Unit 5 Correlation and Regression: Examining and Modeling Relationships Between Variables
Research Methods in Psychology. Correlation A statistical value of the relationship between two...
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Transcript of Research Methods in Psychology. Correlation A statistical value of the relationship between two...
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Research Methods in Psychology
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CorrelationA statistical value of the relationship between two variables
Positive Correlation
As one number increases, the other increases.
Ex: Study time to GPA
Negative Correlation
As one number increases, the other decreases.
Ex: Absences to GPA
No Correlation
Variables do not affect one another in a significant way
Ex: Height to GPA
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Correlation Coefficient
• Ranges from -1.00 to 1.00
• Zero is no relationship
• -0.85 is a stronger relationship than .34
• CORRELATION IS NOT CAUSATION!!(i.e. Just because two variables have a correlation
does not mean one causes the other)
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CORRELATION IS NOT CAUSATION!!!• People that floss everyday live 3 years longer than those that do not.
• Red wine drinkers live longer than those that do not drink red wine.
• As speed limits increased on America’s highways, the death rate went down.
• Women with breast implants commit suicide 3 times as often as those without breast implants.
• Children who are played Mozart in the womb have higher IQ’s.
• Marijuana users in youth are more likely to have mental illness as adults.
• As ice cream sales increased, so did shark attacks.
• More TV’s per person in a country, the longer people live.
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Illusory Correlation
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The Experiment• Only research method capable
of showing cause and effect
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Experimental Method• Review Literature of Past Research
• Formulate Hypothesis• Design Research/Study Method
(naturalistic observation, case studies, surveys, experiments, etc)
• Collect the Data• Analyze the Data • Report the Findings (journal,
critique, replicate)• Draw Conclusion or Theory on
Explanation of Findings
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Hypothesis• A statement about the relationship between two or more
variables• Must be testable and refutable• Instead of proving the hypothesis, science usually tries to
disprove a null hypothesis.
Null Hypothesis (H0): opposite of hypothesis
Statistical Significance : 95% not due to chance
Hypothesis Example:
H1: Gender has an effect on spatial ability
H0: Gender does not have an effect on spatial ability
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Variables• Independent Variable (I.V.): manipulated by
experimenter
• Dependent Variable (D.V.): MEASURED variable influenced by independent
• Operational definition
• Confounding/extraneous variables
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Control Group
• No treatment or placebo
• Serves as basis for comparison
• Serves to eliminate alternative explanations
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Population – The larger group of people from which a sample is drawn
Sample: Representative of the population
Two ways to get sample
Random: Every member of the pop has = chance
Stratified: Sample is put together by picking a
group statistically equal to the population
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Control Measures• Counterbalance: controls for order effects
• Single-Blind: subject unaware of assignment
• Double-Blind: subject and experimenter unaware of placement
• Randomization– From population (sample)– From assignment to groups (assignment)
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Other Research MethodsEx Post Facto (after the fact)
- Independent variable already present
- Not a true independent variable, no cause and effect
- Often used due to ethical concerns
Naturalistic Observation
- Natural setting: behavior is not interfered with or altered
Survey Method
- Gathers data on attitudes and behaviors.
Case Study
- Intense study of an individual
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Flaws in Research
• Sampling Bias
• Overgeneralization
• Placebo effect
• Hawthorne/Barnum effect
• Demand Characteristics
• Experimenter Bias
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Evaluating Research
STRENGTHS AND WEAKNESSES OF
Experiment
Correlation
Surveys
Naturalistic Observation
Case Studies
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Ethics in Research• Participants are free to withdraw at any time
• No undo stress
• Subjects informed of significant factors that may influence their willingness to participate
• Subjects should be debriefed
• Ethical treatment of animals
• Generally research goes before a review board for approval
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StatisticsDescriptive Statistics
Organize and summarize data
Central Tendency: mean, median, mode
Standard deviation: variation in data
Range: distance from smallest to largest
Inferential Statistics
Interpret data and draw conclusions
Used to test validity of hypothesis (t-test)
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Standard Deviation
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Statistical Significance• Probability results are due to chance • Inferential stats (t-test) are used to
check for either a 5% (p<.05) or 1% (p<.01) level of significance.
Lottery tickets 14, 3, 27, 41, 18
1, 2, 3, 4, 5
Coin flips HHHHHHH or HHTHTHT
More likely?
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68% are within One standard deviation from mean
95% are within Two standard deviations from mean
Bell or Normal Curve
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Measures of Central Tendency
A Skewed Distribution
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Skews