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The main application of statistical power is "power analysis", a calculation of power usually done before an experiment is conducted using data from pilot studies or a literature review. Power analyses can be used to calculate the minimum sample size required so that one can be reasonably likely to detect an effect of a given size (in other ...
One of the first tests of this theory was a reversal of Asch conformity experiments by adding two confederates in a six person group, and arranging for them to systematically disagree with the majority decision. Instead of lines, the participants judged (aloud) the color and brightness of a series of 36 colored slides (all were blue with ...
In statistics, confirmatory factor analysis (CFA) is a special form of factor analysis, most commonly used in social science research. [1] It is used to test whether measures of a construct are consistent with a researcher's understanding of the nature of that construct (or factor). As such, the objective of confirmatory factor analysis is to ...
There are three processes of attitude change as defined by Harvard psychologist Herbert Kelman in a 1958 paper published in the Journal of Conflict Resolution. [1] The purpose of defining these processes was to help determine the effects of social influence: for example, to separate public conformity (behavior) from private acceptance (personal belief).
In turn, conversion, otherwise known as private acceptance or "true conformity", involves both publicly and privately agreeing with the group's decision. In the case of private acceptance, the person conforms to the group by changing their beliefs and attitudes. Thus, this represents a true change of opinion to match the majority. [24]
The need for a positive relationship with the people around leads us to conformity. [4] This fact often leads to people exhibiting public compliance—but not necessarily private acceptance—of the group's social norms in order to be accepted by the group. [5] Social norms refers to the unwritten rules that govern social behavior. [6]
The rule can then be derived [2] either from the Poisson approximation to the binomial distribution, or from the formula (1−p) n for the probability of zero events in the binomial distribution. In the latter case, the edge of the confidence interval is given by Pr( X = 0) = 0.05 and hence (1− p ) n = .05 so n ln (1– p ) = ln .05 ≈ −2.996.
Use a non-conformity function to compute α-values A data point in the calibration set will result in an α-value for its true class; Prediction algorithm: For a test data point, generate a new α-value; Find a p-value for each class of the data point; If the p-value is greater than the significance level, include the class in the output [4]