...
334
01:25Statistical Methods for Analyzing Epidemiological Data
366
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
366