Psych 2260 Cheat Sheet

Ch. 3

^{Correlation Coefficient: # that tells degree of correlation (r)}	^{Strength: small ±.10 med ±.30 large ±.50} ^{r=[Σ(z_x)(z_y)]/N-1}
^{Linear Correlation: Line indicating relation is roughly a straight line}
^{Curvilinear correlation: Not Straight}
^{Cross-product: Multiplying a score on one variable by a score on another}	^{Cross-product Z score: Using z-scores instead}
^{Variables: predictor is x and criterion is y}
^{Prediction Model: Using z-scores to make predict}	^{Z_y= (β)(Z_x)}
^{Raw Score Predict:}	^{Form 1: Predicted Y=a+(b)(x)} ^{Form 2: Predicted Y=(SD_y)(Predicted Z_x)+M_y}
^{Correlation Matrix: Table of correlations that's set up so each variable is listed down the left and across the top ex.}
^{Multiple Regression: Making predictions w/ multi correlations}	^{Z_y=(β₁)(Z_x1)+(β₂)(Z_x2)+(β₃)(Z_x3)...}

Ch. 10

^{Chi-Square Tests: For when the variable of interest is a nominal vari. The scores they achieve represent frequencies}
^{Frequencies: How many ppl/observations fall into diff categories}
^{Chi-Square Test for Goodness of Fit: Chi-Square test involving levels of a single nom vari}
^{Goodness of Fit: X2=∑(O-E)2/E}	^{**O- Observed Frequency E- Expected Frequency}
^{df for X2 test: df=NCategories-1}
^{Chi-Square T for Independence: Chi-Square test involving 2 variables each w/ several categories}
^{Independence: Refers to a lack of a relation between 2 nom vari}
^{Ind Means X2 Expected frequencies: Makes # that rep cell E=(R/N)(C)}
^{Figuring X2 for Ind: It is the same as goodness of fit but uses scores from each cell of the contingency table}
^{df for X2 for Ind: df=(NColumns-1)(NRows-1)}
^{For cutoff scores: use table A4 to find cutoff scores}
^{Phi Coefficient(): Measure of association between to dichotomous nom vari. Effect size for a X2 for Ind w/ a 2x2 contingency table}	^=√X2/N
^{Cramer's Phi: Extension of Phi, used when the contingency table is larger than 2x2 AKA Cramer's V and denoted as C or Vc}	^{C=√X2/(N)(dfSmaller)}
^{Data Transformation: Math proc used on each score is a samp, usuall done to make samp dist closer to norm}
^{Square-Root Transformation: Taking the √ of each score in a sample to make the distribution closer to normal}
^{Log Transformation: Taking a logarithm of each score to make the samp dist closer to norm}
^{Rank-Order Transformation: Changing the set of scores to ranks so that the lowest score is 1, next lowest is 2... so on}	^{Rank-Order Test: Hyp Test proc that uses rank-ordered scores. Sometimes called dist-free tests/non-parametric tests}
^{Rank-Order Tests Corresponding to Parametric Tests:}
^{Mann-Whitney U: Rank-order test U1=[(N1)(N2)]+[N1(N1+1)/2)-∑R1 // U2=[(N1)(N2)]+[N2(N2+1)/2)-∑R2}	^{Where: U1/U2- U1 Stat N1/N2- Sample size of each group ∑R1/R2- Sum of rank orders for each condition}

^: √ σ μ ∑

Ch. 3

^{Correlation Coefficient: # that tells degree of correlation (r)}	^{Strength: small ±.10 med ±.30 large ±.50}
^{Linear Correlation: Line indicating relation is roughly a straight line}
^{Curvilinear correlation: Not Straight}
^{Cross-product: Multiplying a score on one variable by a score on another}	^{Cross-product Z score: Using z-scores instead}
^{Variables: predictor is x and criterion is y}
^{Prediction Model: Using z-scores to make predict}

Formulas	^{r=[Σ(z_x)(z_y)]/N-1}^{Z_y= (β)(Z_x)}

Ch. 3

^{Correlation Coefficient: # that tells degree of correlation (r)}	^{Strength: small ±.10 med ±.30 large ±.50}
^{Linear Correlation: Line indicating relation is roughly a straight line}
^{Curvilinear correlation: Not Straight}
^{Cross-product: Multiplying a score on one variable by a score on another}	^{Cross-product Z score: Using z-scores instead}
^{Variables: predictor is x and criterion is y}
^{Prediction Model: Using z-scores to make predict}

Formulas	^{r=[Σ(z_x)(z_y)]/N-1}^{Z_y= (β)(Z_x)}

Ch. 4

^{Inferential Statistics: Conclusions that go beyond the particular group of research participants studied}
^{Normal curve/dis: Variables follow a unimodal, roughly symmetrical, bell-shaped dist}
^{Central Limit Theorem: Principle that the distribution of the sums/means of scores taken at random from any dist. of indiv. will tend to form norm curve}
^{Haphazard Selection: Picking for convenience (Ie, whoever happens to be available)}
^{Population Parameters: M, SD2 and SD of a pop}
^{Sample Stats: M, SD2 and SD figured for scores in a sample}
^{Relative Freq: # of times smt happens relative to # it could happen}	^{Probability: p=Possible successful outcomes/All possible outcomes}
^{Response rate: Proportion of individuals approached for the study who actually participated in the study}

Ch. 5

^{Theory: Set of priciples that attempt to explain 1+ facts/relationships/events}
^{Hypothesis testing process:}	^{Step 1- Restate Question (research/null hypotheses?) Step 2- Determine chara of comparison distribution Step 3- Determine cutoff sample score Step 4- Determine samples score on the comparison distribution Step 5- Decide whether or not to accept/reject the null hypothesis}
^{Comparison Distribution: Represents the population situation if the null hypothesis is true}
^{Meta-analysis: Combo of results from multiple diff studies}
^{Directional Hypothesis: Study that focuses on a specific direction of effect}
^{Decision Errors: Correct procedures leading to faulty results}
^{Type I Error: Conclude the study supports research hypothesis when it is actually is false}
^{Type II Error: Extreme p-value that leads to rejecting a null hypothesis that should actually be accepted}
^{Not Significant: NS}

Ch. 8

^{T test for independent means: using scores obtained from 2 sep groups that're indep of each other}
^{Distribution between means: comp dist used in a t test for ind M. We are not using diff scores and are instead comp 1 groups M to the other groups M}
^{Weighted Avg: An average weighted by the amount of info that each sample provides}	^{Pooled estimate of pop SD2: S2Pooled=[(df1/dftotal)(S12)]+[(df2/dftotal)(S22)]}
^{SD2 of dist of diff between Ms: For pop1: SM12=S2Pooled/N1 For pop2: SM22=S2Pooled/N2}
^{SD2 of dist of diff between Ms: S2Difference}	^{S2Difference=SM12+SM22}
^{SD of the dist of diff between Ms: SDifference}	^{SDifference=√S2Difference}
^{Df for ttest for for ind M: dftotal=df1+df2}
^{ttest for ind M: t=(M1-M2)/SDiffference}
^{Hyp Test Proc: Find S12+S22->S2Pooled->SM12+SM22->S2Difference->SDifference->Cutoff->M1+M2->t}
^{Effect Size for IndM T: Est Eff Size=(M1-M2)/SPooled}
^{Harmonic M: Gives equivalent sample size to groups that have equal group sizes (used for est eff size when group sizes aren't even) Harmonic M=[(2)(N1)(N2)]/(N1+N2)}
^{t test shown in research: t(dftotal)=(tscore), p<.01}

^: √ σ μ ∑

Ch. 6

^{Distribution of Means (DoM): The distribution of the means of each of many samples of = size and all randomly selected from the same population}	^{3 Chara of DoM: 1. Its M 2. Its spread (SD2+SD) 3. Its shape}
^{Rules: Rule 1- PopMm (M of DoM)=PopM (M of pop) Rule 2a- Pop SD2M=SD2/N Rule 2b- Pop SDM=√SD2M Rule 3- The shape of a DoM is approx norm if either a) Each sample has 30+ part b) The dist of the pop of indiv is norm}
^{Z Test: The Z score that is checked against the normal curve}
^{Effect Size: The amount that pops (exp and non exp) are separated/don't overlap}	^{Cohen's d: d=(μ1 (M of exp group)-μ2(M of known pop))/σ (SD of known pop)}
^{d effect size: small 0<d<0.2 med 0.2<d<0.8 large d>0.8}
^{Type I Error: Rejecting the null hypothesis when the null hypothesis is actually true}
^{Type II Error: Accepting the null hypothesis when the null hypothesis is false, aka beta error}
^{Type III Error: Concluding that there is a sig diff in one direction when the true effect is in the other direction}
^{Statistical Power: Likelihood that a study will correctly detect a real treatment effect. In other words, the stat pow is the likelihood that the study will correctly reject a null hypothesis}
^{Hypothesis testing steps: Step 1- Develop Hypothesis ie- H0: μ1≤μ2 H1: μ>μ2 Step 2- Determine chara of comp pop σM=σ/√N Step 3- Determine cutoff score Step 4- Determine samples score on the comp dist Z=(M-μM)/σM Step 5: Decide whether to reject or accept the null hypothesis}
^{Power Distribution Steps: Step 1: Turn Z cutoff score into raw score M=(Z)(σM)+μM Step 2: Figure the zscore for the cuttoff M, Z=(M-μM)/(σM) Step 3: Use Table A-1 to determine prob of getting the resulting score from step 2 Power=1-beta}

^: √ σ μ

Ch. 7

^{T Tests: Hyp test procedures where pop SD2 is unknown(Aka students t)}
^{1 sample t test: scores from one sample where the comp pop has a known M but unknown SD2}
^{1 samp t hyp test: In step 2 we have to find the unbiased estimate of the pop SD2 S2=[∑(X-M)2]/df, in step 3 we use table A-2 instead and for step 4 we need to calculate a t-score t=(M-Pop M)/SM to compare against our cutoff score}	^{Degrees of Freedom:df=n-1}
^{Repeated-Measures design: Research situation where 2 scores are taken from each person in the sample (within-subjects design)}
^{t test for dependent means: Each person has 2 scores, we use diff scores for the participants (1 score-the other) and we assume pop M is 0}	^{For the t test for dep M, calculate diff scores before doing hyp test}
^{Est. Effect Size (for t test w dep M): Mean of diff scores/sd of pop of diff scores Est Eff Size=M/S}

^: √ σ μ ∑

Ch. 9

^{ANOVA: Stat procedurefor testing SD2 among the Ms of >2 groups}	^{The null hyp for anova is that the several pops being compared have the same M}
^{Within-group est of the pop SD2: Avging pop SD2 est from each sample into a single pooled est. Gives an avg of est figured entirely from the scores within each of the samp}
^{Between-group est of the pop SD2: Est of the SD2 in each pop from the SD2 among the Ms of the samples}
^{Treatment effect: Diff treatment received by the groups causes the groups to have diff Ms}
^{F Ratio: The between-groups est divided by the within-groups est}
^{F Distribution: Math defined curve that is the comp dist used in an ANOVA}
^{Before testing, find M and S2 for each group of part}
^{Within-groups SD2 est: S2Within=(S12+S22+...Slast2)/NGroups}
^{Grand M: The overall M of all our scores GM=∑M/NGroups}
^{Est of SD2 of the Dist of Ms: SM2=[∑(M-GM)2]/dfbetween}
^{Comparison of fig the SD2 of a dist of Ms from the SD2 of a dist of indiv: from dist of indiv->dist of M - S2M=S2/N dist of M->Dist of indiv - S2Between=(S2M)(N)}
^{F Ratio: Ratio of between-group est of pop SD2 to the within-group est of pop SD2 F=S2Between/S2Within and use table A-3 for comp}
^{Between-groups df: Numerator df dfBetween=NGroups-1}
^{Within-groups df: Denominator df dfWithin=df1+df2+...dfLast}
^{Hyp Test Proc: Find S2 + M for each group->S2Within->GM->dfBetween->dfWithin->S2M->S2Between->F}
^{Effect size for ANOVA: R2}	^{R2=[(S2Between)(dfBetween)]/[(S2Between)(dfBetween)]+[(S2Within)(dfWithin)]}
^{R2 Power Meaning: small .01 med .06 large .14}
^{Factorial ANOVA: ANOVA for factorial research design}
^{Interaction Effect: X = interaction (effect of one variable impacts the results on the other)}
^{Two-way ANOVA: Considers the effect of 2 variables that separate groups}
^{Grouping Variables/Ind Variables: Variables that separate groups}
^{One-Way ANOVA: Consider the effect of only one grouping}
^{Diff ANOVA Means: Cell Ms- M of scores in each cell Marginal Ms- M of 1 grouping variable (vertical/horizontal grouping)}
^{Dependent Variable: Represents the effect of the exper proc}
^{One-Way ANOVA in Research: Ftest(dfBetween, dfWithin)=F ratio score, p<.01}

^: √ σ μ ∑

Psych 2260 Cheat Sheet (DRAFT) by jagglepop

Ch. 3

Ch. 10

Ch. 3

Ch. 3

Ch. 4

Ch. 5

Ch. 8

Ch. 6

Ch. 7

Ch. 9

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