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Each level of each independent variable has different subjects.
Between-Subjects or Independent Group Design
Each subject participates in all levels of all independent
variables.
Within-Subjects or Repeated Measures
Group Design
There must be at least two independent variables.
Each subject participates in all levels of one
independent variable but not the other.
Mixed Group Design
when the subjects try to figure out the experiment and then alter their
behavior to either "help" the scientist or even hinder the scientist
Demand Characteristics
Standard Error of the Mean
Standard Error of a Sample
when you reject the null hypothesis when shouldn't have because the null
hypothesis is actually true - there is not difference between your groups.
Type I error
when you fail to reject the null hypothesis when you should have because there really is a
significant difference between your groups.
Type II Error
If the scientific hypothesis predicts a
direction of the results, we say it is a
One-Tailed Hypothesis
If the scientific hypothesis does not
predict a direction of the results, we say it is a
Two-Tailed Hypothesis
an analysis of an experimental design with one independent variable and a nominal
dependent variable
One-Way Chi-Square
Chi-Square
df = k -1
Degrees of freedom for a Chi-Square
f
_{e}
of a Two-Way Chi-Square
when you have two independent variables and a nominal dependent variable
Two-Way Chi-Square
df = (number of rows -1) x (number of columns -1)
Degrees of Freedom for a Two-Way Chi-Square
If your sample size
is above 1000 (Comparing Sample to a Population
Single Sample z-test
Single Sample z-test formula
If your sample size
is below 1000 (Comparing Sample to Population)
Single Sample t-test
Single Sample t-test formula
If your two sample groups are independent of each other
t-test for Independent Groups
t-test for Independent Groups formula
Standard Error of the Difference for Independent Groups
(n1 - 1) + (n2 - 1)
df independent groups
If the two samples are not independent of each other but
instead are positively correlated to each other
t-test for Correlated Groups
the standard
error of the difference
(correllated groups)
number of pairs - 1
df correlated groups
t-test for correlated samples: using raw data
D bar
The mean of all the
difference scores. Difference scores are calculated by subtracting each Y value
from its X pair value
Standard Difference for Correlated Groups using the raw data
F = MSbg / MSwg
F ratio formula One-Way ANOVA
MSbg = SSbg / dfbg
MSbg formula One-Way ANOVA
MSwg = SSwg / dfwg
MSwg formula One-Way ANOVA
dfbg = k - 1
dfbg formula One-Way ANOVA
dfwg =
(n1 - 1) + (n2 - 1) + . . . + (nk - 1)
dfwg formula One-Way ANOVA
SSbg = [ (ΣX1)2 / n1 ) + (ΣX2)2 / n2 ) + . . . + (ΣXk)2 / nk ) ] - [ (ΣX1 + ΣX2 + . . . + ΣXk )2 / Ntotal ]
SSbg formula One-Way ANOVA
SSwg = [ (ΣX21 + ΣX22 + . . . + ΣX2k ) ] - [ (ΣX1)2 / n1 ) + (ΣX2)2 / n2 ) + . . . + (ΣXk)2 / nk ) ]
SSwg formula One-Way ANOVA
dfN
dfbg
dfD
dfwg
Nominal Dependent Variable Data
Chi-Square (
X
^{2}
)
Ordinal Dependent Variable Data
An ordinal statistic
Interval/Ratio Dependent Variable
2+ Factors (Independent Variables)
Two-Way ANOVA
Interval/Ratio Dependent Variable
1 Factor (Independent Variables)
2 Levels (i.e. control and experiment)
T-Test
Interval/Ratio Dependent Variable
1 Factor (Independent Variables)
3+ Levels (i.e. control, experiment
_{1}
, experiment
_{2}
)
One-Way ANOVA
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Author
Mental86
ID
2812
Card Set
Statistics
Description
statistics final
Updated
2009-12-11T10:08:56Z
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