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NUR3300 > Exam 2 Quantitative Analysis > Flashcards

Exam 2 Quantitative Analysis Flashcards

1
Q

Statistical Analysis

A

Purpose - findings that are significant, also clinically significant

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2
Q

Probability Sampling

What is it?

A

Sampling method in which elements in the accessible population have an equal chance of being selected for inclusion in the study

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3
Q

Probability Sampling

What is a biased sample?

A

Doesn’t look like the population ‘you’ are wanting to look at

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4
Q

Probability sampling

What is sampling error?

A

What ‘we’ got, vs the truth

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5
Q

Probability sampling

how does sample size affect statistics?

A

Larger the sample, better off to find relationships…if you’re looking for subtleties, need larger groups

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6
Q

Types of stats

Descriptive

A

describe sample, what sample looks like, basic relationships

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7
Q

Types of stats

inferential

A

helps make generalizations

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8
Q

Types of stats

Univariate

A

One variable (ex.age)

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9
Q

Types of stats

Bivariate

A

two variables

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10
Q

Types of stats

multivariate

A

> two variables

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11
Q

Types of stats

Two categories of inferential

A

Parametric

Nonparametric

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12
Q

Descriptive

A

Describing the sample

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13
Q

Descriptive

Frequency

A

Stem-and-leaf plots (hash marks)
histograms (Bar graph)
frequency polygons (line graph)

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14
Q

Central tendency

Mode

A

Most frequent score

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15
Q

Central tendency

Median

A

Middle score

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16
Q

Central tendency

Mean

A

Average score

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17
Q

Normal curve

A

Symmetrical. “Bell curve”

Normal curve is very rare when dealing with people…going to have outliers

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18
Q

Skewed curve

A

Asymmetrical
Tail is right sided…positive
tail is left sided…negative

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19
Q

Kurtosis

A

How tall or flat the curveis

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20
Q

Range

A

spread of scores

difference between highest and lowest score

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21
Q

Semiquartile or interquartile range

A

the range of the middle 50%…divide range into quarters, discard Q1 and Q4…gets rid of the outliers

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22
Q

Standard deviation

A

How far someone is away from the average

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23
Q

Bivariate correlations

A

Two groups

looking for RELATIONSHIP between two scores (similarities)

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24
Q

Direction of relationship - negative

A

X goes up, Y goes down

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25
Q

Direction of relationship - positive

A

X goes up, Y goes up

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26
Q

Magnitude or strength of relationship

A

Weak…score close to 0
Strong…score close to +1 or -1

Remember… -0.6 is stronger than +0.2

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27
Q

When use pearson r

A

if using interval or ratio

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28
Q

When use spearman rho?

A

If using ordinal

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29
Q

Scatter plots

A

If the dots form a PERFECT line, regardless of direction, then perfect correclation

If the dots are completely scattered no semblance of a line, then no correlation.

If dots are in the general form of a line, but not perfectly straight…that is typical correlation.

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30
Q

Inferential statistics

A

Making suppositions about the population from information you know about the sample

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31
Q

Key points

A 
Population = Parameter .... p - p
Sample = Statistic.... s - s
32
Q

What do Greek symbols indicate?

A

Inferring a population parameter

33
Q

Significance level

setting alpha

A

.1, .05, .01, or .001

alpha is the risk we are willing to take that we will make a mistake and WRONGLY REJECT the null hypothesis when it is, in fact, true….ex….we should have accepted the null hypothesis, there is not relationship

34
Q

Chance error

A

mastate results from a fluke or a chance occurrence that did not reflect reality

35
Q

When is alpha set?

A

BEFORE collecting the data

36
Q

Significance Level

A

Calculate our statistic

THEN check a table for the actual probability (p value) the result was due to chance

37
Q

Significance level

when is null hypothesis rejected/accepted?

A

If p is less than alpha, reject null hypothesis

if p is greater than alpha, do not reject null

38
Q

Deciding on alpha

A

Type I error…when researcher rejects null when should have been accepted (alpha)
Type II error…when researcher says there is no relationship (accepts null hypothesis) when there is actually a relationship (power)

all based on null hypothesis

39
Q

Confidence intervals for clinical meaningfulness

A

An interval or range of scores in which the true value lies with a given degree of certainty

40
Q

Confidence intervals for clinical meaningfulness

What does the interval width indicate?

A

A wide interval indicates that our data isn’t good enough to give us good confidence in our inferences about the population.

Wide interval = less confidence
Narrow interval = more confidence

If CI contains 0, there is no effect
If CI does not contain 0 , there is an effect

41
Q

Types of inferential stats

Non-parametric

A

Inferential statistic involving nominal or ordinal level data to infer to the population

42
Q

Types of inferential stats

parametric

A

inferential statistical tests involving interval or ratio level data to infer to the population

43
Q

Types of inferential stats

basic assumptions to be met

A

Probability sample (Sampling method in which elements in the accessible population have an equal chance of being selected for inclusion in the study)

Normal distribution (bell curve)

Interval or ratio level data

reduction of error

44
Q

Nonparametric

A

When you cannot assume that your sample looks almost exactly like the population(small samples…odd bell curve)

45
Q

Chi square

A

Looks at differences in frequency of independent groups.

uses NOMINAL or a combination of NOMINAL and ORDINAL data

tablecompares actual data to the data expected to occur

46
Q

Parametric

A

When you CAN assume that the sample is enough like the population that you can make big leaps in faith with your statistical conclusions

47
Q

t-tests

A

Comparing TWO groups

groups are independent (not related) or dependent (correlated or related)

48
Q

t-tests

what are you looking for?

A

differences in means between groups…get a “t” statistic

49
Q

t-tests

independent variable

A

Nominal

50
Q

t-tests

dependent variable

A

interval or ratio

51
Q

ANOVA

A

comparing MORE THAN TWO groups

looking for differences in means between the groups

Gives you an ‘F’ statistic

52
Q

ANOVA

Independent variable

A

nominal

53
Q

ANOVA

dependent variable

A

must be interval or ratio

54
Q

Simple Regression

A

seeks to predict the value of Y based on the value of X

55
Q

Simple regression

A

ONE independent and ONE dependent variable

56
Q

Simple Regression

Dependent variable

A

MUST be interval or ratio

57
Q

Simple regression

A

calculates a slope (b) and intercept (y)

Reported as the regression coefficient ‘R’…upper case R…lower case r is pearson

R squared is the explained variance

58
Q

Multivariate stats

A

when you have more than two comparisons or groups

59
Q

ANOVA types

A

ANCOVA - analysis of covariance
MANOVA - multiple analysis of variance
MANCOVA - multiple analysis of covariance

All examine differences in mean

60
Q

Regression type

multivariate statistics

A

Tries to predict an unknown value from the values known
Multiple regression - an inferential statistical test used to describe the relationship of three or more variables
logistical regression -
hierarchical regression
canonical regression

61
Q

Other multivariate stats

Looking at structure

A

Discriminate function
factor analysis- a test for construct validity that is a statistical approach to identify items that group together
Structural Equation Modeling

62
Q

Biostatistics

A

Round in Randomized Control Trials and other studies that focus on treatment outcomes or illness predictions

63
Q

Biostatistics

What do you want to look at?

A

Magnitude of effect

Strength of association

64
Q

Magnitude of effect

A

The effect is the rate of occurrence (usually seen as a bad thing…falls, pressure ulcers) in each of the groups on the outcome of interest

The DEGREE of the difference or lack of difference between the groups in the study

65
Q

Strength of association

A

ABSOLUTE risk for EVERYONE getting…what you’re testing (example…breast cancer)

probability that a negative outcome will occur

66
Q

Absolute risk reduction

A

when the risk for the negative outcome is less for the experiment group than the control group

example: experimental group…low fat diet…risk for breast cancer reduced

67
Q

Absolute risk increase

A

when the risk for the negative is more for the experimental group than the control group

example…birth control study…found risk for breast cancer increased

68
Q

Other strength of association

relative risk

A

likelihood that the negative outcome will occur in one group compared to another

69
Q

Other strength of association

relative risk reduction

A

proportion of risk that the negative outcome will occur in the experimental group compared to control group

70
Q

Other strength of association

Odds Ratio

A

***Used heavily in health care

Number exposed who actually get the negative outcome divided by number exposed who do not get the outcome

71
Q

Other strength of association

Odds Ratio values

A

> 3 = strong, 1.3-3.0 = moderate, 1.1-1.3 = small, 1 = even, or 50:50 ods

72
Q

Other strength of association

Number needed to treat

A

Number one would need to treat to prevent one case of the negative outcome from occurring

73
Q

Other strength of association

Number needed to harm

A

Number one would need to treat before one case would be harmed

74
Q

EBP Clinical Significance

A
  • Think on an individual level

* What do the numbers mean when you are talking about real people?

75
Q 
EBP Clinical Significance
Example:
One person has a risk of stroke of 1%
Another has a risk of stroke of 10%
An antihypertisive drug has a relative risk reduction of 22% but has absolute risk increase in gastric bleeding side effects in %5 of cases
Which person should take the medication?
A

The patient who has a 10% risk of stroke. The person with 1% chance…the 5% chance of GI bleed may not be worth it.

76
Q

Using a computer

Complicated statistics
programs:

A

Excel
Access
SPSS
SAS

But…must enter data correctly according to your data dictionary

77
Q

Critiquing stats

A
  • Identify if descriptive, parametric, or nonparametric
  • Identify the level of measurement for the I.V. and D.V. of each hypothesis
  • check the chart in book to see if the statistical test was appropriate for each hypothesis
  • check alpha and p value
  • check significance and non-significance of results
  • check CI if provided
  • Check for good charts or tables

NUR3300 (20 decks)

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