Project Design and Logic Models

UNH Data Science Core

UNH Data Science Core

The goal of the Data Science Core (DSC) is to integrate bioinformatics, statistics, and data science resources across UNH, and the state of New Hampshire through our NH-INBRE partners to build capacity in Data Science by providing access to the expertise, infrastructure, and training necessary for modern research. The DSC is a Service Center with mechanisms for both collaborative and fee-for-service models and will work with investigators to find a solution that fits their needs and budget.

Services include: 
>> Consultation for proposal development, experimental design, and creation of Data Management Plans
>> Direct support services in statistical analysis and bioinformatic analysis

Other questions? Contact Kelley Thomas

 

Web Resources

Basic Research Concepts

BASIC RESEARCH CONCEPTS 
A web based tutorial on Basic Research Concepts developed with funding provided by the US Dept. of Health and Human Services, Office of Research Integrity (ORI) that is targeted individuals new to research.  The materials presented are intended to provide basic information about research and how it’s conducted. With this framework, those new to research (staff, students) may be better able to understand research and carry out their duties/responsibilities in a manner that preserves the integrity of the data collected.

Topics include:

  • What is Research?
  • Research Design
  • Elements of Research
  • Methods of Information Collection
  • Handling Information
  • Glossary of Terms
  • Links to Additional Resources
 
Common Guidelines for Education Research and Development and FAQs

COMMON GUIDELINES FOR EDUCATION RESEARCH AND DEVELOPMENT
FAQS
A report from the Institute of Educational Sciences/US ED and the National Science Foundation wihch provides:

  • Guidance on building the evidence base in STEM learning
  • Provides guidelines for six types of education “research” projects
  • Overview of the nature, justification, evidence generation and so on for each project type

Companion Guidelines on Repliication and Reproducibility in Education Research
A Supplement to the Common Guidelines for Education Research and Development (November, 2018) 

 
NCATS Clinical Research Toolbox

NCATS CLINICAL RESEARCH TOOLBOX
Clinical research tools that facilitate trial design, patient recruitment and partnerships for commercialization National Center for Advancing Translational Sciences (NCATS).

 

NIH Research Methods Resources

NIH Research Methods Resources
Provides investigators with research methods resources to help them design their studies using the best available methods.
The material is relevant to both randomized and non-randomized trials, human and animal studies, and basic and applied research.


Logic Models

Logic Model Templates

Logic Model Templates
Example formats compiled by RLCD staff

Logic Model -- University of Wisconsin-Extension

LOGIC MODEls

Logic Model Development Guide -- W.K. Kellogg Foundation

LOGIC MODEL DEVELOPMENT GUIDE
Guiding Program Direction with Logic Models
This report is a companion piece for the W.K. Kellogg Foundation Logic Model Development Guide

Logic Models  -- Dan Ryan, Kathryn P. Hannam Associate Professor, Mills College

LOGIC MODELS  

Logic Models for Program Design, Implementation, and Evaluation: Workshop Toolkit

Logic Model Workshop Toolkit
Resource from U.S. Dept. of Education/ Institute of Education Sciences/ Regional Educational Laboratory Program designed to help practitioners learn the purpose of logic models, the different elements of a logic model, and the appropriate steps for developing and using a logic model for program evaluation.

TA&D Project Logic Model and Conceptual Framework

TA&D PROJECT LOGIC MODEL AND CONCEPTUAL FRAMEWORK

USDA NIFA Logic Model Resources

Logic Model Planning Process
FAQs about Logic Models
Generic Logic Model
Integrated Programs' Logic Model Planning Process

 

Rigor and Reproducibility

UNH Responsible Conduct of Research and Scholarly Activity Guide -- Rigor and Reproducibilty

Enhancing Reproducibility through Rigor and Transparency