PSYC 330 1st EditionExam # 1 Study GuideIntro/MethodsPersonality- from the Greeks meaning persona, mask-The aspects of individual responses and actions that is consistent across situations and other times.Why study personality?seek to understand ourselveshope to predict others actions many applications from the personal to the commercial, to social policy arenasLooking for patterns, making theories, and making assumptions on those theoriesTheories:Idiographic vs. nomotheticIdiographic- approach taken on individual vs group studies. Focus is on the individual, particular thing, person, etc. Nomothetic-studies general lawsGlobal vs ParticularHistorical trends in personalityDynamicBehavioralTraitCognitiveCross culturalEvolutionaryCriteria for a good theoryInternal consistency External consistencyParsimony-the simpler the betterHeuristic value- leads to interesting as to expand on knowledgeRange- comprehensivenessUsefulness-Does it lead to useful applications?Falsifiability- can the theory generate a hypothesis that can be disproved? Ex: religion isn’t falsifiableTheory BuildingAxioms-logical expressionConstructs-explanatory concepts between things and events, may not be able to observe Ex) self esteemPropositions-relate parts of the theory to other partPredicting-predict over timeOperational definition-how we define things like anxiety, not just as a synonym, sometimes as a series of questionsScience tries to relate theory to facts Positivism & scientific methodTheory => (yields) Hypothesis (falsifiable)Empirical date- by observation —theories need empirical supportHypothesis rejected? Failure to reject hypothesis doesn’t prove something Uri Geller and Amazing RandiNOTHING IS PROVENFacts and TheoriesFact: Out of the top SAT Math scores, only 10% were femaleTherefore: Gender is important Math is important SAT is unreliable at upper range Facts are theory laden (heavy loaded)Theory BuildingTheory defines dataTheory defines the variablesDefinitions constrain measuresMeasures vary:-cost-intrusiveness-reliability/validityReliabilityConsistent ResultsUsually represented as r, correlationInter-rater- same dataTest retest-same data tomorrowTemporal- little variationsSplit/half (internal consistency) Validity-Measures what you intend for it to measure-direct function of reliabilityFace-measures what it seems to measure, others don’t get truthConstruct- does it really measure what you want it toConvergent/concurrentPredictive-predict what will happenCriterion- does it fit the criteriaKinds of DataObservation and interview-well defined, replicable, it can be surreptitious( problem if your observed and you change your behavior)Self Report- Structured Open-ended operational richer reliable Personally relevant Focused Phenomenological Procedural artifacts Interviewer effects Each is vulnerable to social desirabilityRatings- Trained vs untrained Operationally defined vs open Counting vs impressions Objective vs non-neutralPhysiological Data- easily operationalized replicable/reliable intrusive utility and relevance vs precision and validity Test Data: Standardized procedure Normative reference-appropriate sample?Meaningful numbersData Gathering- Cross sectional Longitudinal—prospective—retrospective ArchivalResearch Design- Case study- important, small tool but largely important in generating hypothesis Survey Research- provides a snapshot, descriptive, but not inferential statistics (means percentages %, ex- 4 out of 5 doctors) Polls, Cosmo, or men’s health “research”, marketing analysis Correlational Data-Evaluates whether one variable (predictor) is related to another (criterion).Evaluates linear relationships only, can be positive or negative, often expressed as r. r can vary from -1 to +1. r describes the strength of the relationship (not the significance) r may or may not be significant, depending on the sample size. r^2 = the proportion of variance accounted for in the relationship. Correlation of .8 to 1 is high. We want to look at those that are .3-.4. Significant,not due to chance, statistically unlikely to observe data if there is not variance. Significant Correlations- correlation: r-.3 r^2=.09Statistically significant? (need sample size,N)N=20 Not significantN=2000 Yes significantClinically significant? aka Does it matter?Correlation does NOT = causation Example: Most heroin addicts smoked week, does not equal causation because of the oppositeeffect, how many people who smoked weed did NOT use heroin?Interpret correlations cautiously! Spurious correlation- two completely random unrelated variables that show a correlation Intervening variable- explains a relation or provides a causal link between other variables Directionality- Sampling error-difference between the sample and the population Restriction of range-only sampling select part of the population, unlikely to find any variation in variables Sensitive only to linear relationships NO Causality!Experimental DataIndependent variable influences dependent variable MUST have control groups—to provide comparisons—reduce confounds (mess ups or confusion) in study Random assignment of subjects—representative samples—matched samples May reveal causalityControl Groups Random Assignment Matched samples Placebo control Double blind controls-experimenter doesn’t really know whats going on, not just the participantQuasi Experiments Usually have independent variable and dependent variable Have comparison groups Often don’t have random assignment Naturalistic research Weaker causal inferences than true experimentSpecial Research Strategies Family studies Adoption studies Twin studies Genetic linkage and association studies Prevention research Replication and Meta analysisResearch tools Physiological: assays (cortisol) and imagery (CAT, fMRI) Tests