Methods Part II
Quiz yourself by thinking what should be in
each of the black spaces below before clicking
on it to display the answer.
Help!
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| Experimenter Bias | show 🗑
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| show | Internal Validity, it affects results. External Validity, you can't generalize to natural settings.
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| Demand Characteristics | show 🗑
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| Threats of Participant Bias | show 🗑
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| Sampling Distribution of the means | show 🗑
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| Single Sample T-test | show 🗑
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| show | Xbar. Muxbar. SigmaSQRDxbar. Sigmaxbar <- std error.
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| Standard Error | show 🗑
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| Central Limit Theorem | show 🗑
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| show | Z underestimates population variance and gives too many rejections of null. T has more variability, flatter (platykurtic)
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| Degrees of Freedom | show 🗑
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| Two Sample T-test (Independent) | show 🗑
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| show | Xbar1, Xbar2, S(xbar1-xbar2)
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| show | Sampling distribution of differences between the 2 sample means.
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| show | 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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| show | 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 | show 🗑
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| Intact Groups | show 🗑
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| show | 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) | show 🗑
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| show | 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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| show | 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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| show | All are equal. Two are different from one another. NON DIRECTIONAL
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| show | 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 | show 🗑
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| Fisher's LSD Test | show 🗑
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| Bonferroni | show 🗑
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| Post Hocs | show 🗑
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| show | Individual Differences cause high within group variability and mask treatment effect. Individual differences can also become confounding variables
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| show | 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 | show 🗑
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| show | A research study involving 2 or more IVs.
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| Advantages of Factorial Designs | show 🗑
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| show | The mean differences among the levels of 1 factor
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| Theres an Interaction if | show 🗑
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| show | The effect of one factor at one particular level of another factor
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| Null Hypotheses for Factorial ANOVA | show 🗑
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| Factorial ANOVA | show 🗑
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| Testing Single value against known sample or population | show 🗑
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| Mean of one group against population mean | show 🗑
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| show | Use factorial ANOVA test
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| One IV, two levels, Between Subjects | show 🗑
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| show | Dependent T-test
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| One IV, three+ levels, within subjects | show 🗑
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| show | One way, between subjects anova
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| One Way ANOVA means | show 🗑
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