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Theory Analysis in Data Evaluation

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The theories can help us understand and explain data – for instance, to spot patterns or trends. We can apply theories to help guide the design and conduct of an evaluation, such as when deciding on what kinds of data to collect, how to collect it and who is required to participate in the evaluation.

It is important to conduct an analysis of the theory behind data in order to ensure that the results are accurate and valid. This can be accomplished by analyzing the underlying assumptions of an evaluation or by comparing the results with previous studies or models. It is also beneficial to examine the research questions as well as the data collection methods.

Quantitative data may be collected using questionnaires, surveys or statistical tests. comparing changes in a variable over time or between groups). The most widely used quantitative methods include descriptive statistics, such as standard error and mean, which provide data by group or case as well as inferential statistical tests like parametric and nonparametric tests that make predictions about a whole population based on the data of one of the populations and meta analysis which compares results from several independent tests.

Qualitative data often takes the form of textual documents, such as elicited texts (written specifically for the research, such as food diaries) or extant texts (such as meeting minutes or policy documents), or field notes produced during participant-observation or while conducting interviews and focus groups. This type of data requires a distinct kind of analysis – one which is more inductive than deductive. This method is referred to as grounded theory. Its primary purpose is to come up with new theories from the data by constantly interfacing between the processes of gathering and analysis.

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