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Crim 320 week 12.docx

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Simon Fraser University
CRIM 320
Patrick Lussier

March 26 2012 Crim 320 Research Methods in Criminology (Crim 320) Bivariate Correlation and Partial correlation Bivariate Correlation - Bivariate correlation - Karl Pearson’s Product-Moment Correlation (f) Bivariate Correlation - Topics o What is a correlation? o When it should and should not be used? o What are the underlying assumptions? o How to conduct a correlation? o How to test simple hypothesis Context - Two continuous (ratio/interval) variables - Association between two variables (r xy) - Exploration purpose - Investigate relationship before conducting multivariate analysis The mean score is very important, therefore you can’t use categorical variables. There is a hypothesis that lower iq is likely to be convicted but not more self-reported delinquency More delinquent friends may be associated with more self reported delinquency. But a third variable could be that they participants also have a delinquent member in the family. Larger family size -> delinquency. Third indicator – supervision Bivariate Correlation - Research question o Is there a r/s b/w age of onset the criminal career and the volume of crime committed? - Hypothesis: o H1: the earlier the age of onset of delinquency, the more crimes will be committed - Null hypothesis: o H0: The age of onset of delinquency is not related to the number of crimes committed Age of onset and delinquency Y # convictions XY incl X age of onset ()in( ) The direction of the bivariate correlation, positive or negative? The strength of the bivariate correlation - The coefficient varies from -1.00 to 1.00 - Correlation is a standardized measure o Score divided by standard deviation - Easier to interpret than covariance o Metric of both variables is often different Guidelines for interpreting the strength of the relationship: - .00=none - .10=weak - .20=low - .40=moderate - .60=substantial - .80=strong - .90=very high - 1.00=perfect relationship 3.00, skewness significant at p. 001, indicative of a significant departure from normality Absence of outliers - Extreme scores that may exaggerate/minimize the presence of an association - Best strategy – run the analysis with/without outliers and compare both results - Deleting/recoding the extreme value may allow one to find the relationship present/or not A graphical inspection of your data is really important before going on to do the work.use a scatterplot Example: Presence of 1 outlier r= -.o3 Same analysis without outlier R= -.28 Linear relationship between variables - Correlation ill attempt to fit a line along the slope - Linear relationship between variables means no relationship (absence of a slope) - Careful inspection of data prior data analysis - Correlation not adequate to identify non-linear relationships - May recode the variable to an ordinal-type scale - Inverted U shape – definitely an association between the variable. You can’t do much about the next two for your assignments but make sure you talk about htem when interpreting the findings Measurement error is mini
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