Psych 311 Unit 9 Study Guide

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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

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

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

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

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

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

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

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)

Independent Samples: Advantages and Disadvantages

  • Pro’s:
    • No potential for order effects
    • No potential for time-related factors
  • Con’s:
    • Less efficient (more participants needed)
    • Less ideal for studying changes over time
    • Potential problems with individual differences

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 time-related factors

T Test Vs. ANOVA

  • T-test: can only compare two treatments
  • ANOVA: can compare 2 or more treatments


  • 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
    • The variable (independent or quasi-independent) that designates the groups being compared
    • Individual conditions or values that make up a factor

Types of ANOVA Designs

  • Single-Factor
    • Studies that have one independent variable (one factor)
  • Independent-Measures
    • Separate group of participants for each treatment being compared
  • Repeated-Measures
    • Same group of participants is tested in all treatment conditions
  • Two-Factor
    • Studies that have two independent variables (two factors)
    • Can address different questions than single-factor designs

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

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 t-tests should be run to compare three groups (use ANOVA instead)

Reporting ANOVA

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Post-Hoc 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
    • F-statistic does not indicate where the significance is

Chi-Square 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

Chi-Square Test for Goodness of Fit: Data Presentation

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Types of null hypotheses for Chi-Square

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Types of null hypotheses for Chi-Square

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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

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

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