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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| show | 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 | show 🗑
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| show | Permute data to gather all possible samples of n size. Take mean of each possible permuted sample and build distribution.
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| show | Evaluates sample mean against sampling distribution mean. (population data is known)
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| Basic Units of a Sampling Distribution | show 🗑
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| show | Standard Deviation of a sampling distribution
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| show | 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? | show 🗑
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| show | # of Observations that are free to vary (last has to make dataset have a the set Xbar)
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| show | Compares two means from two groups (usually 1 IV w/ 2 levels).
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| show | Xbar1, Xbar2, S(xbar1-xbar2)
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| Sampling Distribution for Independent T-test | show 🗑
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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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| show | Independent Random Sampling. Normal Populations. Equality of Variance. DV is ratio or interval.
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| show | Groups pre-formed because variable being study cannot be randomly assigned. forces btwn subjects design. May affect validity of test
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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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| Between Subjects ANOVA | show 🗑
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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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| Hypothesis for Btwn Sbjt ANOVA | show 🗑
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| Indications of SSbtwn & SSwithin | show 🗑
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| show | 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 | show 🗑
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| show | Alpha adjustment technique. Alpha family wise is divided by total # of comparisons.
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| show | Use DFwithin to get Tcrit from post hoc comparisons (ttests)
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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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| Factorial Design | show 🗑
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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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| show | 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 | show 🗑
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| Null Hypotheses for Factorial ANOVA | show 🗑
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| show | 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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| show | Z test
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| show | Single sample t-test
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| show | Use factorial ANOVA test
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| show | Independent Ttest
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| One IV, two levels within subjects | show 🗑
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| show | One way, repeated measures ANOVA
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| show | One way, between subjects anova
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| One Way ANOVA means | show 🗑
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Created by:
lpicklesimer
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