PSYC 3510 1st EditionFinal Exam Study GuideETHICS- Particular studies to note:-Tuskegee Syphilis Study- The Nuremberg Code- The Milgram Obedience Studies- Willowbrook State- Zimbardo’s Standford Prison Experiment- Edward Taub and Silver Spring MonkeysI. The Principle for Respect for PersonsA. Informed Consent: Each person learns about the research project, knows the risks and benefits, and decides whether to participate.B. Treat participants as autonomous agents.II. The Principle of BeneficenceA. Precautions must be taken to protect participants from harm and ensure their well-being.III.The Principle of JusticeA. Balance between the participants in research and those who benefit fromit. Participants “bear the burden.”IV.Among the other standards: Confidentiality, Sexual Issues, Competence, Questionable InterventionsA. Deception is particularly tricky because the researchers intentionally withhold details of the study from the participants; however, the deceit issometimes necessary to obtain meaningful data.B. Debriefing- An increase in the knowledge of the participant to hopefully alleviate stress or psychological damage caused by deception.C. Research Misconduct- 1. Data Fabrication: Researchers invent data that fits their hypothesis.2. Data Falsification: Researchers selectively delete observations from a data set or by influencing the subjects.a. * Stapel in the Netherlands in 2012; Over 100 studies have been retracted; The whole field of Social Psychology has now been subject to much criticism.D. Animal Research1. The Three R’sa. Replacement: Alternative; Example: Computer simulationsb. Refinement: Modify procedures to minimize or eliminate distress.c. Reduction: Designs to utilize fewest animals as possible.2. Important to consider the difference between ANIMAL RIGHTS AND ANIMAL WELFARECLAIMS AND VALIDITIESII. Claims1. Frequency: The rate at which something occurs; Construct, External, Statistical2. Association: A correlation; Not manipulated; Construct, Statistical, Externala. Represented by a scatterplot graphb. Positive- Same directionc. Negative- Opposite directiond. Zero- No relationship3. Causal: One thing causes the other; Internal, Construct, Statistical, Externala. Distinguishing verbs; Example: Affects, Prevents, May lead tob. Criteria for establishing causationi. Covariance: As A changes, B changes.ii. Temporal Precedence: A comes first in time, before B. iii. Internal Validity: No possible alternative explanations forchange in B, only A changed.I. Validities1. Construct: How well it is being measured2. External: How well it can be applied to other people; Generalization3. Statistical: Does data support the hypothesis; Type I or Type II Error; Margin of Error4. Internal: Relationship between variables; Are there confounds? ; Causal claimsDecide which validity is the most important, sacrifice one over the other.II. Type I Error: False positive; Type II Error: Swing and a missIII. Variables1. Measured: Observed; INDEPENDENT2. Manipulated: Controlling the levels; DEPENDENT3. Conceptual: A broad idea or concept; Example: Self-esteem4. Operational: How the study is measured; SpecificRESEARCHResearch vs. ExperienceA. Experience has no comparison group1. Research needs to present falsifiable theories2. There must be systematic data collectionB. Experience is confounded1. Several possible explanations for an outcome; ConfusingC. Research is probabilistic Research vs. IntuitionA. Intuition is biased by faulty thinking1. Being swayed by a good story- Believing or accepting a conclusion because it “makes sense”.2. Availability heuristic: Things that pop up easily in our mind that tend to guide our thinking; particularly when events are vivid, recent,or memorable.B. Intuition is biased by motivation1. Focusing on evidence we like best- “Cherry-Picking” information2. Asking biased questions to get expected answers3. Confirmatory Hypothesis Testing4. Bias Blind Spot: The belief that we are unlikely to fall prey to cognitive biases.**NOTE**- Make sure to understand the concept of ethnocentrism- Make sure to review the founding and implementation of the IRB- Understand the scales of measurement when making observations.- Nominal, Ordinal, Interval, Ratio- Understand the 3 types of research- Applied, Basic, Translational- Understand the theory-data cycle- Theory, Research Questions, Research Design, Hypothesis, DataI. Validity: Measure what is supposed to be measuredII. Reliability: Consistency across studies.A. Test-Retest: Apply to any operationalization; Consistent throughtimeB. Interrater: Two or more observers come up with very similar findings.C. Internal: Questions are worded differently, but intended to be a measure of same construct.D. Represented by scatterplot and correlation coefficient r- Reliability is Association Claim III. Validity of MeasurementA. Face: A plausible measure of the variable in question; Subjective judgment; Look at a scale, you know what it is studying.B. Content: Subjective; A measure must capture all parts of the defined construct.C. Criterion: Whether the measure is related to a concrete outcome,such as a behavior, that it should be related to; Example: IQ and the construct of intelligenceD. Known-Groups Paradigm: Researchers see whether the scoreson the measure can discriminate among a set of groups whose behavior is already well understood.E. Concurrent: The scale should correlate with other scales measuring the same thing.F. Predictive: Should predict future behavior.G. Divergent: The scale needs to be negatively correlated.H. Discriminant: Two measures of opposite things will be strong but negatively correlated.I. Convergent: Two different scales should have strong correlation.IV. Be sure to understand Population of InterestA. Biased (Unrepresentative): Some members have a much higher probability of being included in the sample.1. Purposivea. Combination of convenience and self-selection2. Snowball3. Convenience4. Self-SelectedB. Unbiased (Representative): All members of the population have an equal chance. 1. Simple random2. Stratified3. Cluster4. Oversampling5. Multistage6. SystemicSTATISTICSA. Regression (r) and Multiple Regression (r^2)1. Small r=.10……r^2=.012. Medium r=.30…….r^2=.09 {Use to explain significance}3. Large r=.50……r^2=.25 { % of the variance explained by the equation}4. r: Correlation with the dependent variable; How much the criterion and