Comprehensive Notes: Course Structure, Grading, and Crime Data Concepts (CJS/Statistics Overview)

Course Structure and Assessment

• No midterm and no final exam; weekly quizzes instead for ongoing assessment
• Weekly cadence: Tuesdays introduce new content and practice; Thursdays include a short quiz on the prior week
• No required textbook; textbooks listed as optional for extra information, but all content needed to pass will be provided by the instructor
• Canvas is the central hub; full syllabus on Canvas; instructor will provide skeleton notes for each week to guide note-taking
• 13 weekly homework assignments with built-in flexibility: the lowest 3 homeworks are dropped; the best 10 will count toward the final grade
• 11 concept quizzes with the lowest quiz dropped; built-in flexibility means some absences or low performance can be excused if officially excused
• Applied research project due end of semester; integrates and applies all topics covered
• In-class activities are included to encourage attendance and active learning; attendance is emphasized; purpose is active engagement
• The course emphasizes data critique, management, and interpretation using tools like Excel and Google Sheets
• Course aims: re-critique and apply justice data; build data management skills; approach numbers with confidence; explain various criminology/justice topics using data

Course Logistics and Policies

• Location and private space: Classroom is private space; hallway is not; safety/immigration protocol explained; contact CSUN Public Safety if needed
• Office location mentioned: Office 352 (private space)
• Safety procedures: If someone asks to see someone inside the classroom, instructor must talk and then call CSUN Department of Public Safety; chief of police may verify eligibility to be in private space
• Community safety: Schedules and safety talks advertised; information sessions sponsored by Department of Public Safety and Student Affairs
• Attendance and late policy: Attendance is ingrained; late work costs 2% per day; you cannot make up in-class activities or quizzes if missed
• Red cards: Optional tool mentioned; may bring red cards if useful (lightweight classroom safety tool)

Grading and Feedback (Minimum Scale and Rounding)

• Grading approach is unusual: the "minimum grading scale" means the lowest possible grade on any assignment is 50% of its points
  • For an assignment with points $P$, you automatically earn at least $0.5P$ regardless of submission quality
  • This does not guarantee passing; if overall performance is poor, you can still fail the course
• Final grade rounding practice is generous: grades are rounded (e.g., 89.5
  ightarrow 90)
• There are no minus grades; this policy applies to overall grades, not assignment-by-assignment
• The grading floor is designed to cushion temporary setbacks and allow recovery, but consistent effort remains essential

Syllabus and Course Schedule

• Syllabi exist in two forms on Canvas: the full eight-page syllabus and a concise weekly syllabus; the instructor posts skeleton notes and weekly structure instead of full lecture slides
• The course schedule follows a stable weekly rhythm moving forward: new content, practice, homework, quizzes
• The course schedule is designed to be transparent and predictable, with weekly patterns built in to support learning and pacing

Why This Course Matters (Content Goals)

• Core focus: read, critique, and apply data within criminology and criminal justice studies
• Build data literacy: interpret crime statistics, understand how data informs policy and practice
• Develop practical data-handling skills using spreadsheets; understand how to present data to explain trends and patterns
• Equip students with the ability to question and assess claims about crime trends, policy impacts, and media representations of crime
• Emphasize practical readiness for future careers in criminology, justice studies, policing, policy analysis, research, and related areas

Criminology and Statistics: Why Stats in This Field

• Statistics are central because crime data drive policy, enforcement, and public perception
• Crime data come from two main official sources: UCR (Uniform Crime Reports) and NCVS (National Crime Victimization Survey)
• Understanding statistics helps distinguish between perception and reality; it enables critique of how crime is reported and framed in the media
• Criminology heavily relies on data to understand crime rates, disparities, and the effectiveness of interventions

UCR and NCVS: Two Main Official Crime Data Sources

• UCR (Uniform Crime Reports): police-reported crime data submitted to the Bureau of Justice Statistics
  • Two major components: Part I crimes and Part II crimes
  • Part I (serious crimes): murder, aggravated assault, robbery, vehicle theft, burglary, etc. (roughly seven or eight categories)
  • Part II (status offenses): crimes that are offenses mainly because of age (under 18), e.g., running away, truancy
  • Not all agencies participate; participation can be uneven across cities; underreporting is common in some areas
  • The hierarchy rule: if multiple crimes occur in one incident, only the most serious crime type is counted (e.g., robbery overlaid with assault counts as robbery)
  • Limited information on victims and circumstances; largely focused on incident-level data (race, gender, time, place, weapon use) but not detailed victimology
  • Strengths: standardized across jurisdictions; long historical record (since 1931)
  • Weaknesses: dark figure of crime (undercounts true crime), bias in reporting, non-participating agencies, lack of victim details, single incident emphasis
• NCVS (National Crime Victimization Survey): household-based self-report crime data
  • Captures victimization experiences (e.g., domestic violence, fraud, assault) through surveys of people aged 12+ (or older)
  • More complete in capturing victim experiences that are not reported to police (addresses the dark figure to some extent)
  • Nationally representative sample; large samples but still smaller than the population; e.g., a few dozen thousand respondents
  • Strengths: captures unreported crime; includes victim perspectives and details about the crime and its consequences
  • Limitations: memory recall issues, social desirability bias (underreporting or misreporting to conform to social norms), excludes homicide (dead cannot respond), may omit hard-to-reach populations (unhoused, sex workers, some marginalized groups)
  • Does not include businesses; focuses on individuals and households
• Key takeaway: Researchers commonly compare UCR and NCVS to get a fuller picture; gaps and biases in each source are acknowledged, and trends are interpreted with caution

Dark Figure of Crime and Biases in Data

• "Dark figure of crime": crimes that go unreported or unknown to authorities; data sources underestimate total crime
• Reasons for underreporting: victim discouragement, fear of reporting, stigma, distrust of authorities, relationship dynamics (domestic violence), perceived futility, fear of retaliation
• Memory recall issues in surveys: memory decay, forgetting details, or misremembering dates/events; may lead to underreporting or inaccurate reporting
• Social desirability bias: respondents tailor answers to be viewed favorably; can lead to underreporting of stigmatized behaviors or overreporting of socially desirable actions
• Definition changes over time: e.g., rape definition broadened around 2010 to include more cases; historical comparisons require caution due to shifting definitions
• Police focus biases: law enforcement attention often directed toward street crime; white-collar crime and other areas may be under-investigated despite large sums
• Data are not neutral: collection methods, definitions, and reporting practices shape the numbers; not purely objective reflections of reality

Data, Sampling, and How We Generalize

• To study a large population, researchers sample a smaller subset (a representative sample) and generalize results to the population
• Example: CSUN demonstrates sample-based inference; surveying about 500 students from a total population of ~38,000 can yield representative insights if the sample mirrors the population
• Concept: sampling and generalizability are fundamental to statistics and research methods; this course will cover how to design, interpret, and critique such samples
• NCVS provides broad victimization data, complementing UCR’s police-reported data; together they help address sample/response issues and bias

Scenarios and Real-World Relevance Covered in Class

• Examples of crime trends articles: contrasting narratives about crime levels (e.g., homicide rates at an all-time-low vs. wildfires and crime-related headlines) illustrate how framing and selective data can mislead without context
• Historical context: crime in the 1990s was much higher than today; understanding trends requires precise definitions (what type of crime, what population, time period) for accurate interpretation
• Criminology context: numbers matter for public perception and policy debates; students will learn to read and critique crime data when forming arguments or evaluating media claims

Career Context and Academic Pathways Mentioned

• For students interested in forensic-type careers (CSI, etc.): realistic prerequisites include extensive science coursework outside criminology (biology, chemistry, organic chemistry, physics) and additional math coursework
• Criminology is a sister discipline to sociology; it focuses on crime within social structures, not a traditional forensics degree
• Options for students pursuing science-heavy paths: minor in biology, chemistry, or double major; or align electives that fit science prerequisites within Easton and Criminology & Justice Studies (CJS)
• Some students may consider cross-listing or complementing with other majors to meet science prerequisites while remaining in CJS
• The course aims to prepare students for varied career paths by equipping them with data skills applicable to many fields

Data Exploration and Future Topics Preview

• Upcoming activity: data exploration with FBI crime data; analyze violent vs. property crime trends; discuss how to interpret trends and potential data manipulation
• Plan to discuss questions that arise from data exploration and to practice critical thinking about crime trends
• Preview of research methods content in CJS 380: understanding downsides of surveys, sampling, and data collection; how to assess the quality and limitations of data

In-Class Tools and Participation

• Poll Everywhere (pollev.com) used for in-class polling; students can join via web or text message; instructor will provide exact methods and codes
• Active participation and engagement are encouraged through in-class activities and data exploration

Practical Reminders and Quick Tips

• Skeleton notes will be provided weekly to help structure your own notes; expect blanks in early weeks to scaffold learning
• Calculators and technology are encouraged for math tasks; mental math is not relied upon; use calculators when needed
• No heavy emphasis on memorization alone; focus on understanding methods, how to read data critically, and how to apply concepts to real-world examples

Quick Reference: Key Numbers and Formulas (LaTeX)

• Minimum grading floor per assignment: for an assignment with points $P$, you automatically earn at least $0.5P$
• Late penalty: $2\% \text{ per day}$
• Homework policy: 13 homeworks total; lowest 3 dropped; count of 10 homeworks used for final grade
• Quiz policy: 11 quizzes total; lowest quiz dropped
• Rounding rule: e.g., an overall grade of $89.5$ rounds to $90$; no minus grades
• Important date notes: in-class sessions planned through November with occasional breaks; check Canvas for exact dates

Quick Reference: Course Roles and Contacts

• Instructor emphasizes openness to questions; there are no silly questions in this class; encourage asking and clarifying anytime
• Office location for private space: 352; contact for private space policy and safety procedures
• Safety sessions and information sessions are offered and sponsored by Department of Public Safety and Student Affairs; watch for announcements

Final Takeaways

• The course uses a practical, data-driven approach to criminology, emphasizing hands-on data skills, critical thinking about crime statistics, and real-world relevance
• Students will engage with both police-reported data (UCR) and victim-reported data (NCVS) to understand the strengths and limitations of each data source
• The grading structure is designed to reward effort, provide flexibility, and encourage continued participation, while still holding students accountable for learning
• The curriculum integrates theory with practice through projects, quizzes, and in-class activities, aiming to prepare students for analytical and evidence-based careers in criminology and related fields