DOC PREVIEW
CSUN PSY 524 - Lecture 2 Review

This preview shows page 1-2-3-25-26-27 out of 27 pages.

Save
View full document
View full document
Premium Document
Do you want full access? Go Premium and unlock all 27 pages.
Access to all documents
Download any document
Ad free experience
View full document
Premium Document
Do you want full access? Go Premium and unlock all 27 pages.
Access to all documents
Download any document
Ad free experience
View full document
Premium Document
Do you want full access? Go Premium and unlock all 27 pages.
Access to all documents
Download any document
Ad free experience
View full document
Premium Document
Do you want full access? Go Premium and unlock all 27 pages.
Access to all documents
Download any document
Ad free experience
View full document
Premium Document
Do you want full access? Go Premium and unlock all 27 pages.
Access to all documents
Download any document
Ad free experience
View full document
Premium Document
Do you want full access? Go Premium and unlock all 27 pages.
Access to all documents
Download any document
Ad free experience
Premium Document
Do you want full access? Go Premium and unlock all 27 pages.
Access to all documents
Download any document
Ad free experience

Unformatted text preview:

PsyPsy524524Lecture 2Lecture 2Andrew AinsworthAndrew AinsworthMore ReviewMore ReviewHypothesis Testing and Inferential Hypothesis Testing and Inferential StatisticsStatisticsMaking decisions about uncertain eventsMaking decisions about uncertain eventsThe use of samples to represent populationsThe use of samples to represent populationsComparing samples to given values or to other Comparing samples to given values or to other samples based on probability distributions set up samples based on probability distributions set up by the null and alternative hypothesesby the null and alternative hypothesesZZ--test test Where all your misery began!!Where all your misery began!!Assumes that the population mean and Assumes that the population mean and standard deviation are known (therefore standard deviation are known (therefore not realistic for application purposes)not realistic for application purposes)Used as a theoretical exercise to establish Used as a theoretical exercise to establish tests that followtests that followZZ--testtestSampling distributions are established; Sampling distributions are established; either by rote or by estimation either by rote or by estimation (hypotheses deal with means so (hypotheses deal with means so distributions of means are what we use)distributions of means are what we use)compared to yyyyNσσσσ=ZZ--testtestDecision axes established so we leave little Decision axes established so we leave little chance for errorchance for error Reality Reality H0 HA H0 HA “H0” 1 - α β “H0” .95 .16 Your Decision “HA” α 1 - β Your Decision “HA” .05 .84 1.00 1.00 1.00 1.00Making a DecisionMaking a DecisionType 1 error Type 1 error ––rejecting null hypothesis by rejecting null hypothesis by mistake (Alpha)mistake (Alpha)Type 2 error Type 2 error ––keeping the null hypothesis keeping the null hypothesis by mistake (Beta)by mistake (Beta)Hypothesis TestingHypothesis TestingPowerPowerPower is established by the probability of rejecting the Power is established by the probability of rejecting the null given that the alternative is true.null given that the alternative is true.Three ways to increase itThree ways to increase it––Increase the effect sizeIncrease the effect size––Use less stringent alpha levelUse less stringent alpha level––Reduce your variability in scores (narrow the width of the Reduce your variability in scores (narrow the width of the distributions) distributions) more control or more subjectsmore control or more subjectsPowerPower““You can never have too much You can never have too much power!!” power!!” ––––this is not true this is not true ––too much power (e.g. too many too much power (e.g. too many subjects) hypothesis testing becomes subjects) hypothesis testing becomes meaningless (really should look at meaningless (really should look at effects size only)effects size only)tt--teststestsrealistic application of zrealistic application of z--tests because the tests because the population standard deviation is not population standard deviation is not known (need multiple distributions instead known (need multiple distributions instead of just one)of just one)““Why is it called analysis of Why is it called analysis of variance anyway?”variance anyway?”/Total wgbgTotal S A ASS SS SSSS SS SS=+=+Factorial betweenFactorial between--subjects ANOVAssubjects ANOVAsreally just onereally just one--way way ANOVAs for each ANOVAs for each effect and an effect and an additional test for additional test for the interaction. the interaction. What’s an What’s an interaction?interaction?1211 1122122DVIV IVdv g gggggdvN g g##Repeated MeasuresRepeated MeasuresError broken into error due (S) and (S * T)Error broken into error due (S) and (S * T)carryover effects, subject effects, subject carryover effects, subject effects, subject fatigue etc…fatigue etc…1231111213123Subject Trial Trial Trialsrrrsn rn rn rn####Mixed designsMixed designs1231 1 11 12 1311211312Group Subject Trial Trial Trialsrrrsnsnsn n++###################Specific ComparisonsSpecific ComparisonsUse specific a priori comparisons in Use specific a priori comparisons in place of doing any type of ANOVAplace of doing any type of ANOVAAny number of planned comparisons Any number of planned comparisons can be done but if the number of can be done but if the number of comparisons surpasses the number comparisons surpasses the number of of DFsDFsthan a correction is than a correction is preferable (e.g. preferable (e.g. BonferoniBonferoni))Comparisons are done by assigning Comparisons are done by assigning each group a weight given that the each group a weight given that the weights sum to zeroweights sum to zero10kiiw==∑OrthogonalityOrthogonalityrevisitedrevisitedIf the weights are also orthogonal than the If the weights are also orthogonal than the comparisons also have desirable properties in comparisons also have desirable properties in that it covers all of the shared variancethat it covers all of the shared varianceOrthogonal contrast must sum to zero and the Orthogonal contrast must sum to zero and the sum of the cross products must also be sum of the cross products must also be orthogonalorthogonalIf you use polynomial contrasts they are by If you use polynomial contrasts they are by definition orthogonal, but may not be interesting definition orthogonal, but may not be interesting substantivelysubstantively121*2200111111000Constrast Constrast−−−−ComparisonsComparisonswhere where nnccis the number of scores used to get the mean is the number of scores used to get the mean for the group and for the group and MSerrorMSerrorcomes from the omnibus comes from the omnibus ANOVAANOVAThese tests are compared to critical F’s with 1 degree of These tests are compared to critical F’s with 1 degree of freedomfreedomIf post hoc than an adjustment needs to be made in the If post hoc than an adjustment needs to be made in the critical F (critical F is inflated in order to compensate for critical F (critical F is inflated in order to compensate for lack of hypothesis; e.g. lack of hypothesis; e.g. SchefféSchefféadjustment is (kadjustment is (k--1)Fcritical)1)Fcritical)()22/cjj jerrornwY wFMS=∑∑Measuring strength of associationMeasuring strength


View Full Document

CSUN PSY 524 - Lecture 2 Review

Download Lecture 2 Review
Our administrator received your request to download this document. We will send you the file to your email shortly.
Loading Unlocking...
Login

Join to view Lecture 2 Review and access 3M+ class-specific study document.

or
We will never post anything without your permission.
Don't have an account?
Sign Up

Join to view Lecture 2 Review 2 2 and access 3M+ class-specific study document.

or

By creating an account you agree to our Privacy Policy and Terms Of Use

Already a member?