Search results
Results from the WOW.Com Content Network
In statistics, exploratory data analysis (EDA) is an approach of analyzing data sets to summarize their main characteristics, often using statistical graphics and other data visualization methods.
Exploratory research is "the preliminary research to clarify the exact nature of the problem to be solved." It is used to ensure additional research is taken into consideration during an experiment as well as determining research priorities, collecting data and honing in on certain subjects which may be difficult to take note of without exploratory research.
They can be beneficial when one is looking to find new qualitative and quantitative data. [14] Diary studies aim to measure people's behavior over an extended period. They provide the opportunity for exploratory research, collecting large quantities of precise data that are both in-depth and contextual.
Contemporary qualitative research has been influenced by a number of branches of philosophy, for example, positivism, postpositivism, critical theory, and constructivism. [7] The historical transitions or 'moments' in qualitative research, together with the notion of 'paradigms' (Denzin & Lincoln, 2005), have received widespread popularity over ...
[124] [125] It is especially important to exactly determine the structure of the sample (and specifically the size of the subgroups) when subgroup analyses will be performed during the main analysis phase. [126] The characteristics of the data sample can be assessed by looking at: Basic statistics of important variables; Scatter plots
Like all hypotheses, a working hypothesis is constructed as a statement of expectations, which can be linked to deductive, exploratory research [3] [4] in empirical investigation and is often used as a conceptual framework in qualitative research. [5] [6] The term "working" indicates that the hypothesis is subject to change. [3]
Data and information visualization (data viz/vis or info viz/vis) [2] is the practice of designing and creating easy-to-communicate and easy-to-understand graphic or visual representations of a large amount [3] of complex quantitative and qualitative data and information with the help of static, dynamic or interactive visual items.
Causal analysis is the field of experimental design and statistical analysis pertaining to establishing cause and effect. [1] [2] Exploratory causal analysis (ECA), also known as data causality or causal discovery [3] is the use of statistical algorithms to infer associations in observed data sets that are potentially causal under strict assumptions.