Crime Sentencing First Time Offender

Crime

Sentencing First Time Offender

Victim of Personal Crime

Victims of Property Crimes

Abstract crime can be any action that by societies or personal standards may be an action of violating or breaking a law. By western standards or jurisprudence, for a crime to be committed there ususally has to be a combination of a some type of guilty action in addtion to a knowing guilty thinnking parttern by the perpetrator. This report focuses on a specific crime scenario and attempts to answer related questions. Our criminal justice system is both overwelmed and understaffed as crime continues to escalate.

This report is about a particular incident from the year 2003 that involved a 23-year-old white male named Michael Harris. Mr. Harris was arrested and charged with a theft that involved personal property. Allegedly, on December 5, 2003, Mr. Harris picked a lock and broke into a motor vehicle that belonged to Mrs. Elizabeth Porter who was at the time a 35-year-old woman. Mr. Harris was said to have had the intent to steal the car stereo as well has Mrs. Porter's purse which had money in it. During the break in, Mrs. Porter was inside of her own home on the property where the automobile was parked. However, Mr. Harris was apprehended by a police officer who was on routine neighborhood patrol after observing Mr. Harris' suspicious behavior. On the scene, Mr. Harris was subsequently searched and arrested when he was found to have in his possession the stereo and the purse. It was later discovered that this was Mr. Harris' first criminal offense. Later, Mr. Harris was tried and convicted and then sentenced to 6 months imprisonment for the offense.

Based on this scenario, a statistical analysis was conducted to try to understand the feelings by participants on the seriousness of this crime, the justification of the penalty, the satisfaction of the court ruling and how fair the penalty can be considered. By understanding the inherent statistical results, certain assumptions can be made regarding society, crime, ethical behavior and expectations.

Sentencing First Time Offender - Victim of Personal Crime

Victims of Property Crimes

Introduction

Crimes can be seen as offenses against society and they are therefore punished by the state. Crimes are often distinguished by the effect on the crime's victim. but, crimes can also be distinguished as degrees of offenses. So, crime can be against but not limited to:

The State itself

The rights of citizens public order and morality

The economy, industry or commerce

The environment

Crime in and of itself generally reflects the existing attitudes that are prevalent in an enforcing society. Therefore, this type of report and analysis may be used to monitor if and by how much society concurs with the enforcement of paricular punishments, crimes and/or criminally-based scenarios.

This report focused on survey questionaires that asked specific questions that pertained to the scenario that was introduced in the abstract regarding the theft of a stereo and purse, breaking and entering and other related crimial acts and offenses. But the surveys also aimed to answer questions that pertained to the legal enforcement of these crimes committed by a first itme offender. These questions or DV's served as the driving factors or hypothesis that needed to be answered and were therefore the foundation of these statistical experiments.

Hypotheses 1: How Serious is the offense?

Hypotheses 2: How much do you believe the offender deserved the penalty?

Hypotheses 3: How pleased are you with the penalty the offender received?

Hypotheses 4: Based on the crime, how fair do you believe the penalty is?

Methods and Materials

Through survey polls, this research attempted to discover the opinions of citizens as to how the sentencing process applied to them. The crime was not a violent crime and the offender was a white male with no prior convictions who was sentenced to imprisonment.

Tests of Between- Subjects Effects

Dependent Variable: How fair is the penalty?

Source

Type III Sum of Squares df

Mean Squares

Sig

Corrected Model

Intercept

Code

Error

Corrected Total

Multiple Comparisons

Dependent Variable: How fair is the penalty?

I) Version of scenario

J) version of scenario

Mean Diff

I-J)

Std. Error

Sig

Scenario a Scenario B

Scenario a Scenario C

Scenario a Scenario D

Scenario B

Scenario a Scenario B

Scenario C

Scenario B

Scenario D

Scenario C

Scenario a Scenario C

Scenario B

Scenario C

Scenario D

Scenario a Scenario D

Scenario B

Scenario D

Scenario C

Univariate Analysis of Variance

Between-Subjects Factors

Version of scenario

Value Label

Scenario a Scenario B

Scenario C

Scenario D

Tests of Between- Subjects Effects

Dependent Variable: How pleased are you with this penalty?

Source

Type III Sum of Squares df

Mean Squares

Sig

Corrected Model

Intercept

Code

Error

Corrected Total

Multiple Comparisons

Dependent Variable: How pleased are you with this penalty?

I) Version of scenario

J) version of scenario

Mean Diff

I-J)

Std. Error

Sig

Scenario a Scenario B

Scenario a Scenario C

Scenario a Scenario D

Scenario B

Scenario a Scenario B

Scenario C

Scenario B

Scenario D

Scenario C

Scenario a Scenario C

Scenario B

Scenario C

Scenario D

Scenario a Scenario D

Scenario B

Scenario D

Scenario C

Univariate Analysis of Variance

Between-Subjects Factors

Version of scenario

Value Label

Scenario a Scenario B

Scenario C

Scenario D

Post HOC Tests

Versions of Scenario

Multiple Comparisons

Dependent Variable: How serious is this offense?

I) Version of scenario

J) version of scenario

Mean Diff

I-J)

Std. Error

Sig

Scenario a Scenario B

Scenario a Scenario C

Scenario a Scenario D

Scenario B

Scenario a Scenario B

Scenario C

Scenario B

Scenario D

Scenario C

Scenario a Scenario C

Scenario B

Scenario C

Scenario D

Scenario a Scenario D

Scenario B

Scenario D

Scenario C

Tests of Between- Subjects Effects

Dependent Variable: How serious this offense?

Source

Type III Sum of Squares df

Mean Squares

Sig

Corrected Model

Intercept

Code

Error

Corrected Total

Univariate Analysis of Variance

Between-Subjects Factors

Version of scenario

Value Label

Scenario a Scenario B

Scenario C

Scenario D

Post HOC Tests

Versions of Scenario

Tests of Between- Subjects Effects

Dependent Variable: Did the offender deserve the penalty?

Source

Type III Sum of Squares df

Mean Squares

Sig

Corrected Model

Intercept

Code

Error

Corrected Total

Tests of Between- Subjects Effects

Dependent Variable: How serious this offense?

Source

Type III Sum of Squares df

Mean Squares

Sig

Corrected Model

Intercept

Code

Error

Corrected Total

Univariate Analysis of Variance

Between-Subjects Factors

Version of scenario

Value Label

Scenario a Scenario B

Scenario C

Scenario D

Post HOC Tests

Versions of Scenario

Multiple Comparisons

Dependent Variable: Did the offender deserve the penalty?

I) Version of scenario

J) version of scenario

Mean Diff

I-J)

Std. Error

Sig

Scenario a Scenario B

Scenario a Scenario C

Scenario a Scenario D

Scenario B

Scenario a Scenario B

Scenario C

Scenario B

Scenario D

Scenario C

Scenario a Scenario C

Scenario B

Scenario C

Scenario D

Scenario a Scenario D

Scenario B

Scenario D

Scenario C

Multiple Comparisons

Dependent Variable: How serious is this offense?

Based on observed Means

I) Version of scenario

J) version of scenario

Lower Bound

Upper Bound

Scenario a Scenario B

Scenario a Scenario C

Scenario a Scenario D

Scenario B

Scenario a Scenario B

Scenario C

Scenario B

Scenario D

Scenario C

Scenario a Scenario C

Scenario B

Scenario C

Scenario D

Scenario a Scenario D

Scenario B

Scenario D

Scenario C

Statistics

Gender

Ethnicity

Age

Version of scenario

Are you a student

Have you been victimized?