CRJS 3710 01: Social Statistics

CRJS 3710 - Social Statistics

Summer 2026 Syllabus, Section 01, CRN 30252,

Credit hours: 3

Course Meeting Times

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Instructor

Richard Rogers

Professional Qualifications:
Ph. D., Sociology, Princeton University
B.A., Sociology, University of Illinois at Chicago

Professor

Email: rlrogers02@ysu.edu

Office: Cushwa 2145 (suite 2141)

Office Phone: 330-941-3543

Additional Contact Methods:
Available by Webex during student support hours: https://ysu.webex.com/meet/rlrogers02

Preferred Contact Method: Email

Communication Expectations:
Responses to emails normally come with 48 hours. Email is checked at least once a day unless otherwise notified. Video announcements for the week are posted every Sunday or Monday.

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Course Description

3710. Social Statistics. Coverage includes purposes of statistics, its importance and role in the field, descriptive, comparison, relational, and explanatory stats, as well as their set-up, creation, interpretation, application, and critique. Coverage will include stats in both normal and non-normal situations. 3 hours of lecture per week. Prereq.: CRJS 1500. Crosslisted with: SOC 3701, ANTH 3701. 3 s.h.

Course Readings

Group Title Author ISBN
Required Statistical Analysis in Criminal Justice and Criminology Vito, G. F., Blankenship, M. B., Kunselman, J. C ISBN 978-1-4786-3784-4

The course readings are subject to change in the event of extenuating circumstances, research developments, current events, and/or to ensure better learning.  

Additional Course Materials

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Course Learning Outcomes/Objectives/Goals

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How to Succeed in This Course

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Attendance Expectations

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Late Work Submission Policy

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Additional Course Expectations

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Artificial Intelligence Policy Statement

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Assignments/Assessments

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Grading and Grading Scale

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University Policies

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Schedule of Topics and Assignments

Week of Reading(s) Proposed Topic Due/To Prepare for Class
5/11 Readings: Vito et al., chapters 1 and 2
Video: Why smart statistics are the key to fighting crime
Video: Levels of measurement
Intro to Stats 5/13: Discussion: Intro (1st post)
5/13: Quiz: Intro to Statistics
5/17: Terminology & Levels of Measurement
5/17: Exercise: Entering Data (2 parts)
5/18 Vito et al., chap. 3-5, 7
Video: Descriptive statistics for numeric variables
Web: How to make a boxplot in Excel
Web: How to make a histogram in Excel
Video: The normal distribution
Video: Confidence intervals
Descriptive Stats for Interval-Ratio Variables 5/20: Quiz and Exercise: Descriptive Statistics for Numeric Variables
5/20: Graphs for Interval-Ratio Variables
5/24: Normal Curve and Statistical Significance
5/24: Ohio Z-score
5/25 Video: How to. . . create a basic Pivot Tab le in Excel
Video: Intro to Pivot Tables, charts & dashboards
Video: Frequency tables in Excel
Reading: Vito et al., chap 3
Descriptive Stats for Categorical Variables 5/27: Exercise: Frequency Tables for Categorical Data
5/27: Exercise: Frequency Tables for Categorical Data
5/31: Exercise: Graphs for Categorical Variables
5/31: Group Project: Contract
6/1 Vito et al., chaps 3, 6, 10
Web: Scatter plot in Excel
Video: How to calculate a correlation in Excel
Video: How to construct a contingency table using a Pivot Table in Excel
Bivariate Relationships I 6/3: Exercise: Scatterplots and Timeline Groups
6/3: Exercise: Scatterplots and Pearson’s r
6/7: Quiz: Contingency Tables
6/7: Exercise: Contingency Tables
6/8 Vito et al, chap. 8, 9
Video: Independent sample t-test in Excel
Web: Effect size calculator
Video: Excel – one-way ANOVA Analysis Toolpak
Video: An introduction to the general linear model
Bivariate Relationships II 6/10: Two-Sample T-Tests
6/14: One-Way (Single Factor) ANOVA
6/10: Two-Sample T-Tests 6/14: One-Way (Single Factor) ANOVA 6/14: Group Project: First Schedule Contact Point
6/15 Vito et al., chap. 11
Video: Regression analysis on Excel
Video: ANCOVA models in Excel
Video: SoftTech tutorials
OLS Regression 6/17: Exercise: OLS Regression
6/21: Exercise: Multiple Regression and ANCOVA Models
6/21: Group Project: Second Contact Point
6/22 Video: Binary logistic regression in Microsoft Excel using XLMiner Logistic Regression and Group Project 6/24: Binary Logistic Regression
6/26: Reflection
6/26: Group Project: Final Submission

The course schedule, policies, procedures, and assignments in this course are subject to change in the event of extenuating circumstances, by mutual agreement, and/or to ensure better learning.