Download Review Unit 2-research -2014-15

Chapter 2: The Research
Understanding and Prediction
Hypothesis=A tentative statement about two or
more variables-a tentative statement about
how things work (Students that eat breakfast
perform better in school-what are the two
variables here?) - an educated guess.
RESEARCH
Applied Research=has clear, practical application; goal
to control positive/negative situations
• If it is found that students who eat breakfast perform
better, schools will initiate a breakfast program
Basic Research=questions of interest that may not
have immediate, real world application
• How does anxiety affect people’s desire to be with
others (affiliation need)?
• Do different cultures react differently to stress?
THEORY
• If we observe there is a relationship
between breakfast eaters and
performance we formulate a theory
• Theory=predicts behavior or events- only
change as new information available more permanent-have considerable facts
to support it.
Unit 2 Review
longitudinal study study same people over a long
period
disadvantage=expensive; drop out
advantage=same cohort, less confounding variables
cross sectional study different cohorts at the same
time, less expensive and less time;
disadvantage=different cohorts
Survey- questionnaire (can study more people, study
things not ethical through experiment)
case study- in depth study of an individual (used for
rare occurrences of events/illnesses
experimental study-manipulation of Independent
variable (experimental group receives the
treatment/control group does not) to see it’s effect on
the dependent variable
Experimental Research:
Looking for Causes
• Experiment = manipulation of one variable under controlled
conditions so that resulting changes in another variable can be
observed (feeding one group of students)
– Detection of cause-and-effect relationships
Variable=any measurable conditions, controlled or
observed in a study
• Independent variable (IV) = variable manipulated (food) to
see its effect on
• Dependent variable (DV) = variable measured and affected by
manipulation of IV (school performance)
– How does IV (food) affect DV(performance)?
Operational definitions precisely define each
variable(IV-breakfast, DV-school performance)-required for
good experiment
most needed aspect of a study- so study can be refuted or
verified through REPLICATIOIN (makes study scientific)
Independent and Dependent VariablesHypotheses
1. Riding the bus to school (IV) makes students
more intelligent(DV)
2. Kids who view aggressive cartoons(IV) are
more likely to act aggressively(DV)
3. AP Psychology students who eat
chocolate(IV) perform better on vocabulary
tests (DV)
Operational Definitions=clearly
defining independent/independent
variables for replication
Children (Male/female, ages 4 – 6) who
view aggressive cartoons = , viewing all of
Sponge Bob, episode 5, while alone in…..
are more likely to
act aggressively = placed on the playground
for 30 minutes with 10 children who did not
view cartoon, five minutes after cartoon was
shown and strikes another child
Experimental and Control Groups:
The Logic of the Scientific Method
• Experimental group (exposed to manipulation of
independent variable-chocolate given)
• Control group (similar subjects but does not receive
IV manipulation given to the experimental group-no
chocolate given)
• EVERYTHING ELSE FOR THESE TWO GROUPS
MUST BE THE SAME and
Resulting differences in the two groups must be due to
the independent variable
Extraneous and confounding variables
• Levels of independent variable =1.chocolate verses not
getting chocolate; 2. also two independent variables=chocolate
and breakfast verses none for Control Group
The Scientific Method: Terminology
Population=animals or people from which a
sample is drawn (all AP students in Broward)
and researchers want to generalize about
Participants or subjects =organisms whose
behavior observed in a study
Sample=subjects from the population (AP
psych students selected from all schools in
Broward County)
Random sampling-all in population have equal
chance of selection.
Representative Sample is only way to
generalize results to population
Random assignment-participants have equal chance
of placement in control or experimental groups;
lessons confounding variables
Figure 2.16 The relationship between the population and the sample
Unit 2 Review
Descriptive statistics describes data– mean, mode ,
median, standard deviation
Measures of central tendency = typical or average score
in a distribution
• Mean: arithmetic average of scores
• Median: score falling in the exact center, or the average
of the two center scores
• Mode: most frequently occurring score
mean is most useful measure of central tendency except
when
Outliers = mean distorted by extreme scores
statistical inference-conclusions drawn about the
relationship between variables, from a sample to entire
population
Figure 2.11 Measures of central tendency
Descriptive Statistics: Correlation
• When two variables are related to each other, they
are correlated.
• Correlation Coefficient = relationship between two
variables
• How well does A predict B?
– Strength of the correlation
-1.0 to +1.0
positive correlation-as one variable increases,
so does the other ; as one variable decreases,
so does the other
negative correlation-one variable increases the
other decreases
Figure 2.14 Interpreting correlation coefficients
Correlation
Correlation
Correlation
Correlation
Correlation
Correlation
Scatter plot =best way to show
relationship between variables
Hours Spent Watching Television per Day & GPA
PERSON
HRS
GPA
1
0.5
3.50
2
1
3.75
3
2
4.00
4
2.5
2.75
5
3
2.75
6
3.5
1.75
7
4.5
2.25
8
5
1.50
9
5
2.50
10
7
1.00
Which statistic approximates the relationship between the
variables?
50%
N=20
N=10
r= -.90
r=.50
Unit 2 Review
In normal curve-distribution of
scores-68% fall within 1 SD
above/below the mean
percentile scores – the same as or
better than 72% of population/test
takers
Describing Data
Measures of Variability-how scores vary from the center
• Normal Curve (bell shaped)
Descriptive Statistics:
Variability = how much scores vary from each
other and from mean (see the normal curve or bell
curve pp 63-64 or 66-Barrons)
– Standard deviation = how far scores are from the
mean/average; for the Normal curve with IQ, one
standard deviation is 15 points from the mean
If scores deviate 10 points, curve by 10 points, how
far will scores deviate from mean???
In normal curve-distribution of scores-68% fall
within 1 SD above/below the mean
-Range=distance between highest and lowest
scores in data set
Figure 2.12 Variability and the standard deviation
Z scores measure the distance of a score from
the mean (either - or +); a z score of -1 is 15
points below the mean, -2 is 30 points below
mean
Percentile scores – the same as or better
than 72% of population/test takers
38th percentile=you did the same or better than
38 percent of people who took a test
Distribution of Scores-Bell Curve
• Symmetrical distribution-see p. 65 in
Barron’s
• Positively skewed (curve to Left)-more low
scores than high
• Negatively skewed (curve to Right)-more
high scores than low
If Negative skewed due to test scores, can only
assume????
Experimental Research:
• Replication=repeat a study to see if earlier
results are duplicated (this is why the
operational definitions are important)
• Reliable when you can replicate or repeat it
• Valid when it measures what the researcher
set out to measure
Statistics and Research:
Drawing Conclusions
Statistics – using mathematics to organize, summarize,
and interpret numerical data
– Descriptive statistics (the numbers) organizing
and summarizing data (measures of central
tendency, measures of variability, and the
correlation coefficient) to see if there is a
relationship between variables
– Inferential statistics: interpreting data and
drawing conclusions about the larger population
Statistical significance = the relationship found
between the IV and DV is not due to chance (.05
level of significance)=
less than 5 chances in 100.
It can never be 0 because we can never be 100%
certain
Correlation Predicts Strength or
Relationship Between Variables DOES NOT
Say ONE CAUSES OTHER:
Correlation does not =Causation
– Foot size and vocabulary positively
correlated
– larger feet belong to older children
Strengths and Weaknesses
of Experimental Research
• Strengths:
– conclusions about cause-and-effect
relationships can be drawn
• Weaknesses:
– artificial nature of experiments
– ethical and practical issues
Figure 2.10 Comparison of major research methods
Advantages/Disadvantages of these (naturalistic
observation, case studies, surveys) called
Descriptive/ Correlation Methods:
Advantage:
• explore questions that can not be examined
with experimental methods (poor maternal
nutrition and birth defects)
Disadvantage: Cannot control events to
isolate cause and effect
Evaluating Research:
Methodological Pitfalls
• Sampling bias =sample not representative of
population- I CANNOT DRAW CONCLUSIONS
• Placebo effects = participants’ expectations lead
them to experience some change (do better on a test,
less headaches), regardless of the Independent
Variable/Treatment
• Placebo method helps=give both groups a drug (one
real and one a placebo)
Evaluating Research:
Methodological Pitfalls
Distortions in self-report data (survey, interview):
– Social desirability bias = give socially approved
answers to personal questions
– Response set = respond to questions in a
particular way that is unrelated to the content of
the question (agreeing with almost everything on a
questionnaire)
-Hawthorne Effect=changes in subjects behavior
due to the attention of researcher (having
control and experimental groups help)
Evaluating Research:
Methodological Pitfalls
• Experimenter bias = researchers expectations about
outcome of study influences results; treats
experimental and control groups differently to
increase chance of confirming hypothesis
double-blind control/procedure = neither subject
or experimenter know which group is the control or
experimental group
Single blind control/procedure=the subject does
not know if they are the control or experimental
group
Ethics in Psychological Research:
Do the Ends Justify the Means?
Ethical standards for research: the American
Psychological Associationacademic research and the IRB : ensures ethical treatment for
animal and human research:
1. Informed consent – participant’s permission, told
potential risks, offered alternative activity
2. No harm to humans Psychological or physical
3. Minimal harm to animals-Ethical Treatment
4. Debriefing to offset deception
5. Confidentiality- cannot share names (includes test
scores-UNLESS WRITTEN PERMISSION PROVIDE
Deception IS PART OF RESEARCH!!!
Figure 2.17 Ethics in research