Criminal Justice Administration and Human Services Department
Course Prefix and Number:
The study of applied research designs. Data collection methods emphasized are observation and psychometry. Both qualitative and quantitative data analysis methods are studied, with a strong emphasis on results interpretation. Must be taken as a foundational course for the Master of Science in Criminal Justice. Prerequisite: Graduate standing.
Prerequisite(s) / Corequisite(s):
Most current editions of the following:
Practical Research Planning and Design
By Leedy, Paul D. and Jeanne Ellis Ormrod (Prentice Hall) Recommended
Research Methods and Statistics in Criminal Justice
By Fitzerald, Jack D. and Steven M. Cox (Wadsworth Publising) Recommended
Research Methods in Criminal Justice and Criminology
By Hagan, Frank E. (Allyn and Bacon) Recommended
To understand applied research designs.
To use an applied research design.
To employ and evaluate qualitative and quantitative data analysis methods.
To interpret and evaluate qualitative and quantitative research methods.
To understand evaluation and prediction research.
To enhance critical thinking when interpreting and evaluating research studies.
To apply basic statistical methods to real world data to evaluate relationships between crime related variables.
To understand data collection methods.
Explain the differences between qualitative and quantitative data analysis methods.
Distinguish between evaluation and prediction research as they are used in the Criminal Justice system.
Describe and evaluate the importance of ethical issues associated with Criminal Justice research.
Describe and explain probability sampling.
Explain the difference between validity, reliability, and precision in categorization and measurements.
Select an appropriate research design to support a current Criminal Justice research question.
Explain, evaluate and apply research methodology and design.
Complete a research design project to include the development of a research question, hypothesis, variable identification, data source, and analysis method.
NOTE: The intention of this master course syllabus is to provide an outline of the contents of this course, as specified by
the faculty of Columbia College, regardless of who teaches the course, when it is taught, or where it is taught. Faculty members teaching this
course for Columbia College are expected to facilitate learning pursuant to the course objectives and cover the subjects listed in the topical
outline. However, instructors are also encouraged to cover additional topics of interest so long as those topics are relevant to the course's
subject. The master syllabus is, therefore, prescriptive in nature but also allows for a diversity of individual approaches to course material.