week 11-statistical tests Flashcards

1
Q

Comparing tow group means in an RCT

A
  • assume groups at pretest are baseline
  • difference between groups at posttest is due to treatment
  • unlikely to be result of chance
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2
Q

Comparing tow group means in an RCT: parametric assumptions

A
  • groups have normal distributions
  • groups have variances
  • interval/ratio data is used
  • compare the difference between groups with a t-test
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3
Q

Statistical erroe

A
  • all sources of variability within a data set that cannot be explained by IV (not necessarily mistake)
  • applies to the random differences that are not explained by treatment
  • source of error is variance - all subjects in groups do not respond the same
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4
Q

T distribution

A
  • based on properties of normal curve (95% within 2 SD from mean)
  • determine z-score of area of curve above and below that value and probability of obtaining score
  • significance of difference between the two groups judged by ratio
  • t=difference between means/variability within groups
  • H0 is true if error variance increased and t ratio decreased
  • H0 is false if error variance decreases and t-ratio is increased
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5
Q

Independent Unpaired t-test

A
  • compare means from 2 independent groups (between subjects design)
  • each group composed of difference set of subjects
  • t=(X1-X2)/(Sx1-X2) = difference between independent groups/pooled variability within groups
  • compare calculated t-value with critical value
  • probability that calculated t-value will be larger than critical value is 5% or less
  • t-value larger than critical value is considered significant and reject the h0
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6
Q

Two-tailed test (nondirection HA)

A
  • new splint: X
  • Stnadar splint: X
    calculated t-vlue
  • critical value
  • calculated t-value
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7
Q

paired t-test

A
  • subjects serve as own control (within subject design)
  • each measurement has a matched value for each subject
  • determin if these are significantly different from each other
  • t=mean of difference scores/standard error of difference scores
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8
Q

use of multiple t-tes

A
  • compare two means
  • if > 2 means should not compare using multiple t-tests
  • making more comparisons means you are more likely to commit type 1 error
  • 5% chance of type 1 error with each test
  • cummulative error of multiple test >5%
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9
Q

ANOVA basics

A
  • purpose: compare 3+ groups
  • like t-test based on comparison of distance between group means and error of variance of each group
  • parametric assumptions:
    1. normal distribtuion
    2. homogeneity of variance
    3. interval/ratio data
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10
Q

One-way NOVA

A
  • one IV (3+ levels)
  • h0: uA=uB=uC…
  • HA: there will be a difference between at least tow of the groups
  • Calculates f-statistic
  • conclusions: reject H0 if there is a significant different but you do not know where it is
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11
Q

Multiple comparision test: one-way ANOVA

A
  • if a significant difference is found you must carry out a post hoc multiple comparison test to locate differences
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12
Q

two-way ANOVA

A
  • 2 IV (factor a and factor b)
  • null hypothesis for each main effect of the factors
  • if significant difference is found a mulitple comparisions for 2-way ANOVA test should be conducted
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13
Q

Chi-Square: nonparametric test

A
  • not based on assumptions about population distribution
  • use rank or frequency information to draw conclusions about difference between population
  • ## use with categorical variables (nominal data)
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14
Q

Chi-square assumptions

A
  • frequencies represent individual counts
  • categories are exhaustive and mutally exclusive
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15
Q

chi-squared exquation

A
  • summation of (O-E)^2/E
  • statistic based on differences between observed frequencies and frequencies that would be expected if null hypothesis were true
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16
Q

two-groups parametric tests

A
  • independent t-test: between subject design
  • paried t-test: within subjects design
17
Q

3 + groups parametric design

A
  • ANOVA: 1 IV
  • Multifactorial: ANOVA (2-way, 3-way etc) with 2+ IV
  • if significance is found then multiple comparisons to locate difference
18
Q

Categorical data: nonparametric

A
  • chi-squared: compares observed frequency with expected frequency
19
Q

correlation research

A
  • analysis of association between variables
  • nonexperimental, no IV, observe existing phenomena
  • interval/ratio data
20
Q

Correlation coefficient

A
  • test statistic: r - describes strength and direction of relationship between 2 varibles
  • values range from -1 to 1
21
Q

positive value for correlation coefficient

A
  • positive correlation
22
Q

negative value for correlation coefficient

A
  • negative correlation
23
Q

assumptions for correlation

A
  • subjects scores represent normally distributed population
  • each subject contributes a score for X and Y
  • X and Y are independent measures
  • X values are observed not controlled
  • relationship between X and Y is linear
24
Q

Hypothesis testing in correlation

A
  • significant correlation is unlikely to have occured by chance
  • H0: population correlation = 0
  • HA: population correlation doesnot = 0
25
Q

Interpreting correlation coefficients: strength of relationship

A
  • .00-.25=little correlation
  • 0.26-0.49 = low correlation
  • 0.5-0.69 = moderate correlation
  • 0.7-0.89 = high correlation
  • 0.9-1.00 very high correlation
26
Q

Interpreting correlation coefficients: coefficient of determination

A
  • square of correlation coefficient r
  • percentage of variance shared by the 2 variables
  • if r=0.76, r2=0.58 that means 58% of variability in one variable can be accounted for be the other variable
27
Q

Statistical significance

A
  • probability that correlation coefficient would have occurred by change if there was no correlation between variables
  • even weak correlations can have statistical significance