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BSNC 5000 > 5060 > Flashcards

5060 Flashcards

1
Q

research hypothesis

A

statement that claims a relationship exists between the independent and dependent variable
usually what the researcher is trying to prove

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2
Q

null hypothesis

A

statement that claims that NO relationship exists between the independent and dependent variable

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3
Q

independent variable

A

manipulated variable
usually the intervention being researched

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4
Q

dependent variable

A

affected by the independent variable

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5
Q

extraneous variables

A

variables that are not being studied that can affect outcome of research

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6
Q

experimental group

A

group that is being experimented on

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7
Q

control group

A

group that is not experimented on
provides a baseline for comparison

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8
Q

control

A

measures that are taken to reduce the influence of extraneous variables on the dataset

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9
Q

examples of control

A

homogenous sample
consistent data-collection procedures
manipulation of independent variable
randomization

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10
Q

types of research design

A

experimental
quasi-experimental (no randomization)
non-experimental

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11
Q

what type of research design is most common in nursing?

A

quasi-experimental

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12
Q

types of experiment designs

A

true or classic
solomon four group
after only
nonequivalent control group
after-only nonequivalent control group
one group pretest-posttest
time series

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13
Q

types of nonexperimental designs

A

correlational studies
developmental (cross-sectional, longitudinal/prospective, retrospective ex facto)

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14
Q

correlational study

A

examine relationship between 2+ variables

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15
Q

cross-sectional study

A

outcome among individuals at one point in time

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16
Q

longituindal/prospective

A

changes in individuals over time

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17
Q

retrospective/ex post facto

A

variable x is related to variable y, but x can’t be randomized or measured
find a group without x and compare to a group with x and see if there is a difference in y

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18
Q

threats to internal validity

A

history
maturation
testing
instrumentation
mortality
selection bias

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19
Q

threats to external validity

A

selection
reactive
measurement

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20
Q

3 characteristics of a good research question

A

well-defined population
well-defined variables
testability

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21
Q

3 types of research questions

A

correlational
comparative
experimental

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22
Q

directional hypothesis

A

hypothesis states a relationship exists and in what way the data will trend

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23
Q

non-directional hypothesis

A

hypothesis states a relationship exists but does not predict how the data will trend

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24
Q

descriptive statistics

A

summarize or describe features of the data set
re: central tendency or dispersion
not the actual results of the data itself
usually displayed in visuals (table, graph, histogram)

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25
Q

inferential statistics

A

make generalizations about the population based on sample statistics and hypothesis testing

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26
Q

p value

A

probability
represents how likely x is to happen
evaluates how well the data supports the null hypothesis

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27
Q

high p value

A

data supports null hypothesis
>0.5

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28
Q

low p value

A

data does not support null hypothesis
<0.5

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29
Q

confidence interval

A

value that sample is believed to lie within
normally measured at 95%
CI <95% considered not statistically significant

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30
Q

correlation coefficient

A

measures strength and direction of relationship between 2 variables

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31
Q

range of values for correlation coefficient

A

-1.0 - 1.0
-1.0 = strong negative (inverse) relationship
+1.0 = strong positive relationship
0.0 = no relationship

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32
Q

measures of central tendency

A

mode
mean
median

33
Q

mode

A

number that occurs most frequently in a set
unstable

34
Q

median

A

middle number
not very useful

35
Q

mean

A

arithmetic average of all numbers
affected by outliers

36
Q

standard of deviation

A

measure of how data is spread around the mean

37
Q

types of skew

A

positive (right-side occurs w/ lower limit)
negative (left-side, occurs w/ upperlimit)

38
Q

measures of variability

A

range
standard of deviation
percentile

39
Q

percentile

A

percentage of scores that a given score exceeds

40
Q

standard of deviation

A

average deviation of scores from mean
how far each value lies from the mean
high = far from mean
low = clustered around mean

41
Q

semi quartile range

A

range of middle 50% of scores
half of the difference between the upper quartile and lower quartile

42
Q

level of measurement

A

nominal
ordinal
interval
ratio

43
Q

nominal

A

classification
numbers don’t carry any hierarchal value
info organized into descriptors (red hair, blue eyes, etc)

44
Q

ordinal

A

relative ranking
ranks things against each other but differences may not be equal
(likert scale, income, education)

45
Q

interval

A

items on scale are ranked in order with equal intervals between values but there is no absolute zero
(temperature, pH, test scores)

46
Q

ratio

A

items on scale are ranked in order with equal intervals between values and an absolute zero
(income, weight, height)

47
Q

normal distribution or empirical rule

A

1sd = 68%
2sd = 95%
3sd = 99.7%

48
Q

types of statistical tests

A

t test
ANOVA
pearson R
chi-square
fisher’s exact probability test

49
Q

non-probability sampling

A

convenience
quota
purposive

50
Q

probability sampling

A

simple random
stratified random
cluster
systematic

51
Q

qualities of rigour

A

credibility
auditability
fittingness

52
Q

credibility

A

truth of findings as judged by others or experts in the field

53
Q

auditability

A

accountability as judged by adequacy/comprehensiveness
all info should be provided

54
Q

fittingness

A

faithfulness to everyday results of participants

55
Q

reliability

A

does a tool accurately measure what it is supposed to
ratio of accuracy to inaccuracy

56
Q

reliability tests

A

test-retest
parallel or alternate form
split half
item to total
interrater
kuder richardson
cronbachs’ alpha

57
Q

validity

A

does an instrument measure what it is supposed to

58
Q

validity tests

A

content validity
criterion related validity
construct validity

59
Q

levels of measurement

A

nominal
ordinal
interval
ratio

60
Q

data collection procedures

A

physiological measurement
observational method
questionnaires/surveys
interviews
records
available data

61
Q

hypothesis testing

A

testing whether a scientific hypothesis is true or not by rejecting or accepting the null hypothesis
cannot PROVE the scientific hypothesis only find evidence to support/reject it

62
Q

type 1 error

A

wrongly reject the null hypothesis when it is true
false positive –> wrongly conclude there is an effect when there isn’t

63
Q

type 2 erro

A

wrongly accept the null hypothesis when it is false
false negative –> wrongly conclude there is no effect when there is

64
Q

level of significance

A

probability of making a type 1 error
a = 0.05

65
Q

interval measure tests

A

t test
anova

66
Q

nominal or ordinal measure tests

A

chi square
sign test
signed rank
mann-whitney u

67
Q

how to pick a statistical test

A

nature of research question
number of groups (2 vs. >2)
level of measurement
underlying distribution

68
Q

p-value

A

probability of an event occurring. evaluates how well the data supports the null hypothesis.

69
Q

p value < a (0.05)

A

reject null hypothesis
accept scientific hypothesis

70
Q

p value > a (0.05)

A

accept null hypothesis
reject scientific hypothesis

71
Q

level of significance

A

alpha usually set at 0.05 or 5%
maximum acceptable probability of making a type 1 error

72
Q

t test

A

parametric
examine causality
requires interval/ratio LOM
tests for sig differences between 2 samples
most commonly used test of differences

73
Q

anova

A

parametric
tests for differences between means (usually >2)
more flexible than other tests
ratio LOM

74
Q

pearsons correlation

A

parametric
estimate degree of association between 2+ variables
test of CORRELATION
interval/ratio LOM

75
Q

chi square

A

non-parametric
used with nominal data
test for differences between frequencies expected if groups are alike
indicates significant difference

76
Q

how to prevent making a type 1 error

A

set the level of significance (a value) lower
if p is less than the a value the researcher rejects he null hypothesis & concludes results are statistically significant

77
Q

how to prevent making a type 2 error

A

increase the sample size

78
Q

threats to internal validity

A

history
selection bias
mortality
maturation
testing
instrumentation

BSNC 5000 (11 decks)

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