Chapter 11: Dependent Samples T-Tests Flashcards

1
Q

If the confidence interval contains 0

A

then it is not a significant effect

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2
Q

If the interval doesn’t contain 0

A

then the treatment effect was significant

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3
Q

assumptions for an independent t-test

A
  1. Observations from each sample are independent
  2. The two populations the samples come from must have a normally distributed dependent variable
  3. The two populations the samples come from must have equal variances of the dependent variable
    Called the homogeneity of variance assumption
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4
Q

homogeneity of variance assumption

A

The two populations the samples come from must have equal variances of the dependent variable

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5
Q

what is the goal of hartley’s f max test?

A

Test for homogeneity of variance assumption

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6
Q

interpreting hartley’s f max values

A

Small value (near 1.00) indicates similar sample variances

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7
Q

f-max formula

A

f-max= larger variance/ smaller variance

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8
Q

what information do you need to calculate f-max?

A

Alpha level
K (how many samples you have)
Df for each sample variance

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9
Q

what if you don’t meet the homogeneity of variance assumption?

A

you can use a different method to compute the variance and associated standard error (which a computer will do for you!)

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10
Q

when to use dependent t-tests?

A

Used when you have two scores for one participant

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11
Q

matching-subjects design

A

Each individual in one treatment is matched one-to-one with a corresponding individual in the second treatment

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12
Q

difference score

A

D= X₂- X₁
where X₁ is the person’s score in the first treatment, X₂ is the person’s score in the second treatment

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13
Q

null hypothesis for a dependent t-test

A

H₀: μ= 0
States that the population of difference scores has a mean of zero

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14
Q

alternative hypothesis for a dependent t-test

A

H₁: μ≠ 0
States that there is a systematic difference between treatments that causes the difference scores to be consistently positive (or negative) and produces a non-zero mean difference between the treatments

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15
Q

df for dependent t-test

A

n-1

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16
Q

dependent t-test formula

A

t= M-μ/ Sm

17
Q

estimated standard error for dependent t-test formula

A

Sm= s/√n

18
Q

the dependent t-test is

A

the same procedurally as the one-sample t-test

19
Q

advantages of dependent t-test

A
  • less variability
  • more statistical power
20
Q

disadvantages of dependent t-tests

A
  • Sometimes, people stop showing up for the study, decreasing your sample size
  • Order can influence the results of a study
21
Q

measures of effect size for dependent t-tests

A

cohen’s d & variance explained
same formulas as for a regular one sample t-test

22
Q

confidence intervals for dependent sample t-tests

A

μD= MD +/- t Smd

23
Q

assumptions for a dependent t-test

A
  • Observations within each treatment condition must be independent
  • Population distribution of difference scores (D values) must be normally distributed