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The term may also refer to any data collected during the scientific activity. Observations can be qualitative, that is, the absence or presence of a property is noted and the observed phenomenon described, or quantitative if a numerical value is attached to the observed phenomenon by counting or measuring.
Observational research is a method of data collection that has become associated with qualitative research. [1] Compared with quantitative research and experimental research, observational research tends to be less reliable but often more valid [citation needed]. The main advantage of observational research is flexibility.
Phenomenological models have been characterized as being completely independent of theories, [2] though many phenomenological models, while failing to be derivable from a theory, incorporate principles and laws associated with theories. [3]
Two main statistical methods are used in data analysis: descriptive statistics, which summarize data from a sample using indexes such as the mean or standard deviation, and inferential statistics, which draw conclusions from data that are subject to random variation (e.g., observational errors, sampling variation). [4]
This method represents the most extreme form of intervention in observational methods, and researchers are able to exert more control over the study and its participants. [2] Conducting field experiments allows researchers to make causal inferences from their results, and therefore increases external validity.
A fact is an observed phenomenon, and observation means it has been seen, heard or otherwise experienced by researcher. A theory is a systematic explanation for the observations that relate to a particular aspect of social life. Concepts are the basic building blocks of theory and are abstract elements representing classes of phenomena.
Quantitative research using statistical methods starts with the collection of data, based on the hypothesis or theory. Usually a big sample of data is collected – this would require verification, validation and recording before the analysis can take place. Software packages such as SPSS and R are typically used for this purpose. Causal ...
A phenomenographic data analysis sorts qualitatively distinct perceptions which emerge from the data collected into specific "categories of description." [1] [2] [3] [8] The set of these categories is sometimes referred to as an "outcome space." These categories (and the underlying structure) become the phenomenographic essence of the ...