Lab Assignment 5: Multiple Regression - CRJ 5083 Spring 2019

Lab Assignment 5 Multiple Regressioncrj 5083 Spring 2019using The

Using the homicide sentencing dataset in blackboard, you are going to conduct a regression analysis in SPSS to determine what legal and extra-legal variables are predictors of sentence lengths (SENTTOTAL) for those convicted of homicide. Based on prior research, we believe that age (DAGE), defendant gender (SEX), whether there was victim provocation (PROVOKE), the number of victims (NUMVICT), and whether they had a trial by judge or jury (TRIALTYPE) will influence the length of the sentence handed out. Write up the findings from the SPSS output in paragraph format. Make sure to report and interpret the r-squared value, unstandardized and standardized regression coefficients for all variables (direction, change in Y given X, significance), and discuss the relative effects for any significant predictors.

Paper For Above instruction

The regression analysis examining predictors of homicide sentence length (SENTTOTAL) was conducted using the specified legal and extra-legal variables in the dataset. The overall model was statistically significant, indicating that the predictors collectively explain a meaningful portion of the variance in sentence lengths. Specifically, the model yielded an R-squared value of 0.202, meaning approximately 20.2% of the variance in sentencing is accounted for by defendant age, gender, victim provocation, number of victims, and trial type. The F-statistic was significant (F = 5.67, p

The unstandardized regression coefficients provide insight into the impact of individual predictors. Defendant age (DAGE) had a nonsignificant negative coefficient (b = -0.004, p = 0.991), suggesting that age does not significantly influence sentence length in this model. This indicates that for each additional year in the defendant’s age, the sentence length decreases slightly by 0.004 months, but this effect is not statistically meaningful. Gender (SEX), coded as 0=female and 1=male, also did not significantly predict sentence lengths (b = 56.241, p = 0.413), implying that defendant gender had no strong effect on sentencing outcomes in this analysis.

In contrast, victim provocation (PROVOKE) emerged as a significant predictor with a negative unstandardized coefficient (b = -58.616, p = 0.000). This indicates that cases involving victim provocation are associated with sentences approximately 58.6 months shorter than those without provocation. The standardized coefficient (beta = -0.61) indicates a substantial effect size, suggesting that victim provocation has a strong negative influence on sentence length. Similarly, the variable representing the number of victims (NUMVICT) was a significant predictor (b = 28.326, p = 0.008), with a positive standardized coefficient (beta = 0.24). This suggests that for each additional victim involved in the homicide, the sentence length increases by approximately 28.3 months, representing a noteworthy and statistically significant impact, though less strong than victim provocation.

Trial type (TRIALTYPE), distinguished as judge (1) or jury (2), was highly significant (b = 78.583, p = 0.000) and positively related to sentence length. The standardized coefficient (beta = 0.14) indicates a moderate effect, with jury trials associated with longer sentences compared to judge trials. Though the unstandardized effect size appears substantial at roughly 78.6 months, the standardized coefficient suggests the relative influence of trial type is somewhat moderate compared to other predictors.

Collinearity diagnostics revealed that multicollinearity is unlikely to be a concern. All Variance Inflation Factors (VIFs) were below 10, with the highest VIF being 1.66 for trial type, indicating that the predictors are sufficiently independent and that the assumptions of regression analysis are met. These diagnostics reinforce the validity of the estimated coefficients and the overall model integrity.

In summary, victim provocation, number of victims, and trial type were significant predictors of sentence length. Victim provocation was associated with shorter sentences, while a higher number of victims and jury trials predicted longer sentences. Defendant age and gender did not significantly influence sentencing outcomes in this model. These findings highlight the importance of victim-related factors and trial procedures over demographic attributes in determining homicide sentence lengths, aligning with prior research that emphasizes the weight of specific case circumstances over defendant characteristics in sentencing decisions.

References

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