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PLEASE FOLLOW ALL DIRECTIONS ON THIS ASSIGNMENT AND YOU CAN USE OUTSIDE REFERENCE AND I HAVE ATTACH A REWRITE JUST AS A GUIDE ALONG WITH WEEK 1,2 AND 3 FOR YOU TO LOOK BACK AT IN ORDER TO UNDERSTAND WHAT IS NEEDED TO BE DONE FOR THIS ASSIGNMENT MAKE SURE YOU FOLLOW WHAT NEEDS TO BE DONE FROM WEEK 1 ON THIS ASSIGNMENT……THANKS

Discussion: Measures of Central Tendency

One of the main goals of data analysis is to describe data in a succinct manner so that it is easily understandable; this is called data reduction. To succinctly describe data, measures of central tendency (that is, mean, median, and mode) can be used. For example, suppose you collect data on the arrest records of inmates in a particular region. From your data, you might want to determine the most common offenses committed by inmates. If each offense is measured nominally (for example, armed robbery = 1, murder = 2, and aggravated assault = 3), determining the mode would show which offense is committed most frequently. You might also want to determine the number of offenses typically committed by inmates (ratio level of measurement) prior to being sentenced to prison. To do this, you could calculate the mean, or the average number of offenses committed by inmates. You also could rank the number of offenses from the highest to the lowest to determine the middle number, or the median number of offenses committed by inmates.

Before choosing which measure of central tendency to use, it is important to consider the level of measurement that applies to the variable. For instance, it would not make sense to find the mean or median of a variable, such as gender, that is measured nominally. As you complete the following Discussion, keep in mind that not all measures of central tendency are appropriate for all levels of measurement.

Define the three measures of central tendency including mean, median, and mode. Include the factors that must be considered when using each measure of central tendency and explain why they are important.

Explain how you could use each of the measures of central tendency to better understand the criminal justice research topic you chose in Week 1.

THIS IS A REWRITE GUIDE ONLY FOR YOU TO FOLLOW IN HELPING YOU PUT TOGETHER THE DISCUSSION QUESTION THAT NEEDS TO BE ANSWERED FROM THE INSTRUCTION PAPER THAT’S BEEN ATTACHED

Week 4 Discussion

Central tendency is a single number that is used to represent an entire set of data; the
Mean, median, and mode are the three measures of central tendency. The mean is the most used
measure of central tendency. It is the sum of scores divided by the number of scores. The median
is the division point in a group that is divided in half and used with interval, ratio or ordinal data.
The mode is the most used number used in a data set. Mode is most commonly used with
nominal data.
Using the comstat report from the Jackson Police Department in Jackson, MS, I can
discuss the median, mean and mode of central tendency of the homicides throughout Jackson,
MS within a 24- month period. Finding the mean by dividing the number of homicides by the
number of precincts in the city of Jackson, (88 deaths/4 precincts=22).
The median divides the homicide into two equal groups. Half of the precincts has a few
more homicides where the other precincts have less. The mode would be the precincts in which
the homicides occurred.
Utilizing these measures of central tendency are important in the criminal justice field
because it streamlines a clear way to see how crime is committed in any given area and
potentially, a path toward fighting it. It is imperative to be sure the right measures are used when
gathering data to prevent inaccuracies in the results. Using the wrong methods could result in
skewed and ineffective data.

References

Bachman, R., & Schutt, R. K. (2014). The practice of research in criminology and criminal
justice (5th ed.). Thousand Oaks, CA: SAGE Publications.

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Data Analysis and Criminal Justice

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Data analytics helps by combining extensive data in various ways in attempts to display previously unseen patterns. In this case, data analytics applies computer-processing power to correlate and bring together many data points. For instance, a data analyst in the criminal justice system correlates criminal justice data such as recidivism and crime rates with information from other sources such as work history and poverty rates to find patterns necessary to improve the justice system’s efficiency and quality. Data analysis and research are more pertinent to police officers because they are the first contact people make in the criminal justice system. The police collect data about crime reporting, which receives compliments from the judicial system’s data concerning criminals/crimes prosecuted and data from probation or correctional services (Hannah-Moffat, 2019). I would use data analysis as a police officer to prevent crime through timely, accurate, and impartial statistical data. I would analyze and research data to keep citizens safe by knowing areas prone to high crimes, gang formation within the community, and ex-prisoners tactics that facilitate recidivism. In this case, I would use data analysis to input information from crime scenes into databases that would help to find connections between cases. Later, I would create specific criminals’ records and narrow suspects list. For instance, by grouping crime information with data reflecting vandalism incidences, unemployment rates, and truancy rates, I can uncover subtle and significant correlations affecting crime. After geotagging data points, I would narrow the data further to predict where and when certain crimes are likely to occur. The prediction would help in deploying the limited personnel and financial resources necessary to patrol high crime areas. Later, I would make crime data public to increase trust and transparency that would facilitate a good working relationship with community members.

Reference

Hannah-Moffat, K. (2019). Algorithmic risk governance: Big data analytics, race and information activism in criminal justice debates. Theoretical Criminology, 23(4), 453-470.

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Sampling Methods

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Stratified Random Sampling

I would use stratified random sampling, a probabilistic sampling method, to research high rates of substance abuse. Stratified sampling is a technique of sampling that entails population division into smaller sub-groups called strata (Zhao et al., 2019). The strata formation is relative to members’ shared characteristics or attributes such as educational attainment or income. After dividing the population into groups, the researcher randomly chose the sample proportionally. The strata should be distinct, and the data must not overlap. The method allows researchers to attain a sample population that appropriately represents the whole study population.

Strengths and Weaknesses

One advantage of the research method is that if the rehabilitation centers require improvements, research information could be a step in the appropriate direction to address any underlying problems. Another advantage is that it ensures every subgroup in the population gets proper representation in the sample. In this case, the technique offers better population coverage as the researchers have a mandate over the subgroups to facilitate the representation of all substance abusers and community members in the sampling. On the other hand, one of the technique’s disadvantages is that the researcher must meet several conditions for its proper use. For instance, a researcher should identify study population members and classify every one of them into only a single subpopulation. Another disadvantage is the participants’ refusal to offer accurate information during the research. For instance, failing to gather information face to face taints results.

Use

Stratified random sampling would help in obtaining information concerning drug abuse in the United States. My data collection form would be using surveys to gather information from community members and substance abusers in rehabilitation centers. In this case, I would use the research method for finding the general effectiveness, explanation, or applicability of the existing drug policies or programs.

Reference

Zhao, X., Liang, J., & Dang, C. (2019). A stratified sampling based clustering algorithm for large-scale data. Knowledge-Based Systems, 163, 416-428.

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Levels of Measurement

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Levels of Measurement

Levels of measurement consist of interval, ordinal, ratio, and nominal. Nominal is the lowest level where there is no representation of mathematical variables. For instance, when measuring the gender population in rehabilitation centers with more males than females, nominal variables would be more than the real number of individuals in rehabilitation centers. On the other hand, ordinal measurement is a level where there is a ranking of variables in an orderly way (Vaske, 2019). For instance, a researcher may use the Likert scale to survey people in a community through the agree-strongly disagree method at the end of a meeting. In this case, the survey is rank-ordered to show how effective the meeting was relative to survey results.
Additionally, an interval is a level of measurement where variables have meaningful values with the same space between the values. The space is vital in the data collected because zero has no basic value in this measurement. In this case, one can use interval measurements to track substance abusers between two diverse age groups. Hence, the data is essential in understanding what situations can cause people to abuse substances. Finally, the ratio is the measurement level where variables have meaningful value with the same space between the values but have no true value of zero. Hence, a ratio level is countable. In this case, ratio and interval measurements would be the best levels to gather information on the number of substance abusers in a given year by combining them to compare the information. When one wants to gather data in the criminal justice system, I think these levels would apply to any researcher on a study. However, not all measurement levels are compatible since wrong measurement levels can skew outcomes.

Reference

Vaske, J. J. (2019). Survey research and analysis. Sagamore-Venture. 1807 North Federal Drive, Urbana, IL 61801.