Print Options

Card layout: ?

← Back to notecard set|Easy Notecards home page

Instructions for Side by Side Printing
  1. Print the notecards
  2. Fold each page in half along the solid vertical line
  3. Cut out the notecards by cutting along each horizontal dotted line
  4. Optional: Glue, tape or staple the ends of each notecard together
  1. Verify Front of pages is selected for Viewing and print the front of the notecards
  2. Select Back of pages for Viewing and print the back of the notecards
    NOTE: Since the back of the pages are printed in reverse order (last page is printed first), keep the pages in the same order as they were after Step 1. Also, be sure to feed the pages in the same direction as you did in Step 1.
  3. Cut out the notecards by cutting along each horizontal and vertical dotted line
To print: Ctrl+PPrint as a list

25 notecards = 7 pages (4 cards per page)

Viewing:

Psych 311 Unit 9 Study Guide

front 1

Parametric tests

back 1

  • 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

front 2

Nonparametric tests

back 2

  • 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

front 3

When to use nonparametric tests

back 3

  • 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

front 4

Research Designs for T Tests

back 4

  • 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

front 5

T Test: Hypothesis testing

back 5

  • 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

front 6

T Test: Null hypothesis

back 6

  • 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

front 7

T Test: Reporting

back 7

front 8

T Test: Types

back 8

  • 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

front 9

T Test: Types (reporting differences)

back 9

  • 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)

front 10

Independent Samples: Advantages and Disadvantages

back 10

  • 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

front 11

Repeated measures: Advantages and Disadvantages

back 11

  • 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

front 12

T Test Vs. ANOVA

back 12

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

front 13

ANOVA

back 13

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

front 14

Types of ANOVA Designs

back 14

  • 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

front 15

Null Hypothesis for ANOVA

back 15

  • States that there is no treatment effect and that the population mean is unchanged
  • Provides a specific value for the unknown population mean

front 16

Types of ALPHA

back 16

  • 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)

front 17

Reporting ANOVA

back 17

front 18

Post-Hoc Tests

back 18

  • 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

front 19

Chi-Square Test for Goodness of Fit

back 19

  • 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

front 20

Chi-Square Test for Goodness of Fit: Data Presentation

back 20

front 21

Types of null hypotheses for Chi-Square

back 21

front 22

Types of null hypotheses for Chi-Square

back 22

front 23

Correlational Research Strategy

back 23

  • 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

front 24

What correlations describe

back 24

  • 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

front 25

Correlational Vs. Experimental

back 25

  • 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