Title: Psych 230 Lecture 1
Psychological Measurement & Statistics
Fall 2012
MWF 10:00 – 10:50
Aero & Mech Engr S202
.Instructor: Rosemary A Rosser, PhD (rrosser@email.arizona.edu)
Office hours: MW 11-12 Psych 101
Teaching assistants: Melissa Soenke and Lauritz Dieckman
Course Description
This is designed as a first introduction to statistics for students seeking grounding in the
quantitative data analysis associated with conducting behavioral research. I have taught
similar courses, including more advanced ones at the graduate level, for 30+ years; and,
weird as it might seem, I love it.
I approach statistics as a tool for furthering the research enterprise. For me, statistics
provides a mechanism for buttressing conceptual arguments; so we will be talking lots
about the research context as well as focusing on the logic and the math of the endeavor. I
have done many kinds of research in those 30 years--from studies of infant cognition, the
acquisition of knowledge in science, spatial cognition, to name a few of the topics. My
approach to the content could be described as "conceptual", but I need to make clear what
that both means and does not mean. First, the course is not designed as a cookbook
application approach where you learn to plug a bunch of numbers into a computer,
crunch the data, and trust the machine to give you some sort of answer. Data are seldom
self-evident, and without a conceptual understanding of the procedures we use--and what
we are assuming about the nature of reality when we use them--those data are apt not to
be interpretable. You will learn how to crunch data, albeit with small data sets, but more
importantly I hope you will learn what the outcome of the crunching tells us about our
phenomena. Second, I do not expect students to necessarily fully appreciate all of the
formal, elegant mathematics that underlie the procedures we employ, i.e., you won't have
to do lots of derivations and formal mathematical proofs. However, some appreciation of
that mathematical foundation is precisely what makes our use of the tool meaningful and
a good fit with our research problems. Formulas are conceptual statements; that will be
part of the presentation. So, you need to be able to follow some of those proofs and
appreciate the interconnection and underlying logic of what we do and what we assume
when we use these procedures. Basically, then, this course is about the logic and rationale
for using statistical procedures in the behavioral sciences and what those procedures
yield. You will learn how the procedures work, what the outcome of the application
means, including the relevant computation. The main focus will be on quantitative data
analysis and specifically on parametric procedures.
It is not assumed that students enter the class with lots of mathematical training. Statistics
is basically arithmetic, though a good working knowledge of algebra is essential as well.
Calculus is very nice but not necessary at this level and with the approach I
take…acquaintance with logic and probability helps though. Also, I have found that
students without a good grounding in math, at least college algebra, experience
considerable difficulty. Check the material in Kirk’s book in Appendix A and test
yourself. There is also a good review of important concepts related to each of the test
questions.
The textbook and reading
The book is Kirk, R.E. (2007). Statistics: An Introduction. Belmont, CA.
Thomson/Wadsworth. I have used various editions of this book and find it to be very
clear and thorough, similar in philosophical perspective to my own and a good resource
should you choose to continue with the subject matter.
Requirements for the course
There will be 4 midterms and a comprehensive final, (25 points for each midterm and 50
points for the final). You may drop one of the midterms but not the final. The midterms
and final will be primarily multiple-choice items, some short answer, and some simple
computation. We plan to administer the exams on line. There will also be extra credit, on
line quizzes for those who wish to improve a midterm score. If you get 80% or more on
an extra credit quiz, you will earn 10 points toward your final point total. There are no
partial points on the extra credit quizzes. In addition, there will be “pop” quizzes for an
opportunity to earn extra credit points. The purpose of these is to reward those who attend
class and keep up with the posted notes. Students who do not attend class regularly tend
to experience difficulty with this material.
Study Guides
I will provide study guides for all the exams, which will be posted to the website, to help
you master the material. Note also that all my lectures will be posted prior to our
discussion of a topic. These are not power points, as I consider them too sketchy; they are
complete textual outlines of what I will talk about. The only thing missing in them will be
formulas, derivations, diagrams, etc. If you copy the notes and bring them to class, you
can easily fill in the additional mathematics. There will also be practice problems posted
on the website also; and the solutions will be available too. These exercises will not be
graded, but they offer a way for you to check your understanding and progress with the
key concepts and to practice with the mathematics.
Grading and Policies
The midterms are 25 points each (3 x 25 = 75)
The comprehensive final is worth 50….for a total of 125 points possible. My grading
breakdown:
90% and up is an A
80%-89% is a B
70-79% is a C
60-69% is a D
And less than 60% is an E
Attendance is not required, but it is recommended. Many students find this to be a
difficult course; attendance might make it less so. Also, you won't have the computational
fill-ins in the notes if you are not there. And, to reiterate, sometimes I give “pop” quizzes
or problems, which you would not be eligible for if you are not there.
Some nuts and bolts: I will not do make-up exams so do not miss them, and I don't
typically give incompletes unless you are in danger of croaking. Note also that this is
cumulative content and thus this isn't a crammable-for kind of course. We also have help
in the form of weekly reviews through ThinkTank, which students have found invaluable.
We also must address the problem of academic integrity, especially since the exams are
on-line. For a fuller discussion of policy, I refer you to the u of a website. But briefly, it
is not expected that you collaborate in test taking---studying of course, but do not
collaborate on the tests. I do realize that you may consult your notes during an on-line
exam. However, given the length of the exams and the nature of the items, it would be
very difficult to do well on one of my tests just by checking notes at the time of testing.
Studying with the guides I will supply is a better strategy. We will have procedures built
into to the tests themselves designed to detect any cheating. Should I find “cheaters” or
“collaborators”, I am required by university policy to report such conduct to the Dean of
Students. I will introduce you to my “Cheater-eater” later this week.
The course schedule
The following are the dates that topics will be discussed, when exams take place; and the
C notations indicate what chapters in the text are covered.
Aug 20: Introduction to the course
22-24 Logic of science (C1)
27-29 Principles of measurement (C2)
31 Frequency distributions and introduction to central tendency
Sept. 3 Holiday—no class
5 Central tendency and review
7 Midterm #1
10-12 Variability (C4)
14 Extra credit quiz
17-19 More on variability
21 The normal curve and z-scores (C4 and parts of C9)
24 Z-scores continued
26 Review for midterm
28 Midterm #2
Oct 1-8 Correlation (C5)
10 The beginning of regression (C6)
12 Extra credit quiz
15-17 Regression continued (C6)
19-22 Transition to probability (C7, 8, 9)
24 Review for midterm
26 Midterm #3
29-31 Probability & inference (C10)
Nov 2 Extra credit quiz
5-9 More on inference (C11, 12, 13)
12 Holiday—no class
14 Catch up and review
16 Midterm #4
19-21 More inference and catching up
23 Thanksgiving—no class
26 Extra credit quiz
28-Dec 3 Continuation of inference
Dec 5 Comprehensive review for the final
Day of scheduled final: The final exam