We conducted a qualitative content analysis to analyze 1052 pages of text from the 109 laws passed from 2000 to 2017. Content analysis describes a family of approaches for systematic examining of texts [13 ]. Qualitative content analysis is the close, comprehensive, and organized reading of a set of texts to identify themes, intent, or patterns [14 ,15 ]. It is not a mere counting of words but a close reading of texts based on driving research questions [16 ]. Analysts read a purposeful selection of texts to identify themes or coding frames [17 ] which are the primary instruments to sort qualitative texts into categories. All the texts are read line by line by the analyst(s) for comprehension. Analysts select relevant lines of text to be “coded” or sorted into themes for comparative study according to research interests. Early uses of content analysis are from fields of psychology, medical practice, and communication research [8 ,18 (link)]. The analysis is grounded in empirical content rather than interpretive argument [9 ,19 ].
We used QSR’s NVivo 10.0, a software package for text-based analysis, to store policy texts and to organize our systematic reading. Our objective was to capture and describe the most complete spectrum of policy activities related to insect pollinators. To accomplish this, all laws were read in their entirety line-by-line. The software did not perform any automated functions. The authors read the texts looking for the actions (human behaviors) called for by the laws and responsible actors (government agencies, industry, hobbyists) named in the law. Following conventional qualitative content analysis approach, we had no predetermined codes or themes. Under this “open coding” or inductive approach, all of the coded policy actions fit into 18 thematic categories. Reflection brought this number to a manageable 12 themes [20 ]. Through discussing and rereading the texts, the authors refined the dozen themes into five parent categories of targeted human behavior (Box 1 ). Each policy was re-read in its entirety and coded to thematic category for further analysis by one analyst [21 ].Insect pollinator relevant policy targets identified via inductive qualitative content analysis.
We used QSR’s NVivo 10.0, a software package for text-based analysis, to store policy texts and to organize our systematic reading. Our objective was to capture and describe the most complete spectrum of policy activities related to insect pollinators. To accomplish this, all laws were read in their entirety line-by-line. The software did not perform any automated functions. The authors read the texts looking for the actions (human behaviors) called for by the laws and responsible actors (government agencies, industry, hobbyists) named in the law. Following conventional qualitative content analysis approach, we had no predetermined codes or themes. Under this “open coding” or inductive approach, all of the coded policy actions fit into 18 thematic categories. Reflection brought this number to a manageable 12 themes [20 ]. Through discussing and rereading the texts, the authors refined the dozen themes into five parent categories of targeted human behavior (
Policy Targets Apiculture (e.g., registering hives, equipment disposal rules, disease concerns, inspections) Pesticides (e.g., banning of neonicotinoids) Research (e.g., funding for research, Colony Collapse Disorder, research) Habitat (e.g., conservation, enhancement, development) Awareness (e.g., pollinator awareness, knowledge needs)
Alt-text: Box 1
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