PSC 41 Midterm 1 Study Guide
PSC 41 Midterm 1 Study Guide PSC 41
Popular in Research Methods in Psychology
Popular in Psychology
This 9 page Study Guide was uploaded by Kayla Dillard on Saturday October 8, 2016. The Study Guide belongs to PSC 41 at University of California - Davis taught by Dr. Cross in Fall 2016. Since its upload, it has received 119 views. For similar materials see Research Methods in Psychology in Psychology at University of California - Davis.
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Date Created: 10/08/16
9/21/16 Dr. Victoria Cross firstname.lastname@example.org Research Methods in Psychology • http://canvas.ucdavis.edu • required: • research methods in psychology: evaluation a world of information 2nd edition • clicker • final grades are NOT CURVED • exams 60% (3 midterms & optional cumulative final) 40 MC questions (need scantron) and written part • • cheat sheet on each exam single paper 8.5x11 front and back (put name and ID on it) can bring all 3 cheat sheets for final • worksheets 30% (will be on canvas, only 1 submission is accepted) • lowest grade will be dropped, no late worksheets will be accepted • 2 sets of video worksheets on canvas under syllabus • clickers 5% (clicker grade will be recorded each day based on participation, lowest 2 days will be dropped) • 2% added to final grade if all extra credit is earned (based on performance) • syllabus FAQ worksheet due next week • textbook chapter quizzes due before the exam 6 experimental subject pool credits required, see “participation letter” on canvas, sona system Wednesday class before thanksgiving is CANCELLED 9/21/16 Lecture 1 How do we know things? • humans gather and organize info to generate theories about the world Ways to acquire knowledge • intuition, authority, empiricism, rationalism, scientific knowledge • Scientific Knowledge • describes relationships between variables • is objective • based on rationalism and empiricism (Theory-Data Cycle) • people adjust their theories throughout life start with a hypothesis 1 • • collect data • revise and create hypothesis 2 • more data • Theories • Trephining—the practice of drilling or chiseling holes into the skull • some animals experience seizures • cave paintings from Neolithic times indicate that people believed evil spirits were in the skull and they drilled holes into the skull to release them • this practice continued throughout the MiddleAges into the Renaissance • Phrenology—perhaps the brain is the primary control center fro the body and personality • belief that we could see what type of person you are by looking at brain volume and the shape of the skull continued into the 1800s • Autism—many autistic children don’t show clear signs until around 18 months • perhaps it is caused by the MMR vaccine (belief was cause by a coincidence and false data) Goals of Research in Psychology • Describe Behavior (frequency claims) • Predict Behavior (association claims) • Explain Behavior (causal claims) 9/26/16 Lecture 2 Theories and Hypotheses • Empiricism • systematically gather data and answer the questions (reduce subjectivity) • must be open to accept the data (reduce bias) • must have a control/comparison group (don’t just confirm hypothesis, test it) Theories • an integrated set of principles that explain observations and can be used to deduce a large number of hypotheses • a good theory • is consistent with known facts • is logically (internally) consistent • is parsimonious: contains a minimum number of assumptions in order to explain the data, the simpler one is preferred if two assumptions work • is testable: can be confirmed or disconfirmed • and falsifiable: precise enough to be disproven, do not just gather confirming evidence • ex: Psychodynamic theory • personality as an adult may be influenced by unresolved conflicts experienced as a child • not falsifiable Hypotheses • a testable statement (not a question) • a prediction about what will happen between the variables of interest • there should also be a null hypothesis: states that there is no difference or relationship between the variables • research hypothesis: specific predication about the relationship between the variables • In class example: (dollar bill in 1 of 3 envelopes experiment) predictor: strategy outcome: success H0: Strategy does not predict success. null H1: Switching increases success. directional H2: Staying increases success. directional H3: Strategy predicts success. non-directional • Directionality of Hypothesis • Causal orAssociative • causal: the experimenter must be in control of the “cause”, “if I change X, the Y will also change” • associative: “these groups respond differently”, experimenter is not in control of cause 9/28/16 Lecture 3 Critical Thinking • critiquing claims about relationships between variables • we draw our own conclusions on whether a claim was well supported or not and if it is valid • appreciate the strengths of the claim • Steps in critical thinking: • What am I being asked to believe or accept? • What evidence is available to support the assertion? What are alternative ways of interpreting the evidence? • • must accept the findings • must not accept the author’s explanation • Were the groups equal to begin with? • Work backwards from the outcome variable • HINT: If the groups were not randomly assigned, then they are almost certainly different to begin with (self-selection bias) • Were the groups treated the same way during and after the experiment? • What additional evidence would be helpful? • Must relate to and address your proposed alternate explanation • Propose how to fix the experiment • Can you design an experiment where you randomly assign them to the conditions? • Can you make equivalent groups? • Is it true “Have they met the threshold to convince a scientific thinker?” • You may question how they have measured the outcome • how did they define success? • was it a survey? (people may have lied) • did they assess all important aspects of the outcome? • Agood third variable: • is specific and plausible • does NOT suggest a mechanism by which the original claim works • Ex: People who eat Champion Cereal for breakfast will be more successful than people who eat Great Gruel. • alternate explanation: • Maybe Champion Cereal costs more and the people that buy it had a higher income to begin with. • Maybe it is marketed in a neighborhood with better school. • Ex: Men who tuck in their shirts make more money. • the relationship is reversed: making more money leads to tucking in your shirt • the groups were different to begin with Outcome level 1: $$$$ Outcome Level 2: $ associated variable: associated variable: shirts tucked in shirts untucked wearing suits, professional job, more wearing casual, non-professional job, conscientious, more motivated to less conscientious, less motivated to make money (maybe taking on extra make money (not taking on extra tasks) tasks) different treatment: taken seriously Different treatment: not taken based on their clothing seriously Groups not the same to begin with: Make the groups equal: different jobs: one CEO, one McDonalds randomly assign? choose 100 McD’s and have 50 tuck and 50 not tuck Groups making other different choices: Make the groups equal: conscientious/ volunteering for more work randomly assign Groups being treated differently: Make the treatment more similar: fabric Questionable outcome of measure: Redeﬁne outcome measures: self-report of income tax returns Reasoning • inductive: reasoning from a specific case to general principles • deductive: reasoning from general principles to a specific case • Ex:All men are mortal. Socrates is a man. Therefore, Socrates is mortal. • Valid: • Modus Ponens (Affirming by affirming) • Modus Tollens (denying by denying) • Invalid: • Denying the antecedent • Ex: All fruit contains seeds. Eggplant are not a fruit. Therefore, eggplant do not contain seeds 10/3/16 Lecture 4 Studying Behavior Variables • any characteristic or quality that varies • something we measure of manipulate (under our control) • operational definition: defining the variable by the means used to measure it • variables must vary, if there is only one level, then it is a constant • some are measured in categories (ex: variable: freshman housing, levels: dorm or not) • some are measured continuously (ex: variable: time, values: milliseconds) external, physiological, emotional, cognitive, behavioral variables • • measuring variables: • directly observe behavior (action, performance, archive, giving a test and seeing how many were answered correctly) • self-report (best way to find out attitudes, beliefs, feelings, memories) • monitor physiology (biological responses, physical states) Thing to Measure Method Used actions observation ideas self-report physical state monitor • associated claims: I wonder if x is related to y? • x is the predictor variable • pre-existing characteristic • y is the outcome • outcome, response • causal claims: What is the effect of x on y? • x is the predictor variable (independent) • directly manipulated by the experimenter • y is the outcome variable (dependent) • “depends on” the level of independent variable outcome, response, measured • • predictor variables • pre-existing characteristics • individual differences of the participants • not under the experimenter’s control • participants are grouped according to their level or value • independent variables • manipulated • under the experimenter’s control • participants are assigned a level or value Ex: What is the most therapeutic approach to treat depression? Predictor Variable= type of therapy (manipulated) • levels= meds, group, breathing exercises • categorical Outcome Variable= level of depression • levels= self-report (1. I can't get out of bed SA(5)A(4) N(3) D(2) SD(1)) • categorical Ex: Do boys and girls play in the same size groups of friends? Predictor Variable= gender (pre-existing characteristic) • levels= male, female • categorical Outcome Variable= group size • levels= 0,1,2,3,4… • continuos Claims Goals of Research in Psychology • Describe Behavior (frequency claims) • identify regularly occurring sequences of events • clarify behaviors • Predict Behavior (association of claims) • identify relationships between variables • strength of relationship leads to degree of confidence in prediction • Explain Behavior (casual claims) • understand the cause and effect Relationship Between Variables • Positive linear relationship • as one variable increases, the other variable increases Negative linear relationship • • as one variable decreases, the other variable increases • Curvilinear relationship • No relationship • Ex: Negative linear GPA Time on Facebook 10/5/16 Lecture 5 Determining Causation • Observing a relationship between two variables does to mean that one cause the other • Correlation does not imply causation • To conclude causation, we need to isolate the effect of one variable • start with two groups that are the same —> use random assignment • manipulate one variable • observe the outcome • Cause and Effect Criteria • Co-occurance • when v1 is present, is v2 also present? • when v1 is absent, is v2 also absent? if yes, then there is a relationship • • Time sequence • Alternative causes must be ruled out • if single headed arrow between v1 and v2, then it is a causal • if double headed arrows between v1 and v2, then there is a relationship • Method Overview • Correlational Research/Association Claims • two measured variables • searching for an association • • Experimental Research/Causal Claims • one manipulated variable, on measured variable • searching for causation • isolating the effect of the independent variable • goal: eliminate all but one explanation • starting with different groups and treating them the same is not an experiment Things to keep in mind: • no overall “best” method • question drives methodology • multi-methodological replication • scientists are skeptical and persistent Measuring VariablesAccurately and Consistently • “If you can’t measure it, it doesn’t exist” • Almost all measurements include some degree of error or noise • We must evaluate the accuracy and consistency of the measurement • Some things can’t be directly measured • create a construct that can be measured and approximates the variable of interest • Aconstruct: hypothetical variable that we can’t direct observe (intelligence, happiness…) • Validity (accuracy) • genuine, credible, true • accuracy or correctness • can be very narrow or very broad • not directly measurable • Construct validity • is the operational definition of a variable accurate? • Face Validity: does your operational definition appear to measure the construct? • procedure: did anything about the procedure add noise or error to the measurement? • • what was it like to be a participant? • method match: • is there an appropriate method to measure the construct? • Reliability (consistency) • repeat, replicate • if measured again, will you get the same result? • calculate correlations to assess reliability • test-retest • split-half • parallel or alternate forms • item-total • coefficient alpha (cronbachs) • inter-rater reliability • high: judges example 10/10, 9/10, 10/10 • low: judges example 10/10, 5/10, 1/10 “What might have been” • getting 2nd place in a race • better than 3rd but so close to 1st • bronze medalists displayed more positive emotions that silver medalists both immediately and later on the podium
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