Analyze Data. Review Coachella census data and Coachella policing agency's cannabis arrest records. Analyze Social Equity programs of cities with similar demographics to identify barriers for social equity applicants and equity policies with proven success.
Analyze Data. After receiving the testing report from the geotechnical firm, the CONSULTANT will analyze the data and any existing previous geotechnical data, consisting of the following tasks:
Analyze Data. After receiving the testing report from the geotechnical firm, that is under contract separately with the sponsor, the Consultant will analyze the data and any existing geotechnical data received, consisting of the following tasks: o Review geotechnical recommendations o Determine appropriate data for pavement design. o Input data for computer modeling with topographical survey data o Prepare pavement data and soil information for incorporation on plan sheets o Evaluate existing pavement sections for potential recycling and reuse
Analyze Data. CONSULTANT will analyze the geotechnical report and existing geotechnical data received from COUNTY, consisting of the following tasks:
Analyze Data. On the basis of existing data, the Comprehensive Preliminary Watershed As- sessment, data analysis and the site conceptual model, stakeholders will determine whether additional data are needed and, if so, how they will be collected. Data needs will depend on specific programmatic requirements. The studies conducted for the major assessment and cleanup programs are described in Chapter 4, along with potential opportunities for integra- tion, but participants should also consider additional areas for integration that might apply to the contaminants, watershed, and participants in the specific watershed project. After carefully considering the types of additional data required for each agency or program and evaluat- ing opportunities to consolidate data collection, managers can determine the methods and mechanisms for collecting the data. The data can be collected independently by stakeholders with available authorities and resources (as long as it is collected according to an agreed-upon quality assurance/quality control (QA/QC) plan, as described below), or a collaborative data collection effort can be launched.
Analyze Data. The analysis and interpretation of the collected data is conducted during feedback sessions (i.e., a meeting of the people representing the point of view in the goal template and the data providers). The data, raw or graphically processed, are studied in advance by the participants of the feedback sessions. During the feedback sessions the presented material is analyzed and interpreted by the people in the point of view of the goal template. The results are conclusions with respect to the goal under investigation.
Analyze Data. The evaluation partner will analyze data according to the Evaluation Plan (from Task 2b) and any addenda to answer the evaluation questions.
Analyze Data. This is the most important phase in the study. In this step, the researchers turn the raw data into structured and logically interconnected conclusions. On the other hand, due to its creative component, it is the less straightforward phase in the cycle. To help researchers to analyze the data and to draw the conclusions, there exists several methods for qualitative data analysis that can be followed. In the DevOps exploratory study considered here, the authors conducted a thematic analysis approach [10, 44]. Thematic analysis is a method for identifying, analyzing, and reporting patterns within the data. For that purpose, the data is chopped into small pieces of information, the quotations or segments, that are minimal units of data. Then, some individuals (typically some of the researchers) act as judges, codifying the segments to highlight the relevant information and to assign it a condensate description, the code. In the literature, codes are defined as “descriptive labels that are applied to segments of text from each study” [10]. In order to easy the task of the coders, the codes can be grouped into bigger categories that share come higher level characteristics, forming the semantic domains (also known as themes in this context). This introduce a multi-level codification that usually leads to richer analysis. A very important point is that splitting of the matter under study into quotations can be provided by a non-coder individual (typically, the thematic analysis designer), or it can be a task delegated to the coders. In the former case, all the coders work with the same segments, so it is easier to achieve a high level of consensus that leads to high reliability in the results of the analysis. In the later case, the coders can decide by themselves how to cut the stream of data, so hidden phenomena can be uncovered. However, the cuts may vary from a coder to another, so there exists a high risk of getting too diverse codifications that cannot be analyzed under a common framework. Thematic analysis can be instrumented through Atlas [1, 17], which provides an integrated frame- work for defining the quotations, codes and semantic domains, as well as for gathering the codifications and to compute the attained ICA. In the study considered in this section, the method for data analysis followed is described in the four phases described below (see also Figure 3).
