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EAB > Summarising Data > Flashcards

Summarising Data Flashcards

1
Q

Why should data be summarised?

A
  • Data quality monitoring
    • Data checking + cleaning - check for invalid/missing entries
    • Baseline data in a study - describe characteristics of participants in study e.g. 1st table in many research articles - to set study + results in context
    • Before doing a complex analysis - so it makes sense
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2
Q

What is quantitative data and the 2 types?

A
  • data which can be measured numerically

- continuous or discrete

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3
Q

What is continuous data and give e.g.s?

A
  • data lie on a continuum
  • can take any value between 2 limits
  • e.g. weight, height
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4
Q

What is a limitation of continuous data?

A

accuracy of data depends on accuracy of method of measurement so that some continuous data may be recorded as integers although that is an approx to true value

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5
Q

What is discrete data and e.g?

A
  • data do not lie on a continuum
  • can only take certain values, usually counts (integers)
  • no. of children in a family
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6
Q

Why is weight a continuous variable and what is the limitation?

A
  • it is measured using weighing scales
  • lies on a continuum
  • limitation is the accuracy of the scales
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7
Q

Why is the number of previous pregnancies in a pregnant woman discrete data?

A
  • it is counted

- only whole numbers are possible

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8
Q

What is ordinal data and which type of data is always ordinal?

A

– the data values can be arranged in a numerical order from the smallest to the largest.
- Quantitative data are always ordinal

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9
Q

What are e.g.s of ordinal data?

A
  • Questionnaire scale data - often counts, e.g. when adding the no. of +ve responses to a set of questions to get a total score.
  • Categorical data may also have an inherent orde, such as stage of disease.
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10
Q

What is an e.g. where continuous data can look discrete?

A
  • because of the way they are measured and/or
    reported.
  • e.g. gestational age of babies often reported in whole weeks, e.g. 38 weeks, - appears to be discrete.
  • It is however continuous - could be reported to a greater degree of accuracy, e.g. as a decimal, such as 38.5 weeks
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11
Q

What are all continuous measurements limited by?

A
  • the accuracy of the instrument used to measure
    them,
  • many quantities are reported in whole numbers for convenience such as age and height
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12
Q

What is categorical data?

A

data where individuals fall into a number of separate categories or classes

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13
Q

Give e.g.s of categorical data

A
  • gender: male or female = 2 classes
  • disease status: alive or dead = 2 classes
  • stage of cancer: I, II, III or IV = 4 classes
  • marital status: married, single, divorced, widowed or legally separated = 5 classes
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14
Q

Give e.g.s of when categorical data can be ordinal?

A
  • Different categories of categorical data may be assigned a number for coding purposes
  • if there are several categories there may be an implied ordering, such as with stage of cancer where stage I is the least advanced and stage IV the most advanced.
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15
Q

What is dichotomous data and give e.g.?

A
  • only 2 classes
  • all individuals fall into one or other of the classes
  • aka as binary data.
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16
Q

Is it possible to categorise continuous data?

A
  • possible to re-classify continuous data into groups, for ease of reporting.
  • e.g. it is common to report birthweight in bands, giving the numbers of babies who fall into each
    birthweight band.
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17
Q

What are the consequences of dichotomising?

A
  • lots of info + statistical power lost in the analysis.
  • nature of any relationships may be masked. e.g. if relationship was curved, this may be weaker if the data were categorized
  • if relationship was U-shaped, categorization may totally obscure it
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18
Q

Why is it better if continuous data are re-classified into several groups?

A
  • effect on statistical power is less than when dichotomizing.
  • Grouping causes no problem if re-classification done simply to present summary statistics but the original data are used in the analysis
  • Sometimes can be useful when examining a non-linear relationship. The analysis may be more straightforward and more meaningful
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19
Q

How can continuous data be summarised?

A
  • a measure of the centre of the data distribution

- measure of the variability of the data.

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20
Q

What are measures of centre of data?

A
  • Mean

* Median

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21
Q

What are measures of variability of data?

A
  • Standard deviation (variance)
  • Range (minimum, maximum)
  • Interquartile range
22
Q

What is the mean and how is it calc?

A
  • simple average of all the data:

- sum of all values divided by the total number of values aka the arithmetic mean.

23
Q

What is the median and what is it when there is an even/odd no. of values in the sample?

A
  • the middle value when the data are arranged in ascending order of size. (n + 1)/2 = pos of median
  • odd no. of values in the sample: median will be the value with the same number of values both bigger than it and smaller than it.
  • even number of values: there will be two middle values and the median will be the mean of the two.
24
Q

What is standard deviation and what does it indicate?

A
  • a measure of the average difference between the mean and each data value.
  • indicates how dispersed the data are
25
Q

How is SD calc?

A
  • square root of the variance.
26
Q

How is variance calc?

A

summing the squared differences between the overall mean and each value and then dividing by the number of values minus one.

27
Q

What is the advantage of the standard deviation over the variance?

A

it is in the same units as the original data and so is easier to interpret

28
Q

What happens to the equation when the whole population variance is calculated?

A
  • a different denominator is used; we divide by n (but this almost never happens)
  • Since we virtually always have a sample, the SD is obtained by dividing by n-1 because it can be shown to give a more accurate estimate of the population standard deviation
29
Q

What is the range?

A
  • diff between smallest + largest value

- usually expressed as min + max (sometimes actually diff between 2 values shown but is not as good)

30
Q

What is interquartile range?

A
  • range of values that includes middle 50% values

- bounded by upper + lower quartile

31
Q

How are lower + upper quartile calc?

A
  • Lower: ranking data sim to median and then taking value below which bottom 25% of data sit (n + 1)/4 = pos
  • Upper: ranking data sim to median and then taking value above which top 25% of data sit 3 (n + 1)/4 = pos
32
Q

Which summary measure of centre of dis be used for continuous data with symmetric distribution?

A

Arithmetic mean

33
Q

Which summary measure of centre of dis be used for continuous data with +vely skewed distribution?

A

geometric/harmonic mean (but these don’t allow for 0 values)

34
Q

Which summary measure of centre of dis be used for continuous data with skewed distribution?

A

Median

35
Q

Which summary measure of centre of dis be used for discrete data?

A
  • Median unless range of data is large enough to make calc of mean sensible
  • e.g. no. of children in a family is discrete, while sometimes mean is calc e.g. 2.4 children - may be diff to interpret
36
Q

Which summary measure of spread of dis be used for continuous data?

A
  • SD

- range often useful if there’s room to present it

37
Q

Which summary measure of spread of dis be used for continuous data with skew (unsymm)?

A

IQR

38
Q

How can unordered/nominal data be summarised?

A
  • using freq in each category together with either overall prop/%
  • complete set of freq is freq dis
39
Q

How can ordered/ordinal data be summarised?

A
  • using freq + % but can also calc cumulative freq + % which is useful to show % below certain cut-off
40
Q

What is a histogram and its features?

A
  • diagram which shows dis of data by plotting data in rectangles (bins) corresponding to categories along x axis
  • rectangles have heights/areas prop to freq (no.) in the categories
  • y axis is freq per interval
41
Q

How are the bins interpreted in a histogram?

A
  • if widths of bins are same, height of each rectangle prop to its freq but if not then area ind freq
42
Q

What does a histogram show?

A
  • shape of dis
  • range
  • middle
43
Q

What does a box & whisper plot show?

A
  • median: horizontal line in box
  • UQ: top edge of box
  • LQ: lower edge of box
  • LQ: lower edge of box
  • max: top of whisker
  • min: bottom of whisker
44
Q

What can the shape of dis show about the data?

A
  • central values
  • extreme values
  • where bulk of data lie
45
Q

What is +vely skewed data?

A
  • tail on RHS longer than tail on left
  • large no. of indiv have low data values and few indiv have v.high values which stretches right tail
    e. g. alcohol intake in pregnancy, chol, weight, blood pressure
46
Q

What is -vely skewed data?

A
  • tail on LHS longer than tail on right
  • e.g. gestational age - pre-term births stretch left tail and many on the right as clinical practice of induction beyond 40 weeks + limiting size of mother/foetus
47
Q

How are bar charts presented?

A
  • each category given its own bar along x axis

- height of each prop to freq of observations

48
Q

What are advs of bar charts?

A
  • show freq /% in each category

- may be quicker to absorb than a table

49
Q

What do pie charts show?

A
  • dis of indiv in diff categories of variable where every indiv belongs to only 1 category
  • each category given an area/slice of graph
  • area of each slice prop to freq of observations within that category + calc by div whole pie (360 degrees) into slices
50
Q

What is adv + disadv of pie charts?

A
  • comparison of prop in diff pop groups

- hard to judge size of angle so cant judge figs/prop

51
Q

How is data shown on normal dis graph and its use?

A
  • 95% data lies within mean +/- 2xSD
  • 65% data lies within mean +/- 1xSD
  • normal dis used for normal ranges where you expect normal, healthy values to lie

EAB (5 decks)

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