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PSYC 2002
Chris Herdman

Psych 2002 FINAL CHEAT SHEET f Proportion=p= f=frequencN=for pop.n=for sampSS=sum of squareSample Deviation= Pop. Deviation= n x−μ f (100) θ =SS/N ∑ x Percentage=p(100N = Pop. Variance= Sample Mean=M= n Pop. Mean= ∑ x SS 2 SS μ= Pop. SD=θ=√( ) Sample Variance== x= μ+z(ϑ) N N n−1 2 ∑ (x−μ) 2 ∑ x −( ∑ x ) Sum of Squared Deviation (Pop.)=SS= (def=) n (comp)Sum of Squares (sample) = 2 2 x2 SS x−μ ∑ x−M )Deff. =∑ x − ( ) (comp) Sample SD= s=√( ) z-score= n n−1 θ Discrete:INDIVISABLE separate categories ex.# of kids Continuous:Infinite values Nominal:Colour Ordinal:Rank by size Interval:Ordered intervals Ratio:Interval w zero point Descriptive Stats:Describe group of raw scores Inferential Stats:Goes beyond data, can’t be manipulated Unimodal:1 Peak Bimodal:2 peaks Multimodal:2+ peaks Rectangular:0 Kurtosis:How flat/peaked a distribution is LEPTOKURTIC:Tall/Peaked MESOKURTIC: Normal PLATYKURTIC:Broad/flat Correlational Method:No control, no manipulation, observing ex.smoking Experimental Method:1 variable manipulated, other controlled, no confounds, cause/effect θ θ μ μ1−μ2=0 θM= (¿¿M) M− μ Standard Error= √n z-test= T-test=t= sM -> if pop. Known df= z=M−μ /¿ M n−1 M−μ M−μ sM= s SS Effect Size(z)=dθ Effect Size(t)=s= √n s= √ df 2 t2 2 μM=pop.mean explained variability= effect size=d=(M ¿2 s√ p t +df (M 1M −2) (μ1 2) T-test for independent means=t= whether 2 means are diff. enough to be from diff. populations S(M 1M 2) 2 2 SS +SS p sp sp= 1 2 df =df 1df 2 sM1−M2 √(n1)+ n2 ( 11 +) (21 ) *when using this test, use to compare* Effect size: sm: d<0.2 Md: 0.20.25 Confidence Interval=CI  Determines range of values likely to contain an unknown mean. Depends on desired interval ex. 95%, 99% μ=z θ +M ( M Z-test T-test H0 H 1 H 0 H 1 1. State and 1. State and 2. Set α to locate critical region. 2. Set α , determine df, find critical region on chart. 3. Collect data, preform z-test 3. Collect data, perform t-test H 0 H 0 4. Make decision about 4. Make decision about Statistical Power: probability a test will produce significant results if research hypothesis us true. 2 distributions can have HIGH POWER but LITTLE OVERLAP if: 1) 2 means are very different or 2) Standard error is very small (small SD large n). Power is higher if 1) Bigger diff. between means α (effect) 2) More participants (lg n) 3) smaller SD. To increase power 1) Increase sample3) Use one tailed testngent 2 Explained Variability) : how much of the variability is accounted for by the treatment effect. “40% of variability explained by effect”. T-test: Estimates pop. SD using sample SD.Type I decision error: reject null when it is true Type II: fail to reject null when null is false. ANOVA 1. Compute df for between-groups and within groups. 2. Calculate SS for each 3. Calculate MS for each 4. Calculate F-ratio df 5.Find F-critical using F-table. Useand df between 6.Compare F-obtained to F-critical. Formulas for Indep. Measures ANOVA ^  This I for a repeated measures ANOVA. Notation for Repeated Measures ANOVA n # of individuals tested in aPl Participant/personal total treatments k Number of treatments T Sum of scores in each group N Number of scores G Total of all scores * use same steps as you would use for an independent measures ANOVA 2 2 G SStotal= ∑ x - SSwithin= ∑ SSinside eachtrea=ment1SS …2 df withi= N 2 2 ∑ T G ∑ dfwithineachtreatment SS bet.treatments − dfbet.treatments n N 2 2 P G SS =SS −SS
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