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PSY2012 Exam 1

by: Hugo Notetaker

PSY2012 Exam 1 PSY2012

Hugo Notetaker

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General Psychology
Gaby Pogge
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This 8 page Study Guide was uploaded by Hugo Notetaker on Sunday September 18, 2016. The Study Guide belongs to PSY2012 at University of Florida taught by Gaby Pogge in Fall 2016. Since its upload, it has received 7 views.


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Date Created: 09/18/16
PSY2012 Week 1-3 Make Sure to Review all of the concepts below for the applied questions on the Exam ! Expect to cover a chapter over every two class periods 3 Evaluations throughout the class + The Final  Course Goals: 1. Science behind Psychology  2. Introduction to the different areas within the field of psychology 3. To teach you the concepts of psychology GET TEXTBOOK Office Hours:  Monday ... Tuesday 10:30­11:30 My Psych Lab  CourseID: pogge97871 Chapter 1 Psychology ­ the study of the mind, brain, and behavior Common Themes: ­Scientific Method ­Behavior has many causes ­Individuals vary but research focuses on group tendencies ­Multiple theories explain same behavior ­Nature (biology) & nurture (learning, experience) ­Context/sociocultural setting matter Levels of analysis ­ Molecular ­> Neurochemical ­> Neurological ­> Mental Level (Cognitive) ­> Behavioral  level ­> Social Level  Human behavior is difficult to predict. ­ People have a hard time predicting their own behavior  ­ Actions are multiply determined ­ Psychological influences are rarely independent of each other  ­Ex. Motivation and cognition  Individual differences in thinking, emotion, and personality. People influence one another. ­Reciprocal Determinism  ­Asch Experiment (using different sized lines with confederates (fake participants)) Behavior is shaped by culture. Naïve Realism "Seeing is believing" Are you objective? ­Ex: political beliefs (are you right about all of them?) Common Sense Often contradictory ­ Birds of a feather flock together vs opposites attract ­ Absence makes the heart grow fonder vs out of sight, out of mind Sometimes wrong Sometimes right ­ Snap judgments Not always so common  ­ Curse of knowledge  ­ Importance of scientific observation On being "scientific" A process to generate knowledge, not knowledge itself  ­ Systematic observations  Generally: ­ Start with an observation (empiricism) ­ Generate and test hypothesis  ­ Seek explanations that fit data ­ Repeat ­ Publish Chapter 1 Continued Is psychology scientific? ­ Intuition  ­ We are all Naïve psychologists  Hypothesis ­ a specific prediction based on a theory or experience, which can then be tested. ­ An expetation or prediction about a relationhip between two or more things ­ "We anticipated/predicited/expected that..." ­ Classic format: If X, then Y Theory ­ organized set of ideas that explain many findings ­Specific (no "theory of everything" here) ­Falsifiable ­Tie together related data  ­Explains/predicts behavior ­Directs research  ­Are NOT truth (falsification vs failure to falsify) A scientific theory vs your friend's "theory" ­Rigorous methods ­Multiple studies, different tests ­Explains data Science as a Safeguard ­Psychologists (scientists) are people too ­Confirmation Bias ­Belief perseverance Science protects us from fooling ourselves Tests of hypotheses must be falsifiable ­ we must be able to be wrong The scope of science ­Can test observations about the natural world ­Cannot test metaphysical  ­Do people have souls? On being wrong Science is not perfect ­Good scientists accept that they might be wrong Scientific knowledge is always tentative ­Degrees of confidence A set of claims that seems scientific but isn't ­No scientific method ­Lacks scientific safeguards Warning signs: ­Hypothesizing loopholes ­Over­reliance on anecdotes ­Lack of peer review ­Lack of self correction  ­Psychobabble (big scientific words that seem like they have meaning) "Patternicity" ­The "hot hand" (basketball shoots) ­Face on Mars? ­ FAKE Terror Management Theory ­1. Evolve self­awarness ­2. Realize inevitability of death ­3. Leads to paralyzing terror and sense that life is meaningless Solution? ­4.Symbolic immortality ­Reminders of death lead to more: ­endorsement of cultural worldviews (including belief in the paranormal) ­pattern perception  Logical Fallacies  ­Emotional reasoning fallacy ­Bandwagon Fallacy ­"Not me" fallacy Bias blind spot Why is pseudoscience problematic? Three major reasons: ­Opportunity cost ­Direct harm ­Inability to think scientifically  Not foolproof, but scientific thinking is our best safeguard against human error ­Peer review ­Be open to ideas, but only accept the ones that have been tested. Scientific Thinking involves thinking critically and being skeptical Being skeptical does not mean being closed minded. Principles of scientific thinking 1. ruling out rival hypotheses 2. Correlation is not causation 3.Falsifiability  4.Replicability 5.Extraordinary claims require extraordinary evidence 6.Occam's razor Basic vs. Applied research  ­Basic Research  ­understanding the principles of behavior  ­E.g., What is the function of self­esteem? ­Applied Research ­how to apply the principles to solve important problems ­E.g., How can we change people's thinking so that they want to engage in proactive health behaviors Types of Psychologists ­Experimental ­ conduct research ­Kind of a misnomer here ­All researchers on this may conduct experimental rsearch ­Developmental ­ research may change over the lifetime ­...... Chapter 2 ­ Methods ­ We are ALL susceptible to biases that can cause faulty thinking ­These biases are generally adaptive ­ But can lead us to be wrong, especially when we think we are right ­Proper research design can help lower these biases Validity ­Are we measuring what we think we are measuring? ­Two ways to asses validity ­Internal Validity ­Can we make a causal claim? ­Did X cause Y? ­Most account/control for alternative explanations ­Remember the ice cream/homicide example ­External Validity ­Is this how people behave in the real world? ­Does the conclusion generalize outside of the lab? 1. Naturalistic Observation ­Watching behavior in the real­world ­Example: Jane Goodall ­Observed and recorded chimpanzees for years ­Observed without intervening ­High External Validity ­Definitely how chimpanzees behave because that's watch she was watching  ­Low Internal Validity  ­We cannot establish causation ­No control over important variables ­Observer/Hawthorne effects ­Single observer problem in interpreting behavior 2. Case Study ­Studying one person or group for an extended time ­Ex. Phineas Gage ­Good for: ­Rare occurrences ­Existence proofs ­The Case of Genie ­Genie was kept in isolation by her abusive parents until the age of 13 ­She spent almost her entire childhood locked in a bedroom, isolated and abused ­External Validity ­Not really ­ certainly valid for the case you're studying, but ultimately anecdotal (unknown generalizability)  ­Internal Validity  ­No ­ again cannot infer causation 3. Correlational Designs ­Measuring two or more things and seeing if they are related. ­Correlation vary from r = ­1 to +1 ­+/­1 = perfect correlation ­Zero (no relationship) ­Can be: ­Positive (as one increases, so does the other) ­Negative (as one increases, the other decreases) ­Observing correlations allows us to generate predictions  ­Requires statistical tests of the correlation to avoid illusory correlations ­ perception of an association where  none exists ­Similar to "patternicity" ­Researchers use lots of terms to refer to correlational relationships between variables ­Illusory Correlations ­E.g., Full moon and crime frequency ­Humans tend to focus on box "A" and ignore the others ­Correlation vs Causation ­Three possible explanations: ­A causes B ­B causes A ­C causes both A and B ­Internal Validity: ­No ­ cannot infer causation ­External Validity: ­It depends ­Good for studying things we cannot manipulate (e.g., cannot study experimentally) ­Ex: Health outcomes from smoking ­Determining Causation ­You must manipulate one variable. and measure how it affects the other ­Ex. Manipulate level of ice cream consumption, measure aggression ­Starting to demonstrate causality ­E.g., ruling out reverse­causation ­Nomenclature: ­Variable: anything that can be measured ­Independent variable ­ whatever was manipulated ­Dependent variable ­ the other measured variable (the outcome) Chapter 2 Continued 4. Experimental Designs: Determining causation  Key features of experiment: 1. Manipulate the IV and observe effect on DV ­Create two or more "conditions" 2.Random assignment to conditions ­Intervention (ice cream) group versus control (no ice cream) group ­Ruling out confounds (alternative explanations) ­Other variables (not the IV or DV) that systematically vary along with the independent variable ­Ex. some people don't like ice cream; lactose­intolerance ­Determining causation requires isolating the variable of interest ­Attempt to control for all other factors ­Participants randomly selected from a representative sample within a population  ­Ex. A nationally representative sample based on age, sex, and household income ­Problems: ­Impossible to match participants with everything ­Internal Validity:  ­High ­ we can determine causation (when designed well)  ­Importance of good methods ­External Validity:  ­Depends, but typically lower than other designs Threats to Experimental Designs: Potential Alternative Explanations  1. Placebo effect ­Improvement due to mere expectation of improvement  ­May show similar effects to real drugs Solution: Blind studies ­Often a part of random assignment  ­Nocebo effect (opposite of placebo effect) 2. Experimenter expectancy effect ­Researcher expectations influence participant behavior  ­Ex. Clever Hans (Math horse) ­Solution: Double­blind studies 3. Demand characteristics  ­Participants try to guess the hypothesis and alter their behavior ­Prevents researchers from accurate/unbiased observation of participant behavior  Solution: Cover stories, "filler"items ­Cover story: Taking a participant's picture for a study of "attraction" ­Filler item: "I get paid bi­weekly by leprechauns" Types of Measures: Self­Report ­Self reports rely on participants' self­assessments rather than experimenter observation ­What is your favorite color? ­Sensitive to format ­Sensitive to wording Advantages ­Easy/low cost ­Most accurate in some cases Disadvantages ­Lack of introspective access ­Participants may lie or be biased ­Social desirability 1. Measure behavior  2. Indirect measures (e.g., infer based on reaction time)  Statistics  1) Descriptive : Communicate pattern of results 2) Inferential: Draw conclusions from results Central tendency 1) Measures of central tendency  ­Mean: average ­Median: Middle score in the data set ­Mode: most frequent score in the data set 2) Variability ­ how loosely or tightly bunched scores are ­ Standard deviation ­ measures how far each data point is from the mean ­ Range ­ difference between the highest and lowest scores Inferential Statistics  ­Allow us to infer real­world conclusions form data ­Significance testing (p­values) indicate the probability of obtaining your results ­The SMALLER the p­value, the more evidence against the null (more confidence that we can reject the null  hypothesis that there is no effect) ­Statistical significance: ­ p < 0.05 (common convention) ­ If p < 0.05, we reject the null hypothesis....  ­Practical significance: ­Statistical significance does not indicate the real­world importance of finding ­Just because p < 0.05 does not mean we should care ­ p < 0.05 does not say anything about size of effect ­Need to look at effect size (e.g., correlation coefficient, Cohen's D) ­Size of effects may say something about the importance or predictive value of an effect ­Small effects can be important  Evaluating Research ­Statistics can be lucky ­"1 in 20 papers are false at p<0.05" ­Statistics can also be misleading (intentionally or unintentionally) ­(1) Report unrepresentative measures  ­E.g., reporting the mean instead of median for skewed data ­(2) Truncate graphs  ­(3) Neglect base rates ­Peer review ­Recently, push for transparency  ­Open (public) data ­Open materials ­Replicability  Evaluating Psychology in the Media ­ Reporters' incentives may not align with truth­telling  ­May exaggerate a claim ­May minimize less central (but important) details ­May create a false sense of scientific controversy (misleading "balanced coverage")


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