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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.

Creating Digital Terrain Models from 3D Light Detection and Ranging (LiDAR) Data

Yesenia Sánchez, Amely Bauer, and Lindsay Campbell

Light Detection And Ranging, or LiDAR, is a technology that uses laser pulses to create detailed 3D maps of the Earth’s surface. The technology has been around since the 1960s, but recent advances have made it more common for mapping both natural and human-made environments. LiDAR data is useful in many fields. It can be used to improve car safety and self-driving technology and to map areas to identify archaeological sites. It is useful for precision agriculture applications, land management decisions, and even mosquito control. This fact sheet provides practical instructions on how to create a DTM and a hillshade visualization from LiDAR data using ArcGIS Pro software. The guide is intended for anyone interested in processing publicly accessible LiDAR data or their own LiDAR data for further use, including community partners in industry and governmental agencies, scientists, and the public.

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.”

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.