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«EDMS 451: Introduction to Educational Statistics FALL SEMESTER 2014 Professor Office hours Dr. Gregory R. Hancock Tu/Th 12:30-1:30pm 1230D Benjamin ...»

EDMS 451: Introduction to Educational Statistics

FALL SEMESTER 2014

Professor Office hours

Dr. Gregory R. Hancock Tu/Th 12:30-1:30pm

1230D Benjamin Building or at other times by appointment

tel: 301.405.3621 fax: 301.314.9245 e-mail: ghancock@umd.edu

mailbox: 1230 Benjamin (inside the second door on the right) Teaching Assistants Office hours Ms. Ji An (anji.nihao@gmail.com) to be announced Mr. Daniel Yangsup Lee (yangsup@umd.edu) to be announced Mr. James Xiaying Zheng (xyzheng86@gmail.com) to be announced Course description This course is designed to teach introductory concepts in statistics as applied in the social sciences, particularly education. The course will provide a presentation of commonly used statistical procedures. Students will learn both conceptual and technical aspects of location and dispersion measures, regression and correlation, hypothesis testing, z-tests, t-tests, and analysis of contingency tables.

Proficiency with algebra is necessary; however, no mathematics beyond algebra will be taught in this course.

Course website and class materials https://elms.umd.edu (use your Testudo login to access the ELMS site) Prior to coming to class each day students are expected to access whatever lecture materials are posted, if any. Materials should be posted by noon on the day of class, although probably earlier. Note, however, that whereas students may be accustomed in many courses to receiving handouts containing all of the lecture notes, this class will typically require you to take notes in addition to whatever handouts may be provided. Please be prepared to take notes. This also underscores the importance of not missing class.

Required Course Material Readings: There is no textbook for this course. Readings will be posted on-line for students to access. As lectures are only able to provide a skeleton of the course content, students are expected to read the associated materials.

Software: We will use SPSS (Statistical Package for the Social Sciences), which you may purchase for your laptop or home computer.

There are versions for PC (https://terpware.umd.edu/Windows/Package/2181) and Mac (https://terpware.umd.edu/Mac/Package/2182). You may purchase a license from the Terrapin Technology Store (http://www.it.umd.edu/techstore/), and get it installed on your computer. In addition, the computer lab in the College of Behavioral and Social Sciences has SPSS on its computers (http://www.oacs.umd.edu/ComputerLabServices.asp) and can be used for homework.

Teaching Assistants / Tutors / Study Groups The TAs are here to help. Use them as soon as you feel something is unclear. Do not wait. Also, although I don’t believe that paying a tutor is necessary to do well in this course, you are of course welcome to secure a tutor if you feel that is necessary for you. Of course, I can’t endorse any tutors nor can I guarantee the accuracy of any information provided by tutors to students. Responsibility for course material ultimately rests solely with each student. I do highly recommend forming study groups that meet regularly (once or twice a week) to go over class materials; this can be an extremely valuable (and cheaper) option.

Calculator You will need a calculator that is capable of calculating square roots for the homework, quizzes, and examinations. On in-class assessments you may NOT use your smartphone. Instructors will not provide calculators on in-class assessment days so be sure to bring a calculator on these days.

Classroom etiquette Please have phones off and away during class meetings. Also, if you use a laptop or tablet to take notes, please refrain from personal web browsing, chatting, and other distracting and discourteous behaviors. Class is only 75 minutes; it demands your full attention.

Possible Guest Lecturers Occasionally a guest lecturer may appear in class, either because I need to be away from class due to conflicting commitments or because I am giving others mentored teaching experience. In such cases class material is still mandatory and students are expected to show our guest the highest courtesy.

Keys to success in this class BE HERE. In every sense – physically, mentally, and attitudinally. When you’re in class, pay attention. If you fall behind, this is not an easy class to play catch-up; so as soon as you need help, ask. Go over materials study with others regularly. And above all, be willing to learn. The instructor and TA can’t make you learn; you have to choose to engage.

Special Needs If you have a registered disability that will require accommodation, please see the instructor so necessary arrangements can be made.

If you have a disability and have not yet registered with the University, please contact Disability Support Services in the Shoemaker Building (301.314.7682, or 301.405.7683 TTD; http://www.counseling.umd.edu/DSS/) as soon as possible. All requests for academic accommodations should be made at the beginning of the semester.

Religious Observances The University of Maryland policy on religious observances states that students not be penalized in any way for participation in religious observances. Students shall be allowed, whenever possible, to make up academic assignments that are missed due to such absences. However, the srudent must contact the instructor before the absence with a written notification of the projected absence, and arrangements will be made for make-up work or examinations.





Academic Integrity The University of Maryland, College Park has a student-administered Honor Code and Honor Pledge. For more information on the Code of Academic Integrity or the Student Honor Council, please visit http://www.studenthonorcouncil.umd.edu/whatis.html. This Code sets standards for academic integrity at Maryland for all undergraduate and graduate students. As a student you are responsible for upholding these standards for this course. It is very important for you to be aware of the consequences of cheating, fabrication, facilitation, and plagiarism. The code prohibits students from cheating, fabrication, facilitating academic dishonesty, and plagiarism.

Instances of this include submitting someone else’s work as your own, submitting your own work completed for another class without permission, or failing to properly cite information other than your own (found in journals, books, online, or otherwise). Any form of academic dishonesty will not be tolerated, and any sign of academic dishonesty will be reported to the appropriate University officials.

Importance of attendance In a class of this size I cannot possibly manage your attendance. YOU are responsible for attending this course, and for all of the material covered. In the rare instance that you miss a lecture, TAs are not responsible for giving you personal lectures that you missed.

Also, and importantly, the nature of this subject matter is highly cumulative. Typically, if you miss any material then you lack the vocabulary and/or principles needed to succeed in the next topic. Then this snowballs out of control very quickly. Do not miss class.

Missed single class due to illness:

Once during a semester, a student’s self-authored note will be accepted as an excuse for missing a minor scheduled grading event in a single class session if the note documents the date of the illness, acknowledgement from the student that information provided in the note is correct, and a statement that the student understands that providing false information is a violation of the Code of Student Conduct. Students are expected to attempt to inform the instructor of the illness prior to the date of the missed class.

Major scheduled grading events:

Major Scheduled Grading Events (MSGEs) are indicated on the syllabus. The conditions for accepting a self-signed note do not apply to these events. Written, signed documentation by a health care professional, or other professional in the case of non-medical reasons (see below) of a University-approved excuse for the student’s absence must be supplied. This documentation must include verification of treatment dates and the time period for which the student was unable to meet course requirements. Providers should not include diagnostic information. Without this documentation, opportunities to make up missed assignments or assessments will not be provided.

Non-consecutive, medically necessitated absences from multiple class sessions:

Students who throughout the semester miss multiple, non-consecutive class sessions due to medical problems must provide written documentation from a health care professional that their attendance on those days was prohibited for medical reasons.

Non-medical excused absences:

According to University policy, non-medical excused absences for missed assignments or assessments may include illness of a dependent, religious observance, involvement in University activities at the request of University officials, or circumstances that are beyond the control of the student. Students asking for excused absence for any of those reasons must also supply appropriate written documentation of the cause and make every attempt to inform the instructor prior to the date of the missed class.

Course Evaluations As a member of our academic community, students have a number of important responsibilities. One of these responsibilities is to submit course evaluations each term though CourseEvalUM in order to help faculty and administrators improve teaching and learning at Maryland. All information submitted to CourseEvalUM is confidential. Campus will notify you when CourseEvalUM is open for you to complete your evaluations for fall semester courses. Please go directly to the website (www.courseevalum.umd.edu) to complete your evaluations. By completing all of your evaluations each semester, you will have the privilege of accessing online, at Testudo, the evaluation reports for the thousands of courses for which 70% or more students submitted their evaluations.

Formal Course Assessment

Homework to check yourself:

There will not be homework in this course that you have to hand in. HOWEVER, there will be practice questions provided routinely for you to be able to check yourself. These do not cover all materials for which students are responsible, but do give students a chance to work on some of the core skills. DO THESE IMMEDIATELY, and if you have problems check with your study group and/or a TA. As I stated before, the material in this course snowballs very quickly and it is not a course you want to play catch-up in.

Quizzes (“Minor Scheduled Grading Events”):

On four days (tentatively: 11 Sept, 9 Oct, 6 Nov, 4 Dec) a relatively short quiz will be administered either in-class or on-line (to be determined). Each quiz will cover material from the lessons since the last quiz or exam (unless otherwise specified). Students who miss a quiz will not be able to make it up unless prior arrangements have been made with the instructor. Quizzes will typically require a calculator. Students are to complete quizzes entirely on their own.

Exams (“Major Scheduled Grading Events”):

There will be three in-class examinations (tentatively: 30 Sept, 23 Oct, 20 Nov) and a “quasi-cumulative” final exam (date to be announced). The final exam will primarily cover material since the last examination, but then also some areas where I believe students may have had trouble throughout the semester. For each exam, students may use one 8.5"x11" two-sided page of notes; tables and scratch paper will be provided at the time of the exam as needed. Students should bring a calculator to the exams, NOT a smartphone.

Course grades This course is not graded on a curve. Assessments will be combined according to the percentages shown on the left. A worksheet for

computing grades is provided. Final grades will be assigned using the scale to the right (without rounding):

–  –  –

Topic Introduction / Basic Concepts Displaying Data / Frequency tables Percentiles and Percentile Ranks Measures of Central Tendency Measures of Variability Probability Normal Distribution / z-score Sampling Distributions Introduction to Hypothesis Testing One-Sample z-test One-Sample t-test Interpreting Hypothesis Testing Results Confidence Intervals Independent Samples t-test Dependent Samples t-test Correlation Simple Linear Regression Probability Revisited Chi-square Goodness-of-Fit Test Chi-square Test of Independence _____________________________________________________________________________________________________

Course Grade Worksheet

–  –  –

_______________________________________________________________________________________________

TOTAL WEIGHTED COURSE PERCENTAGE:

FINAL COURSE GRADE:

–  –  –





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