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13246660
Statistics
Description
Research Design and Analysis
No tags specified
statistics
t-test
anova
descriptive statistics
inferential statistics
statistics
Mind Map by
Minthia Meghan
, updated more than 1 year ago
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Created by
Minthia Meghan
over 6 years ago
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Resource summary
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
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