External Validity- Can the results of the study be generalized to the rest of the population.Topic Outline for Exam 2Intro to Experimentation: Ch 9 & 10, Lecture Thurs 2/21The Experimental Ideal- Create a situation in which two groups are perfectly equal at baseline; then introduce a single treatment, a single change, a single manipulation to one of the groups, and take a measurementWhy it falls short when studying people: The “everything equal at baseline” ideal is currently impossible when we’re studying humansIndependent Variable (IV): Variable in which you manipulate to show a change in your dependent variable (ex: how sports events affect mood; IV=sports game)Dependent Variable (DV): Variable in which you test for changes (ex: how sports events affect mood; DV=mood levels due to the effects of sports game)Operationalizing the IV: Researchers always need to make trade-offs between practicality and experimental realism when operationalizing Independent Variables (the ability to test non-objective things such as: fear, love, jealousy, self- esteem, low social status, injustice, etc.)Manipulate the IV: Changing the independent variable to see the different effects it has on the dependent variable (ex: changing sports events, both negatively and positively, to see the effects in which it has on people)Random Assignment to Condition: Random assignment refers to the use of chance procedures in experiments to ensure that each participant has the same opportunity to be assigned to any given group.Experimental Group- The group of participants who receive the drug or treatment being studiedControl Group- The control group is composed of participants who do not receive the experimental treatmentPlacebo Group- Some patients in a study may be administered a placebo (fake treatment) while other participants receive the actual treatment. The purpose of doing this is to determine whether or not the treatment has an actual effectRandomized- minimizes the differences among groups by equally distributing people with particular characteristics among each of the trialsDouble-Blind- even the experimenter doesn’t know what condition/treatment the participant is given. Placebo-control Experiment-Some patients in a study may be administered a placebo (fake treatment) while other participants receive the actual treatment (controlled group). The purpose of doing this is to determine whether or not the treatment has an actual effectExternal Validity- Can the results of the study be generalized to the rest of the population.Internal Validity-Refers to how confidently one can conclude that the observed effect(s) were produced solely by the independent variable and not extraneous ones Why we might have poor Internal Validity:Random error- Aspects of the testing environment that affect both groups equally and that create “noise” in our data (ex: a flickering light, anything that would distract us from detecting and effect) *Accounted for by controlled testing (NULL HYPOTHESIS TESTING) Systematic Error (a.k.a. Confounds)-Groups differ on a dimension that makes them unequal at baseline that may influence dependent variable. (ex: having two conditions always run in two separate rooms, or by two separate experimenters, or at two times of day)Selection bias- Our two groups may be predetermined by a characteristic other than our IV (ex: personalities of people who sit at the front of the room verses people in the back of the room)*Needs random assignmentMaturation effects- Sometimes participants change over time, and it has nothing to do with your manipulation (short term factors that cause maturation: boredom, fatigue, practice) Counterbalance- Switching the order in which you present to participants to eliminate the factor of practice/maturation. (ex: ordering 1,2,3 then 3,2,1 then 2,1,3…etc.)Internal Validity Threats:History effects: Sometimes participants change over time, and it has nothing to do with your manipulation (ex: testing participants before and after a tragic event such as Sept. 11, 2001) *controlled group would require the participants to go through the same conditions (history).Attrition: Losing subjects over time in a way that may be systematically related to the IV or DV. (ex: testing marital happiness with 10 couples over a span of 5 years; loosing participants throughout those 5 years throw off your data) *prevention requires careful planning, permission into databases for contact info and imputations) Instrumentation & Experimenter Effects: If the testing “apparatus” changes over the course of the experiment, it introduces error unrelated to your DVDemand Characteristics:Between Groups- Randomly assign subjects to experimental vs. control conditionPro’s: Compares separate groups of individuals; allows one score per participant; participants score in not influenced by other factors (fatigue, practice, etc.)Con’s: Individual differences (high variability); confounding variables; large number of participantsWithin Groups Designs (Repeated Measures) - Measure each subject twice: in the control & experimental conditionsPro’s: Reduces in error variance; powerCon’s: Participation in one condition may effect performance in other conditions (carryover effect) Null Hypothesis Testing & T-Tests: Supplemental Reading & Lectures Tues 2-26 & Thurs 2-28The Logic of Null Hypothesis Testing: Testing whether the effects of our IV has an effect on our DV or if it is simply caused by random error.Indirect Proof (Null Hypothesis testing): We have to “prove” that “random chance” (random error) is such an unlikely explanation for our why group averages differ that the only likely explanation is the IV did it. Random Chance (Random error): we know 2 groups won’t be perfectly equal at baseline.Effect of IV as explanations for group differences: If the difference between two group means is big enough, it’s unlikely that random error is the sole cause of group differences (in which case the IV is more likely to be the cause)Alpha: To prove the null (IV didn’t effect DV) wrong our group means need to be different enough that this difference would occur by chance alone less than 5% of the time (to rule out “random chance” as the better explanation) [alpha (α)= .05, or 5%]P-values: tells us the probability that we DID get this outcome just by chance aloneStatistical Significance: p-value is less than alpha = .05 (large “N”)Practical Significance: it’s too small to
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