Transcript Examples - Solon City Schools

Research Methods
It is actually way more exciting
than it sounds!!!!
Why do we have to learn this
stuff?
.
Be aware however of two hurdles that tend to skew our
logic when we research
•
Hindsight Bias
Monday Morning
Quarterbacking!!!
• Consider findings to be
common sense
Example:
Overconfidence
• Overconfidence –
• 82% of U.S. drivers consider
themselves to be in the top 30% of
their group in terms of safety.
• 81% of new business owners felt
they had an excellent chance of
their businesses succeeding. When
asked about the success of their
peers, the answer was only 39%.
(Now that's overconfidence!!!)
Overconfidence
“There
is no reason for anyone to have a computerin their home.” (Ken Olson, president
of Digital Equipment Company, 1977)
“Heavier-than-air flying machines are impossible.”(Lord Kelvin, British mathematician,
physicist, and president of the British Royal Society, 1895)
“Reagan doesn’t have the presidential look.”(United Artists executive when asked
whether Ronald Reagan should be offered the starring role in the movie The Best
Man, 1964)
“A severe depression like that of 1920–1921 is outside the range of probability.”
(Harvard Economic Society, Weekly Letter, November 16, 1929)
“Man will never reach the Moon, regardless of all future scientific advances.” (Lee
DeForest, inventor of the vacuum tube, 1957)
The Scientific Attitude
• Three main components
– Curious eagerness
– Skeptically scrutinize competing ideas
– Open-minded humility before nature
Critical Thinking
• Critical Thinking - thinking that does
not blindly accept arguments and
conclusions
– “Smart thinking”
– Four elements
• Examines assumptions
• Discerns hidden values
• Evaluates evidence
• Assesses conclusions
– Empirical Approach
Scientific Method
1. Observe some aspect of the universe.
2. Invent a theory (an explanation) that is
consistent with what you have observed.
3. Use the theory to make predictions, a
hypothesis is a testable prediction
4. Test those predictions by experiments or
further observations.
5. Modify the theory in the light of your results.
6. Go to step 3.
3Types of Research
• Descriptive
• Correlational • Experimental
3 Types of Descriptive Research
• The Case Study
• The Survey
• Naturalistic Observation
Case Studies
• Case study –
• Provides oppty to study unusual
cases
• Offers suggestions for further
study
• Results often can’t be
generalized
• Can’t establish cause and effect
Example:
Case Study Methodology
– Methodology
• Gather data from one
person (or small group)
through:
Naturalistic Observation
• Gather data by
watch subjects in a
• Do not manipulate
the environment.
• May be done when it
is
• Does not show cause
and effect.
Example:
Survey
• Gather Data –
–
–
–
–
Personal facts
Behaviors
Attitudes
Opinions
Survey Method
Method:
•Questionnaire
•Interview
Can be Descriptive or Correlational
Advantages:
Example:
1. On ave. how many hours do you study per night?
2. What is your grade point ave?
Conducting a Survey
• Population - all the possible subjects in a group you
want to study
• Example:
• Random Sampling – a portion of the population that
fairly represents the population because each
person has an equal chance of getting chosen
• Ensures that
• Helps avoid false generalizations
• Example:
Survey Method: The Bad
• Low Response Rate
• Response Bias/social
desirability bias –
• Wording Effects –
way questions are
worded may change your
results
How accurate would a survey be
about the frequency of diarrhea?
Correlational Method
• Correlation - expresses a
relationship between two
variables.
–
• Can be efficient
• Can make
• Can use pre-existing or
archival data
• Make it difficult to assess
the impact of a third variable
As more ice cream is eaten,
more people are murdered.
Does ice cream cause murder,
or murder cause people to eat
ice cream?
Results of Correlational
Studies
Positive Correlation
• The variables go in
the
Example:
Negative Correlation
• The variables go in
Example:
Correlation Coefficient
• Correlation coefficient - A
number that measures the
relationship between 2
variables.
• Range is from – The relationship gets
the closer you get to zero.
• Scatterplot – a visual
representation of the
relationship between the
variables
– shown as a graphed cluster of
dots
Which is a stronger
correlation?
• -.13 or +.38
• -.72 or +.59
• -.91 or +.04
Which of the following would be a
negative correlation, and which
would be a positive
correlation?
Education and years in jail
Weight and hours of TV watched
Education and income
Holding babies and crying
Food and calories ingested
Correlation
Correlation
Correlation
The table below lists the scores of eight research participants on a test to
measure anxiety, as well as the typical number of cigarettes each person
smokes daily. Scores on the anxiety test can range anywhere from a low of 0
(indicating very low anxiety) to a high of 30 (indicating very high anxiety).
Research
Participant
1
2
3
4
5
6
7
8
Anxiety
Cigarettes
Test Score____________
8
11
9
3
15
11
14
16
21
26
12
10
22
24
17
18
Construct a scatterplot to represent the correlation between smoking and
anxiety. Describe the direction of the correlation and give two possible
explanations for it.
Correlation
Illusory Correlations
• Illusory Correlation
–
–A random coincidence
• Speaking at a college graduation ceremony,
Professor Robson compared college graduates
with adults who are less educated. She
correctly noted that college graduates pay
more taxes, vote more frequently, engage in
more volunteer activities in their
communities, and are less likely to go to jail
than less-educated adults. The professor
concluded that colleges obviously do great
things for society. How might you reasonably
challenge the way the professor reached her
conclusion?
Experimental Method
• Experiment - Manipulation
of one or more variables
• Advantages:
• Can verify results –
• Can eliminate bias • Shows
• Disadvantages:
Smoking causes health issues.
Theory
• Theory -
• Example:
– My hunch:
• A hunch
Hypothesis
• Hypothesis - A
testable prediction.
• Example:
Operational Definitions
• Operational Definition - • Example:
A statement that tells a
person clearly the
• Meaning of
• measure of a
• Must be clear and
precise
• Must be
• Allow
Independent Variable
• Independent Variable
Whatever is being
manipulated in the
experiment.
If there is a drug in an
experiment, the drug is
almost always the
independent variable.
Example:
Dependent Variable
• Dependent Variable Example:
• It is dependent on the
independent variable.
The dependent variable
would be the effect
of the drug.
Beware of
Confounding Variables
• Confounding variables A factor other than the
independent variable
that might produce an
effect on the
experiment
• Confounds often arise
due to differences
between the groups that
exist before the
independent variable is
imposed!
Examples:
(We’ll talk about the last two
later)
.
Assignment
• Random Assignment
• Examples:
– Assigning participants in an
experiment to experimental and Experimental control groups by chance
Control group –
– Minimizes differences between
two groups
– Different than Random Sample
– Reduces the impact of
confounding variables
• Experimental Group –
• Control Group –
2 other confounding variables
• Experimenter Bias - expectations by
the experimenter that are subtly
communicated to the participants
– Example:
• Placebo effect – an experimental
effect caused by expectations of
participants or caused by a substance
which the recipient assumes is the
independent variable but is not
– Example:
Double-blind Procedure
• Double-blind -
• Minimizes placebo effect and
experimenter bias
Replication
• Replication Repeating the
research study to
see whether the
basic finding
extends to other
participants and
circumstances.
• What helps the
researcher to insure
the study can be
replicated?
Quasi-experimental
• Quasi-experimental
No random
assignment
• Used to study
differences
between:
• Confounding
variables so no cause
and effect
Controlled Observation
• Controlled
Observation - Type
of Observational
Research
– Conditions are
contrived by
researcher
– Independent and
dependent variable
– Does not show cause
and effect
• Early Psych
Research
Statistics
• Recording the
results from our
studies.
• Must use a common
language so we all
know what we are
talking about.
Descriptive Statistics
• Descriptive Statistics numbers that
summarize a set of data
obtained from a sample
– describes sets of data.
• Examples:
Types of Data
• Nominal Data – identifies
categories
• Ordinal Data – identifies the
order in which data falls in a
set
• Interval data – data that
falls within a number line
with a zero starting point
• Ratio data – data that falls
in a number line where zero
is just another number
• Examples:
Measures of Central Tendency
• Mode • Mean • Median -
Central Tendency
• Mean, Median and Mode.
Let’s look at the salaries of the
employees at Dunder Mifflen Paper
in Scranton:
$25,000-Pam
$25,000- Kevin
$25,000- Angela
$100,000- Andy
$100,000- Dwight
$200,000- Jim
$300,000- Michael
The median salary looks good at $_______________________
The mean salary also looks good at about $________________
But the mode salary is only $___________________________
Watch out for extreme scores or outliers.
is a better measure than the
when there are extremes
Normal Distribution
• In a normal
distribution, the
are all the same.
Examples:
Height, Weight, IQ Scores
Distributions
• Outliers skew
distributions.
•
If group has one high
score (contains more
low scores)
•
If a group has a low
outlier, the curve has
a (contains more high If most students scored well on a
test, what would the distribution look
scores)
like?
If most students scored poorly?
What does the data tell us?
Measures of variability
• Range: distance from highest
to lowest scores.
• Standard Deviation: the
variance of scores around the
mean.
• The higher the variance or SD,
• Do scientists want a big or
small SD?
• Variance - The average of
the
differences from the mean –
another measure of how the
data is distributed around the
mean
Shaq and Kobe may both
score 30 ppg (same mean).
But their SDs are very
different…meaning?
Calculating Standard Deviation
Step 1 – calculate the mean –
add all of the raw scores and
divide by the # of scores
Step 2 – calculate the deviation
from the mean by
subtracting each of the raw
scores from the mean
Step 3 – square the deviation
from the mean for each
score
• Step 4 – Sum the squared
deviations
• Step 5 – divide the sum of
the squared deviation by the
number of scores and find
the square root
Calculating the Standard
Deviation
Calculating the Standard Deviation
your turn
Scores – 10, 3, 7, 8, 7
Step 1 – calculate the mean – add
the all of the raw scores and
divide by the # of scores
Step 2 – calculate the deviation
from the mean by subtracting
each of the raw scores from the
mean
• Step 3 – square the deviation
from the mean for each score
• Step 4 – Sum the squared
deviations
• Step 5 – divide the sum of the
squared deviation by the number
of scores and find the square
root
Variance
• The average of the
squared differences
from the Mean.
• = Standard Deviation2
• Example:
Standard Deviation = 5
Variance =
• *if you know the variance, how
can you calculate the standard
deviation?
Scores
• A unit that measures
the distance of a score
from the mean in units
of standard deviations
• Observation – Mean
Standard Deviation
• 10-15 = -1
5
• Equals 0 at the mean
• A positive z score =
Example: If John scored a 72 on a test •
with a mean of 80 and a standard deviation
of 8, John’s z score would be
negative z score =
Normal Distribution
What is the probability an observation is less than the
z score or more than the z score?
Normal Distribution
Calculating the % of the observed
Step 1 – calculate the mean
Step 2 - calculate/find the standard deviation
or variance. If you only have the variance you
must calculate the standard deviation
Step 3 draw a normal distribution curve and
find the scores for each standard deviation
from the mean and place them on the graph
Step 4: calculating the % of students who
scored within a range of scores by finding the
corresponding scores on the curve, then add
the percentages from each standard
deviation.
Inferential Statistics
• Inferential Statistics - The purpose is to
discover whether the finding can be
• Statistical Significance – the observed
difference between the means of the
experimental and control group are not due
to
• Measured by P-value =
– 5% likely the results are due to chance or
– 95% confidence level the results are due
to the independent variable
– You can apply the findings to the
population
• What does a p-value= .80 mean?
Statistical Significance
Key Ideas
• The bigger the difference between groups the less likely it is
that it's due to chance.
• The larger the sample size (number of patients) the more likely
it is that the observed difference is close to the actual
difference. This is an example of the "law of large numbers."
– Ie. The smaller the real difference is, the more patients you need
to be likely to detect a statistically significant difference in a
clinical trial. The larger the real difference is, the fewer patients
you need to be likely to be detect a statistically significant
difference in an actual clinical trial.
• The larger the sample size, the smaller an observed difference
has to be in order to be statistically significant.
• The smaller the sample size, the larger an observed difference
would have to be in order to be statistically significant.
APA Ethical Guidelines for
Research
• IRB• Both for humans and
animals.
Animal Research
• Clear purpose
•
• Acquire animals
legally
• Least amount of
Human Research
•
•
•
•
C
No
I
D
Milgram’s Experiment
Milgram’s Experiment
• Obedience High
• Obedience Lower