Summary lecture 1, Emperical research project for IB
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Lecture 7:Factor analysis:Technique to reduce a large amount of information (contained in a number of original variables into a simple message (fewer variables, factors) with a minimum loss of informationDone at the start of your empirical analysis, before your regression analysisLatent variables:Often we need to measure things that cannot be measure directly or cannot be observed (e.g. burnout), because it could have multi dimensions. So you can measure faucets. Next we can see whether these facets reflect a single variable. So are these different facets driven by the same underlying variableMain use of factor analysis:To understand the structure of a set of variables (structure of a latent variable)To construct a questionnaire to measure an underlying variable (design a questionnaire to measure the variable)To reduce a dataset to manageable size while retaining as much of the original information as possible (e.g. merging two variables)Step 1:Calculate the correlation coefficients for each pair of these variables. Step 2:Cluster variables, clusters of large correlation coefficients suggest that those variables could be measuring aspects of the same underlying dimension. These underlying dimensions are known as factors (latent variables)You can also make is graphical:Imagine two factors as being axes of a graph, (plot these variables). Coordinate of variables indicate the strength of the relationship between the variable and each factor. The coordinates of a variable are known as factor loadings (correlation between a factor and a variable , axes {-1.1}. ideally a variable should have large coordinate for one axis, small coordinate for the other axis. Variable relate to one axisMathematical:Factor matrix: matrix of factor loadings. Assumption: the factors represent real-world dimensions, which must be guessed at by inspecting which variable have high loads on the same factorsIt is possible to find as many factors as...
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