Statistics for the Behavioral Sciences NYU Syllabus PDF

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Syllabus for spring 2020 general chemistry with Professor Elizabeth Bauer. It includes her policies for in person classes which should be helpful....

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Statistics for the Behavioral Sciences Department of Psychology New York University Class meets: M & W 9:30-10:45, Cantor 102 Professor: Elizabeth A. Bauer, Ph.D. Office: 407 Meyer Office Hours: M, W & R 11:00-12:30 or by appointment

PSYCH-UA.10 Spring 2020 4 Points Section 007 Phone: 212-998-3866 Email: [email protected]

Course goals: This course aims to provide psychology majors with tools for evaluating data from different kinds of studies, in preparation for department lab courses. Students will work on examples of data sets, learn approaches to problems of statistical prediction, and learn the application of statistical reasoning to decision making. Students will learn to analyze data using SPSS and how to write up results in APA format. At the end of the term, students will be familiar with data description, variance and variability, significance tests, confidence bounds, tests of differences of means, correlation of variables, linear regression and nonparametric tests. Textbook: Cohen, Barry. (2013). Explaining Psychological Statistics (4th ed): Wiley & Sons (Kindle edition is fine). This is available for free through the NYU library: Go to NYU Home and select the tab labeled Research. In “NYU Libraries”, search for Explaining Psychological Statistics. Choose the top selection, which has 5 versions. On the next page, again click on the top selection. Click on Check Availability, then click on Ebook Central (NYU Access Only). Then you can read it online or download it as you like. You can also use the third edition, but there are differences in page numbers and problem sets between the two editions, so make sure you check those against the fourth edition. Be aware that the third edition has none of the helpful SPSS information of the fourth edition. Calculators: A scientific calculator is required (preferably Texas Instruments or HP; a graphing calculator is unnecessary). Your calculator must be able to calculate standard deviation (or variance) as a built-in function. It is your responsibility to understand how your calculator works. Calculators should always be brought to lab. It’s helpful to make sure you have extra batteries with you on exam days. Poll Everywhere: Student Response System (sometimes known as a clicker system): We will be using a student response system to increase student engagement and gauge student comprehension during lecture. Instead of using clickers, we will be using a system called Poll Everywhere (https://polleverywhere.com). Here is a link to the student guide: https://www.polleverywhere.com/guides/student There is no charge for using this system this semester and you can use any device you already have – cell phone, iPhone, tablet, or laptop. I will post a link on NYU Classes to register. You must use this link so that we know which class you belong to. Please use your NYU email address for Poll Everywhere. Recitation (aka Lab): During recitation, students will be taught how to use SPSS for data exploration and analysis and how to report results in APA style format. Students will also be able to get assistance from their TA for problems with assignments or any other class problems. In addition, TAs will have office hours so that they are available to students outside of class time. Attendance for recitation is mandatory. Please see “Rules for Recitation” posted on NYU Classes under Resources>Lab/Recitation. Note: Labs usually meet in 194 Mercer, room 304, LC19, in the Tisch building, or 668 Waverly. Check Albert to be sure. Requirements & Grading: Grades will be based on best three out of four exams (52% of grade), a cumulative final exam (22% of grade), four SPSS computer assignments (20% of grade), Poll Everywhere participation (3% of grade) and recitation attendance (3% of grade). Exams (52% of final grade) There are 4 in-class exams during the semester. Exams are based upon lectures, readings, recitation examples and SPSS assignments. Lecture notes are posted on NYU Classes but will not necessarily cover all the material presented in lecture or tested on exams. Exams will consist of multiple-choice questions, computational problems and occasionally fill-in or true/false questions. The type of questions and problems that you will have

to answer for the exams will be similar to the ones you will have worked on in class and in recitation. You will be allowed to bring in your calculator and one sheet (back and front) of notes – it must be handwritten and no more than 8.5” x 11”. Anything else (including typed notes, extra post-its, etc.) will be confiscated and you will receive a failing grade for that exam. Exams cannot be made up but your lowest exam score will be dropped. Attendance for exams is mandatory. The possibility for a makeup exam will only be considered in the event of a serious, documented medical or family emergency. I must be notified by phone or email before the exam time and documentation must be provided. There will be no make ups without appropriate documentation. Make up exams will not be provided in the event of transportation problems, alarm clock failures, etc. You will receive a zero for a missed, unexcused exam. Final Exam (22% of final grade) The final exam is CUMULATIVE and will be the same format as the other exams. You will be allowed to bring in your calculator and one sheet (back and front) of notes – it must be handwritten and no more than 8.5” x 11”. Anything else (including typed notes, extra post-its, etc.) will be confiscated and you will receive a failing grade for the final. SPSS Assignments (20% of final grade) There are four SPSS assignments during the semester posted on NYU Classes. There is also a document that will help you with these assignments called “A One Stop Shop for Nailing your SPSS Assignments” posted on NYU Classes under Resources>Assignments.  Assignments must be handed in to me in class before lecture starts on the date they are due (check your syllabus for the due date). Once I’ve started lecturing you must wait until the end of class to turn in your assignment. Any time after class starts will be considered late.  The entire assignment must be handed in to receive credit as on time; any pages submitted later will make your entire assignment late.  An assignment is not considered “accepted” until it is in my hands.  Under no circumstances should assignments be handed in to your TA.  Nothing should ever be put in my mailbox; you must bring late assignments to my office.  Please follow the rules for the assignments listed on the document “A One Stop Shop for Nailing your SPSS Assignments” posted on NYU Classes under Resources>Assignments.  We take Academic integrity very seriously. Please make sure to review the information on Academic Integrity at NYU: https://cas.nyu.edu/content/nyu-as/cas/academic-integrity.html  Every assignment should have a proper header, which includes the Academic Integrity statement of NYU (this is posted on NYU Classes). Assignments will not be accepted without this header.  L Penalties: You will receive a notice (usually an L) for any of the following: o Assignment turned in after the start of class but before 12:30 o Assignment is not stapled o Assignment header is missing Ls are cumulative across all assignments throughout the semester. You may receive one “free” L with no penalty. After that, 2Ls will receive a deduction of 1 point, 3Ls will receive 2 points deduction, and 4Ls will receive 3 points deduction. 

Automatic Late Penalties: Day assignment is due but after 12:30: -1 pt. After day assignment is due: -3 pts. per day up to 4 days after due date. After 4 days past due date: Assignments will not be accepted after 4 days past due date.

Late penalties will not apply if you have an excused (documented) absence that is cleared with me before the due date.

Poll Everywhere Participation (3% of final grade) Questions will be asked during lecture using Poll Everywhere. This will be used to encourage participation and gauge your comprehension in the class. There will be no makeups* for Poll Everywhere, so the grading is lenient. Your grade on Poll Everywhere at the end of the semester will be used to determine your participation grade using the following formula: Number of points you will receive (up to 3 points on final grade) = Poll Everywhere grade/80*3 Therefore, a Poll Everywhere grade of 80% or better = all 3 points credit. (This means you could miss 20% of the questions and still receive full participation credit.) There will be approximately 2 questions every class, so you can usually miss approximately 8 questions with no penalty. This is equivalent to 4 unexcused absences from lecture. Note that these values are approximate and may change depending on the number of questions I ask – you still need to answer 80% of the questions. Please note: If you are having technical problems with Poll Everywhere, please consult the company. I am unable to fix these issues. *Special exceptions (after 20% of the questions are missed) will only be made in the event of religious holidays or extended illness. This must be discussed with me beforehand. Recitation Attendance (3% of final grade) Recitation attendance is mandatory. You may have one unexcused absence. After that, there is a 1 point deduction for each unexcused absence. Attendance is taken at every recitation. Grading Scheme:

A: 91.60 and up A-: 88.60-91.50 B+: 85.60-88.50 B: 81.60-85.50

B-: 78.60-81.50 C+: 75.60-78.50 C: 68.60-75.50 C-: 66.60-68.50

D+: 64.60-66.50 D: 59.60-64.50 F: Below 59.60

Please note: This scale is approximate and subject to change. These grades already include rounding; no further rounding will be done.

Research Participation/Extra Credit: There is no research requirement for this course. However, students can receive one point of extra credit on their final grade for one research hour (one point is the maximum amount of extra credit). Information on this is posted on the Announcements page on NYU Classes. EXTRA HELP: 1) Practice problems. There are optional textbook problem sets posted on NYU Classes (under Resources>Recommended HW & Answer Keys) for practice. 2) See me. If you start to struggle at any time during this course, please seek assistance immediately so you don’t fall behind. Failing the first exam is a sign that you need immediate help in the course. I won’t be able to help you if you come to me at the end of the semester. I am available to meet with students during office hours and sometimes outside of office hours by appointment. 3) See your TA. You can also see your TAs during their appointed office hours. 4) Visit The University Learning Center. The ULC has free tutoring for this course: (http://www.nyu.edu/students/undergraduates/academic-services/undergraduate-advisement/academicresource-center/tutoring-and-learning.html). Please note: Students who do not come to lab or lecture regularly should not expect to make up labs or lessons during office hours. However, if you read the book, we are happy to answer specific questions you may have on the material.

Students with Disabilities: The Henry and Lucy Moses Center for Students with Disabilities (CSD) determines qualified disability status and assists students in obtaining appropriate accommodations and services. Any student who needs a reasonable accommodation based on a qualified disability is required to register with the CSD for assistance: https://www.nyu.edu/students/communities-andgroups/students-with-disabilities.html

Helpful Tips to Succeed in Class: 

Read the assigned readings before class.

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Participate in class regularly. Think, ask questions, and make comments about the material being discussed. This does not necessarily mean that you have to talk out loud. Writing questions and comments in your notes can also be effective – if you follow up on your comments and get answers to your questions.

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Actively take notes in class (and while reading the text). Active note-taking means that you take the time to think about your personal understanding of the material and write that down. (Note: the ability to take notes this way is much easier if you come to class having already read the material). Also, when you study the material after class you should revise your notes as you develop a better understanding of the concepts and theories being discussed.

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Electronic devices have a negative effect on learning. Electronic devices are very distracting, to yourself and other students. So aside from the Poll Everywhere questions, or if you need them because of a disability, it is recommended that you put them away. Hand-written note taking is much better for learning.

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Distribute your studying rather than cramming. Research in memory has demonstrated that people learn material more effectively when they spread out their learning over a period of time rather than cramming it all into one session.

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Study in groups. This makes learning the material more fun. Importantly, research has shown that studying in groups helps people learn because they teach the material to each other.

Please note: There are no extra credit assignments for this course, but you can earn one point of extra credit for one hour of research participation (one point is the maximum amount of extra credit).

Spring 2020 Schedule   Week

Section C in the textbook is about SPSS. This section is not required reading, but highly recommended, especially for earlier chapters when you are just learning SPSS. This section will help you with your SPSS assignments. “Advanced Material” is not required reading unless specified. Date

SPSS due

Day

2

1/27 1/29 2/3 2/5

M W M W

3

2/10 2/12

M W

1

4

2/19 2/24

5 6 7 8 9 10 11 12 13 14 15

SPSS#1

W M

Topic/Activity (Chapter in parentheses with reading adjustments for that chapter) Intro to psych statistics (Ch. 1 & 2) Frequency tables, graphs and distributions (Ch. 2) Measures of central tendency and variability (Ch. 3 to mid 84) Standardized scores and the normal distribution (Ch. 4 to mid 122) Ch 4 continued Intro to hypothesis testing; one group z-test (Ch. 5; Advanced Material to mid 167) Intro to hypothesis testing continued (Ch. 5)

No recitations this week Variables, graphs, data entry; intro to SPSS Week 1 cont’d; Central tendency, variability, standard scores Normal distribution; hypothesis testing One group t-tests and confidence intervals

Interval est and the t distribution (Ch. 6; Advanced Material 185; no 192-193 Exam #1 (Ch. 1-4) 2/26 W M t-test for two independent sample means (Ch. 7) Two group t-tests 3/2 3/4 W Statistical power & effect size (Ch. 8, no 254-257) SPSS#2 M Power continued (Ch. 8) Power and effect size 3/9 3/11 W Linear Correlation (Ch. 9) M Linear Regression (Ch. 10, no 322-327) Hypothesis testing & 3/23 3/25 W Regression continued (Ch. 10) power review 3/30 M Matched t-test (Ch. 11, no bottom 350-353 top) t-tests in SPSS; 4/1 W Exam #2 (Ch. 5-8) correlation Linear regression; 4/6 M One-way independent groups ANOVA (Ch. 12, no top 383Matched t-test 385 bot; no 388-392) ANOVA continued (Ch. 12) 4/8 W SPSS#3 M Multiple Comparisons (Ch. 13, no 430-440) One-way ANOVA 4/13 4/15 W Linear Contrasts (Ch. 13) Multiple comparisons; 4/20 M Exam #3 (Ch. 9-11) linear contrasts 4/22 W Two-way ANOVA (Ch. 14, no 475-477 top; top 479-481 bot; no 485-487) SPSS#4 M Interactions (Ch. 14) Two-way ANOVA & 4/27 4/29 W Two-way ANOVA continued (Ch. 14) ANOVA review M Chi-Square (Ch. 20) Chi-square 5/4 5/6 W Exam #4 (Ch. 12-14) 5/11 M Final Review Review Final Exam: Friday, May 15th, 2020 at 8:00-9:50 am. Location is lecture room unless announced otherwise.

The schedule is subject to change. Please check out all announcements & class changes on the NYU Classes site....