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# Experimental Psychology PSYCH 225

UW

GPA 3.5

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This 8 page Class Notes was uploaded by Brody O'Hara on Thursday September 17, 2015. The Class Notes belongs to PSYCH 225 at University of Wisconsin - Madison taught by Bryan Hendricks in Fall. Since its upload, it has received 39 views. For similar materials see /class/205204/psych-225-university-of-wisconsin-madison in Psychlogy at University of Wisconsin - Madison.

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Date Created: 09/17/15

Conflicts re the nature of science Science as attitudes Empiricism emphasizes the role of experience and evidence especially sensory perception in the formation of ideas over the notion of innate ideas or tradition in contrast to Limits of empiricism Limits of empiricism eg FC What if observations are distorted can t directly observe thinking for example Accuracy SelectivityBias Canshould science focus solely on observables What about traits emotions unconscious Perhaps indirect observation eg obs priming Alternatives Trianguation using multiple assessments Scientific control control groups holding things constant balancing order effects laboratory work manipulations The intent is to rule out alternative influences but it s difficultimpossible to implement complete control there is always some error we can t control everything Precisionaccuracy Precise repeatable reliable in getting the same measurement each timequot Accurate capable of providing a correct reading or measurement Operational definition the precise specification of the procedures used in our experimentation Eg how are you are measuring something le Shyness amount of eye contact number of words muttered number of children they associate with Limits of OD the construct may not match up with the operational definition For instance is quoteye contact the same as quotshynessquot Honestytruthfulness falsification of datamisrepresentation one of the cardinal sins of science Questionable Examples Cyril Burt intelligence suspicions on his data for twin studies Hwang cloning controversy embryonic stem cells Faking photographs for his data Marc Hauser Harvard Professor misrepresentation of data He was charged with 8 instances in 20072009 students couldn t replicate his data Criticalskeptical information must be disconfirmable example Facilitated Communication for individuals with autism there wasn t enough testing and skepticism Curiosityopenness openness for new ideas potential conflict with skepticism Serendipity an aptitude for making desirable discoveries by accident luck Prevailing theory might constrain openness Parsimony extreme or excessive economy or frugality stinginess niggardliness Abstractness a focus on variables and not particular situations instances or examplars search for generalities common misunderstanding science is often not aimed at understanding particulars role of theory Determinism finding the initiator to a cause science searches for orderly causes for predictability Neutralityobjectivity sponsorship or vested interested may skew this desire to minimize bias quotPublicnessquot releasing findings to the public for scrutiny public scrutiny Peer review criticism by other scientists Public ownership patenting private investments in science university research parks Cumulativesequential examination of long term trends impact of the literaturetheory selfcorrecting metaanalyses Rationallogical reason behind an argument scientific arguments are often strengthened if interwoven with another theory other rational explanations can be difficult to evaluate alternatives often seem logical as well Testable theory can be applied quotshow me attitude Basic science broad research on a topic Applied science very narrow specific research on a topic Eg curing AIDS or cancer Primary sources research journals thousands of APA journals conference reportspresentationsposter sessions review articles books handbooks metaanalysis time is an important factor how uptodate is the research Review article psych bulletin psych review annual review of psychology Metaanalysis statistical combination of a set of related studies compilation of data and its analysis Box score tactic count of successes each success of the brand of the experiment Effect size d g magnitude of treatment effect in standard deviation units d Mt Mc or Hedges g Cohen s d unweighted use d when studies composing the metaanalysis primarily report ANOVAs and ttests comparisons between groups Hedge s g weighted Mean effect size the difference between two groups in the form of a mean Subgroup analysis refers to looking for pattern in a subset ofthe subjects File drawer problem difficulty getting published reasons why something may not get published legitimate quotnonsignificance underpowered studies unpopular against prevailing view TopDown Theorizing a broad theory less emphasis on data BottomUp Theorizing large scale collection of data frequently narrowly focused quotminitheories Forward search Descriptive statistics are used to describe the basic features of the data in a study They provide simple summaries about the sample and the measures it s opposed with inferential statistics which tries to reach conclusions that go beyond the immediate data alone Sample vs population Sample representative subset of the population Population a set of entities which statistical inferences are to be drawn Statistics vs parameter Statistic measure from a sample sample mean sample deviation English Parameter measure from the population Greek Standard deviation square root of variance 3 Standard Deviation Variance The average of the squared differences from the Mean to find variance first calculate the mean take each difference from the mean if person 1 had 400 and the mean is 450 the difference is 50 square it then average the result Z Score standardized score representing so many standard deviations plus or minus Null hypothesis nothing happened status quo treatment failed Sampling distribution distribution of statistics measure of the sample the probability that something happened by chance Central Limit Theorem specifies nature of the sampling distribution don t have to empirically derive sampling distribution For sampling distrib of differ betw means Middle zero Variability standard error sd for sampling dist Shape normal almost always Standard error the standard deviation of those sample means over all possible samples of a given size drawn from the population Secondly the standard error of the mean can refer to an estimate ofthat standard deviation Shape of sampling distribution differences expected to occur by chance without treatment Significance the chances that the experiment worked not due to chance alone Plt05 The standard level of significance used to justify a claim of a statistically significant effect Type I error definition logic influences claiming that a treatment worked when in fact it did not probability set by experimenter set probability very low eg 5 p value reports these odds p lt 05 alpha Type II error definition logic failing to detect a legitimate treatment effect a false negative Alphathe probability that you will wrongly reject the null hypothesis This is also referred to as a false positive Beta the probability that you will wrongly retain the null hypothesis false negative Power probability that the test will reject a false null hypothesis ie that it will not make a Type II error Increasing Power raising the sampling size increasing the effect size changing the significance criterion Underpowered Research research that is more to create a Type II error failing to detect a legitimate treatment effect Influences on power n alpha s tx intensity magnitude to be detected directionality independentdependent groups Directional test onetailed test likewise a nondirectional test is a twotailed test Independent Groups test differences between groups Dependent Groups test differences within the same group Homogeneity of variance The assumption of homogeneity of variance is that the variance within each of the populations is equal This is an assumption of analysis of variance ANOVA ANOVA works well even when this assumption is violated except in the case where there are unequal numbers of subjects in the various groups If the variances are not homogeneous they are said to be heterogeneous Repeated measures A repeated measures design refers to studies in which the same measures are collected multiple times for each subject but under different conditions For instance repeated measures are collected in a longitudinal study in which change over time is assessed Other studies compare the same measure under two or more different conditions ANOVA intentlogic analysis of variance intent comparing multiple means partitioning of variance Partitioning of variance 3 models ANOVA Independent Groups F Treatment Variable Error Variable ANOVA 2Way Independent ANOVA Repeated Measures Dependent Groups Treatment variance between groups variance not a pure measure of treatment effects affected both by random error and treatment effects Error variance within groups variance variability not due to the treatment but due to random error That is differences within a treatment group can t be due to the treatment because everyone in the group is getting the same treatment F ratio sampling distribution treatment effect differences between means Variability within groups error F Treatment Var Error Var Main effect marginal means the main variable including all levels Interaction cell means a combination of two variables Subject variance is used as a measure of how far a set of numbers are spread out from each other Post hoc tests usually refers to a statistical test that has been performed after an ANOVA has obtained a significant effect for a factor Because the ANOVA says only that at least two of the groups differ from one another post hoc tests are performed to find out which groups differ from one another Alpha inflation risk of a Type I error Tukey test a post hoc test used for testing 3 levels Bonferroni adjustment Take 05 3 to raise threshold needed for significance SPSS does not do this Very conservative If there is significance here you can find anything significant Interaction post hocs slicingdicing Pearson r Best when variables are on a continuum measure of correlation between variables X and Y giving a value between 1 and 1 inclusive Continuous data numerical data which can hold any value For instance human height is continuous There is no set of allowable heights It can be any number In contrast human sex ie male or female is categorical data because there are only two possible values Measures of magnitude d r2 eta2 7 Effect size d 7 r squared r2 proportion of variance accounted for 7 Eta squared n2 Proportion for each piece of the pie 7 Partial eta squared n2 I Proportion of variance accounted for once other known sources are removed Publication bias the tendency of researchers editors and pharmaceutical companies to handle the reporting of experimental results that are positive ie showing a significant finding differently from results that are negative ie supporting the null hypothesis or inconclusive leading to bias in the overall published literature Construct validity refers to whether a scale measures or correlates with the theorized psychological scientific construct eg quotfluid intelligencequot that it purports to measure lnternal validity The degree to which a study establishes that a factor causes a difference in behavior If a study lacks internal validity the researcher may falsely believe that a factor causes an effect when it really doesn t External validity The degree to which the results of a study can be generalized to other participants settings and times Statistical validity refers to whether a statistical study is able to draw conclusions that are in agreement with statistical and scientific laws lnductionbottomup making a specific observation and applying them to broader generalizations and theories Deduction topdown make a broad theory to a more specific theory Deductive arguments are attempts to show that a conclusion necessarily follows from a set of premises or hypotheses Constructs a mental state such as love intelligence hunger and aggression that cannot be directly observed or manipulated with our present technology Moderators variable that can intensify weaken or reverse the effects of another variable For example the effect of wearing perfume may be moderated by gender if you are a woman wearing perfume may make you more liked if you are a man wearing perfume may make you less liked Mediators variables inside the individual such as thoughts feelings or physiological responses that come between a stimulus and a response In other words the stimulus has its effect because it causes changes in mediating variable which in turn cause changes in behavior Generalizations concept is an extension of the concept to lessspecific criteria It is a foundational element of logic and human reasoning Testability ability to investigate an hypothesis vague statements might be untestable broad statements that cannot be proven wrong may be useless Kuhn has made several notable claims concerning the progress of knowledge Science undergoes periodic llparadigm shifts instead of progressing in a linear and continuous way These paradigm shifts open up new approaches to understanding that scientists would never have considered valid before Scientists can never divorce their subjective perspective from their work thus our comprehension of science can never rely on full quotobjectivityquot we must account for subjective perspectives as well Teflon theory Reliability a general term often referring to the degree to which a participant would get the same score if retested testretest reliability Reliability can however refer to the degree to which scores are free from random error A measure can be reliable but not valid However a measure cannot be valid if it is not also reliable Validity a reference to whether a conclusion or claim is justified Error the contrast of between the true values of a definition Bias systematic errors that can push the scores in a given direction Bias may to finding the results that the researcher wanted Control tactics removing variables that may interfere with the experiment

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