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| Pregunta | Respuesta |
| MDS - Multidimensional Scaling Analysis | The general purpose of this is to detect the meaningful underlying dimensions that all the researcher to explain the similarities and differences between the investigated object |
| MANOVA | Generalizes ANOVA, where there is more than one continuous outcome variable |
| Principle Component Analysis | Is a method of data reduction, therefore the original data is reduced in size. The new variables are linear combinations of the original variables. |
| Discriminant Function Analysis | It predicts a categorical dependent variable by an analysis performed on one or more continuous or binary independent variables, the groups are known prior to anaysis |
| Multiple Regressions | It predicts changes in the single continuous dependent or outcome variable, in response to changes in several independent variables, to find which of the independent variables have an effect on the outcome measure |
| Canonical Correlation analysis | It there are two vectors of random variables, and there are correlations among the variables, then it will find linear combinations among them |
| Multi Logistic Regression | A dependent Variable has 2 possible outcomes, success and failure, where pie is the probability of success |
| Confirmatory Factor Analysis | Is a special form of factor analysis. It tests if the data fits a hypothesised measurement model. |
| Factor Analysis | reduced correlational data to a smaller number of dimensions or factors |
| Cluster Analysis | To identify relatively homogeneous groups of cases based on selected attributes, the groups are originally unknown and should have high internal/external variation in - The distance calculated between every 2 cases involved - Must be measure on some type of ranking scale |
| Wards Method - Cluster Analysis | Ward’s method aims to construct clusters which are compact within themselves |
| Principle Component Analysis - Data Type | If the original variables are uncorrelated or have a small correlation then nothing will be achieved by a PCA |
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