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WSU PSYCH 312 - Inferential Statistics & threats to internal validity

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PSYCH 312 1st Edition Lecture 9Outline of Last Lecture I. evaluation of research hypothesis II. variance inferential statsIII. types of variationa. chanceb. IV (systematic)c. Confound (systematic)Outline of Current Lecture I. Statistical hypothesis testing II. Threats to internal validity Current Lecture oStatistical hypothesis testing oAlpha level Probability of committing a type I error Set at .05 (p< .05)These notes represent a detailed interpretation of the professor’s lecture. GradeBuddy is best used as a supplement to your own notes, not as a substitute.5 in 100 chance of committing type I error 95% of time we wont make type I erroroBeta level Probability of committing a type II error Represents inverse power (probability of avoiding a type II error)Power= 1-Beta**want power to be highoWhy do we avoid type I errors?Eliminate confounds oWhat do we do to decrease type II errors?Increase sample size (increases power)]Design study to minimize factors that reduces size of F ratioIncrease within grp Var and/orDecrease between grp VaroThreats to internal validity Major confounding variables Primarily affect bt-grp varMaturation confound Problems of interpretation resulting from participant maturation either between tests or over time Growing older, stronger, healthier, more tired, more bored, ectFatigue effectsCurrent exp: 3 year olds get tired fasterSolutionRandomize the order of conditionsTesting/practice confound Problems that result from repeated measurement of same individualSolution?Randomize order of conditions Use bt-subjects design rather than within-subjectWithin: same participants in all conditions Between: different participants in different conditions (left, right, original)History confoundEvents that take place between measurements in pretest-posttest design (not related to IV)Ex: record mood at pretest, administer drug, then test again 6 mo laterPretest during winter Posttest during summerSelection confoundParticipants vary on one or more variable (other than that IV) across the conditionsLikely when using an experimenter selected IVRegression toward the meanExtreme scores (DV) move toward the mean with repeated testing over time (unrelated to effect of IV)Diffusion or imitation of treatments Individuals in an experiment may communicate with each other, can reduce differences between conditions due to diffusion Solutions?Make participants blind to hypothesis Ask them not to talk about study Test over short periodInstrumentation Instrument (human or machine) used to measure DV is unreliable (gives different reading over time)Solutions?Training and practice Use multiple raters (check for inter-rater reliability)Counterbalance order of conditions across ratersMortality Loss participants due to drop out or refusal to participate Solution?Removing similar participant from other conditions Noting problem in results and


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