URBAN SOCIOLOGY (SOCI 311-01)



SOCIAL RESEARCH II: STATISTICS

Rutgers University, Newark Campus

HILL HALL – 125; Tuesdays 6:00-9:00 PM

SPRING 2018 - 21:920:302:61

Instructor: Professor Kevin Keogan

Office Hours: Before/After class and by arrangement

Email: keogank@mail.montclair.edu

RECOMMENDED TEXTS/RESOURCES

Any basic statistics text can be used, but I recommend the following for those that find math/statistics difficult:

1. Pyrczak, Fred. Making Sense of Statistics: A Conceptual Overview [ISBN: 1-884585-88-4]

2. Holcomb, Zealure. SPSS Basics: Techniques for a First Course in Statistics [ISBN: 978-1-936523-16-0]

3. The Khan Academy is a great website for help with math/statistics:

OVERVIEW

The main objective of this course is to gain a practical understanding of statistics for quantitative analysis of social science data. Specifically, a successful student will learn: 1. to produce descriptive, correlation, and inferential statistics; 2. to interpret univariate, bivariate, and multivariate statistics; 3. to construct tables and graphs for a clear presentation of data; 4. to select the appropriate type of statistical procedure to summarize data and test hypotheses; 5. to use SPSS software to manage and analyze a large data set.

COURSE GRADING AND REQUIREMENTS

Grading will be based on the following scale:

Assignment Max. Points

Quizzes 10 (Highest 2 quizzes x 5 points each)

Exam 1 20 points (MUST score 70% to PASS)

Lab Assignments 40 points (10 assignments x 4 points each)

Exam 2 30 points (MUST score 70% to PASS)

Total 100 points

Points Earned Final Grade

90-100 A

85-89 B+

80-84 B

75-79 C+

70-74 C

60-69 D

59 or below F

ADDITIONAL DETAIL

• Students are allowed a MAXIMUM of two (2) absences before being penalized. Each absence beyond the 2nd will result in a lowering of your FINAL grade by three points (e.g., 4 absences will result in 6 points being deducted from your FINAL grade). If you can’t make it to class it is YOUR responsibility to get the pertinent information from the missed class meeting (notes, announcements, assignments, etc.). NOTE: Even missing 1 class will probably put you at a disadvantage, especially if you are already struggling with the material

• Arriving late on a regular basis, or being disruptive in class, will lower your final grade. If I notice chronic lateness, or other disruptive behavior, I will bring it to your attention. If you have reason to believe you may be late for class, or if you know in advance that you must miss a class, it’s always better to let me know ahead of time (via email )

• Make-up exams will only be given due to DOCUMENTED EMERGENCIES. However, the test questions and format will be more difficult, so, it would be advantageous if you do NOT miss an exam!

• ALL students must earn a minimum grade of 70% on BOTH exams to pass this class. If you earn less than 70% you will have to schedule a makeup exam. This may result in a (IN) Incomplete grade for the course, especially if you need to re-take both tests. The best way to prepare for the exams is to INDEPENDENTLY and CONSCIENTIOUSLY complete ALL assignments!!! ASK QUESTIONS EARLY AND OFTEN IF YOU DON’T UNDERSTAND THE MATERIAL!!!!!

• Scheduled exams will involve a substantial practical use of SPSS software. A review will be provided before each exam—see schedule for details. Quizzes may or may not be announced. There will be NO makeups for missed quizzes. Exams/Quizzes will consist of short answer questions.

• Students must successfully complete AT LEAST ten (10) lab assignments during the semester. Individual assignments will be given roughly every week or so, and are due at the BEGINNING of class the following week. Late assignments will be penalized, and no assignments will be accepted more than one class meeting after the due date. WARNING: YOU WILL NOT PASS THIS CLASS IF YOU FALL BEHIND ON YOUR LAB ASSIGNMENTS!!!!!!!!!!!!

• Although I encourage students to work together, the individual assignments you hand in must be your own. If you are caught copying all or part of an assignment from another student, you will receive a grade of “0” on the assignment and be subject to further disciplinary action. Of course, the same policy applies to exams as well.

COURSE SCHEDULE (Please note--this is a tentative schedule and subject to change):

Date Reading Lecture/Assignment

Jan. 16 Prologue: Basic Math Review Course Overview

Introduction/Probability Quiz 1: Assessment

Jan. 23 Descriptive Statistics I: Assignment 1

Central Tendency

Jan. 30 Descriptive Statistics II: Assignment 1 Due

Dispersion Assignment 2

Feb. 6 The Normal Curve Assignment 2 Due

Assignment 3

Feb. 13 Inferential Statistics Assignment 3 Due

Study Guide Assignment 4

Feb. 20 Estimation Procedures Assignment 4 Due

Exam Review Assignment 5

Feb. 27 Review All Previous Assignment 5 Due

Exam 1

March 6 Comparing Means I Return Exam 1/ Assignment 6

March 13 Spring Break

March 20 Comparing Means II Assignment 6 Due Assignment 7

March 27 ANOVA Assignment 7 Due Assignment 8

April 3 Chi Square Assignment 8 Due

Assignment 9

April 10 Association I Assignment 9 Due Assignment 10

April 17 Association II Assignment 10 Due

Study Guide Start Review

April 24 Multivariate Analysis Review for Final Exam

May 1st OPTIONAL REVIEW SESSION (NO scheduled class—“Reading Day”)

May 8th FINAL EXAM 6:15-8:15 PM

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