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Foundations of Research & Evidence Based Practice > Week 10-14 > Flashcards

Week 10-14 Flashcards

1
Q

Give an example: Nominal level of measurement

A

Ethnic group- Chinese

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2
Q

Give an example: Ordinal level of measurement

A

University grades- pass, credit

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3
Q

Give an example: Interval/Discrete level of measurement

A

IQ scores

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4
Q

Give an example: Ratio/Continuous level of measurement

A

Height

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5
Q

Give an example: Categorical Data

A

Nominal and Ordinal

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6
Q

Give an example: Continuous data

A

Interval and Ratio

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7
Q

Define: Continuous data

A

is an actual value of the measurement

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8
Q

Define: Categorical data

A

is the number of cases that fall into a category

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9
Q

What are the 3 measures of Central Tendency?

A

Mean
Median
Mode

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10
Q

Define: Median

A

is the score that divides a ordered set of scores into two equal halve

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11
Q

Define: Mean

A

the average

Sum of a set of scores, divided by the number of scores

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12
Q

What are the measures of Variability?

A

Range
Percentiles and Quartiles
Standard Deviation

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13
Q

Measures of Variability

Define: Variability

A

Dispersion or spread of scores

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14
Q

Define: Range

A

Difference between the highest and lowest score

highest score - lowest score = range

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15
Q

Measures of Variability

Define: Percentiles and Quartiles

A

Percentiles divide data into 100 equal portions
Quartiles divide data into 4 quarters
• Q1=25%,Q2=50%,Q3=75%, Q4=100%

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16
Q

Measures of variability

Define: Standard Deviation

A

It is the average difference between any score and the mean

  • Important because it includes information on all scores
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17
Q

What is called the Point Estimate?

A

the mean or median

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18
Q

What is called the Measure of Variability?

A

the Standard Deviation or the Inter-quartile range

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19
Q

What is the purpose of Descriptive Results?

A

Concerned with organising and summarising information about a collection of actual observations

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20
Q

Descriptive Results

Name the 3 ways data is described?

A

1) By measures of central tendency2) By measures of dispersion (variabiltiy)
3) By measures of association

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21
Q

Descriptive Results

Name the methods of describing data

A

with:

1) Numbers- percentages, SD, central tendency
2) Tables/Figures- Bar chart, histogram, polygon, scattergram

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22
Q

Name the 2 Frequency Measures in epidemiology

A

Incidence

Prevalence

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23
Q

Define: Incidence

A

The frequency of new occurrences of disease, condition, or death in a defined population over a period of time

24
Q

Define: Prevalence

A

The number of persons in a defined population who have a specified disease or condition at a point in time

25
Q

(Understanding results- step 1)

Name the 2 types of Data results

A

Categorical and Continuous

26
Q

(Understanding Results)

What are the 4 levels of measurement?

A

Nominal- categories only
Ordinal- categories and ranks
Interval or Discrete- categories, ranks and equal intervals between

Ratio or Continuous- all of the above, plus a true 0 point

27
Q

(Understanding results- step 2)

What are the 2 types of Results?

A

Descriptive

Inferential

28
Q

Define: Inferential Statistics (results)

A

are about what can be inferred from the sample to the population

29
Q

Explain the purpose of Inferential Statistics

A

are necessary for answering questions for those beyond the sample

  • cause, prevention, diagnosis, treatment, prognosis
30
Q

How do we estimate Population characteristics from Sample data?

A

We use the normal curve as a model for making statistical assumptions and estimations

31
Q

If results are Inferential, the results are about what 2 types of significance?

A

Statistical significance

Clinical significance

32
Q

(Hypothesis Testing)

Define: Null Hypothesis (Ho)

A

proposes that there is no effect

33
Q

(Hypothesis Testing)

Define: Alternative Hypothesis (Ha)

A

the opposite of the null hypothesis

34
Q

Define: Hypothesis Testing

A

the process of deciding statistically whether the findings of an investigation reflect chance or “real” effects at a given level of probability

35
Q

What are the 2 possible explanations for a positive outcome in a study?

A
  1. that the research hypothesis is correct

2. that the observed difference between groups occurred by chance

36
Q

Define: Probability

A

is the likelihood an event will occur, given all possible outcomes

37
Q

Define: p-value

A

probability due to chance

p=0.05

38
Q

Explain: Statistical Significance (inferential results)

A
  • hypothesis testing
  • result is a probability value (p-value= 0.05)
  • results are statistically significant if they are below 0.05
39
Q

Explain: Clinical Significance (inferential results)

A
  • provides an estimate we can use in real practice
  • result is a confidence interval
  • results must be above the MID to be clinically significant
40
Q

Define: Point Estimate

A

a single number regarded as the most plausible value from the sample data

41
Q

Explain: Estimation

A

will provide a result AND a degree of certainty

42
Q

Define: Measure of Variability

A

turns the sample result (point estimate) into what we estimate for the whole population

43
Q

What is the most important statistic to find in a results section?

A

measure of variability- what we estimate for the whole population

44
Q

What is the common measure of variability used for estimation?

A

the Confidence Interval

45
Q

Explain: Confidence interval

A
  • the 95% suggests the degree of certainty of the estimation
46
Q

Define: Minimal Important Difference (MID)

A

is the smallest worthwhile difference expected by a patient to proceed with the treatment

47
Q

What is Clinical Significance?

A

is when the treatment effect (confidence interval) is equal or more than the MID

48
Q

Define: Risk ratio

A

is a comparison of risks

49
Q

What are the Difference Values in Continuous and Categorical data?

A

Continuous: difference value=0

Categorical: difference value=1

50
Q

A result is clinically significant if…

A
  • the point estimate is larger than the MID
  • most of the confidence interval is larger than the MID
  • a narrow confidence interval (narrow=precise)
51
Q

(Tree/Forest Plots)

When is 0 used as the point of no effect?

A

when data isn’t a ratio

52
Q

(Tree/Forest Plots)

When is 1 used as the point of no effect?

A

when data is a ratio

53
Q

Explain with an example: Cohort Study

A

Prospective:
Pick people who smoke and people who don’t smoke
Follow participants over time- see who gets lung cancer

54
Q

Explain with an example: Case-Control Study

A

Retrospective:
Pick cases of people who already have lung cancer and people who don’t
Go back in time and find out who smoked

55
Q

When is a result statistically significant?

A

when the p-value of the results are below p=0.05

56
Q

Define: Measures of Central Tendency

A

These are numerical indices that provide a quantitative summary of the centre of the distribution

(Mean, mode and median)

Foundations of Research & Evidence Based Practice (3 decks)

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