EXPERIMENTAL METHODS 1/15/18. Experimental Designs. Experiments Uncover Causation. Experiments examining behavior in a lab setting
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1 EXPERIMENTAL METHODS Experimental Designs Experiments examining behavior in a lab setting Controlled lab setting separates experiments from non-experiments Allows experimenter to know exactly what s causing behavior Always approached : Describing behavior using numbers Experiments Uncover Causation Experiments try to control everything that affects participants Making sure all participants have the same experience Same room Same temperature / lighting Same materials Same experimenter Same confederate(s) 1
2 1/15/18 Experiments Uncover Causation Experiments try to control everything that affects participants Making sure all participants have the same experience Every aspect of their experience is Room is checked and reset between each participant Confederate(s) behavior is all scripted Experiments = Control Example: Experiment designed to test effectiveness of new test-taking strategy Experimental group: Comes to lab and is trained and tested in groups group: Takes test at home online Experiments = Control Example: Experiment showed those trained in the new strategy got higher scores What might have caused this effect? Home distractions,, social facilitation, etc. 2
3 Experiments = Control Example: Experiment designed to test effectiveness of learned test-taking strategy Experimental group: Comes to lab, is trained in groups, then tested Control group: Comes to lab, is trained in an unrelated task in groups, then tested Experiment showed those trained in the new strategy got higher scores Since everything was the same except for IV, the IV likely the effect Experiments = Control Experiments cannot control everything What if control and experimental groups just happened to differ in intelligence? We cannot make everyone coming into the lab have equal intelligence, schooling, etc. Randomization = Whatever cannot be controlled can be randomized between conditions With just 30 people in each condition, most participant factors get spread out evenly Experiments Uncover Causation Through control of environmental factors and randomization of participant factors We can know exactly what affects the DV in the experimental groups Rules out possible confounds Any variable (environmental/ participant) that varies along with the and affects the E.g., Distractions at home (confound) only being present in control group (varies along with IV) affecting test scores (affects the DV) 3
4 Experiments Uncover Causation Good experiments = High validity Ability to know exactly what is affecting the DV That s the hallmark of good science! All sciences, at their core, are methods of understanding cause and effect The Basic Experiment Design Participants randomly assigned to Experimental Group Includes the affecting IV Control Group Doesn t include the affecting IV After the manipulation, the DV is measured in the same way for both groups Virus Saline The Basic Experiment Example: Bargh, Chen, & Burrows, 1996 Hypothesis: Subtle primes can affect behavior Experimental Group Completed word unscrambling task, where many of the words were related to the (e.g., wrinkle, grey, wise) Control Group Completed similar word unscrambling task that didn t include any elderly-related primes After manipulation, experimenters recorded the time it took participants to exit the lab room 4
5 The Basic Experiment Example: Bargh, Chen, & Burrows, 1996 It was expected that those primed with elderly-related words would walk when exiting the study (emulating the elderly) That s exactly what they found across two studies The Basic Experiment Example: Daniel Pink s DRIVE (2010) Hypothesis: Higher pay hinders quality of work, because it leads to job-related Computer programmers in India asked to create new database program (like Excel) Experimental Group participants given 2 months salary for 2 days work Control Group given 2 days salary for 2 days work Afterward, their work (the program) was judged according to its efficacy and elegance (DVs) The Basic Experiment Example: Daniel Pink s DRIVE (2010) Found those who received 2 days salary created a much better product than those paid 2 months salary In both examples Everything was kept constant between groups except for the IV E.g., primed words, salary So, even if the results are a bit hard to believe, they were most likely caused by the IV 5
6 Basic Experiments Advantages Can be used to infer Produces results that are clear and easy to understand Disadvantages Cannot test most participant variables as IVs E.g., income, intelligence, personality Since you cannot directly these factors, you cannot infer that they affect the DV More Complex Experiments To provide more evidence of an effect, experimenters can test of the IV Example: Bargh, Chen, & Burrows, 1996 Hypothesis: Subtle primes can affect behavior Experimental Group #1 primed with words related to rudeness (e.g., bother, disturb, annoy) Experimental Group #2 primed with words related to politeness (e.g., considerate, patient, courteous) Control Group primed with words unrelated to either rudeness or politeness More Complex Experiments Example: Bargh, Chen, & Burrows, 1996 Participants told to contact experimenter standing outside Experimenter talked with someone else, ignoring participant for up to 10 minutes, unless interrupted Recorded whether participant rudely interrupted experimenter 6
7 More Complex Experiments Example: Daniel Pink s DRIVE (2010) Repeated previous experiment, but added a third group that got paid 2 weeks salary Performance High Med Low 2-days pay 2-weeks pay 2-months pay More Complex Experiments By adding extra levels to the IV Able to effects of IV multiple times Adds to internal validity (believability) of the results E.g., showing effect of priming through both rudeness- and politeness-related words Able to show exactly how the IV affects the DV E.g., at what point does an increased salary begin to impede performance Performance High Med Low 2-days pay 2-weeks pay 2-months pay Participants assessed both before and after the manipulation Often used to assess the effectiveness of Example: Effect of behavioral therapy on smoking Number of cigarettes smoked per day is recorded Experimental group: Participants put on fixed schedule for smoking less and less Control group: Participants are allowed to lessen smoking in any way they wish After 1 month, # of cigarettes per day is assessed 7
8 Example: Effects of behavioral therapy on smoking # of Cigarettes per Day > Experimental group Control group Pretest Posttest Example: Effects of behavioral therapy on smoking Does this necessarily mean the treatment worked? What if half the experimental group dropped out of the study before it was completed? participants who drop out mid-study Creates problems if participants who drop out are different from those who stay in # of Cigarettes per Day > Pretest Posttest Experimental group Control group Effect of Mortality Difference between mortality and non-mortality pars. # of Cigarettes per Day > Many people who were heavy smokers dropped, because they couldn t handle the treatment Participants who dropped Participants who stayed 8
9 Effect of Mortality Example: Effects of behavioral therapy on smoking # of Cigarettes per Day > Pretest Posttest Experimental group Control group Treatment had no effect, except driving away heavy smokers Effect of Mortality Example: Wierzbicki & Pekarik, 1993 Meta-analysis of 125 studies testing effectiveness of psychotherapy Discovered an average of 47% of participants dropped out of each study before its completion Which participants were most likely to drop? Those with more psychological illness Dropped because of hospitalization, jailing, suicide, distrust of therapist (paranoia), etc Those who weren t from therapy Advantages Can be a good way to clearly display effects of treatment vs. non-treatment Sometimes it s to have a control group (e.g., depriving people of beneficial treatment) Helps effect of IV to stand out with fewer pars. Effect of treatment is often clouded by between-person variability By assessing the same participants before and after treatment, you take away this extra noise in the data 9
10 Self-esteem Example: Testing effect of social rejection on self-esteem Posttest only design: effect of IV has to rise above within-group variability in DV High Medium Low No Social Rejection Group (Control) Social Rejection Group (Experimental) Self-esteem Example: Testing effect of social rejection on self-esteem Pretest posttest design: effect of IV is shown in each individual High Medium Low Before Social Rejection After Social Rejection Effect much more Each individual participant acts as a replication of the effect Self-esteem High Medium Low Before Social Rejection (Average Effect of Rejection) After Social Rejection 10
11 Disadvantages Have to make sure most participants stick with the experiment right to the end Should track participants who drop out Figure out if they are significantly different from those who stay in How can you lessen mortality rates? Try to make experiment as short as possible (days vs. weeks makes a big difference) Offer (e.g., cash) for returning Make it clear how important it is for participants to keep coming back Disadvantages Effects simply taking a pretest can affect the posttest measure Practice effect repeated performance of the same test leads to better performance over time E.g., taking the same IQ test over and over again effect pretest sensitizes or desensitizes participants to the manipulation (changes reaction) How might exposure self-esteem measure affect reactions to subsequent social rejection? 11
12 Disadvantages Effects simply taking a pretest can affect the posttest measure effect pretest clues participants into what the hypothesis is Most participants want to help the experimenter May fake what they think is the desired reaction How can you lessen order effects? Increase the between pre- and posttest Participants tend to forget about the pretest pretty quickly Add tasks between pre- and posttest E.g., have participants play a video game Try to use pre-tests that seem unrelated to the manipulation E.g., use an implicit measure of self-esteem (testing association between self and positive vs. negative words) Type of Participant Usage Design participants are randomly assigned to experimental or control conditions Design participants are run through both experimental and control conditions E.g., Participants play round of cyberball where they are rejected Self-esteem is measured Participants play round of cyberball where they aren t rejected Self-esteem is measured again 12
13 Repeated Measures Design Advantages Requires far participants Saves lots of time / resources Some samples are very difficult/ expensive to attain E.g., studying effects of stress on expert meditators (monks from Tibet) Repeated Measures Design Advantages Cuts out in data caused by individual variability Helps effects to better stand out Sometimes exposure to one condition after another is more naturalistic E.g., Testing effects of TV ads on mood Examining effects of political slogans on attitudes Investigating effects of online dating profiles on mate preferences Repeated Measures Design Disadvantages Again, there is the problem of effects Example: In first round of cyberball, participants are rejected and then their self-esteem is measured In second round of cyberball, participants are not rejected and then their self-esteem is measured How might participants interpret this order of events? 13
14 Repeated Measures Design Disadvantages If study contains many conditions, the experiment can end up being really long Danger of participants Could cause satisfyzing Participants stop caring enough to perform to the best of their ability Better Repeated Measures Design Counterbalancing can nullify order effects Participants assigned to different order of conditions Allows order effects to cancel each other out E.g., initial rejection lowers self-esteem, but initial acceptance heightens selfesteem Allows experimenter to examine differences caused by effects Making the Most of Participants Design - participants are prescreened and then assigned to different conditions Important participant factors are equalized between conditions E.g., Study of effects of social rejection on self-esteem Use prescreening measure of self-esteem Assign equal numbers of low and high esteem participants to each condition Removes noise caused by participant factors But, you don t have to worry about order effects, fatigue, or other pitfalls of repeated measure designs 14
15 Self-esteem Matched Pairs Design Example: Effect of social rejection on self-esteem Without prescreening, experimental and control groups may just happen to differ in self-esteem Especially likely if sample size is High Medium Low Group 1 Group 2 Matched Pairs Design Example: Effect of social rejection on self-esteem With prescreening, you can randomly assign same number of low- and high-self esteem participants to each condition High Self-esteem Medium Low Group 1 Group 2 Overview Experiments show through control or randomization of everything that affects participants Different types of experimental designs: Posttest only design Pretest posttest design Independent groups design Repeated measures design Matched pairs design 15
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