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Análisis de datos

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thumbnail for publication: Phases of Data Analysis

Phases of Data Analysis

Glenn D. Israel

A UF/IFAS numbered Fact Sheet. This document outlines the phases of data analysis for evaluating Extension programs, emphasizing the importance of tailoring the process to available resources and evaluation rigor. It covers steps such as screening data for errors, selecting key impact indicators, measuring and testing changes, comparing participant groups, and elaborating program impacts using statistical techniques. The goal is to enhance the credibility of findings and improve program effectiveness through rigorous analysis and collaboration among Extension professionals. Original publication date September 1992. 

thumbnail for publication: Cluster Analysis for Extension and Other Behavior Change Practitioners: Information and Terminology Needed for Cluster Analysis

Cluster Analysis for Extension and Other Behavior Change Practitioners: Information and Terminology Needed for Cluster Analysis

Laura A. Warner

By identifying audience subgroups, cluster analysis (a quantitative technique) helps Extension agents tailor their education and communication programs to specific audiences. This publication falls under the Cluster Analysis for Extension and Other Behavior Change Practitioners series and discusses key information and terminology associated with cluster analysis. The publication thus builds upon “Cluster Analysis for Extension and Other Behavior Change Practitioners: Introduction,” which introduced cluster analysis as a technique for audience segmentation. This publication provides essential information for the publications which follow, “A Practical Example” and “Integrating the Results of Cluster Analysis into Meaningful Audience Engagement.”

thumbnail for publication: Elaborating Program Impacts through Data Analysis

Elaborating Program Impacts through Data Analysis

Glenn D. Israel

A UF/IFAS numbered Fact Sheet. This document explains how to analyze Extension program impacts using multivariate statistical techniques. It outlines phases of data analysis, including identifying data errors, assessing changes in impact indicators, and examining relationships between program participation and outcomes. The paper emphasizes the role of contextual variables in clarifying or distorting program effects and introduces concepts such as spurious, conditional, distorter, and suppressor relationships. By incorporating these variables, evaluators can avoid misleading conclusions and improve the validity of their findings. Publication date: September 1992.