PSYC 2100 LUNDQUIST

greatest christmas song ever

EL 1100 2100 2500

PSYC 2100WQ sec 04 05 06 07
Principles Of Research In Psychology, Fall 2010
UConn Storrs Campus, ARJ 311
TUE THUR 5:00-6:15
Eric Lundquist

EXAM 3 RESULTS AND COURSE GRADES
FINAL GRADES HAVE BEEN POSTED TO PEOPLESOFT.

FINAL EXAM THURSDAY 12/16/10, 3:30-5:30 PM, ARJ 311
NOT ARJ 143 LIKE I ACCIDENTALLY SAID ON THE REVIEW SHEET
FINAL EXAM REVIEW SESSION TUESDAY 12/14/10, 7:30-9:00 PM, BOUS 160
FINAL EXAM REVIEW INFO

EXAM 2 EXTRA ASSIGNMENT RESULTS
Includes score out of 20, revised Exam 2 score with HALF the extra points added (so adding 10 points max, up to 40), and new Exam 2 letter grade.

EXAM 2 EXTRA ASSIGNMENT in Microsoft Word format

EXAM 2 RESULTS
EXAM 2 REVIEW INFO
NOTE: Bring textbook for tables and formulas, also calculator and #2 pencil!

EXAM 1 EXTRA ASSIGNMENT RESULTS
Includes score out of 20, revised Exam 1 score with HALF the extra points added (so adding 10 points max, up to 40), and new Exam 1 letter grade.

EXAM 1 EXTRA ASSIGNMENT in Microsoft Word format

EXAM 1 RESULTS
EXAM 1 REVIEW INFO
Bring to exam: textbook for chi-square table, calculator, #2 pencil!

These are listed with the readings below but for convenience here are the PowerPoint slides from class:
Introductory material and experimental methodology
Data display and descriptive statistics

For those using the 7th edition of the textbook, here are the corresponding page numbers for that edition:
GW Ch. 1; Ch. 18 (pp. 582-601, 608-609 summary points 1-12) describes the chi-square test in more detail than was offered in lecture
GW Ch. 1; Ch. 8 (pp. 226-231, 237-239, Box 8.1 p. 234); Ch. 16 (p. 510) includes null and alternative hypotheses, Type I and II errors, reliability and validity
GW Ch. 2, 3, 4
GW Ch. 5 (only pp. 138-143)
Here is the corresponding review sheet for the first exam which is fairly similar to ours (ONLY use this for finding page numbers, other than that go by the Fall 2010 review link above):
Spring 2009 review info for first exam

[Guinness Brewery] [ANOVA seat]



Office: BOUS 136
Office Hours: Mon Wed 4:00-5:00, and by appointment
Phone: (860) 486-4084
E-mail: Eric.Lundquist@uconn.edu

LAB SECTION INFORMATION
SECTION TIME ROOM INSTRUCTOR EMAIL
04 MON 11:00-12:50 WGC 300-A Dobromir Dotov dobromir.dotov@uconn.edu
05 TUE 9:00-10:50 WGC 300-D Zsolt Palatinus zsolt.palatinus@uconn.edu
06 WED 1:00-2:50 WGC 300-A Lauren Gindin lauren.gindin@uconn.edu
07 THU 9:00-10:50 WGC 300-D Alex Demos alexander.demos@uconn.edu

READING:
  1. REQUIRED: Gravetter, F. J., & Wallnau, L. B. (2009) Statistics for the Behavioral Sciences (8th ed.). Belmont, CA: Wadsworth Cengage. (ISBN-10: 0495602205; ISBN-13: 9780495602200)
  2. OPTIONAL: Trochim, W. M. K. (2005). Research methods: The concise knowledge base. Cincinnati, OH: Atomic Dog Publishing. (ISBN-10: 1592601464; ISBN-13: 9781592601462)
  3. REQUIRED: On-Line Readings and Reserve Readings (TBA)

GRADING:
Lecture:   65%  
  Exam 1 20% approximately 6th week of class (~ Thu 10/7)
  Exam 2 20% approximately 11th week of class (~ Thu 11/11)
  Final Exam 25% THURSDAY DECEMBER 16, 3:30 PM
Lab:   35%  
  Homework    
  Group Project Presentation   Data Collection Night 1: Wednesday October 20, 6:30-8:30 PM
Data Collection Night 2: Tuesday October 26, 6:30-8:30 PM
Project Presentation Night: Wednesday December 8, 5:00-6:30 PM
  Final Paper    

This course is an introduction to the methods and tools of psychology as a science. The course introduces the basics of research design and statistical analysis. Much of lecture time will be spent considering the statistical techniques appropriate to various research designs for addressing questions in psychology. You will need a calculator.

Lab will involve weekly exercises in research techniques and appropriate analyses of data, as well as a semester-long small-group research project to be presented on
Wednesday December 8 from 5:00 PM to 6:30 PM
at which attendance is required. Attendance is also required at the data collection sessions on
Wednesday October 20 and Tuesday October 26 from 6:30 to 8:30 pm.
All group research projects must include at least two independent variables (at least one of which must be manipulated) and at least three dependent variables.

Students must obtain a passing grade (D- or better) in both class work and lab work to pass the course. Also, please note that you must receive a passing grade in the "W" component to pass the course. An "F" in the lecture component, in the laboratory component, or the writing portion of the class will result in a grade of "F" for the entire course.

Academic Misconduct in any form is in violation of the University of Connecticut Student Conduct Code and will not be tolerated. This includes, but is not limited to: copying or sharing answers on tests or assignments, plagiarism, having someone else do your academic work, and allowing someone else to pass off your work as their own. Depending on the act, a student could receive an F grade on the test/assignment, F grade for the course, or could be suspended or expelled from the university. The University's Student Conduct Code is on-line at http://www.dosa.uconn.edu/student_code.html; refer to http://www.dosa.uconn.edu/community_standards.html for details on the University's policies concerning academic misconduct (plagiarism, cheating, etc.)

Study Tips that I compiled for my PSYC 132 students long ago, which may be of some general use. Ignore the parts specifically about PSYC 132, naturally. (These ideas may be less relevant in a class like PSYC 2100, but I'll leave that for you to judge for yourselves.)

LINKS OF INTEREST: Some web pages of at least tangential relevance to psychology class topics.

Eric's personal homepage: nothing especially interesting, unless you want to browse the stuff that I browse.

The importance of stupidity in scientific research: a short essay by biologist Martin Schwartz that could help prepare you for grad school, and make you feel better about your research project.

I Really Don't Hate Christmas: Dr. Doofenshmirtz's big number from Phineas And Ferb's Christmas Vacation which I strongly recommend you watch in its entirety. For the uninitiated, the kids are genius inventors who try to come up with ways to make the most of every day, their sister always tries to catch them and get them in trouble, and their pet platypus Perry is secretly a member of a spy organization in which capacity he combats the supervillain Dr. Hans Doofenshmirtz. Just accept it.


TOPICS AND READING ASSIGNMENTS:
to be updated throughout the semester
GW = Gravetter and Wallnau
Additional links are optional for elaborating on class discussions.

CLASS SYLLABUS in Microsoft Word format, should you lose your original.

TOPIC READING
Science, psychology, & statistics GW Ch. 1; Ch. 18 (pp. 607-625, 633-634 summary points 1-12)
describes the chi-square test in more detail than was offered in lecture
An examination of the supposed "Cmabrigde Uinervtisy" demonstration of the irrelevance of letter order within words.
A list of the types of research designs and corresponding statistical analyses most likely to be used in PSYC 2100 research projects.
The rationale for the concept of Institutional Review Boards (IRBs) is touched on in The Immortal Life of Henrietta Lacks, an amazing book whose story is nicely framed in this quote from Henrietta's daughter.
An ABC News segment about how changing views of gender roles affect adults' and children's ability to answer a riddle, which could be elaborated as a research project.
Experimental design and measurement issues GW Ch. 1; Ch. 8 (pp. 230-235, 242-244, Box 8.1 p. 238); Ch. 16 (p. 524-525)
includes null and alternative hypotheses, Type I and II errors, reliability and validity
PowerPoint slides on introductory material. (You're not seriously thinking that just reading these slides would be a substitute for what I talk about in class, are you?)
Trochim's web page on reliability and validity, with the diagram shown in class plus a bit of explanation that goes beyond the brief mention you'll see in GW pp. 524-525. (Ignore the bottom part about the 2x2 table, etc.)
Trochim's web page on scales of measurement, in case you're looking for more explanation of nominal, ordinal, interval, and ratio scales.

PrimeTime Live clip about Gregory Berns's research on conformity and independence, including the clip of the Man With A Toolbelt.
  • Neurobiological Correlates Of Social Conformity And Independence During Mental Rotation - the paper the TV clip was based on.
  • New York Times story on Berns's research - interesting to see the difference between a scientific article and a popular press account.
  • Wikipedia's article on Asch's original experiments, from which you can link to Milgram's obedience study as well if you want to see what all that fuss is about, though it's not at all the same thing. (FYI the similarly famous Stanford Prison Experiment by Zimbardo is more a silly stunt than a scientific experiment so don't confuse them.)
    • Data display and descriptive statistics GW Ch. 2, 3, 4
    An illustration of the three types of kurtosis
    My web page about everyone's favorite monotreme
    PowerPoint slides on data display and descriptive statistics
    Why the sample variance has a denominator of N-1 instead of N: a proof that dividing the sample sum of squares by N-1 instead of N gives an unbiased estimate (i.e. accurate in the long-run average) of the population variance. This is purely for the mathematically inclined -- others should steer clear. The "expectation" operator notated as E(X) means roughly the long-run average of X or the mean of all X's in the population, but note that doesn't necessarily indicate a mean of some score -- X could be a variance for instance, and then E(X) would be the population value of that variance, as it is in this proof. If that helps clear anything up. (Other pages from the same book follow but are unrelated to this topic.)
    Z-scores and standardized distributions GW Ch. 5
    END OF EXAM 1 MATERIAL
    Correlation Ch. 16 (pp. 520-535)
    [correlation coefficient formula that I use:
    r = covxy / (sx*sy)
    where covxy = SPxy / (N-1), and SPxy = Σ(X-Mx)(Y-My),
    which means the SUM of everybody's (X-Mx)*(Y-My),
    and Mx and My are the mean of X and Y respectively (cause you can't type an X with a bar over it.)]
    A diagram on this page shows some scatterplots and the correlation coefficients calculated from them, just to give you an idea of what typical correlations might look like, but also of how unpredictable they might be if you don't look at your data in a scatterplot. This point is made even more obvious by another diagram further down the same page, which shows some very different sets of data that all give the exact same value of the correlation coefficient r.
    Normal distribution and probability as area under curve GW Ch. 6
    The opening scene of Rosencrantz And Guildenstern Are Dead by Tom Stoppard, in which an unlikely extended run of coin flips gives rise to some existential angst. Note that even though each coin flip is perfectly in line with the "laws" of probability, we still don't quite believe this run of events should occur. (If you're curious, the play is a modern comedic take on two minor characters from Shakespeare's Hamlet who are unwittingly involved in a plot to kill Hamlet; this 1966 update focuses on their misadventures before their own eventual deaths.)
    A diagram of a "quincunx", sometimes called a "Galton Board" after its inventor Francis Galton. It's a wooden board with pins inserted into it, and when a ball is dropped into the top it will bounce randomly either right or left at each pin it encounters. Most of the balls will bounce about an equal number of times in both directions, canceling out the left and right directions and landing in the middle. By chance, some of them will bounce to the left or the right more times, landing further from the middle. The end result is the accumulation of balls forming a normal distribution, which shows the decreasing likelihood of extreme patterns of bouncing. Just for overkill, here's a video that shows a quincunx in action, where something more sand-like than ball-like is poured through the opening; hey, if you find a better demonstration, send it along -- at least this one's really short.
    Distribution of sample means GW Ch. 7
    Deriving the estimate of the standard error of the mean: something you don't need to be able to do at all but may be curious about, and if you are, it's explained clearly in section 10.17 of this text by Glass and Hopkins.
    Hypothesis Testing GW Ch. 8, Ch. 16 (pp. 537-540)
    Some somewhat shocking quotes appear on p. 127 of this chapter, if you can take it.
    How many observations does it take to disconfirm the hypothesis that "all dogs have four legs"?
    T-test for one sample GW Ch. 9
    T-test for 2 related samples GW Ch. 11
    END OF EXAM 2 MATERIAL
    T-test for 2 independent samples GW Ch. 10
    A clear and interesting lecture on significance tests vs. effect size measures by Bruce Thompson, one of the smartest people working in statistics today. It's purely optional for this class but it is understandable and closely related to things I've said in lecture, so it may actually help you understand the material! If you get a chance just let it play for a bit and see if you get sucked in. It's an hour long and I think you'll easily follow the first forty minutes, probably the whole thing.
    Book Review of The Cult Of Statistical Significance from the journal Science from June 2008. This one-page article raises many of the issues I mention during the semester about the misplaced emphasis psychology places on null hypothesis significance testing. Optional, of course, but pretty accessible.
    Confidence Intervals (Estimation) GW Ch. 12
    Notes on Confidence Intervals: this was written for my graduate stats class and may be more detail than you want to see, but it does try to capture the meaning and interpretation of confidence intervals in the way I tried to do in class. If the textbook isn't clear enough, have a look and see if this helps. (Some of the discussion is about a statistic called b that appears in regression analysis, but you don't have to know anything about that -- it all applies equally well to the sample mean M.)
    Analysis Of Variance for more than two independent samples GW Ch. 13
    ANOVA notes: Here is a summary of the way I explain ANOVA in class, along with my (simpler) versions of the formulas that you'll be using in the exam. (Still bring your book though!) And here is the more nicely formatted (and probably more accurate, in terms of how the Greek letters look) Microsoft Word version.
    Understanding ANOVA Visually: An animation that's helpful for understanding what's going on in ANOVA conceptually.
    Analysis Of Variance for repeated measures (related samples) GW Ch. 14
    REPEATED MEASURES ANOVA notes in Microsfot Word format.
    Analysis Of Variance for factorial designs and interactions GW Ch. 15
    Chi-Square and non-parametric tests GW Ch. 18 & 20
    Message from Skip Lowe, Psychology Department Head, Fall 2008:

    2100WQ Evening of Psychological Science
    From:Charles Lowe
    Wednesday, December 3, 2008 11:13:30 PM
    To:PSYFAC-L@LISTSERV.UCONN.EDU

    Colleagues,
    Because I don't have access to the 2100WQ TA list, I'm sending this email to the entire department.
    I want to acknowledge the absolutely terrific job that our undergraduate students enrolled in 2100WQ did this semester. Here is one comment from one of our faculty colleagues....
    "...just have to say how impressed I was with the 2100WQ event tonight. The kids did great and the atmosphere was incredibly positive and upbeat. I was blown away. Congrats to everyone whose hard work made it what it was! I suspect that the TA's and the instructors have really done a superb job."
    I echo this colleagues' comments. So thanks are due to...
    1. The faculty who were instructors this semester for 2100WQ (Lundquist, Magnuson, Mellor, and Quinn). My thanks, or more appropriate, the department's thanks for a job well done.
    2. The graduate students who served as TA lab instructors or as our 2100WQ TA coordinator (Jennifer Bailey, Julia Blau, Lyndsey Collins, Sarah Copeland, Alicia Dugan, Randi Garcia, Rob Isenhower, Greg Kerwin, Stephenie Petrusz, Amanda Snook, Damian Stephen, Curtis Walker, Pamela Whitney, Ya-Ching Yeh, and Jessica Gallus as our 2100WQ TA Coordinator). I talked with the majority of our 2100WQ students, and all of these students provided rave reviews for our TA lab instructors. You guys can't imagine how important your contributions are. We had visits to this year's 2100WQ poster presentation night this year from President Mike Hogan, from VPGRE Suman Singha, from our new Dean Jeremy Teitelbaum, from several Associate Deans and other dignitaries as well. All of these important people communicated to me that they were absolutely in awe of what these undergraduate students enrolled in 2100WQ have done. You cannot imagine the good will that tonight created among those administrators who really do matter in deciding our department's future. Every year, indeed twice each year, I make it a priority point to thank our graduate students for their contributions to our department's success. Tonight's 2100WQ poster presentation night is one reason why I do so. The above grad students have made very meaningful and very important contributions to the success of our department this semester, both in the eyes of our undergraduate majors and in the eyes of high level administrators who matter. My sincere thanks.
    3. The undergraduates who are enrolled in Psyc 2100WQ this semester. Please, if you are a 2100WQ instructor or a 2100WQ lab TA, take the time to thank these undergraduates for their good work this semester, and for their contributions to our department, and please express my recognition and appreciation, as Department Head, for their performance this semester.
    All of the above being said, I admit that I take full credit.
    Best,
    Skip

    Some important figures in the history of statistics:

  • Abraham De Moivre around 1730 derived the normal distribution as the limit of the binary distribution when the number of binary decisions (e.g., coin tosses) is infinite.
  • Johann Carl Friedrich Gauss often gets credit for discovering the normal distribution because in 1809 he proved that it described errors of measurement (in astronomy, etc.), which is why the normal distribution is sometimes called the Gaussian distribution.
  • Adolphe Quetelet in 1835 first applied the normal distribution to biological and behavioral traits rather than merely to measurement error, describing the concept of "the average man"; he also invented the Quetelet Index which today we usually refer to as the Body Mass Index (BMI).
  • Francis Galton invented the concepts of correlation and regression around 1886. He also read and wrote at age 2-1/2, went ballooning and did experiments with electricity for fun, mapped previously unexplored African territories, taught soldiers camping procedures and how to deal with wild animals and "savages," tried to objectively determine which part of Britain had the most attractive women, studied the efficacy of prayer empirically, observed the amount of fidgeting at scientific lectures to measure the degree of boredom, invented fingerprinting and weather maps along with the meteorological terms "highs," "lows," and "fronts," coined the phrase "nature and nurture," and pioneered mental testing, twin studies of heritability, the composite photograph, the study of mental imagery, the free-association technique for probing unconscious thought processes, the psychological survey questionnaire, and... umm... eugenics. Oops.
  • Karl Pearson founded modern statistics beginning in the 1890's, inventing the chi-square distribution and test and coining the term "standard deviation" among others; he formalized the calculation of the correlation coefficient (where Galton had arrived at it graphically) and so that calculation bears his name today.
  • George Udny Yule worked on the concepts and mathematics of partial correlation and regression in the 1890's, making multiple regression as we know it possible.
  • William Sealy Gosset in 1908 worked out the distribution of sample means ("standard error" in modern terminology) for cases where the population standard deviation is unknown -- hence he is the inventor of the t-test.
  • Ronald Fisher was a key figure in bridging the gap between the Darwinian theory of natural selection and its underlying mechanism of Mendelian genetics; from about 1915 onwards he also invented experimental design as we know it today, and developed Analysis Of Variance (ANOVA) as a generalization of Gosset's work to more than two groups (Snedecor in his influential early textbook named the 'F' statistic for Fisher).
  • Jerzy Neyman and Egon Pearson (son of Karl) invented and refined many of the concepts of null hypothesis significance testing in the 1930's (e.g. the alternative hypothesis, power, Type II error, confidence intervals), though Fisher had a constant ongoing argument with everything they did -- mainly because it wasn't the way HE did it.

    If you're wondering about classes being canceled due to weather, see http://today.uconn.edu/?page_id=1041 or call 486-3768.