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5060 Flashcards

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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
inferential statistics
make generalizations about the population based on sample statistics and hypothesis testing
26
p value
probability represents how likely x is to happen evaluates how well the data supports the null hypothesis
27
high p value
data supports null hypothesis >0.5
28
low p value
data does not support null hypothesis <0.5
29
confidence interval
value that sample is believed to lie within normally measured at 95% CI <95% considered not statistically significant
30
correlation coefficient
measures strength and direction of relationship between 2 variables
31
range of values for correlation coefficient
-1.0 - 1.0 -1.0 = strong negative (inverse) relationship +1.0 = strong positive relationship 0.0 = no relationship
32
measures of central tendency
mode mean median
33
mode
number that occurs most frequently in a set unstable
34
median
middle number not very useful
35
mean
arithmetic average of all numbers affected by outliers
36
standard of deviation
measure of how data is spread around the mean
37
types of skew
positive (right-side occurs w/ lower limit) negative (left-side, occurs w/ upperlimit)
38
measures of variability
range standard of deviation percentile
39
percentile
percentage of scores that a given score exceeds
40
standard of deviation
average deviation of scores from mean how far each value lies from the mean high = far from mean low = clustered around mean
41
semi quartile range
range of middle 50% of scores half of the difference between the upper quartile and lower quartile
42
level of measurement
nominal ordinal interval ratio
43
nominal
classification numbers don't carry any hierarchal value info organized into descriptors (red hair, blue eyes, etc)
44
ordinal
relative ranking ranks things against each other but differences may not be equal (likert scale, income, education)
45
interval
items on scale are ranked in order with equal intervals between values but there is no absolute zero (temperature, pH, test scores)
46
ratio
items on scale are ranked in order with equal intervals between values and an absolute zero (income, weight, height)
47
normal distribution or empirical rule
1sd = 68% 2sd = 95% 3sd = 99.7%
48
types of statistical tests
t test ANOVA pearson R chi-square fisher's exact probability test
49
non-probability sampling
convenience quota purposive
50
probability sampling
simple random stratified random cluster systematic
51
qualities of rigour
credibility auditability fittingness
52
credibility
truth of findings as judged by others or experts in the field
53
auditability
accountability as judged by adequacy/comprehensiveness all info should be provided
54
fittingness
faithfulness to everyday results of participants
55
reliability
does a tool accurately measure what it is supposed to ratio of accuracy to inaccuracy
56
reliability tests
test-retest parallel or alternate form split half item to total interrater kuder richardson cronbachs' alpha
57
validity
does an instrument measure what it is supposed to
58
validity tests
content validity criterion related validity construct validity
59
levels of measurement
nominal ordinal interval ratio
60
data collection procedures
physiological measurement observational method questionnaires/surveys interviews records available data
61
hypothesis testing
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
type 1 error
wrongly reject the null hypothesis when it is true false positive --> wrongly conclude there is an effect when there isn't
63
type 2 erro
wrongly accept the null hypothesis when it is false false negative --> wrongly conclude there is no effect when there is
64
level of significance
probability of making a type 1 error a = 0.05
65
interval measure tests
t test anova
66
nominal or ordinal measure tests
chi square sign test signed rank mann-whitney u
67
how to pick a statistical test
nature of research question number of groups (2 vs. >2) level of measurement underlying distribution
68
p-value
probability of an event occurring. evaluates how well the data supports the null hypothesis.
69
p value < a (0.05)
reject null hypothesis accept scientific hypothesis
70
p value > a (0.05)
accept null hypothesis reject scientific hypothesis
71
level of significance
alpha usually set at 0.05 or 5% maximum acceptable probability of making a type 1 error
72
t test
parametric examine causality requires interval/ratio LOM tests for sig differences between 2 samples most commonly used test of differences
73
anova
parametric tests for differences between means (usually >2) more flexible than other tests ratio LOM
74
pearsons correlation
parametric estimate degree of association between 2+ variables test of CORRELATION interval/ratio LOM
75
chi square
non-parametric used with nominal data test for differences between frequencies expected if groups are alike indicates significant difference
76
how to prevent making a type 1 error
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
how to prevent making a type 2 error
increase the sample size
78
threats to internal validity
history selection bias mortality maturation testing instrumentation

BSNC 5000 (11 decks)

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