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research methods and data analysis > Levels, sample size and power > Flashcards

Levels, sample size and power Flashcards

1
Q

what is data collected at?

A
  • data collected at different levels
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2
Q

what do levels of data analysis depend on?

A
  • depends on design and measures
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3
Q

what are the two different levels of data analysis?

A
  • within a person
  • between people
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4
Q

what are four examples of data within a person?

A
  • response on questionnaire
  • reaction time
  • blood concentration
  • physiological measurement
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5
Q

what are four examples of data analysis between people?

A
  • individuals in a sample
  • samples in a population
  • classes/ cohorts in a school
  • districts in a city
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6
Q

what is the most common level of data analysis?

A
  • between people is most common
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7
Q

how many repetitions should you use?

A
  • no hard rule
  • more is better
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8
Q

what are the limitations of large sample sizes?

A
  • time, money and resources limited
  • participants’ effort, endurance, boredom, will- power and kindness are limited
  • benefit increases only with square root of N
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9
Q

how do you double statistical power?

A
  • quadruple number of repetitions
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10
Q

what do you do with all the repetitions?

A
  • take averages
  • one per design level
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11
Q

what is the alternative with all the averages?

A
  • take average of differences
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12
Q

what do you do with all the averages of averages?

A
  • analyse them
  • use t- test, correlation, ANOVA, GLM
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13
Q

how many levels of data analysis is there?

A
  • three
    1-3
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14
Q

what is level 1 of data analysis? - give an example

A
  • within- person, within- measurement
    e.g., using 100 individual heart beats to estimate the heart rate
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15
Q

what type of data is level 1?

A
  • raw data
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16
Q

what is level 2 data analysis?- give an example

A
  • within- person, within condition
    e.g., using 10 repetitions of each condition to estimate pre/ post HRs
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17
Q

what data/ statistic is level 2?

A
  • summary data
  • descriptive statistics
18
Q

what is level 3 data analysis? - give an example

A
  • between people
    e.g., using 16 athletes in each group to estimate intervention effect
19
Q

what statistics is level 3?

A
  • inferential statistics
20
Q

what do all general linear model statistical tests assume?

A
  • assumes your data are independently sampled
21
Q

how is the assumption not true for a heart rate experiment?

A
  • heart rate now is not independent from your HR 5 seconds ago
22
Q

what happens to the measurements from the same individual?

A
  • correlated or dependent
23
Q

what should you use for a valid GLM test?

A
  • only use one value per person and condition
24
Q

how can you use all the level 1 data?

A
  • use a multi- level mixed model regression (MLMM)
25
Q

what designs usually need less data?

A
  • within- subject (repeated) designs
26
Q

what is statistical power?

A
  • the probability that you will find a significant result
27
Q

what does statistical power increase with?

A
  • increases with the square root of N
28
Q

what do you need to set in statistics?

A
  • set an arbitrary level of significance
29
Q

how do you evaluate sample size?

A
  • evaluate sample size in context of other things
30
Q

do you need more than 20 for parametric GLM stats?

A
  • NO, false assumption
  • t-tests were created for N= 4 to 10
  • more is usually better
31
Q

what should you do if the study has a small sample size?

A
  • poor criticism
  • if you know it is too small just tell me what size it should be
32
Q

what is assumed about the effect when there is a significant result?

A
  • assuming that there is a real effect
  • whatever your hypothesis, you assume it is exactly true
33
Q

what is assumed about the sample when finding a significant result?

A
  • that it is exactly as big as you say it is
34
Q

what do you need to specify? what is the equation?

A
  • specify an effect size> R2, f, cohen’s d
    Cohen’s d = t / sqrt (N)
35
Q

what does statistical power assume about other statistical assumptions?

A
  • assuming that other statistical assumptions are true
  • independent sampling
  • similar variance, independent residuals, etc
36
Q

what is the simplest case when calculating the effect size?

37
Q

how do you guess the sample size?

A
  • independent samples t-test
  • calculate N required
  • calculate Cohen’s d
38
Q

are there many hard rules in statistics?

A
  • no
  • very few hard rules
39
Q

what is sample size not easy to do?

A
  • not easy to assess or criticise
40
Q

how many datapoints should be used?

A
  • more datapoints is usually better
41
Q

how can sample size be analysed?

A
  • re effect size
42
Q

what can power analysis help with?

A
  • helps you design a study

research methods and data analysis (12 decks)

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