In this course, you have learned about research statistics and their relevance to the world of criminal justice. This week, you will synthesize your learning and explain how criminal justice researchers use statistics to effect change throughout the criminal justice system.
A criminal justice research specialist will not only conduct research, they must also be prepared to present and articulate statistical inferences from research results in a clear and efficient manner.
Review the conclusion of “Police Officers’ Best Friend?: An Exploratory Analysis of the Effect of Service Dogs on Perceived Organizational Support in Policing”
Impact on Change: These statistics establish a baseline understanding of problems, highlight where resources are needed (e.g., high-crime areas), and reveal disparities (e.g., in sentencing or arrests) that call for policy intervention.
2. Identifying Relationships and Predicting Outcomes (Inferential Statistics)
Inferential statistics (like t-tests, ANOVA, and regression analysis) are used to draw conclusions about a larger population based on a sample and to test hypotheses.
Example: Analyzing whether a new policing strategy significantly reduces property crime (testing a hypothesis), or using regression to predict an individual's risk of recidivism based on multiple factors.
Impact on Change: They determine what works and what doesn't. If a program shows a statistically significant effect on reducing recidivism, policymakers have a data-driven rationale to fund and expand it (evidence-based policy).
3. Evaluating Program and Policy Effectiveness (Evaluation Research)
Statistical methods are the backbone of evaluation research, which assesses the efficacy of interventions.
Example: Using a quasi-experimental design with statistical controls to compare crime rates in a neighborhood that received a new youth mentor program versus a similar neighborhood that didn't.
Impact on Change: The statistical findings (e.g., a p-value indicating the difference is unlikely due to chance) allow administrators to make informed decisions to retain, modify, or eliminate ineffective programs, ensuring efficiency and better public safety outcomes.
Statistical Inferences and the Role of the Research Specialist
A criminal justice research specialist must not only conduct the analyses but also be able to clearly communicate the statistical inferences derived from them.
Understanding Statistical Inferences
A statistical inference is a conclusion reached about a population based on sample data, often expressed in terms of probability. The key steps involve:
Hypothesis Testing: Starting with a null hypothesis (e.g., the intervention had no effect) and an alternative hypothesis (e.g., the intervention did have an effect).
Calculating the P-value: Determining the probability (p-value) of observing the data if the null hypothesis were true.
Drawing an Inference:
If the p-value is small (≤0.05), the researcher rejects the null hypothesis and infers that the observed effect is statistically significant and likely exists in the larger population.
Sample Answer
Statistics are fundamental to criminal justice research, providing the empirical evidence needed to evaluate programs, identify trends, and ultimately effect change throughout the criminal justice system.
How Criminal Justice Researchers Use Statistics to Effect Change
Criminal justice researchers use statistics in several key ways to drive data-driven reforms:
1. Describing and Measuring Phenomena (Descriptive Statistics)
Researchers use descriptive statistics (like means, medians, modes, frequencies, and standard deviations) to profile and quantify elements of the system.
Example: Calculating crime rates over time, the average sentence length for a particular offense, or the racial distribution of the prison population.