Quantitative - data analysis issues: levels of measurement, error types, descriptive Flashcards Preview

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Flashcards in Quantitative - data analysis issues: levels of measurement, error types, descriptive Deck (18)
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1
Q

What are the data in quantitative research?

A

primarily numerical

descriptive statistics - ways of displaying data and summarising it in ways that are easily understood

2
Q

In what ways are numbers used to display data?

A
  • numerical result (e.g.BP, age)
  • coded category (e.g. 1 = male 2= female)
  • ordered categories (e.g. pain scale)
3
Q

What are the levels of measurement?

A

nominal, ordinal, interval, ratio

4
Q

What are the properties of nominal data?

A

-different categories

5
Q

What are the properties of ordinal data?

A
  • different categories

- categories can be ranked

6
Q

What are the properties of interval data?

A
  • different categories
  • categories can be ranked
  • equal distances between categories
7
Q

What are the properties of ratio data?

A
  • different categories
  • categories can be ranked
  • equal distances between categories
  • fixed zero
8
Q

What are the ways of presenting descriptive data?

A
  • tables: allows data from different variables to be displayed togethe
  • charts: immediate visual impact
  • measures of central tendancy: mean, median, mode
  • measures of dispersion: range, interquartile range, standard deviation, variance
9
Q

Why do we perform statistical analysis?

A

to draw inferences from the sample that we studied about the population of interest

10
Q

What are the two basic approaches to statistical analysis?

A
  • hypothesis testing (using P values)

- estimation (using confidence intervals)

11
Q

How does hypothesis testing happen?

A

-set null hypothesis, set study hypothesis, carry out significance test, obtain test statistic, compare test statistic to hypothesised critical value, obtain P value, make decision

12
Q

What is a P value?

A

P value = PROBABILITY of obtaining the study results in the Ho is true

  • can be between 0 and 1
  • the closer it is to 0, the more likely it is that the Ho should be rejected
  • statistical sig - often set at 5
  • only tells you how likely the results are when the Ho is true
13
Q

How do you know if there is sufficient or insufficient evidence to reject or accept the Ho?

A

if P > or = 0.05 there is insufficient evidence to reject the Ho

14
Q

What is a Type I error?

A

(false positive error)

incorrect rejection of a true null hypothesis

15
Q

What is a Type II error?

A

(false negative error)

failure to reject a false null hypothesis

16
Q

What is the power of a study?

A

the probability of being able to detect a difference between the study groups, should one exist

  • usually expressed as a %
    e. g. - for a study with 80% power theres an 80% chance of detecting a real difference between study groups
17
Q

When is a confidence interval calculated?

A

when info about the effect size and whether the results are of clinical significance

-measure of the precision (accuracy) with which the quantity of interest is estimated

18
Q

All you know about confidence intervals

A
  • calcualted from any estimated quantity from the sample data such as mean and median etc
  • 95% CI is the range of values within which the true population quantity would fall 95% of the time if the study were repeated multiple times
  • 95% confident that the ‘true’ value lies within the specified range
  • if range includes 0 then may be no difference between groups