Summative Data Analysis
Summative Data Analysis
Summative Data Analysis is the method of examining data gathered for a specific time to find out the results of a program, project, or initiative. It is important for informing the stakeholders about the efficiency, facilitating future decisions, and assuring accountability.
What are the main objectives of summative data analysis?
The primary goals of summative data analysis are to evaluate the total success of a program or intervention, to find out whether or not the goals and objectives had been achieved, and to give decision-making evidence. To illustrate, a school might employ summative data analysis to analyze the effect of the new teaching method on the performance of the students at the year's end.
How does summative data analysis differ from formative data analysis?
The main difference between summative data analysis and formative data analysis is that summative data is done at the end of a project or program to judge its overall success, while formative data is done during the implementation process to check if adjustments need to be made. For instance, a health program can make changes in their strategies using formative analysis halfway through the program, while the summative analysis would evaluate the program's effectiveness once it is over.
What types of data are commonly used in summative data analysis?
The summative data analysis commonly involves two types of data, namely the quantitative data like test scores, sales figures, or survey results, and qualitative data from individual interviews or focus groups. As an example, a non-profit organization might undertake analysis using quantitative data on the service usage and qualitative feedback from beneficiaries to gauge the impact of its services.
What are some common methods for conducting summative data analysis?
The statistical analysis, comparative studies, and outcome evaluations are among the common methods for undertaking summative data analysis. The regression analysis method can help to detect the connections between the variables and the pre-and post-intervention comparisons might be the best tool to prove the changes that were caused by the program like a healthier lifestyle after the initiative.Summarize the data and then study it for quality control issues using conclusion analysis.