Transcript 2030 exam review 2

PSYC2030
Exam Review
#2
March 13th 2014
Chapter 5
Essential Features of
Experimental Research
•
What is an independent variable and what is meant by the
term “levels” of an independent variable?
 Independent variable:
 The factor of interest to the researcher
 Must have a minimum of two levels
(Situations/Conditions)
 Can be manipulated by the experimenter
 E.g., experimentally eliciting anxiety in Group A
but not in Group B
 Participants can be selected based on their
attributes
 E.g., selecting two groups that differ in anxiety
level
Essential Features of
Experimental Research
•
Distinguish between an experimental and a
control group.
 Experimental group:
 Receives treatment
 Control group:
 Treatment is withheld
 = comparison group
Essential Features of
Experimental Research
•
What is a confounding variable and why is it a problem
in experimental research?
 Confounding variable:
 Any uncontrolled extraneous variable that covaries
with the independent variable and could provide
an alternative explanation of the results
 Problem?
 Difficult to interpret results
 Results could be due to the effect of the
independent variable, confounding variable, or
some combination of both
Essential Features of
Experimental Research
•
In psychological research, what is a dependent
variable?
 Behaviour measured as an outcome of an
experiment
 If we study the effect of X on Y
 X = Independent Variable
 Y = Dependent Variable
Essential Features of
Experimental Research
•
What is the difference between a ceiling and a floor
effect?
 Ceiling effect
 The average score in both groups is so high that
no difference can be determined
 E.g., questions are too easy
 Floor effect
 The average score in both groups is so low that
no difference can be determined
 E.g., questions are too difficult
Manipulated versus Subject
Variables
•
What is meant by a subject variable? Give two
examples.
 Subject variable: Existing characteristics of the
individual participating in the study
 Examples
 Sex
 Age
 Socioeconomic status
Manipulated versus Subject
Variables
•
When drawing conclusions from studies using
manipulated variables, statements about cause can
be made. These statements cannot be made when
using subject variables. Why?

With manipulated variables, groups are created based
on random assignment


The researcher can be fairly confident that the groups are
are equal (i.e., any differences will be random)
With subject variables, groups are created based on
participants’ characteristics


The researcher can’t be certain that the groups are equal
(i.e., differences might not be random)
Any differences might be due to individual differences than
covary with the subject variables
The Validity of Experimental
Research
•
Give two examples of mistakes that result in a loss of
statistical conclusion validity.
 What is statistical conclusion validity?
 The extent to which the researcher uses
statistics properly and draws the appropriate
conclusions from the statistical analysis
 Examples of mistakes:
 Using the wrong analysis technique or violate
some of the assumptions required for
performing a particular analysis
 Reporting some results but not others
The Validity of Experimental
Research
 Distinguish


internal from external validity.
Internal validity: the degree to which an
experiment is methodologically sound and
confound-free
External validity: the degree to which research
findings generalize beyond the specific experiment
being conducted
Threats to Internal Validity
•
How might attrition reduce an experiment’s internal
validity?


Attrition: participants leaving the study over the course
of time
Problem?

The group finishing the study is made up of different types
of people than is the group that started the study.
Threats to Internal Validity
•
Distinguish between testing and instrumentation as
threats to the internal validity of a study.


Testing: Occurs when the fact of taking a pretest
influences posttest scores (perhaps by sensitizing
participants to the purpose of the study)
Instrumentation: Occurs when the measuring
instrument changes from the pretest to the posttest
Chapter 6
Between-Subjects Designs
 In
a between-subjects design, why is it
important to aim for equivalent groups?


Between subjects design: an experimental design
in which different groups of participants serve in
the different conditions of the study
we want to make sure that any differences
between the groups is due to the levels of the
independent variable
Between-Subjects Designs
 Other
than the problem of equivalent groups,
what is a disadvantage of using a betweensubjects design?
 Large numbers of people may be need to be
recruited, tested, and debriefed during the
course of the study
The Problem of Creating
Equivalent Groups
 What
is the difference between random
selection and random assignment?
 Random selection: A procedure for getting
volunteers to come into your study. Every
person has an equal chance to participating.
 Random assignment: A procedure to assign
participants to conditions. Every person
participating has an equal chance of bring
placed in any of the conditions/groups in the
study.
The Problem of Creating
Equivalent Groups
 Explain
how random assignment accomplishes
the goal of creating equivalent groups
 Individual differences are randomly spread out
across conditions
The Problem of Creating
Equivalent Groups
 Explain
how simple random assignment differs
from blocked random assignment
 With simple random assignment, there is no
guarantee that the groups will be equal in size
(e.g., flipping a coin 20 times can result in
heads all 20 times)
 With blocked random assignment, participants
are assigned to blocks of alternating
conditions
Within-Subjects Designs
 What
are the advantages of within-subjects
designs over between-subjects designs?
 Within-subjects designs: an experimental
design in which the same participants serve in
each of the conditions of the study
 Advantage over between-subjects designs?
 Fewer people are required!
 Eliminate the equivalent groups problem
Within-Subjects Designs
 What
is the main problem that a within-subjects
design has to solve?
 Order effect: the experience of participating in
one condition of the study influences
performance in subsequent conditions
Within-Subjects Designs
 What
is the difference between a progressive
effect and a carryover effect?
 Progressive effect: performance changes
steadily from trial to trial (e.g., fatigue)
 Carryover effect: performance changes as a
result of one sequence of events rather than
another (e.g., easy-difficult Qs vs. difficult-easy
Qs)
The Problem of Controlling
Sequence Effects
•
What is the general purpose of
counterbalancing?
 The order of conditions/presentation is
randomized for each subject, thus eliminating
order effects
The Problem of Controlling
Sequence Effects
 what
is “complete” counterbalancing? If there
are five different conditions in a study, how many
sequences will be needed if complete
counterbalancing is going to be used?
 complete counterbalancing occurs when all
possible orders are used
 Five conditions: 5! = 120 sequences
The Problem of Controlling
Sequence Effects
•
What is “partial” counterbalancing? What are
two ways to accomplish partial
counterbalancing?
 Partial counterbalancing: a subset of all
possible orders of conditions is used
 Sampling from the complete set of all
possible orders
 Randomizing the order of conditions for
each subject
Control Problems in
Developmental Research
 What
is a cross-sectional design?
 In developmental psychology, a design in
which age is the independent variable and
different groups of people are tested
 Each group is of different age
Control Problems in
Developmental Research
 What
are cohort effects and how might they
make the interpretation of cross-sectional
designs difficult?
 Cohort: group of people born at the same
time
 Cohort effects: differences between groups
are due to growing up in different historical
times
Control Problems in
Developmental Research
 What
is a longitudinal design?
 In developmental psychology, a design in
which age is the independent variable and
the same group of people is being tested
repeatedly at different ages
Control Problems in
Developmental Research
 Describe
how a cohort sequential design
combines elements of cross-sectional and
longitudinal designs
 A new cohort is added to a study every few
years and then studied periodically
throughout the time course of the study
 Attempts to balance cohort and attrition
problems
Problems with Biasing
 What
is experimenter bias and how can it be
demonstrated experimentally?
 Occurs when an experimenter’s
expectations about a study affect its
outcome
Problems with Biasing
 What
are two ways to control experimenter bias?
 Research protocols: detailed descriptions of of
the sequence of events in a research session
 Ensures uniformity of treatment of research
participants
 A double blind procedure: neither the
participant nor the experimenter knows which
condition of study is being tested
Problems with Biasing
 What

is the Hawthorne effect?
A form of participant bias in which a participant’s
behaviour is influenced by the mere knowledge of
being in an experiment and therefore important to
the experimenter
Problems with Biasing
•
What is the good subject effect, how does it relate to
demand characteristics, and how might it influence
the outcome of a study?


Good subject effect: A form of participant bias in which
participants try to guess the experimenter’s hypothesis
and then behave in such a way to confirm it
Demand characteristics: A feature of the experimental
design that increases the chance that participants will
detect the true purpose of the study
Problems with Biasing
•
How can participant bias be controlled?
 Reduce demand characteristics
 Deception
 Placebo control group
 Manipulation check:
 Asks participants what they thought the
study was about
 Checks for the presence of demand
characteristics
 Conduct field research
Chapter 7
Single-Factor – Two Levels
•
Describe the Stroop effect and explain why his
first two studies were examples of single factor
repeated-measures designs.
 Single factor design: An experimental design
with a single independent variable
 Stroop effect: When the name of a color is
printed in a color that doesn’t match the
name, naming the color of the word takes
longer and is more prone to errors than when
the color of the ink matches the name of the
color
Single-Factor – Two Levels
 Identify
the situations when a t test for
independent samples will be used and when a t
test for related samples will be used.

Independent t-test
 Used
when two groups are completely independent
from one another



Random assignment
Subject variables with two groups (males vs. females)
Dependent samples t-test:
 Matched
groups design
 Repeated-measures designs
Presenting Data
 There
are Three Ways:
 1. Numbers can be presented in sentence
form
 2. Construct a table of results
 3. Graphical Representation
Presenting Data
•
How you present your data depends on the
type of IV you are interested in...
 Continuous Data: Use a line graph, but a
bar graph is adequate
 Discrete Data: Use a bar graph, line graph
is inappropriate.
 The IV is always presented on the X axis or
the horizontal line
 The DV is always presented on the Y axis or
vertical line
Control Group Designs
•
What is a placebo and what is the reason for
including a placebo control group in an
experiment?
 Placebo: a substance given to a participant
in a form suggesting a specific
pharmacological effect when in fact the
substance in inactive
 Purpose? Sometimes effect is caused simply
by believing in the drug.
Control Group Designs
 Placebo
Control Groups: Participants
are led to believe they are expecting
some treatment when they are
actually not. This type of control group
is often used in pharmacological
studies
Control Group Designs
 Waitlist
Control Groups: Often used to
assess the effectiveness of the
program and helps eliminate
alternative explanations for the results
you obtain such as general
improvement because of passage of
time
Control Group Designs
 Yoked
Control Groups: when each
member of the control group is
matched or “yoked” to a member of
the experimental group so that the
amount of time spent participating in
the events in each condition is
equivalent across your groups.