Module 3
Research Strategies:
How Psychologists Ask and
Answer Questions
Can a 4 year old keep themselves from eating a marshmallow now,
If they are told they will get two later?
Video
Y Psychology: Scientific Study of Behavior
and Mental Processes
Goals
• Describe Behavior
• Predict Behavior
• Explain Behavior
• Control Behavior
Psychology is Empirical
Knowledge acquired through observation
Psychologists must be skeptical
and think critically
What is the evidence?
How was it collected?
Like
all
verdicts,
the value and validity
of the evidence
must to examined.
Studies provide EVIDENCE to support or
refute a theory.
Proof Studies NEVER provide PROOF.
Like all verdicts,
the value and validity
of the evidence must
to examined.
General Principles
(Theory)
Empirical
Evidence
Predictions
Hypothesis
Observations
(data)
Advantage: Self correcting cycle.
Scientific Method in Psychology
Develop
theory
Caffeine improves people’s study skills
Form
hypothesis
Students who drink 2 or more caffeine
beverages per day have higher GPAs
Test
hypothesis
Record amount of caffeine consumed
and GPA for each student in the study.
Refine
theory
Do the data support the theory?
Revise the theory to better fit the data.
General Questions to Ask About a Study
1. Identify the variables!
Variable: Anything that takes on different
values, at different times, places,
or in different individuals.
All studies have variables. They are the
constructs (qualities) we measure (e.g.,
intelligence, personality, or reaction
times).
You need to ask how the variables are defined?
Is the measure Valid? (Does it measure what it
claims to? Is the measure meaningful? Useful?)
Operational Definitions - Detailed descriptions
of measurement criterion.
How was ability to delay gratification (self
control, self discipline) operationally defined in
the Marshmallow Study?
2. Is the measuring device reliable?
- will the same value be obtained
- by different researcher
- at different times
Statements of precise operational definitions
allow scientists to Replicate studies.
Replications allow us to determine if the
results of a study are reliable.
Concept Check
1.What variable/construct measured in this study?
2. How is this variable operationally defined?
3. Is this a valid way of measuring this construct?
4. Is it a reliable way of measuring the construct?
5. Do the results support the conclusions made by the researcher?
2. You need to ask who is in the study?
Sample
Collection of Ss used in a study
Population
Larger collection of people
about which we want
to generalize
Sampling Bias
Sample
When the sample is
not representative of
the larger population
Population
If sample is restricted or biased this
limits the generalizability.
Volunteers?
Convenience Samples (University
Students)?
Small numbers of subjects can be used
to estimate the behavior of a larger
group as long as the sample is not
biased.
Representative Samples - matched
demographics.
Random Samples - eliminates bias.
Replications to Test Generalizability
to other Populations and Times
Often studies are done of specific populations
and researchers may want to know if you
would get similar results with other
populations. So they replicate the study with
samples from other populations.
e.g., Marshmallow Study was replicated with
Children in Columbia. Would we get same
results if we replicated the study today?
Types of Research
1) Descriptive: No Hypothesis
2) Relational Studies: (Circumstantial Evidence)
3) Experiments - evidence of cause and effect.
Remember: None of these are Proof.
Descriptive Studies
1) Case Studies
Detailed description of one person’s behavior
Useful for Rare Cases (e.g., Serial Killers,
or Rare Disorders).
• Concerns

Experimental Bias

Subject Bias

Generalizability
2) Naturalistic Observation
Studies
- real world settings
Concerns
 - defining variables
 - sampling (who, where and when)
 - Experimenter Bias
- Observation can change behavior
3) Surveys
Ask People about behaviors or attitudes
Main Advantage: Can survey large numbers.
Concerns
 Biased Samples
4 out of 5 dentists recommend brand X.
 Generalizability
 Subject Bias
 Wording and method effects results
Class Survey.
Reasons for taking Class
Interesting
Gen Ed Requirement
(need the credits)
Learn about people
Fun or Amusing
Liked in High School
Considering as Major
Useful for career
Understand Self
Recommended to me
Better than other options
Cards
All
78%
15%
93%
97%
41%
83%
24%
5%
23%
0%
3%
0%
11%
3%
44%
42%
24%
3%
42%
14%
5%
0%
15%
7%
17%
0%
18%
3%
3%
0%
5 Hour Energy Video
Top 2
Relational Studies: what variables tend to go
together in predictable ways.
Correlations
Two variables measured on same person.
Correlation statistic ( r ). Ranges from -1 to +1
Positive - change in same direction.
High on one variable predicts high on the other.
Negative - change in opposite directions
Low on one variable predicts high on the other.
Absolute Value of r.
Strength of the relationship.
r = +1 or r = -1 perfect prediction.
r = 0 no ability to predict.
The closer to +1 or -1 the stronger the relationship
The closer to 0 the weaker the relationship
Correlations
Age
Do young hockey players
take more penalties than
old hockey players?
*
* * *
*
** * *
(-) r
* *
* * *
* *
* * * *
* *
* *
Penalty Minutes
r - statistical relationship
twixt 2 variables (age &
penalties)
Concept Check
Predict the correlation between these variables
(High, Medium or Low? Negative or Positive)
Weight and Height
IQ and shoe size
SAT scores and Grades in College
Miles you have drive since a fill-up and amount of gas
in your tank.
Number of Storks and Birthrate in a town
Correlation  Causation
Several Interpretations
A could cause B
B could cause A
C could be causing both A and B
Warning: People often try to use correlations as evidence
of cause. This is wrong.
True Experiments
Independent Variable (IV) - Variable that is
manipulated by Researcher. (Cause)
Dependant Variable (DV)- Measure of the
effect of manipulating the IV
•Purpose:
To see if changes in the IV cause changes in
the DV.
True Experiments
- objective measure
- a difference is produced (manipulated)
- all other variables held constant
If these three requirements are met, what
can we conclude about our results?
Cause
Effect
Light
Growth
When we conduct Experiments with
People we have an Experimental Group
and a Control Group. The two groups are
treated exactly the same except for the IV.
The level of the IV is under the control of
the experimenter (I.e., any person in the
study could be assigned to either the
experimental or the control group.)
Some studies compare different
groups of people to each other.
Random Assignment ensures that
there is no systematic reason why the
two groups should differ. Any
differences are due to chance.
Confound – anything other than the
manipulated variable that is different
between two conditions.
Serves as an alternative explanation of the
cause of differences between conditions.
Natural Experiments - look like experiments,
but they are confounded.
-Variable is not manipulated by experimenter.
e.g., comparisons of pre-existing or selfselecting groups.
Males vs. Females
People who use Match.com vs. those who do
not.
If Males score better on Math tests than
Females can I say being Male causes better
math scores?
Other explanations?
Other Biases (Can occur in any type of study)
Experimenter Bias
- blind Observers
Expectancy Effects
- e.g., placebo effect
Double blind procedures.
- both observer and subjects are blind to expected
results.
Statistics
- allow us to describe findings
Measures of Central Tendency
Mean – arithmetic average
Medium – Midpoint
Mode – Most Frequent
Measures of Variation
How much variation is there is the measures?
Range – lowest through highest.
Standard Deviation – Average distance of the
scores from the Mean. Larger the Standard
Deviation the more variable the scores in the
distribution are.
Shape of Distributions
Symmetrical Distribution
Symmetrical - scores evenly
distributed around the midpoint of the distribution.
Frequency
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0
1
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5
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9
10
Variable Measured
Skewed Distributions - scores pile up on one end of the curve.
Skewed Negitively
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5
5
Frequency
Frequency
Skewed Positivily
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3
2
1
4
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0
0
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Variable Measured
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Variable Measured
IE(DS)
43
In a symmetrical, unimodal, distribution the
Mode, Median and Mean will all be the same.
When the distribution is skewed, or contains
some deviant scores, these three measures can
be very different.
IE(DS)
44
IE(DS)
45
Normal Curve
Bell shaped curve describes
many variables in the
natural world.
Allows us to estimate the
probability of scores at any
place in the distribution.
Tests of Statistical Significance
If a result is statistically significant, the
difference between conditions is unlikely to
be due to chance alone.
Statistical Significant  Important
Better question: How big is the
difference?