The main techniques used to interpret information about an intervention for use in an evaluation are statistical analysis, the use of models, non-statistical analysis and judgement techniques, such as cost-benefit analysis, cost-effectiveness analysis and multi-criteria analysis. See also cost-benefit analysis, cost-effectiveness analysis, data collection, models, multi-criteria analysis, non-statistical analysis, statistical analysis.
The process of organizing, summarizing, and interpreting numerical, narrative, or artifact data, so that the results can be validly interpreted and used to guide future development of students.
Evaluation requires that data not only be gathered but that it be analyzed. Impressionistic conclusions can be drawn from qualitative data. Quantitative data is best analyzed using statistical procedures.
A Really Big Deal in CRM. Data analysis, also called business intelligence, is using software for ad hoc query, reporting and analysis, and supporting strategic decision-making processes with a data warehouse or data mart. Basically it means slicing and dicing your data to figure out how to keep customers and find new ones. Isn't that why you were collecting all that data in the first place
The systematic study of data so that its meaning, structure, relationships, origins, etc. are understood.
Processing the information or data that has been gathered in order to draw conclusions.
Provides access to tools allowing users to perform sophisticated data analysis of both native data content and meta-data. Features include: Basic keyword and Boolean search functionality Natural language and search query support Fuzzy logic and thesaurus-based search Advanced data mining capabilities, such as artificial intelligence, neural-network, and thematic data mapping search
Data analysis (also called business intelligence) commonly used in CRM applications, is using software for ad hoc query, reporting and analysis and supporting strategic decision-making processes with a data warehouse or data mart. It can help firms keep their customers and find new ones.
(IEEE) (1) Evaluation of the description and intended use of each data item in the software design to ensure the structure and intended use will not result in a hazard. Data structures are assessed for data dependencies that circumvent isolation, partitioning, data aliasing, and fault containment issues affecting safety, and the control or mitigation of hazards. (2) Evaluation of the data structure and usage in the code to ensure each is defined and used properly by the program. Usually performed in conjunction with logic analysis.
The evaluation of data collected.
an evaluation of collected observations and information.
The method by which the physiological data produced from the administration of the psychological structure test is analyzed and evaluated for a conclusion of truth or deception.
Processing, interpretation and analysis of findings
The process of systematically applying statistical and logical techniques to describe, summarize, and compare data.
Organization, processing, and presentation of information you collected for the purpose of making recommendations or drawing conclusions
Data cleansing Data mining Data navigation Data visualization Data Warehouse Database Database marketing DBM (Database Management) DBMS (Database Management System) Decision tree Dimension Direct Marketing Distribution and Logistics Dynamic segmentation
The processing of marketing research findings to summarize a situation, discover relationships between elements of the information, or to draw conclusions from them. See Marketing Research.
Data analysis is the act of transforming data with the aim of extracting useful information and facilitating conclusions. Depending on the type of data and the question, this might include application of statistical methods, curve fitting, selecting or discarding certain subsets based on specific criteria, or other techniques. Respect to Data mining, data analysis is usually more narrowly intended as not aiming to the discovery of unforeseen patterns hidden in the data, but to the verification or disproval of an existing model, or to the extraction of parameters necessary to adapt a theoretical model to (experimental) reality.