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CRIM 320 (64)
Lecture

Crim 320 Week 11.docx

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Department
Criminology
Course
CRIM 320
Professor
Patrick Lussier
Semester
Winter

Description
Difference between more than two population means One-way Anova -Also called single-factor ANOVA -Statistical test for comparing the sample means of three or more INDEPENDENT groups in terms of a continuous dependent measure ANOVA versus t-test -T-test can only compare two samples at the same time. -With t-test are tested at .05, 1 in 20 chance of falsely rejecting the null hypothesis. -Therefore, with 21 t-test, one should be significant by chance alone. THREE OR MORE GROUPS One nominal or ordinal variable with more than two attributes considered as the IV -onset of delinquency: 1)childhood-onset (before 12) 2)adolescent-onset (between 13-17) 3) adult-onset offenders (18 and older) -Type of offenders: 1)first-time offenders (no prior record) 2)recidivists (only 1 prior conviction) 3) multi-recidivists (2 or more prior convictions) -Police interventions for a call of partner abuse: 1)arrest 2)mediation 3)ask the husband to leave the house for 24 hours Outcome (DV) -One interval or ratio variable considered -persistence of criminal activity -time spend without committing a crime after release from prison -Number EXAMPLE OF RESEARCH QUESTIONS -H(1): Age of onset of criminal activity has an impact on the persistence of criminal activity -H(2): Type of offenders differ as to the time spent without committing a crime after being released from prison -H(3): Type of police interventions influence the number of subsequent police contact for partner abuse ANALYSIS OF VARIANCE -Does not look at the mean directly but at the variance-based on the ratio between-group variance and the within-group variance EXAMPLE -Laerence Sherman (1984) -Minneaplois Domestic Violence (DV) Experiment -Policeès BETWEEN-GROUP VARIANCE -Difference between the group mean and the grand mean (or overall mean) WITHIN-GROUP VARIANCE -Difference between an individual score and the mean of the group to which an individual belongs Homogeneity of within-group variance and heterogeneity of between-group variance Scores cluster well around the mean group Within-group variance Heterogeneity of within-group variance and heterogeneity of between group variance The scores do not cluster so well around the group mean (within group variance) Means are difference (between-group variance) Homogeneity of within-group variance and homogeneity of between group variance ANOVA CHART (important) State research question and null hypothesis – Selection of variables – Descriptive statistics – Normal distribution or Outliers – Test for the homogeneity of Variance -Lavens Test is significant – Levenes test is not significant -Interpret the Welch statistic – Interpret the F statistic -Reject the null hypothesis – Reject null hypothesis -Perform post hoc Comparisons (Tamane) – reform post hoc comparisons (Sheffee) Accept the null hypothesis – Accept the null hypothesis Analysis of variance is based on the sum of squares of between and within group variance
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