TB6/7 RM Topic Overview

HideShow resource information
  • Created by: mint75
  • Created on: 17-11-15 15:42
View mindmap
  • RM TB6/7 Overview
    • Linear Regression
      • A way of predicting outcomes you have not measured
      • Creates a linear model of the relationship between two variables
      • What to look for
        • R(squared)= The % of variance accounted for by the model
        • SSM (SST/SSR)= The amount of improvement by using the 'best' model over the 'dumb' model
        • The F Ratio = The ratio of the MSM (regression) error to the mean square residual error. A larger F = smaller p value
      • y = b1x + b0
    • Correlation
      • A way of measuring the extent to which two variables are related
      • DOES NOT IMPLY CAUSATION
      • What to look for
        • Correlation Coefficient (r) = Divide the covariance by the product of the individual STDDES
          • Should always be between -1 and +1, 0 = no relationship.
          • A standardised measure of the size AND direction of the relationship
        • Covariance = Sum of the cross-product deviations, divided by n -  1
          • Tells you the DIRECTION but not size of the relationship
      • Non-parametric alternatives
        • Spearmans Rho (assumptions violated)
        • Kendalls Tau (small data set with tied ranks)
      • Partial correlations
        • Used to control for other potential mediating variables, the EXCLUSIVE relationship of x to y
    • Multiple Regression
      • The multiple regression equation
        • y = b0 + b1x1 + b2x2 +...bnxn + residuals (eta)
      • Different methods of regression
        • Managing the order of variables you're fitting
          • Forced Entry
          • Hierarchical
          • Stepwise
      • Interpreting multiple regression
        • R2 - The proportion of variance accounted for by the model
          • Adjusted R2 = An estimate of R2 in the population (shrinkage)
            • You want these to be similar, meaning the model could generalise to the population
        • ANOVA F Statistic; Tells us whether the regression model is a better predictor than the mean (dumb) model
        • Beta Values
          • The change in the outcome associted with a unit change in the predictor
          • Standardised beta values = the same but as STDDEV
            • The change in the outcome associted with a unit change in the predictor
      • Predicts the values of an outcome variable from several predictors
      • How well does the model fit the data?
        • Residual statistics (Standardised residuals)
          • Confidence intervals, 1.96+-
        • Influential cases (Cooks distance)
          • Measures the influence of a single case on the whole model, values > 1 = bad
    • Standardised Beta Values
      • A beta value tells us the CHANGE in the outcome variable associated with unit change in predictor
        • The standardised betas tell us the same but expressed as a STDDEV
          • e.g B1 = 0.523, as adverts increased by 1 STDDEV, sales increased by 0.523 of a STDDEV
          • These allow us to directly compare the effects of variables
        • e.g b1 = 0.087, as adverts incresed by £100 sales increased by 0.087 units
      • Factor Analysis
        • Tests for 'clusters' of variables. Which measures are ASPECTS of a common dimension? How many are there?
        • Use correlation (R) matrix
          • Aiming to reduce the R-matrix into SMALL sets of UN-CORRELATED dimensions
        • Use Factor matrix to assess factor loadings
          • How much does each question contribute to each factor?
        • Principal Component Analysis (PCA) = finds the principal axis of a cloud of data points, allowing you to find factors (FA)
          • Use Eigenvectors, reduce multidimensional data sets into a set of components
            • Eigenvalues tell you how important each eigenvector is
            • Scree Plots
              • Look for the point of inflexion, as opposed to Kaiser extraction where you only use eigenvalues > 1
            • Rotation
              • Maximises the loading of a variable on one factor whilst minimising load on all other factors
              • Orthoganol (VARIMAX) both axes are rotated to go through the center of the clouds, factors are UNCORRELATED
        • Reliability
          • Test-retest method and Split half method
          • Cronbachs alpha, data is reliable if a > 0.7
            • Although affected by number of items
            • Reverse score reverse phrased items!
    • Also note assumptions of each test

Comments

No comments have yet been made

Similar Psychology resources:

See all Psychology resources »See all TB6 RM resources »