Research Methods in Psychology

Correlation A 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

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)

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.

Illusory Correlation 5

The Experiment Only research method capable of showing cause and effect

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

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

Variables Independent Variable (I.V.): manipulated by experimenter Dependent Variable (D.V.): MEASURED variable influenced by independent Operational definition Confounding/extraneous variables

Control Group No treatment or placebo Serves as basis for comparison Serves to eliminate alternative explanations

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

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)

Other Research Methods Ex 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

Flaws in Research Sampling Bias Overgeneralization Placebo effect Hawthorne/Barnum effect Demand Characteristics Experimenter Bias

STRENGTHS AND WEAKNESSES OF Evaluating Research STRENGTHS AND WEAKNESSES OF Experiment Correlation Surveys Naturalistic Observation Case Studies

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

Statistics Descriptive 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)

Standard Deviation

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?

Bell or Normal Curve 68% are within One standard deviation from mean 95% are within Two standard deviations from mean

Measures of Central Tendency A Skewed Distribution

Skews