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

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1

## What are the data in quantitative research?

###
primarily numerical

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

2

## In what ways are numbers used to display data?

###
-numerical result (e.g.BP, age)

-coded category (e.g. 1 = male 2= female)

-ordered categories (e.g. pain scale)

3

## What are the levels of measurement?

### nominal, ordinal, interval, ratio

4

## What are the properties of nominal data?

### -different categories

5

## What are the properties of ordinal data?

###
-different categories

-categories can be ranked

6

## What are the properties of interval data?

###
-different categories

-categories can be ranked

-equal distances between categories

7

## What are the properties of ratio data?

###
-different categories

-categories can be ranked

-equal distances between categories

-fixed zero

8

## What are the ways of presenting descriptive data?

###
-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

## Why do we perform statistical analysis?

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

10

## What are the two basic approaches to statistical analysis?

###
-hypothesis testing (using P values)

-estimation (using confidence intervals)

11

## How does hypothesis testing happen?

### -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

## What is a P value?

###
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

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

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

14

## What is a Type I error?

###
(false positive error)

incorrect rejection of a true null hypothesis

15

## What is a Type II error?

###
(false negative error)

failure to reject a false null hypothesis

16

## What is the power of a study?

###
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

## When is a confidence interval calculated?

###
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