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Statistics for the Behavioral Sciences Department of Psychology New York University Class meets: T & R 9:30-10:45, GCASL C95 Professor: Elizabeth A. Bauer, Ph.D. Email: [email protected] Office: 407 Meyer

PSYCH-UA.10 Section 007 Fall 2021 4 Points

Office Hours: M, W & R 11:30-12:30 and by appointment

Course goals: This course aims to provide psychology majors with tools for evaluating data from different kinds of studies. 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, as well as SPSS. Textbook: Cohen, Barry. (2013). Explaining Psychological Statistics (4 th 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 4 versions. On the next page, click on “Online Access” for the top selection. Then click on Ebook Central. 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. If you’re unsure, you can check out videos on YouTube to help you understand your calculator. It’s useful to have your calculator available during recitation or lecture. It’s helpful to make sure you have extra batteries with you on exam days. Poll Everywhere: Student Response System: We will be using a student response system with questions to increase student engagement and gauge student comprehension during lecture. The system we’ll be using is 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 register everyone who is enrolled on NYU LMS; if you join the class late you may not be enrolled, so please contact me to make sure. Poll Everywhere will not start to count for credit until the day after the add/drop period ends. Please remember that we will be using 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 LMS under Content>Lab/Recitation. Note: Check Albert to be sure where your recitation is located. Requirements & Grading: Grades will be based on the 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 participation (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 LMS 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. Exam questions and problems will be similar to the ones you will have worked on in class and in recitation, including the Poll Everywhere multiple choice questions. 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, you will receive a failing grade for that exam, and you will be reported to Academic Affairs. 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 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 misreading the syllabus, 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 and be reported to Academic Affairs. SPSS Assignments (20% of final grade) There are four SPSS assignments during the semester posted on NYU LMS. 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 LMS>Content>SPSS Assignments. ● Assignments will be submitted to a program called Gradescope. Information on Gradescope is posted on NYU LMS under Content>SPSS Assignments and the link for Gradescope can be found under NYU LMS>Content>SPSS Assignments>Gradescope. Assignments must be submitted to Gradescope as pdf files only. ● Assignments may be submitted earlier than the due date, but they must be submitted by 11:59pm on the day they are due. Any time after that will be considered late. ● The entire assignment must be submitted 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 submitted through Gradescope. ● Under no circumstances should assignments be emailed to me or your TA. ● Please follow the rules for the assignments listed on the document “A One Stop Shop for Nailing your SPSS Assignments” posted on NYU LMS>Content>SPSS 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 in the One Stop Shop document). Assignments will not be accepted without this header. ● Late Penalties: o o

After 11:59pm on the due date but before 6:00am the next morning: -1 pt After day assignment is due (from 6:00am on): -3 pts. per day up to 4 days after due date.

o o

After 4 days past due date: Assignments will not be accepted after 4 days past due date. Assignments will not be considered as “accepted” until we have the integrity header.

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. Questions are graded only for participation, not accuracy. **If extenuating circumstances arise, please let me know immediately. Please note: If you are having technical problems with Poll Everywhere, please consult the company. I am unfortunately unable to fix these issues. Recitation/Lab Participation (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.

Extra Credit: There are no extra credit assignments for this course. However, students can receive one point of extra credit on their final grade for participating in one research hour through SONA (one point is the maximum amount of extra credit). Information on this is posted on the Announcements page on NYU LMS. Please note: this only applies to research done through the SONA program; we cannot give credit for other types of research. EXTRA HELP: 1) Practice problems. There are optional textbook problem sets posted on NYU LMS (under Content>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. 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: If someone misses a lecture or a recitation, it’s easy to fall behind. Students who do not come to lab or lecture regularly will not be able 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 Student Accessibility (CSA) 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 CSA 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. ● 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. ● 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. ● 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. ● 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. ● 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.

Fall 2021 Schedule ● 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. Week 1

Date

SPSS due

Day

9/2

R

9/7

T

9/9 9/14

R T

9/16 9/21

R T

2

3

4

9/23

SPSS#1* *

R

9/28

T

9/30 10/5 10/7 10/12 10/14 10/19 10/21 10/26 10/28 11/2 11/4 11/9 11/11 11/16 11/18 11/23

R T R T R T R T R T R T R T R T

5 6 7 8 9 10 11 12 13

SPSS#2

SPSS#3

Topic/Activity (Chapter in parentheses with reading adjustments for that chapter) Intro to psych statistics (Ch. 1) Frequency tables, graphs and distributions (cont’d Ch. 1 & Ch. 2) Measures of central tendency and variability (Ch. 3 to mid 84) Standardized scores and the normal distribution (Ch. 3 & 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) Interval est and the t distribution (Ch. 6; Advanced Material 185; no 192-193 t-test for two independent sample means (Ch. 7) Exam #1 (Ch. 1-4) Statistical power & effect size (Ch. 8, no 254-257) *NO CLASS/MONDAY SCHEDULE - SEE BELOW* Power continued (Ch. 8) Linear Correlation (Ch. 9) Linear Regression (Ch. 10, no 322-327) Regression continued (Ch. 10) Matched t-test (Ch. 11, no bottom 350-353 top) Exam #2 (Ch. 5-8)One-way independent groups ANOVA (Ch. 12, no top 383-385 bot; no 388-392) ANOVA continued (Ch. 12) Multiple Comparisons (Ch. 13, no 430-440) Exam #3 (Ch. 9-11) Linear Contrasts (Ch. 13) Two-way ANOVA (Ch. 14, no 475-477 top; top 479-481 bot; no 485-487)

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

Two group t-tests

Power and effect size Hypothesis testing & power review t-tests in SPSS; correlation Linear regression; Matched t-test One-way ANOVA Multiple comparisons; linear contrasts Two-way ANOVA ANOVA review

11/30 SPSS#4 T Interactions (Ch. 14) Chi-square 12/2 R Two-way ANOVA continued (Ch. 14) T Chi-Square (Ch. 20) Review 12/7 15 12/9 R Exam #4 (Ch. 12-14) 12/14 T Final Review Final Exam Date: Thursday, 12/16/21 from 10:00-11:50am. Location is our classroom unless otherwise announced.

14

The schedule is subject to change. Please check out all announcements & class changes on the NYU LMS site. **SPSS#1** This assignment was changed to be due 9/27/21** * TUESDAY, OCTOBER 12TH IS A MONDAY SCHEDULE. ● TUESDAY LECTURE & RECITATIONS DO NOT MEET THAT DAY. ● MW CLASS LECTURE MEETS THAT DAY. ● MONDAY RECITATIONS MEET THAT DAY....