Psych 311 Unit 9 Study Guide
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1
Parametric tests
 Require assumptions about population parameters
 Require numerical scores
 Can be used to calculate mean & standard deviation
 Use ratio & interval data
 Testing hypotheses: Use population parameters
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Nonparametric tests
 DO NOT require assumptions about population parameters
 DO NOT require numerical scores; use categories, names, and groupings'
 CANNOT be used to calculate mean & standard deviation
 Use nominal and ordinal data
 Testing hypotheses: DO NOT state specific, numeric, population parameters
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When to use nonparametric tests
 When Simplicity is Needed
 Sometimes categories are simpler than scores (and still useful)
 When
scores violate assumptions of parametric tests
 For instance, parametric tests require normal distributions
 When variance is extremely high
 Extreme variance makes statistical significance unlikely
 Categories allow diverse scores to fit (ex. High, Medium, Low)
 With Indeterminate or Infinite Scores
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Research Designs for T Tests
 Single Sample
 Uses a single sample to make inferences about a single population
 Two Unrelated Samples
 Uses two samples to make inferences between two unknown populations
 Two Related Samples
 Uses one sample with each individual tested in two treatment conditions to make inferences about mean population differences
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T Test: Hypothesis testing
 GOAL: Use sample from treated population (treated sample) to
determine whether treatment has effect.
 Population mean is UNKNOWN
 Sample Mean is KNOWN, as is estimated standard error
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T Test: Null hypothesis
 Null hypothesis states that there is no treatment effect and that the population mean is unchanged
 Null hypothesis provides a specific value for the unknown population mean
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T Test: Reporting
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T Test: Types
 Single Sample
 Uses sample data (one sample) to test a population mean
 Independent Samples
 Uses data from two separate samples to evaluate mean difference between two different treatment conditions or populations
 Two sets of data
 Completely separate (independent) groups of participants
 Repeated Measures Designs
 Uses two sets of data from the same sample of participants to evaluate mean difference
 Two sets of data
 The same group of participants
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T Test: Types (reporting differences)

Independent
Samples:
 Since it involves two separate samples, extra notation is needed
 Subscripts are used, with number indicating which sample (1 or 2)
 n _{ 1}, n _{ 2} (sample size); M 1, M 2 (means), SS _{ 1}, SS _{ 2} (sum of squares; μ_{ 1 } μ_{ 2 }(population mean difference)

Repeated Measures
Designs
 Uses sample mean difference (M _{ D}) to estimate population mean difference (μ_{ D})
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Independent Samples: Advantages and Disadvantages

Pro’s:
 No potential for order effects
 No potential for timerelated factors

Con’s:
 Less efficient (more participants needed)
 Less ideal for studying changes over time
 Potential problems with individual differences
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Repeated measures: Advantages and Disadvantages

Pro’s:
 More efficient (fewer participants needed)
 Good for studying changes over time
 Reduces problems caused by individual differences

Con’s:
 Potential for order effects
 Potential for timerelated factors
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T Test Vs. ANOVA
 Ttest: can only compare two treatments
 ANOVA: can compare 2 or more treatments
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ANOVA
 Definition
 Analysis of Variance
 It is a hypothesis testing procedure used to evaluate mean differences between two or more populations or treatments
 Uses sample data to make inferences about populations
 FACTOR
 The variable (independent or quasiindependent) that designates the groups being compared
 LEVELS
 Individual conditions or values that make up a factor
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Types of ANOVA Designs
 SingleFactor
 Studies that have one independent variable (one factor)
 IndependentMeasures
 Separate group of participants for each treatment being compared
 RepeatedMeasures
 Same group of participants is tested in all treatment conditions
 TwoFactor
 Studies that have two independent variables (two factors)
 Can address different questions than singlefactor designs
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Null Hypothesis for ANOVA
 States that there is no treatment effect and that the population mean is unchanged
 Provides a specific value for the unknown population mean
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Types of ALPHA
 Testwise Alpha
 The risk of a Type I error, or alpha level, for an individual hypothesis test
 This is what we’ve talked about for the whole semester
 Experimentwise Alpha
 Is the total probability of a Type I error that accumulates when several hypothesis tests are used in a single study
 Typically larger than testwise alpha
 Is the reason why multiple ttests should be run to compare three groups (use ANOVA instead)
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Reporting ANOVA
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PostHoc Tests
 What are they?
 Additional hypothesis tests that are run after an ANOVA to determine exactly which mean differences are significant and which are not
 When to use them?
 When you have a significant F statistic and there are more than two groups
 Fstatistic does not indicate where the significance is
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ChiSquare Test for Goodness of Fit
 Uses sample data to test hypotheses about the shape or proportion of a population distribution
 Determines how well sample proportions fit population proportions of null hypothesis
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ChiSquare Test for Goodness of Fit: Data Presentation
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Types of null hypotheses for ChiSquare
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Types of null hypotheses for ChiSquare
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Correlational Research Strategy
 Two or more variables are measured to obtain a set of scores (usually 2) for each individual (or source)
 Measurements are examined to identify patterns that exist between the variables and to measure the strength of the relationship
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What correlations describe
 The direction of the relationship
 Positive or negative relationships
 The form of relationship
 Linear (using Pearson correlation)
 Monotonic (using Spearman correlation)
 The consistency or
strength of relationship
 How close the relationship is to a perfect linear or a perfect monotone relationship
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Correlational Vs. Experimental

Correlational
 Goal: Demonstrating the existence of a relationship between variables (not explaining)
 NO manipulating or controlling variables
 Measures two different variables

Experimental
 Goal: Demonstrating cause and effect, explaining relationship between variables
 DOES manipulate and control certain variables
 Usually only one measured (dependent) variable and looks for differences between two or more groups of scores