Transcript 1. Describe a phenomenon 2. 3. 4.

Research and Statistics
AP Psychology
Questions:
► Why
do scientists conduct research?
 answer
Questions that Social Psychologists
wanted to explore:
► What
impact does attractiveness have on
perception?
► How do we explain peoples’ behavior?
 Famous Experiment
► What
are the reasons for attitudes or
prejudices?
► Why do we conform?
► Why are some people altruistic?
4 Main Goals of Psychological
Research:
► Describe
a phenomenon
► Make
predictions about
the phenomenon
► Control
► Explain
variables
phenomenon
with some degree of confidence
In order to conduct good
psychological research one must:
 Use the scientific method.
► Have
a clear operational definition.
 Allows for replication.
► Have
informed consent.
► Follow APA guidelines.
Various Types of Research Methods
Video
► Descriptive
Research: Scientists use
naturalistic observations, case studies, and
surveys to describe and predict behavior
and mental processes.
Various Types of
Research Methods
► Correlational
Research: predicts naturally
occurring relationships.
Various Types of
Research Methods
► Experimental
Research: Scientists use
experiments to control variables and to
establish Cause and Effect Relationships
(in which one variable can be shown to have
caused a change in another.)
Descriptive
Research
►
Naturalistic Observation:
 Seeing subjects in natural
environment
 Best way to gather
descriptive data.
 Problem- when people know
that they are being watched,
they act differently.
(Hawthorn effect)
 Test 1
 Video 1
 Video 2
►
Case Studies:
 Intense examination of a
phenomenon.
 Useful when something is
new and complex.
 Problem- limited to what
researchers consider
important.
Descriptive Research
Surveys
► Researchers use questionnaires and
interviews to gather information about
behavior, beliefs, and opinions.
► Gives a broad view of a large group.
► Validity depends on representativeness
and how questions are worded.
► Problem- people are reluctant to say what
they really believe, say what is desired.
Correlation:
Used to say that two traits or behaviors
accompany one another and the Correlation
Coefficient measures the relationship
between the two.
► Correlation does not mean Causation!!!
► Types of correlation:
►
 Positive- two variables same direction
 Negative- two variables opposite direction
Correlation Coefficient
►A
number that
measures the
strength of a
relationship.
► Range is from -1 to +1
► The relationship gets
weaker the closer you
get to zero.
Which is a stronger
correlation?
► -.13 or +.38
► -.72 or +.59
► -.91 or +.04
Experiments:
► Establish
►
a Cause and Effect relationship.
Allow researchers to control variables.
 By manipulating variables, researchers view effects.
 Types of Variables:
►Independent- manipulated factor.
►Dependent- factor that is observed and measured.
(depends on the application of the independent
variable)
► Confounding a.k.a.Random Variablesuncontrolled factors in research design.
Experimental Method:
► 1.
► 2.
Observe some aspect of the universe.
Invent a tentative description, called a
hypothesis, that is consistent with what you have
observed.
► 3. Use the hypothesis to make predictions.
► 4. Test those predictions by experiments or further
observations and modify the hypothesis in the
light of your results.
► 5. Repeat steps 3 and 4 until there are no
discrepancies between theory and experiment
and/or observation.
How do I pick the people to study?
► Pick
a sample of people from a population so
that you can generalize your findings. Regardless
of research design)
 Population  Random Sample Random Assignment
 Control Group OR Experimental Group
 Larger samples yield more reliable results (External
Validity)
So now, what do we do with all the
data collected???????
► We
use STATISTICS! Yay…..
Important stuff to know about data…
► In
order for data to be considered worthy or
good, sound data it must be…
 Reliable- stable and consistent
 Valid- it is reporting what it set out to report
Statistical Analysis of Research Results:
Descriptive or Inferential?
► Statistics
are methods for demystifying and
making meaning from data.
► Numbers
have little meaning unless it is
organized. An organized list allows us to
see clusters or patterns in data.
► Graphing allows us to see some level of
meaning from numbers. (Pie Charts, Line
Graphs, Frequency Polygon
Descriptive Statistics:
► Used
to describe and present data.
► They can tell us the difference between two or
more groups.
► Basic categories of DS are
 1. Central Tendency
 2. Variability
 3. Correlation
Central Tendencies
►A
way to describe the typical or average
score distribution.
► Mean- average, can be skewed.
► Median- best indicator of central tendency,
middle score.
► Mode- Most frequently occurring score.
Variability:
► Indicates
how much spread there is in a
distribution.
► Range- difference between lowest and
highest score.
► Variance- how different scores are from
each other.
► Standard Deviation- most common measure
of variability. (how far each value is from
the mean.)
Let’s practice with Central
Tendencies
► What
options does Mars Co have when
filling bags of M&Ms in regard to color?
►FIXED…..RANDOM…..You
decide!
Choose a sample from the
population…..
 Propose a hypothesis by recording a % on
worksheet. (f = frequency) Calculate %.
 Observe and record actual colors in your
sample. (how closely do they match?)
►Will
you alter your hypothesis?
 Get into groups of 5 people. Record data.
►Will
you alter your hypothesis?
 Get into 2 big groups. Record data.
►Will
you alter your hypothesis?
 Get into 1 group. Record data.
►Will
you alter your hypothesis?
So what…
► ….does
this tell us about sampling?
► …does this tell us about randomness?
► …does this tell us about the relationship
between research and statistics?
Normal/Bell Shaped Curve:
Intelligence and IQ
Correlation:
► Used
to describe the relationship between
two variables. (co- relation)
► Measured in numbers from -1.0 to +1.0.
► -1.0 indicates negative relationship
► 0 indicates no relationship
► +1.0 indicates positive relationship
► Scatter plots are used for visual
representation.
Inferential Statistics:
► Provides
the researcher with a measure of
confidence that the difference between groups is
large enough to be important (or small enough to
be
insignificant.) In other words, IS provide a
measure of how likely
it was that results came about by chance. In
summary it answers the question- was
the difference mostly due to the Independent
Variable rather than chance factors.
Statistically Significant? (Tests of
Statistical Significance.)
►
t-test: establishes if results were by chance
or because of IV.
► ANOVA:
groups
compares means of 2 or more