Methods Part II
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| Experimenter Bias | Researchers beliefs about the expected outcome influence the results (leading questions, inadvertant reinforcement)
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| Threats of Experimenter Bias | Internal Validity, it affects results. External Validity, you can't generalize to natural settings.
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| Demand Characteristics | Aspects of the study or study environment that reveal the hypothesis being tested...may lead subjects to exhibit subject role (good, negativistic, apprehensive, faithful)
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| Threats of Participant Bias | Internal, innaccurate results. External, can't generalize. ( use deception, single blind study, control group)
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| Sampling Distribution of the means | Permute data to gather all possible samples of n size. Take mean of each possible permuted sample and build distribution.
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| Single Sample T-test | Evaluates sample mean against sampling distribution mean. (population data is known)
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| Basic Units of a Sampling Distribution | Xbar. Muxbar. SigmaSQRDxbar. Sigmaxbar <- std error.
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| Standard Error | Standard Deviation of a sampling distribution
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| Central Limit Theorem | Specifies nature of sampling distribution. *mean of sampling distribution is a pretty good estimate of the pop. mean for samples larger than N=1. Sampling distributions are more normal, with less variability.
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| Why T-test? | Z underestimates population variance and gives too many rejections of null. T has more variability, flatter (platykurtic)
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| Degrees of Freedom | # of Observations that are free to vary (last has to make dataset have a the set Xbar)
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| Two Sample T-test (Independent) | Compares two means from two groups (usually 1 IV w/ 2 levels).
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| Independent T-test Notation | Xbar1, Xbar2, S(xbar1-xbar2)
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| Sampling Distribution for Independent T-test | Sampling distribution of differences between the 2 sample means.
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| Variance Sum Law | The variance of the sampling distribution is the sum of the variances for the component sampling variances (i.e. std dev = S(xbar1-xbar2))
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| Pooled Standard Deviation | Assume equal variances in an Independent T-test, we factor variance out (still under radical)
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| Assumptions for an Independent (Two Sample) Ttest | Independent Random Sampling. Normal Populations. Equality of Variance. DV is ratio or interval.
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| Intact Groups | Groups pre-formed because variable being study cannot be randomly assigned. forces btwn subjects design. May affect validity of test
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| Confidence Intervals | Obtained sample mean(s) +- (TCRIT*STD ERROR). std error bars will be smaller than Confidence interval
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| Paired Sample T-test (Dependent) | ALL ABOUT DIFFERENCES. within subjects. Difference score from each individual tested against expected difference of 0.
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| Between Subjects ANOVA | a multi-group generalization of the t-test w/ 3 distinctions: more groups, focus on variance instead of means, uses F. Same assumptions as t
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| ANOVA is one tailed because | F=t^2. Also made of SS/df and SUM of squared values cannot be negative and df cannot be negative.
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| Hypothesis for Btwn Sbjt ANOVA | All are equal. Two are different from one another. NON DIRECTIONAL
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| Indications of SSbtwn & SSwithin | If SSbtwn is large & SSwithin is small, null is probably true. If btwn is a fair amount and within is somewhat less, alternative is probably true. (because F=between/within)
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| Family-Wise Error Rate | Refers to the chance of committing at least one type-1 error among a set of analyses
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| Fisher's LSD Test | Least Significant Difference. We run modified tests between pairs ONLY IF ANOVA is significant. pretty liberal.
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| Bonferroni | Alpha adjustment technique. Alpha family wise is divided by total # of comparisons.
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| Post Hocs | Use DFwithin to get Tcrit from post hoc comparisons (ttests)
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| Problems with Btwn Subjects ANOVA | Individual Differences cause high within group variability and mask treatment effect. Individual differences can also become confounding variables
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| Subjects variability | Makes up part of Within group variability in One-way anova. Tells how much within groups variability can be attributed to individual differences
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| One Way ANOVA | more sensitive to treatment effect because individual differences are accounted for. We use means of groups and means of individual subjects
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| Factorial Design | A research study involving 2 or more IVs.
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| Advantages of Factorial Designs | More realistic...DVs of interest are rarely ever effected by only 1 thing in real life. Shows interactions of IVS on DVs. Economical, can test multiple hypotheses at once.
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| Main Effect | The mean differences among the levels of 1 factor
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| Theres an Interaction if | The effects of one factor depend upon the level of another factor. If it is significant we can no longer talk about main effects.
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| Simple Main Effect | The effect of one factor at one particular level of another factor
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| Null Hypotheses for Factorial ANOVA | All treatments for factor A are equal. All treatments for factor B are equal. Factors are independent.
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| Factorial ANOVA | we split SSbtwn(called SScells) into 3 groups. A, B & Interaction btwn A&B. Indent three groups if including Fcells in source table
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| Testing Single value against known sample or population | Z test
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| Mean of one group against population mean | Single sample t-test
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| More than one independent variable | Use factorial ANOVA test
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| One IV, two levels, Between Subjects | Independent Ttest
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| One IV, two levels within subjects | Dependent T-test
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| One IV, three+ levels, within subjects | One way, repeated measures ANOVA
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| One IV, three+ levels, between subjects | One way, between subjects anova
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| One Way ANOVA means | One IV, multiple levels
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