Zusammenfassung der Ressource
Statistics
- Experiment Types
- Levels of Constraint
- Correlational
- True Experimental
- Only one that can predict, rest are
causality or description
- Quasi-Experimental
- No random assignment
- Observation
- Naturalistic
- with
Intervention
- Design
- Dependent
Groups/Within
Subject Design
- To find change in
participant, eliminates
individual differences, for
rare cases
- Can't use with
carry over effects
(time, fatigue,
etc.)
- Can be controlled for
with counterbalancing,
pre-training,rest or
using a different design
- Independent
Measures/Between Subject
- 2 samples are compared
before and after
- Matched Samples
- Scale of Measurement
- Interval
- Absolute 0
- Ratio
- Can be negative
- Ordinal
- Ranked Categories,
not equally
different
- Nominal
- Mutually Exclusive
Categories,
Collectively
Exhaustive
- Descriptive Statistics
- Variabilitiy
- Range
- Continuous uses upper
and lower real limit for
calculations
- Semi-interquartile Range
uses Middle 50% of
scores
- Standard Deviation
- Z-Scores
- Distance from mean in
SD
- Critical = 1.65
- Average Distance of a
score form the mean
- Central Tendency
- Mean
- Informative, but
affected by outliers
- Median
- Central score when ordered
(N+1/2)
- Mode
- Most frequent score
(only one for
nominal)
- Graphs
- Histogram
- Ratio/Interval
- No Spaces
- Bar Graph
- Nominal/Ordinal
- Frequency Table
- Polygraph (line chart)
- Inferential Statistics
- Parametric Tests
- T-Tests
- One-sample
t-test
- Use when know pop. mean,
and can calc sample mean
and SD
- To test whether a
sample is significantly
different from
population
- Independent-Groups t-Test
- Test if two samples are significantly
different from one another
- Dependent Groups t-Test
- Test changes in participants
based on measurements from
two times.
- Assumptions: Independent
Observations, normal pop,
homogenity of variance
(variance of groups is similar,
tested with Harthy FMAX test
or Levene)
- ANOVA
- Oneway
ANOVA
- Used with 1 factor, with
independent groups
- Repeated-Measures ANOVA
- Assumption: Homogeneity of
co-variance (Sphericity
variances for each set of
difference scores are equal)
- Mauchley Shpericity test;
greenhouse geisser,
hynn-feldt
- Factorial ANOVA
- Main Effects and
Interaction between
them
- Use simple main effects if
there is an interaction
- Types of Factorial ANOVA
- Simple 2 factors with 2-3 levels
- Higher order ANOVA 3+ factors
- 3 2-way interactions
- 1 3-way interactions
- Mixed ANOVA 1 between and
1 within subjects factors
- 2-way repeated measure
tested two separate times
twice
- Used to combat
experiment-wise
error rate which
increases with each
t-test.
- Assumptions:
Normal
distribution,
homogenity of
variance,
,independent
observations.
- Post Hoc Tests
- Fisher's LSD
(3 groups)
- Tukey's HSD
(4 groups,
conservative)
- Student-Neuman
Keuls (4 groups,
liberal)
- Dunnett's
(somparing one
group to series
of experimetnal
groups)
- Scheffe's Test
(Complex contrasts)
- Planned
Comparisons
(a priori)
- Trend Analysis
(Sequential Groups)
- Non-Parametric
Tests
- Chi-Square
(Nominal Only)
- Goodness of Fit
(1 group)
- Are individuals spread
across cats evenly
- Are the number of cases
distributed equally across
categories
- Test of
Independence (2
groups)
- Are groups equally
distributed across
categories
- Rank Test
(Ordinal Only)
- Mann-Whitney U
(Independent Measures)
- Wilcoxon T
(Dependent
Measures)
- Friedmann's Rank Test
(3+ Groups
- Kruskall Wallis (3+ Groups)
- (Only for
nominal/ordinal
data)
- Less power than parametric
- Indication of significance
- Effect Size
- Measurement of the
magnitude of treatment
effect
- Alpha Level
- One-tailed
(directional) and
two-tailed tests
- Type I Error: Concluding there is an
effect when none exists
- Testwise Error: Error per test
- Experimentwise Error: Type
I error in the test overall
- Type II Error: Concluding there is no
significance when there is