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In statistics and research, internal consistency is typically a measure based on the correlations between different items on the same test (or the same subscale on a larger test). It measures whether several items that propose to measure the same general construct produce similar scores. For example, if a respondent expressed agreement with the ...
Cronbach's alpha (Cronbach's ), also known as tau-equivalent reliability ( ) or coefficient alpha (coefficient ), is a reliability coefficient and a measure of the internal consistency of tests and measures. [1][2][3] It was named after the American psychologist Lee Cronbach. Numerous studies warn against using Cronbach's alpha unconditionally.
The name of this formula stems from the fact that is the twentieth formula discussed in Kuder and Richardson's seminal paper on test reliability. [1] It is a special case of Cronbach's α, computed for dichotomous scores. [2] [3] It is often claimed that a high KR-20 coefficient (e.g., > 0.90) indicates a homogeneous test. However, like ...
Administering one form of the test to a group of individuals. At some later time, administering an alternate form of the same test to the same group of people. Correlating scores on form A with scores on form B. The correlation between scores on the two alternate forms is used to estimate the reliability of the test.
Internal validity. Internal validity is the extent to which a piece of evidence supports a claim about cause and effect, within the context of a particular study. It is one of the most important properties of scientific studies and is an important concept in reasoning about evidence more generally. Internal validity is determined by how well a ...
Data quality refers to the state of qualitative or quantitative pieces of information. There are many definitions of data quality, but data is generally considered high quality if it is "fit for [its] intended uses in operations, decision making and planning ". [1][2][3] Moreover, data is deemed of high quality if it correctly represents the ...
Causal inference is the process of determining the independent, actual effect of a particular phenomenon that is a component of a larger system. The main difference between causal inference and inference of association is that causal inference analyzes the response of an effect variable when a cause of the effect variable is changed. [1][2] The ...
Kelley's main research goal was to emphasize the central ideas Heider discovered in Attribution theory. [11] The first focus of Kelley's research was a look at external and internal attributions. [11] His second focus was determining whether the procedure to arrive at external and internal attributes was related to experimental methodology. [11]
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