Content Analysis of Public Input on Forest Management SOAN355

Assignment 4: Content Analysis of Public Input on Forest Management

SOAN355

Spring 2017

 

Due Date:  TBA

 

Objective:  Along with survey and census data (quantitative information), social scientists use qualitative data to describe forest values, beliefs and attitudes.   This assignment will demonstrate your ability 1) to find forest values, beliefs and attitudes expressed in speech or writing (qualitative data) , 2) to code that text for themes, and 3) to communicate in writing key insights about element of culture based on qualitative data.

Description: Conduct a content analysis of “public input” on the Shasta Agness Project, a US Forest Service landscape restoration plan for a site in the Coast Range near the Rogue River northwest of Gold Beach, OR.  See the following link:

http://data.ecosystem-management.org/nepaweb/nepa_project_exp.php?project=49607

Select one public comment from an environmental group and one public comment from an industry group.  Systematically code the text for forest values and attitudes toward the restoration project.  (Include your coding effort with you summary.) Provide a narrative summary of what you learned about the forest values and restoration attitudes of your two stakeholders.  Do they support the Shasta Agness Project, and if so, what did they like about the project?  If not, what were their concerns or objections?

Your summary should pose an empirical question, identify your stakeholders, describe your qualitative data, describe your coding strategy, and summarize your results.  Conclude by answering your research question.

Formatting Guidelines:  The narrative part of this assignment should conform to the common writing conventions for your portfolio.

Attachments:

application/vnd.openxmlformats-officedocument.wordprocessingml.document iconsoan355_assignment_4_1_1.docx

application/vnd.openxmlformats-officedocument.wordprocessingml.document iconexample_of_a_format_for_assignment_4_1.docx

 

Qualitative Data Analysis

 

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