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Experimental Psychology 225 Lecture 1, Fall 2004

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Title: Experimental Psychology 225 Lecture 1, Fall 2004


1
Experimental Psychology 225 Lecture 1, Fall 2004
Course Instructor John Curtin Teaching
Assistants Katie Graf Estes Joanne
Hogle Stephanie Schell Chris Ripley
2
Todays Outline
  • Introductions
  • Go over syllabus policies
  • Collect data for Experiment 1
  • Out of here a few minutes early (hopefully! -)

3
Introductions
Instructor John Curtin, John, Professor
Curtin, Dr. Curtin Office Room 326
Psychology Office Phone 262-0387 Office Hours
Tuesdays 1030-1230 EMail
jjcurtin_at_wisc.edu
4
Teaching Assistants/Lab Instructors
Teaching Assistant Katie Graf Estes Office
Room 528 Psychology Office Phone
262-9942/265-0592 Office Hours MO
1230-130 WE 200-300 EMAIL
kmgraf_at_wisc.edu Teaching Assistant Joanne
Hogle Office Room B30 Psychology Office Phone
262-4028 Office Hours TU/TH 230-
330pm EMAIL jmhogle_at_wisc.edu Teaching
Assistant Stephanie Schell Office Room 623
Psychology Office Phone 262-6647 Office Hours
FR, 11am-1pm EMAIL saschell_at_wisc.edu
5
Your turn.
6
The Syllabus
  • Course website
  • http//dionysus.psych.wisc.edu/coursewebsites/psy2
    25/psy225.htm
  • Lecture slides (ppt and pdf formats) available
    evening before (or sooner). Announce on class
    listserv
  • Provides updates to the syllabus, handouts, etc.
  • Can view directly or print out hard copy
  • Course email listserv
  • fall225-curtin_at_lists.students.wisc.edu
  • Will distribute information frequently
  • Use as discussion list for class
  • Copy sent to TAs and me

7
Class Organization
  • Two 1 ¼ hour lectures/week led by me
  • One Discussion (1 hour) and one Lab (2 hours)
    per week w/TA
  • No distinction between discussion and lab. Think
    of them as three hours of applied instruction
    each week
  • Not specifically designed to answer questions
    about lecture
  • Think of lecture and lab as independent but
    related courses covering same material from
    different perspectives
  • Lecture will cover research methodology/statistics
    at conceptual/abstract level
  • Lab will teach from a hands on practical level
  • Many topics will be covered in both lecture and
    lab. However each will also contain unique
    material
  • Lab will also emphasize writing instruction
  • Timing of coverage will vary across lecture and
    lab

8
Course Objectives
  • Introduce the research process in Psychology
  • Understand and critically evaluate published
    research (informed consumer)
  • Learn to design, conduct, and analyze research
    projects
  • Learn to communicate research results (written
    and oral)
  • Learn scientific writing
  • Critical consumer of information in general
    (e.g., politics, policy)

9
Required Recommended Texts
  • Ray, W. J. (2003). Methods Toward a science
    of behavior and experience (7th ed.). Pacific
    Grove, CA Brooks/Cole Publishing Company.
  • American Psychological Association. (2001).
    Publication manual of the American Psychological
    Association (5th ed.). Washington, DC American
    Psychological Association.
  • Recommended Text Kirkpatrick, L. A. Brooke,
    C. F. (2003). A Simple Guide to SPSS for Windows
    (revised ed.) Belmont CA Wadsworth/Thomson
    Learning.

Available at University Bookstore and Underground
Textbook Exchange. Used APA manuals available
on Amazon
10
Grading Policies
Examinations
  • Three exams during lecture for 350 points (100,
    100, 150)
  • Two sections multiple choice and short
    answer/essay,
  • Third exam will be somewhat (50 pts) cumulative
  • Make up exams will be given for those with
    legitimate, verifiable excuses (i.e., illness
    with doctors note). Make-up exams will be
    different and more difficult!
  • Will go over exams in class following exam
  • Scheduled exam dates are Sept. 30th, Nov. 11th,
    and Dec. 17th _at_ 225PM) Dates are subject to
    change

11
Grading Policies
Class exercises/homework
  • Occasionally, I will ask you to complete
    something before class in order to facilitate
    class discussions or to reinforce learning of
    specific points.
  • Graded as complete or incomplete. No late class
    exercises will be accepted.
  • Class participation/Expression of ideas
  • I am reserving 20 pts for class participation
  • I will be open for suggestions on the format
    (more on this later)
  • My current plan is
  • 2 pts per session
  • Cant get more than 2 pts per session
  • 10 sessions gets all points

12
Grading Policies
  • Laboratory papers
  • Six primary writing assignments based on
    experiments you conduct (3 graded, 3 P/F)
  • Assignment 1 Method results P/F 5pts
  • Assignment 2 Method results Graded 50pts
  • Assignment 3 Introduction P/F 5pts
  • Assignment 4 Introduction, results Graded 75
    pts
  • Assignment 5 Discussion P/F 5 pts
  • Assignment 6 (IP) Full scientific report
    Graded 125 pts
  • Graded assignments will be assessed a penalty of
    10 deduction per day late
  • P/F assignments not accepted late
  • The 225 CURVE
  • How it works

13
Instructor/TA Evaluation
  • Evaluations of course progress, and teaching
    style of instructor/TAs
  • Allows for online changes in course that benefit
    you (and me)
  • Anonymous Use Student Number and I will not
    decode until end of class
  • Will provide 1 point for each one completed.
    Easy way to get points added into your final grade

14
Attendance
  • Strongly encouraged but not required
  • Much material in lecture and labs will not be
    covered in the book (e.g., content, critical
    thinking)
  • Need to attend to get participation points

15
Problems w/ Course
  • Please see me. I will do everything possible to
    correct the problem
  • Comment in evaluations

16
Schedule of Readings and Topics
  • Topics are listed by week and date for lecture
  • Text readings are listed by week. Have readings
    completed by the end of the week in which they
    are assigned
  • Additional readings (primary source journal
    articles will be assigned as course progresses
  • Schedule can change but will be announced in
    class and updated on the web
  • Additional readings in APA manual outlined in lab
    syllabus

17
Note-taking recommendations
  • Print out notes ahead of time or afterwards
  • If afterwards, slide numbers are provided Dont
    copy slide content into notes
  • Blank spaces in slides for answers to questions
    (class points for answers). Designed to
    encourage and allow for critical thinking
  • My perspective on learning Think actively and
    critically in class
  • This course is very different from other
    psychology classes. Not a content/memorization
    course
  • Reminder This is a 5 credit course with a very
    large workload (equivalent to two regular courses)

18
Homework
I want you to find (and make a copy of) an
article in a magazine or newspaper. This article
should be covering what you think are scientific
findings. You will write three BRIEF paragraphs
on this article. In the first paragraph,
summarize the main points of the article. Next,
write 1 paragraph on what you think science is.
Finally, write 1 paragraph about what you think
makes your particular article scientific (so you
relate it back to your second paragraph). Bring
both the article and this writing assignment
(TYPED) to class Tuesday 9/07/04.
19
Homework
  • Two homework assignments for next week
  • Read and answer questions about Stanovich
    article Due 9/14/04
  • Read and answer questions about Platt article
    Due 9/16/04
  • Turn in on due date
  • Must be typed. Must be completed by due date. No
    late homework
  • Put your name TA name on homework
  • Due dates and digital copy of questions on web

20
Experiment 1
  • Randomly divide class into three groups (count
    off 1-3)
  • Group one stays here with me
  • Group two goes to room XXX with XXXXX
  • Group three goes to room XXX with XXXX

21
Announcements
  • Turn in Media Assignment at end of class today
  • Read and answer questions about Stanovich
    article Due 9/14/04
  • Read and answer questions about Platt article
    Due 9/16/04
  • Any lecture slide printing problems?
  • Re-introduce participation points procedure

22
Methods of Obtaining Knowledge
  • Tenacity Accepting an idea as valid knowledge
    b/c that idea has been accepted for a long period
    of time
  • Common Sense Obtaining knowledge without any
    intellectual effort/thinking or involvement of
    sensory processes. Includes both Intuition and
    Cultural knowledge
  • Authority Acceptance of an idea as valid
    knowledge b/c some respected source claims it is
    valid
  • Rationalism Knowledge is developed through
    reasoning processes alone. (Reason)
  • Empiricism Gaining knowledge through direct
    observation

23
Write a brief description of what you see
24
Rules for Acceptable Observations
  • An observation must be available to more than one
    person-- intersubjective verification.
  • The description of the observation from multiple
    observers must be quite similar-- reliable.
  • The conditions under which observation made and
    the details about how the measurement was
    accomplished must be clearly specified--
    operational definition.

25
Announcements
  • Read and answer questions about Stanovich
    article Due 9/14/04
  • Read and answer questions about Platt article
    Due 9/16/04
  • Library research sign-up
  • ID number on participation points (5058, 9888,
    2299, 9383, 5851, 7044)?

26
What is Science?
Lets hear some examples that you found of science
in the media.
Which of the previous methods are not part of the
scientific method of acquiring knowledge? Tenacity
, common sense, authority
Why are they not scientific? i.e., what are
their shortcomings relative to the scientific
method?
  • Don't always conform to data (evidence)
  • Can be uncritical and accepting
  • Subjective (can be overly influenced by cultural,
    personal factors)

27
What is Science?
  • Science is a process of acquiring knowledge
  • Based on experience and observation (i.e.,
    empiricism)
  • Incorporates rationalism to build theories
  • Characterized by a more critical and systematic
    approach to obtaining knowledge
  • Goal is understanding how and why things happen
  • Characteristics of Science
  • Based on empirical observation guided by theory
  • Uses logic/reasoning to support conclusions
  • Observations are systematic, verifiable,
    repeatable, and well-defined
  • Science is cyclical and self-correcting

28
Phases of Scientific Research
  • 1. Idea generating phase Identify a topic of
    interest to study.
  • 2. Problem-definition phase Refine vague and
    general idea(s) generated into precise question
    to be studied. Good psychological science should
    be guided strongly by theory.
  • 3. Procedures-design phase Decide on specific
    procedures to be used in gathering and
    statistical analysis of the data.
  • 4. Observation phase Using the procedures
    devised, collect your observations from the
    participants in your study.
  • 5. Interpretation phase Compare your results
    with results you predicted. Do they support your
    theory? Leads to further theory development,
    modification or discard theory.
  • 6. Communication phase Prepare a written or
    oral report of your study for publication.
    Application can follow from here.

29
Phases of Scientific Research
Idea generating phase
Problem-definition phase
Procedures-design phase
Observation phase
Interpretation phase
Communication/Application
30
Announcements
  • Turn in Stanovich homework
  • Read and answer questions about Platt article
    Due 9/16/04
  • Library research sign-up if missed class

31
Levels of Constraint
Naturalistic Observation Behavior observed in
the natural environment with no attempt to change
or limit the environment or behavior of the
subjects. Case Study Researcher intervenes
somewhat by asking questions and directing line
of inquiry. Flexible with attention following
interesting responses that seem relevant at that
time. Correlational Research Setting can be
natural or laboratory but variables of interest
and measurement methods are precisely defined and
decided prior to the start of the
study. Differential/Quasi-experimental
Involves comparison of two or more groups.
Groups are selected to be closely matched on all
factors other than the one pre-existing
difference being studied. Experimental
Research Different groups are compared but the
researcher controls assignment to the groups and
directly manipulates the variable/factor being
investigated.
Lower Constraint Higher
Constraint
32
Scientific Theory
Theory defined A formalized set of concepts that
organizes observations and inferences and
predicts and explains phenomena. Theory
specifies relationships between constructs
  • Characteristics of a good theory
  • Usually based on a large body of empirical
    findings
  • Testable (Precise, Specific Falsifiable)
  • Parsimonious (simple)
  • Organizes the collection of subsequent
    observations

33
Scientific Theory
Hypothesis A specific prediction based on theory.
A hypothesis is a statement or prediction about
a relationship that should be observed between
two or more variables if the theory is correct.
Variable Any directly observable characteristic
that can take on different values. These include
demographic/subject variables, independent
variables, dependent variables and many other
categories.
Construct Generalized, abstract concept that is
constructed to explain relationships among
observed variables in a particular situation.
Once formulated, constructs are used, as if they
exist, to theoretically predict (hypothesize)
effects on other constructs and their associated
variables in situations that had not been
previously observed.
34
Reasoning/Logic in Scientific Theory
  • Logic is at the heart of science (Rationalism
    component)
  • Concepts of falsifiability and strong inference
    are derived from basic forms of logical reasoning
  • Used to organize observations into theory
  • Used to make predictions about future events from
    theory
  • Inductive and Deductive Reasoning
  • Inductive reasoning When a researcher begins
    with observation of specific variables and then
    infers constructs and develops theory.
  • Deductive reasoning When the theory about
    psychological constructs serves as a basis for
    making predictions about new observations for
    specific variables.

35
Reasoning/Logic in Scientific Theory
Deduction Theory ? Data
  • Induction
  • Data ? Theory

36
Propositional Logic
  • If p then q
  • If you are playing the badgers (or Florida State
    Seminoles) in football, then you will lose.
  • First part of the logical statement is called the
    antecedent (p)
  • Second part is called the consequent (q)
  • There are four types of propositions that can be
    made in this framework
  • We can affirm or deny the antecedent (2)
  • We can affirm or deny the consequent (2)
  • Two are correct and two are incorrect

37
Announcements
  • Turn in Platt homework at end of class
  • Pick up Stanovich homework now
  • IDs 5226, see me after class
  • IDs 5058, 2299 (9347 Wicke), 7044, 2289, 3232
    (Schrammel), 5038
  • 2287 2297(Guralski)
  • Hands for not registered
  • Library research sign-up if missed class

38
Affirming the Antecedent (Modus Ponens)
  • Confirmatory logic
  • 1. If p then q
  • p (p is true)
  • Therefore, q (q is true)

Is this correct or incorrect logic?
CORRECT
  • Example
  • 1. Individuals with depression lack the normal
    (but inaccurate) protective cognitions about self
    worth and efficacy Depressive realism. They
    are more accurate in their self-appraisals and
    this causes them to be depressed.
  • Jay has accurate self-appraisal of his
    worth/efficacy
  • .and Jay is depressed

39
Affirming the Antecedent (Modus Ponens)
  • We use this in everyday life to predict what will
    happen based on our understanding of the
    consequences of our actions.
  • This is also how theories inform us about our
    world.
  • You are using this form of logic when forming
    hypotheses to test from a theory. Basically, you
    are saying what you expect to observe if your
    theory is true.
  • If Construct A changes, then Construct B will
    change

40
Denying the Antecedent
  • 1. If p then q
  • Not p (p is false)
  • Therefore, not q (q is false)

This logical reasoning is INCORRECT but not
really used in science
41
Affirming the Antecedent
  • When forming hypotheses, we are affirming the
    antecedent
  • In this future experiment, if I manipulate the
    Independent variable, then the Dependent variable
    will change
  • (1) If p then q (2) p (3) q
  • This is valid/correct reasoning deductive
    reasoning

42
Affirming/Denying the Consequent
  • When testing hypotheses, we are either affirming
    or denying the consequent
  • If Theory is true then these results will be
    observed in this experiment our hypothesis will
    be supported/true
  • (1) if p then q (2) q or not q (3) p or not p
  • We conclude based on empirical evidence
    (observations/data) and statistical analyses that
    our hypotheses/predictions in an experiment were
    either true (q) or false (not q)
  • From this, we want to conclude that our theory is
    true (p) or false (not p)

43
Affirming the Consequent
  • 1. If p then q
  • q (q is true)
  • Therefore, p (p if true)
  • Is this correct or incorrect and when in does it
    occur?
  • Although this logic is not correct, this
    situation is frequently encountered in research.
  • This is the case when our predictions/hypotheses
    are supported

Explain this and its implications?
44
Recognizing Logic in Research
You develop an intervention to improve school
performance for disadvantaged children in the
inner-city. The intervention involves 1 year of
classes, involving both parent child. It is
intensive and not many families volunteer to
participate. Two years after completion you
compare school performance of children who
completed the intervention with a sample of
children not offered this intervention. The
intervention kids outperform the other kids.
  • What proposition is suggested by this?
  • What conclusion might you reach?
  • What form of logic must you use to do this?
  • What is the problem with this?
  • What else needs to be done?

45
Affirming the Consequent
  • What proposition is suggested by this?
  • What conclusion can you reach?
  • What form of logic must you use to do this?
  • What is the problem with this?
  • What else needs to be done?

Your proposition might be If the intervention
is effective, intervention kids will outperform
others Intervention kids will have higher mean
performance that non-intervention kids on some
test. The natural conclusion to reach is that
the intervention was effective. However, this
is an example of affirming the consequent
. .and it is invalid to conclude based on this
type of logic alone that the antecedent
(intervention is effective) is true. Many
other potential explanations for the observation
(intervention kids outperform others) may
exist. Must rule out other explanations in this
study and conceptual replications.
46
Affirming the Consequent
  • In research design we attempt to rule out all
    other causes for the observed outcome but we can
    never conclude with certainty that we have proved
    our theory with this form of logic.
  • This is where replication (repeating an
    experiment either exactly rarely done not
    worthwhile, or conceptually) becomes important.
    Although never proved, when predictions from a
    theory are repeatedly confirmed, we become more
    confident that they are true. Why?

47
Affirming the Consequent
  • Scientists attempt to overcome the flaws of this
    method of logic by exercising as much control
    over conditions as possible.
  • By eliminating as many possible alternatives for
    explaining q (superior performance of the
    intervention kids), you can increase confidence
    in the conclusions about your theories.
  • This is done through many different methods
    including
  • The use of control groups
  • Controlling important variables
  • Experimental methods (e.g., random assignment)
    are especially effective in establishing control
    and eliminating alternatives

48
Denying the Consequent (Modus Tollens)
  • Disconfirmatory logic
  • 1. If p then q
  • Not q (q is false)
  • Therefore, not p (p if false)

Is this correct or incorrect logic?
CORRECT
The stress response dampening theory states that
alcohol acts directly on fear centers in the
brain and interferes with activity in this
system. Therefore, intoxicated individuals will
not experience as robust (strong) a stress
response as sober ones.
  • If alcohol intoxication leads to stress reduction
    If SRD theory is true, then intoxicated
    individuals will be less stressed than
    non-intoxicated individuals
  • Intoxicated participants are not less stressed
    than non-intoxicated participants
  • Therefore, intoxication does not lead to stress
    reduction SRD theory is false

49
Denying the Consequent (Modus Tollens)
  • This (logic of falsification) is at the heart of
    scientific testing of theory (Popper)
  • By successfully falsifying a theory (i.e., by
    falsifying a specific hypothesis), we advance
    knowledge confidently.
  • Unlike the situation where confirm our
    predictions, when we fail to find the observed
    effect, we can be logically confident that the
    theory is incorrect
  • However, it is not always that simple. WHY?
  • Not enough power to find effect due to
  • Small sample
  • Weak manipulation e.g., effects of nicotine in
    withdrawn smokers
  • Measures werent sensitive enough
  • More on this during reliability and also strong
    inference

50
Recognizing Logic in Research
The FDA considers it unnecessary to label food
that has been genetically engineered, despite
public demand, since the no risks to our health
from eating these foods has been documented.
Industry spokespersons argue that the problem is
one of consumer ignorance and to persuade people
of their viewpoint they have only to educate us
about the genetic engineering (GE) process. The
following argument is used to promote spending on
consumer education.
  • If people dont understand GE they will be
    reluctant to consume GE food products.
  • Numerous studies demonstrate that people are
    indeed reluctant to eat GE food products.
  • Therefore, they must not understand GE.
  • Education can potentially change this.

Is this a logical argument? Explain? What else
should this person do to strengthen their
argument for the need for education?
51
Recognizing Logic in Research
Schachter studied the relationship between
anxiety and the need to affiliate (to be with
others). His theory suggested that anxiety would
cause the need to be with others (basically,
misery loves company!) From this theory, you
can derive the following proposition
  • If anxiety causes the need to affiliate, then
    anxious people will not choose to wait alone (as
    often as non-anxious people)
  • He conducts a study and finds that anxious people
    DO choose to wait alone (as often as non-anxious
    people).

What type of logic does this involve and what if,
if anything, can you conclude from this?
52
Announcements
  • Pick up Platt homework now
  • Library research sessions tonight and tomorrow
  • STAI homework assignment (email by 830am
    Thursday)

53
Theory Development and Logic
  • Theory advances in the following fashion
  • Predict a falsifiable hypothesis based on theory
  • Design and conduct an adequate test of the
    hypothesis
  • (a) If prediction is false, modify or discard
    theory. This is denying the consequent and it is
    logically valid.
  • (b) If prediction is true conduct
    additional tests of theory (replications) which
    are aimed at eliminating other explanations.
    These replications must be conducted to rule out
    other explanations b/c affirming the consequent
    does not inevitably indicate that the antecedent
    (i.e., theory) is true.

54
Wason Example
Determine what my rule is for generating a
correct series. First Series 2 - 4 - 6
Rule/Theory Numbers increase by 2 Tests?
55
Wason Example
The rule was All numbers that increase.
  • Two strategies for determining rule
  • Enumerative Generate examples that confirm your
    current theory
  • Eliminative Generate examples that can
    disconfirm your current theory
  • Individuals using eliminative strategy determine
    rule 3x faster than those using enumerative
    strategy
  • Can you connect these ideas to other discussions
    we have had about theory and more generally
    science?
  • Theory develops through falsification (Popper).
  • This is the self-correcting part of science,
    again

56
Falisfication Stanovich Article
  • Why did Stanovich object to Benjamin Rushs
    defense of bloodletting as a cure for yellow
    fever?
  • How do proponents of ESP explain the inability of
    skeptics to demonstrate effects?
  • According to Stanovich, why is specificity of
    predictions important?
  • According to Stanovich, what is the difference
    between a theory and a hypothesis?
  • Why isnt repeated confirmation of a theory
    sufficient evidence of its validity?

57
Falsification Stanovich Article
  • How could the strategy of falsification be used
    in everyday life?
  • Describe Stanovichs simple model of scientific
    progress. Is the public too lazy to think
    scientifically?
  • What should new theories be capable of doing?

58
Announcements
  • Answers to homeworks on the web
  • Exam date
  • Exam review

59
Platt Strong Inference Article
  • List the steps a scientist should take in
    applying the method of strong inference to a
    problem.
  • How does strong inference go beyond Poppers
    falsification strategy? (How is it similar and
    how is it different?)
  • What are the advantages of strong inference?
  • What are the difficulties?
  • What question does Platt say you should ask
    yourself when planning a new study?

60
Operational Definitions
Operational definition Detailed set of
procedures used to measure or manipulate the
levels of a construct. Operational definitions
transform abstract/conceptual constructs into
objective, reliably measurable variables.
61
An Indian Fable
It is said that once upon a time a king gathered
a few men who were born blind. They were asked to
describe an elephant, but each one was presented
with only a certain part of it. To one was
presented the head of the elephant, to another
the trunk, to another its ears, to another the
leg, the body, the tail, tuft of the tail, etc.
The one who was presented with the head said
"The elephant is like a pot!" The one who was
presented the trunk answered, "The elephant is
like a hose". The one who touched only the ears
thought that the elephant was a fan, the others
said that it was a pillar, a wall, a rope, a
brush, etc. Then they quarreled among
themselves, each thinking that he was the only
one right and the others were wrong. The obvious
truth is that the elephant is a unity of many
parts, a unity that they could not grasp in their
ignorance.
62
Operational Definitions
There are four broad categories of operational
definitions. They are as follows
  • Actual behavior
  • Physiological/biological
  • Self-report
  • Manipulation
  • It is best to use all four categories, (often in
    different studies), to converge on the true
    concept you wish to examine.
  • Using one of the four categories may make the
    concept you are trying to examine look different
    compared to if you had used a different category.

63
Conceptually Defining Anxiety
  • As an exercise, pause for a few minutes and write
    down, in the space below, your definition of
    anxiety, as if you were writing a book of
    psychological terms.
  • Discuss your various conceptual definitions of
    anxiety.

A state of apprehension, uncertainty, and
uneasiness resulting from the anticipation of a
realistic or imagined threatening event or
situation from which you can not escape, often
impairing physical and psychological functioning
64
Operationally Defining Anxiety
List at least THREE ways by which you could
measure anxiety at a physiological level. Be
quite specific, remembering that your definition
must be clear enough so that another researcher
could replicate it.
  • Increased heart rate (arousal)
  • Increased skin conductance (arousal)
  • Potentiated startle (Yes)
  • Increased right frontal brain activity (Yes)
  • Increased cortisol (HPA axis activation) (Yes)

65
Operationally Defining Anxiety
List at least THREE ways by which you can measure
anxiety at a nonverbal, observational level.
Specifically, what behaviors and/or conditions of
an individual would indicate to an observer that
s/he was experiencing anxiety?
  • Speed of approach toward anxiety-eliciting
    stimulus
  • Time spent in an anxiety eliciting environment
  • Disruption of performance of an independent task

66
Announcements
  • New Exam Date Oct 7th
  • Review session on Monday or Tuesday _at_ 6pm?
  • First class evaluation Due Thursday
  • Complete the online human subjects training
    module at
  • http//info.gradsch.wisc.edu/research/compliance/h
    umansubjects/tutorial/
  • Due 10/07/04
  • KT Travis

67
Operationally Defining Anxiety
List several ways in which you can measure
anxiety through verbal reports. Specifically,
what kinds of questions would you ask to
determine if an individual was experiencing
anxiety? These can be true/false, multiple
choice, or open-ended questions.
  • Complete the STAI

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Operationally Defining Anxiety
Now list several ways in which an researcher
could experimentally produce anxiety with certain
procedures and/or stimuli. Be sure that they are
ethical. Once again be specific enough so that
another researcher could replicate your procedure.
  • Give a speech about most embarrassing body part
  • Threat of electric shock or other noxious
    stimulus
  • IAPS

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Defining Other Constructs for Homework
  • Provide a conceptual definition and
    operationalize it across the 4 categories
  • CREATIVITY
  • DEPRESSION
  • INTELLIGENCE
  • LOVE
  • MOTIVATION
  • OBESITY
  • SELF-ESTEEM

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6 Main Ideas From Exercise
  • Sometimes there are different possible conceptual
    definitions. Differences in these conceptual
    definitions are often times theoretically driven.
  • The conceptual definition often will influence
    how you operationally define something.
  • Some terms have more relation or less relation
    between their conceptual and operational
    definition (obesity vs. love).
  • There are 4 broad categories of operational
    definitions (actual behavior, physiological/biolog
    ical, self-report, manipulation)
  • Any one definition can be wrong (incomplete). The
    best operational definitions sample across all
    areas. Often it's best NOT to stay with one
    operational definition only--probably want at
    least 2 or 3 categories.
  • Operational definitions (as opposed to conceptual
    definitions) MUST BE precise, specific, and
    measurable.

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Measurement Issues
The Measurement of Intelligence An Example
(trivia tape)
  • The single trivia question approach. Problems?
  • Very inconsistent. (Same person might be judged
    intelligent or unintelligent on different
    occasions depending on the specific question
    asked)
  • Can not be related to intelligence b/c
    inconsistent (and probably other reasons)
  • The tape measure approach. Problems?
  • Consistent but may not be related to intelligence
  • The IQ test
  • Consistent (in what ways?)
  • Related to intelligence (what evidence?)

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Quantifying Reliability and Validity
To index various types of reliability and
validity we will look at relationships between
observed variables. What (descriptive/inferenti
al) statistic allows us to quantify the magnitude
(and direction) of a relationship between two
variables?
  • The correlation coefficient

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Correlation Coefficients
What are the important properties of correlation
coefficients (How do we use them to describe
relationships?
  • Correlations range from 1.0 to 1.0
  • Strength of relationship is indexed by the
    absolute value of the coefficient (bigger is
    better)
  • Direction of relationship is indexed by the sign
    of the coefficient

Measures of reliability and validity are
typically expressed as correlations that relate
to the property (specific type of reliability or
validity) we are trying to demonstrate for our
variable.
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Reliability Shooting Bulls
Reliable
Reliable
Unreliable
  • Reliability is an index of the consistency of
    measurement
  • Test-retest reliability
  • Inter-rater reliability
  • Internal consistency

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Test-Retest Reliability
Definition Consistency of measurement across
multiple administrations/measurements separated
by some length of time.
  • What is the appropriate length of time to assess?
  • Too short of a period of time (hours) may
    overestimate reliability by allowing people to
    be consistent b/c they remember their previous
    responses
  • Too long of a period of time (e.g., years) may
    underestimate reliability because construct may
    actually have changed (e.g., height)
  • Should all tests/measures possess this form of
    reliability?
  • Not necessarily. It depends on whether the
    construct is expected to remain stable over time
    (e.g., intelligence/aptitude) or to vary (e.g.,
    mood)

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Inter-rater Reliability
Definition Consistency of measurement across
multiple observers/raters who are performing the
measurement. Rate how attractive the person is
on a 10 point scale (1 10) with higher scores
indicating more attractiveness.
  • Ratings of Psychopathy with PCL
  • DSM diagnosis

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Inter-rater Reliability
  • How could we improve our ratings of
    attractiveness?
  • Provide a clear definition of what we are rating
    (our opinion, group consensus, what makes someone
    attractive)
  • Provide anchors to the scale
  • Provide training with feedback

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Inter-rater Reliability The Psychopathy Checklist
  • Promiscuous sexual behavior
  • Behavioral problems early in life
  • Lack of realistic long-term plans
  • Impulsiveness
  • Irresponsible behavior
  • Lack of remorse
  • Many marital relationships
  • Juvenile delinquency
  • Callousness
  • Criminal versatility
  • Glibness/superficial charm
  • Grandiose sense of self-worth
  • Tendency to boredom/need for

    stimulation
  • Pathological lying
  • Conning/manipulative behavior
  • Failure to accept the consequences of actions
  • Shallow affect
  • Lack of empathy
  • Parasitic lifestyle
  • Poor behavioral controls

Inter-rater reliability ranges from 0.87 to 0.97
for trained raters Hare, R. D., Harpur, T. J.,
Hakistian, A. R., Forth, A. E., Hart, S. D.,
Newman, J. P. (1990). The revised psychopathy
checklist reliability and factor structure.
Psychological Assessment, 2, 338-341.
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Internal Consistency Reliability
  • Definition Consistency across items (typically)
    that combine to form the questionnaire/measure
  • STAI example ( extra item)
  • 0.83 0.91 for PCL-R Cooke Michie, 1997

Is the goal to have the highest internal
consistency possible? Not always. Sometimes a
construct is heterogeneous (i.e., consists of
multiple dimensions). To have construct
(content) validity, you will need to measure all
the independent dimensions. Items from these
dimensions may not relate to each other (i.e.,
NEM from MPQ)
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Validity Shooting Bulls Revisited
Valid
Not Valid
Not Valid
Validity (of measurement) is an index whether
your test (or other measure including
behaviors, etc.) truly measures what you think it
measures (i.e., the construct of interest).
  • Face validity
  • Construct validity
  • Content validity
  • Criterion validity (predictive concurrent
    validity convergent divergent validity)

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Face Validity
Definition Does the test/measure appear to
measure what it purports to measure (e.g., STAI
vs. MMPI)
Why is face validity desirable?
  • It is a simple, convenient way to provide initial
    support for the validity of a measure.
  • It can be related to cooperation on the part of
    the participant (again, MMPI).

Is face validity always good?
  • High face valid measures are open to bias on the
    part of the participant.
  • Face validity is not sufficient to demonstrate
    validity. Often something that appears face
    valid may not really measure what you think it
    does.

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Construct Validity
Definition The degree to which the test/measure
provides an adequate measure of the construct.
  • It is an umbrella term that includes concepts of
    content validity and criterion validity
  • It must occur within a theoretical framework
    (i.e., you need to be able to theoretically/concep
    tually describe the construct to proceed in
    assessing the construct validity of its measures.)

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Content Validity
Definition The degree to which the test/measure
adequately covers the construct to be measured
(I.e., does it assess all aspects/dimensions of
the construct)
  • It is particularly relevant for achievement tests
    (e.g., exams in this class shortstop
    performance)
  • It is often performed in a somewhat subjective
    manner (i.e., developing a measure of depression
    with expert consensus)
  • Empirical methods also exist (Factor analysis)

84
Criterion Validity
Definition The degree to which scores on the
test are related to other measures of the
construct (e.g., criterion validation of an IQ
test)
  • Must assess the relationship between your new
    measure and other established measures (ideal if
    a gold standard exists)
  • Need to demonstrate both convergent (relates to
    other measures that it should relate to) and
    divergent (does not relate to measures that it
    should not relate to) validity
  • This may be done with both concurrent measures
    (other measures obtained at the same time) and in
    a predictive (look at the ability of the measure
    to predict future scores on another measure)
    fashion

85
Inferential Statistics
Descriptive Statistics Are used to describe,
summarize and simplify data. Provides a single
(typically) numeric value to summarize some
aspect of the overall data set.
Inferential Statistics Are used to infer the
status of a question (about descriptive
statistics) in a full population of individuals
based on a sample from that population. Answers
from inferential statistical are probabilistic.
In other words, all answers have the potential to
be wrong and you will provide an index of that
probability along with your results.
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Describing Data Scales of Measurement
  • Nominal Are naming scales. They only have
    the property of identity
  • Ordinal Measure a variable in order of
    magnitude. Therefore they have properties of
    identity and magnitude
  • Interval Have properties of ordinal scale and
    there are equal intervals between values
  • Ratio Have properties of interval scale and
    there is an absolute zero point

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Describing Data
  • Graphical Representation of Data
  • Bar Graph
  • Histogram
  • Frequency Polygon
  • Frequency Counts
  • Summary Statistics
  • Measures of Central Tendency
  • Mean
  • Median
  • Mode
  • Measures of Variability/Dispersion
  • Range
  • Variance
  • Standard deviation
  • Measures of Shape

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Bar Graph
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Sample Histogram
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Frequency Polygon
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Bar Graph How Much Do Students Drink?
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Frequency Polygon How Much Do Students Drink?
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What is wrong with this picture?
102
Frequency Counts
  • Drinks/week Frequency
  • 0 - 4 203
  • 5 - 9 49
  • 10 -14 25
  • 15 - 19 27
  • 20 - 24 16
  • 25 - 29 19
  • 30 - 34 4
  • 35 - 39 6
  • 40 - 44 1
  • 45 - 49 8
  • 50 - 54 0
  • 55 - 59 0
  • 60 - 64 1
  • 65 - 69 1
  • 70 - 74 3
  • 75 - 79 0
  • 80 - 84 2
  • 85 - 89 1

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Summary Statistics
  • Measures of Central Tendency
  • Mode
  • Median
  • Mean
  • Measures of Variability
  • Range
  • Variance
  • Standard deviation
  • Measures of Shape
  • Skewness
  • Kurtosis

104
Central Tendency Mode
Definition Most frequent score
  • ADVANTAGES
  • 1. Most probable score in data set
  • 2. Only measure of CT for nominal data
  • DISADVANTAGES
  • 1. Only based on one score in data set
  • 2. Can be more than one of them

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Central Tendency Median
Definition Score at the 50 percentile (middle
value)
  • ADVANTAGES
  • 1. Not very sensitive to outliers
  • 2. Only measure of CT for ordinal data
  • 3. Can be used to split data set into equal
    parts. (Median split)
  • DISADVANTAGES
  • 1. Uses less information from data set than
    mean

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Central Tendency Mean
Definition Arithmetic average
  • ADVANTAGES
  • 1. Uses most information from data set
  • 2. Is used in many parametric inferential
    statistics
  • DISADVANTAGES
  • 1. Very sensitive to outliers

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Variability Range
Definition Distance between end points of
distribution (spread from end to end)
  • ADVANTAGES
  • 1. Lets you know range of values
  • DISADVANTAGES
  • 1. Very sensitive to outliers
  • 2. Only based on two scores in data set

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Variability Variance
Definition Average squared distance of scores
from mean (spread from mean in squared units)
  • ADVANTAGES
  • 1. Based on all scores in data set
  • 2. Used often in parametric inferential
    statistics
  • DISADVANTAGES
  • 1. Units of measurement often dont make
    sense

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Variability Standard Deviation
Definition Square root of variance (spread from
mean in original units)
  • ADVANTAGES
  • 1. Based on all scores in data set
  • 2. Has same units as variable being
    described

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Measures of Shape
  • Skewness
  • How symmetric is distribution?
  • Kurtosis
  • How peaked is distribution?
  • Ratio scores in center vs. tails of distribution

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Skewness 0 Symmetric
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Skewness gt0 Right tail too long
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Skewness lt 0 Left tail too long
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Which Distribution has larger SD?
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Which Distribution has the Higher Mean?
116
What is the Modal Grade?
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What is the Median Grade?
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