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Department
Psychology
Course
PSYC-2400
Professor
Kelley Robinson
Semester
Fall

Description
Social Psychology I 18/09/2013 2:51:00 PM Experimental Research  With experiments, you can determine causality; with correlational studies, you cannot  Correlational research is valuable – used for prediction (what’s going to happen neck) but will not know why or how Focus: causality  Does x cause y? (x->y)  There are different ways to explain causality (x->y, y->x, or z->xy)  Manipulate independent variable (IV): cause o Take control and manipulate variable (change levels, presence) o You want to know whether alcohol has an effect on memory for social psychological experiments o Ex. reading textbook, drinking wine or grape juice – also how much? One cup? Four?  Evaluate effect on dependent variable (DV): effect o Effect on memory – what do you remember studying? o Ex. People who drank four glasses of wine remembered less?  Need random assignment! o Put people in different groups – those eating chocolate, those forced to chain smoke, etc. o Everyone has equal possibility of being in either group o Different than random sample! – random sample is when everyone has equal possibility of being part of the experiment o Random assignment is a “great equalizer”  Not based on personality aspects or height//weight/sex etc.  Individual differences average out! What does it mean if X causes Y?  If someone tells us smoking causes lung cancer, what does it mean? Does it mean EVERYONE who has ever smoked gets lung cancer? Does it mean people who don’t smoke cannot get lung cancer?  Causal influences on Dr. Robinson’s Happiness o Chocolate, class participation, watching first dates, high class average, getting paid, other (variety)  If you give her chocolate she will be happy, but if she hasn’t slept then her happiness won’t improve as much  Control sleep, test in experimental way  Valid/reliable/consistent influence on what will occur o Goal is to account for as much variation in happiness as we possibly can – identify more and more variables o There are always going to be unidentified variables (error) – things we haven’t assessed or predicted o IV coin flip  Heads first group, tails second group then assess an outcome  Manipulation I = heads, Manipulation II = tails; outcome = DV  Ex. Hypothesis -> similarity causes liking o Reading info on others o Liking = desire to spend time with other person (operational definition) o Importance of control groups for conclusions  Comparison to experimental group  Tells us what the data actually means  IV:  manipulation I (similar other)  manipulation II (dissimilar other)  DV:  Outcome o What would you conclude if you found this?  Similar group – liking score 9/10  Dissimilar group – liking score 4/10 o What if there was a control group?  Neutral control – liking score 8/10  Control group = similarity group; baseline! Doesn’t change anything  Shows us that dissimilarity causes people to reduce their liking, NOT that similarity causes liking o Not all experiments have control group!  We especially see this in drug trials – pharm company developing new drug, compare administration of their drug vs. no drug, but NOT to other drugs already on the market  We are able to interpret data in various ways; were there other ways to manipulate the IV? Issues with Experiments  Importance of control groups for conclusions o Comparison to experimental group  Ethical, practical considerations, or both o Just because we want to do the experiment, doesn’t mean we always can o Practicality – depending on methods – can take a LONG time – expensive – no evidence…do you want to invest in the experiment? Gather correlational data first!!! o Ethicality – what are we doing to people?  Realism o Mundane vs. psychological  Mundane realism  how close to real life is the situation that you’re manipulating?  Hard to recreate since people are always looking for cameras, adjusting their behaviour, etc.  Psychological realism  about the experience you’re going through; is that an approximation of what is real?  What most experiments have – stress is real in the laboratory  Internal vs. external validity o the more similar to real-life, the more difficult it is to attribute the effect to just ONE cause – balancing act  Internal validity  In the lab, small space – control everything else – the more you can control everything BUT the variable you’re manipulating  The more internal validity, the less external validity  External validity  How much can you really generalize? Can you extend the causal variety to other situations? If you take the experiment out of the lab, will the results be the same?  Experimenter and subject bias o Hans the counting horse – experimenter thought the horse was smart enough to count, BUT horse was readin
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