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Scientific Basis > Test 3 > Flashcards

Test 3 Flashcards

1
Q

Population

A

Patients who share a similar characteristic, condition, disease, etc. of interest to researcher

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2
Q

Who is the population

A

Among women with breast cancer undergoing outpatient chemotherapy, how does receiving massage therapy in addition to usual care as compared to usual care only affect pain and fatigue?

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3
Q

POPULATION vs sample

A

complete set of persona or objects
Common characteristic
of interest to the researcher

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4
Q

population vs. SAMPLE

A

subset of a population

sample represents the population

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5
Q

Probability

A
  • -uses random methods
  • -Each element in the population has an equal, independent chance of of being selected
  • -increased likelihood to yield representative sample
  • -increased cost and complexity
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6
Q

types of probability

A

simple
stratified
systematic
cluster

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7
Q

Non-Probability

A
  • -Random methods not used
  • -Less likely to approximate target population
  • -Creates a biased sample
  • -increased convenience
  • -decreased cost and complexity
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8
Q

types of Non-probability

A

convenience
quota
purposive

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9
Q

Probability sampling: simple

A
  • -most basic form of probability
  • -importance of this sampling strategy
  • ——–Equal chance of selection
  • ——–independent chance of selection
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10
Q

Advantages of SIMPLE

A

Little knowledge of population is needed
Most unbiased of probability method
Easy to analyze data and compute errors

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11
Q

Disadvantages of SIMPLE

A

Compete listing of population is necessary

Time consuming

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12
Q

Probability Sampling: STRATIFIED

A
  • -type of probability sampling
  • -population divided into subgroups or strata
  • -Example of strata:
  • ——gender
  • ——age groups
  • ——years of experience
  • -Random sample taken from each strata
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13
Q

Proportional stratified sampling

A

sampling fraction for each stratum determined by proportion in total population

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14
Q

Disproportional stratified sampling

A

determine stratum is represented

used when strata are very unequal

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15
Q

Advantages Stratified

A
  • -Increases probability of being representative

- -Ensures adequate number of cases for strata

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16
Q

Disadvantages of stratified

A
  • -requires accurate knowledge of population
  • -may be costly to prepare stratified lists
  • -statistics are more complicated
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17
Q

Probability Sampling: Systematic

A

Selection of every kth case
—–selection interval determined by overall size of population divided by # needed for sample size
Example: Sample size of 100 needed from 1000 potential participants.
1000/100=10
So every 10th individual on the list is selected to create the sample of 100 people

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18
Q

Advantages of systematic

A

Easy to draw sample
Economical
Time-saving technique

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19
Q

Disadvantages of systematic

A

Samples may be biased

After first sample is chosen, no longer equal chance

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20
Q

Probability Sampling: Cluster

A
  • -Larger groups or clusters, not people, are selected from population
  • -Simple, stratified or systematic random sampling may be used during each phase of sampling
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21
Q

Advantages of Cluster

A
  • -Saves time and money

- -Arrangements made with small number sampling units—-Characteristics of clusters or population can be estimated

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22
Q

Disadvantages of Clusters

A
  • -Causes a larger sampling error
  • -Requires each member assignment of population to cluster
  • -Uses a more complicated statistic analysis
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23
Q

Nonprobability Sampling

A
  • -Sample elements are chosen nonrandomly
  • -Produces biased sample
  • -Each element of the population may not be included in the sample
  • -Restricts generalizations made about study findings
  • -Common among nursing research studies
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24
Q

Advantages of nonprobability sampling

A

Costs less

Takes less time

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25
Q

Disadvantages of nonprobability sampling

A

Nonrandom

Not able to generalize findings

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26
Q

Nonprobability Sampling: convenience

A
  • -chooses the most readily available subject or object
  • -Does not guarantee that the subject or object is typical of the population
  • -Snowball sampling
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27
Q

Snowball sampling

A
  • -Type of convenience sampling method
  • -Study subjects recruit other potential subjects
  • -Also known as network sampling
  • -May find people reluctant to volunteer
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28
Q

Nonprobabilty Sampling: Quota

A
  • -Type of nonprobability sampling
  • -Researcher selects sample to reflect characteristics
  • -Examples of stratum
  • —–age
  • —–gender
  • —–Educational background
  • -Number of elements in each stratum
  • —–number is in proportion to size of total population
  • —–But elements not selected at random
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29
Q

Nonprobability sampling: purposive

A
  • -type of nonprobability sampling
  • -researcher uses personal judgment in subject selection
  • -each subject chosen is considered representative of population
  • -many qualitative studies use this technique
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30
Q

Sampling Technique to be used

A
  • -Use voluntary subjects
  • -Follow the ethics of research
  • —subjects must voluntarily agree
  • —subjects may refuse to participate
  • -Research data
  • —based on voluntary responses form subjects
  • —biased sample occurs if subjects do not participate
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31
Q

Volunteers as Subjects

A
  • -Participation in research is voluntary
  • -Differences between volunteers and individuals approached by researcher
  • —volunteers
  • —questionable motivation (ex: money, other rewards)
  • —May differ from those obtained via sampling (ex: greater risk-takers_
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32
Q

volunteers

A

subjects who approach the researcher asking to participate

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33
Q

Random sampling

A

each subject has equal probability of being included in the study

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34
Q

random assignment

A

Procedure to ensure that each subject has equal chance of being placed in the experimental or control group

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35
Q

Study Timeframe: Cross-sectional

A
  • -subjects checked at one point in time
  • -data collected from groups of people
  • -data may represent differences in
  • —age
  • —time periods
  • —developmental states
  • —important considerations
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36
Q

Limitaitons

A

factors may influence internal validity of data

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37
Q

Study timeframe: Longitudinal

A
  • -subjects are followed over time
  • —a cohort study is one example
  • -subjects are studied based on
  • —similar age group
  • —similar background
  • -data are gathered
  • —same subjects
  • —several times
  • -Tells influence of time
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38
Q

Cross-sectional

A

less expensive
take less time
easier to conduct

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39
Q

Longitudinal

A

Accurate means of studying changes over time

Studies take a long time to perform

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40
Q

Timeframe

A

used should be adequate to answer the study’s research question

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41
Q

Determining sample size

A
  • No simple rules
  • qualitative studies use much smaller samples than quantitative studies
  • factors to consider for sample sizes in quantitative studies
  • –homogeneity of population
  • –degree of precision desired by the researcher
  • –type of sampling procedure that is used
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42
Q

Power Analysis

A
  • helps to determine sample size
  • may prevent type II error
  • helps to detect statistical significance
  • —presence of a difference or correlation
  • low power –> likelihood of type II error high
  • external funding sources require it
  • helps determine the optimum sample size
  • —prevents under- or over-sampling
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43
Q

Nursing Research Studies

A
  • Usually limited to small convenience samples
  • Generalizations to total population difficult
  • Small sample sizes warrant replication studies
  • Similar results from replication help with generalization
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44
Q

Sampling Error

A
  • random fluctuations in data
  • not under the control of the researcher
  • chance variations occur when sample is chosen
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45
Q

Sampling Bias

A
  • Bias when samples are not carefully selected
  • all nonprobability sampling methods have it
  • may occur in probability sampling methods
  • —subjects decide not to participate when chosen
  • —final sample is now not representative of population
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46
Q

Data collection method types

A 
questionnaire
interview
observation
physiological measure
psychological measure
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47
Q

data collection methods factors influencing selection

A 
research question
research method
variable(s) of interest
access to population
availability of appropriate instruments
cost
timeframe
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48
Q

Questionnaires

A
  • self-report
  • only method for certain human response data
  • –Ex: attitudes, beliefs, knowledge level
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49
Q

questionnaires: categories of questions

A 
demographic
open-ended
closed-ended
contingency
filler
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50
Q

questionnaires: distribution

A

-made available at a convenient location
-through a mailing or distribution system
-through internet
========responses rate influenced by many factors

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51
Q

Advantages of questionnaires

A
  • quick and generally inexpensive
  • easy to test for reliability and validity
  • administration is time efficient
  • can obtain data from widespread geographical areas
  • allows for anonymous responses
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52
Q

disadvantages of questionnaires

A
  • costly to mail if large volume
  • potential low response rate
  • respondents give socially acceptable answers or fail to answer
  • respondents may not be representative of the population
  • no opportunity to clarify items for respondents
  • respondents must be literate or have no physical limitation
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53
Q

Interview

A
  • method of data collection
  • interviewer obtains responses
  • face-to-face encounter, by telephone, or through an internet connection
  • QUANT and QUAL studies
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54
Q

Types of interviews

A

unstructured
structured
semi-structured

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55
Q

Influencing factors of interviews

A

face-to-face
interviewer influence
telephone interview

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56
Q

face-to-face interview

A
  • ethnic background
  • age
  • gender
  • manner of speaking
  • manner of dress
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57
Q

Interviewer influence

A
  • non-experimental research: Rosenthal effect

- Experimental studies: experimenter effect

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58
Q

Telephone interview

A

tone

dialect

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59
Q

Advantages of interviews

A
  • high response rate
  • in-depth responses
  • wide range of participants
  • high percentage of unstable data
  • ability to observe verbal an nonverbal behavior
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60
Q

disadvantages of interviews

A
  • time consuming
  • expensive
  • arrangements may be difficult
  • participants may
  • —be influenced by the interviewers’ characteristics
  • —intentionally provide socially acceptable responses
  • —be anxious because answers are being recorded
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61
Q

observation data

A
  • data gathered through visual observation
  • nurses are well qualified to use this method
  • carefully developed plan is essential
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62
Q

Examples of observable behavior

A
  • psychomotor skills
  • personal habits
  • nonverbal communication patterns
  • inter-rater reliability
  • —The degree to which two or more raters or observers assign the same rating or score to an observation
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63
Q

Structured observations

A
  • data-collection tool, usually some kind of checklist
  • expected behaviors are identified on the checklist
  • observer indicates the frequency of behavior occurrence
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64
Q

Unstructured obervations

A
  • researcher attempts to describe events or behaviors freely

- process requires a high degree of concentration and attention

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65
Q

Physiological measures

A
  • involve the collection of physical data from subjects
  • measures are objective and accurate
  • Ex: lab values, weight, vital signs
  • advantage–precision and accuracy
  • disadvantage–expertise required for using devices
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66
Q

Psychological Measures

A
  • Attitude scales (ex: attitudes, feelings)
  • —-Likert scale, semantic differential scales
  • Personality tests
  • Visual analogue scale
  • —0-10 pain scale
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67
Q

Visual Analogue Scale

A
  • Presents subjects with a 100mm straight line drawn on a piece of paper
  • Subjects are asked to make a mark on the line at the point that corresponds to their experience of the phenomenon
  • Quantitative data is obtained from measurements of the responses
  • useful for measuring: nausea, pain, fatigue, shortness of breath
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68
Q

Pre-existing Data

A
  • Data is used from previous research
  • Existing information is reanalyzed for new research
  • Preexisting data sources
  • –patient charts
  • –records from agencies and organizations
  • –personal documents
  • –almanacs
  • –professional journals
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69
Q

Measurement

A
  • Process of assigning numbers to variables
  • ways to assign numbers
  • in research, measurement is…
  • –quantification of information
  • –Applied mostly in quantitative research designs
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70
Q

Ways of assign numbers

A

counting
ranking
comparing objects or events

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71
Q

Qualitative research designs

A
  • Concept of measurement does not apply to qualitative data in narrative form
  • concept of measurement may apply to qualitative data that is summarized an places into categories
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72
Q

Level of Measurement: Nominal

A
  • lowest level of measurement
  • objects or events are named or categorized
  • Categories must be exhaustive and mutually exclusive
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73
Q

Examples of nominal level

A

gender
marital status
religious affiliation

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74
Q

Level of Measurement: Ordinal

A
  • second level of measurement
  • data can be rank ordered and placed into categories
  • exact differences between rank not possible
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75
Q

Examples of ordinal level

A

mild
moderate
severe

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76
Q

Level of Measurement: Interval

A
  • Third level of measurement
  • data can be rank ordered and placed into categories
  • Distance between ranks can be measured
  • actual numbers on a scale
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77
Q

Level of Measurement: Ratio

A
  • highest level of measurement
  • data is categorized and ranked
  • distance between ranks is “true” or natural zero
  • zero means a total absence of quantity measured
  • debate usually exists between interval and ratio level
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78
Q

Examples of ratio level

A
  • money in bank account

- body weight

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79
Q

Appropriate level of meausrement

A
  • Precision- interval or ratio
  • ranked or categorized sufficient- ordinal
  • categories of data only needed- nominal
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80
Q

level of measurement considerations

A
  • level is appropriate for the type of data desired

- degree of precision that is desired for the study

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81
Q

Data collection factors

A
  • research question(s) or hypothesis or hypotheses
  • design of the study
  • amount of knowledge available about the variable(s)
  • Methods and resources available
82
Q

Variety of Data Collection Methods

A
  • More than one method used
  • Similar results form variety of methods
  • —Also known as triangulation
  • –Greater confidence in study findings
83
Q

Data collection instrument criteria

A

practicality
reliability
validity

84
Q

Practicality of an instrument

A
  • cost
  • appropriateness
  • determine practicality before reliability or validity
85
Q

Example of questions to consider

A
  • length of time to administer to subjects?

- Special training required to administer or score?

86
Q

reliability of an Instrument

A
  • Consistency and stability

- Degree of reliability determined by correlational procedures

87
Q

Types of reliability

A
  • stability reliability
  • equivalence reliability
  • internal reliability
88
Q

Validity of an Instrument

A
  • quantity of variable of interest can be calculated with use of instrument
  • greater validity, more confidence in instrument
  • statistical measurement may be used for correlational procedures
89
Q

Ways to measure validity

A
  • panel of experts

- Examination of literature

90
Q

Stability of Reliability of an Instrument

A
  • Consistency over time

- Stability and accuracy

91
Q

Ways to measure

A
  • physiological instruments- stable and accurate
  • questionnaire instruments- test and retesting
  • high correlation coefficient- close to 1.00
92
Q

Alternate or parallel forms of reliability

A

The degree to which tow different forms of instrument obtain the same results

93
Q

Interrater or interobserver reliability

A

the degree to which two or more observers use the same instrument to obtain the same results

94
Q

High correlation coefficient- close to 1.00

A

Higher confidence that the two forms of the test are gathering the same information

95
Q

Internal consistency Reliablity

A
  • individual items measure the same vairable
  • one concept or construct (ex: variable) is measured
  • sample of items is its main consideration
  • split-half procedures
  • coefficient alpha (a) or Cronbach’s alpha
96
Q

Face Validity of an Instrument

A
  • Appears to measure what it is supposed to measure
  • Experts review the instrument ot validate it
  • use of the instrument with people with characteristics similar to study
97
Q

Content Validity of an instrument

A 
-items represent the content
ways to measure
---comparison with literature
---panel of experts in subject area
---test blueprint designed for content and level
98
Q

criterion validity of an instrument

A

-scores are correlated with external criterion

99
Q

two types of criterion validity

A
  • concurrent validity
  • –comparison to other behavior
  • –correlation coefficient closer to 1.00 means higher validity
  • predictive validity
  • –prediction of behavior in future
100
Q

Construct validity of an instrument

A
  • most difficult to measure

- does the instrument measure the construct it is supposed to measure?

101
Q

Ways to measure (construct validity)

A

Known-groups proceudre
–administer tool to two groups who should differ in responses on the variable of interest
factor analysis
–determine whether the tool is measuring only one construct or several constructs

102
Q

Reliability and Validity

A
  • Close association
  • Reliability a condition for validity
  • –an instrument cannot be valid unless it is reliable
  • Reliability tells nothing about degree of validity
  • –an instrument can be very reliable but with low validity
103
Q

Sources of Error

A
  • all research contains some error
  • error introduced by
  • –human beings
  • –environment or setting
104
Q

Types of sources of error

A
  • instrument inadequacies
  • Instrument administration biases
  • Environment variations during collection of data
  • Temporary subject characteristics during the collection of data
105
Q

Instrument inadequacies

A
  • –Do items collect appropriate data?
  • –Does order of items influence response?
  • –Are instrument’s directions clear & unbiased?
106
Q

Instrument administration biases

A
  • –Instruments administered consistently?

- –Observational data collected consistently?

107
Q

Environment variations during collection of data

A

was there consistency in location and condition where data collection took place? (ex: temperature, noise level, lighting)

108
Q

Temporary subject characteristics during the collection of data

A

were there differences in subjects at time of data collection which influenced responses? (ex: anxiety level, hunger, tiredness)

109
Q

Descriptive

A
  • organize and summarize numerical data collected from samples
  • Allows examination of study participants’
  • —characteristics
  • —behaviors
  • –experiences
110
Q

Inferential

A
  • concerned with populations
  • use sample data to make inferences about a population
  • help determine real differences versus chance differences
111
Q

Descriptive vs. Inferential statistics

A

descriptive
-examine characteristics of study participants
inferential
-determine if sample is representative of the population

112
Q

Four major classes of descriptive statistics

A

condense data
central tendency
variability
relationships

113
Q

Measures to condense data

A

Summarize and condense

  • frequency distributions
  • graphic presentations
  • percentages
114
Q

Measure of central tendency

A

Average distribution or most common value for a group of data

  • -mean
  • -mode
  • -median
115
Q

median

A

middle point of the data set

116
Q

mean

A

statistical average

117
Q

mode

A

number that occurs most often

118
Q

Measures of variability

A

Describe how spread out the values are in a distribution

  • range
  • standard deviation (SD)
119
Q

more varied scores

A

heterogeneous

120
Q

less varied scores

A

homogeneous

121
Q

68-95-99.7

A

About 2/3rds of the cases )or 68%) lie within one standard deviation unit of the mean in a normal distribution

122
Q

Measures of Relationships

A

Concern the correlations between variables

  • -correlation coefficients
  • -scatter plots
  • -contingency tables
  • -correlational procedures
123
Q

Bivariate Descriptive Statistics

A
  • used fro describing the relationship between two variables
  • two common approaches:
  • -crosstabs (contingency tables)
  • -correlation coefficients
124
Q

Correlation Coefficient (r)

A
  • results range from -1.00 to +1.00
  • The greater the absolute value of the coefficient, the stronger the relationship (negative is stronger than positive)
  • With multiple variables, a correlation matrix can be displayed to show all pairs of correlations when multiple variables are present
125
Q

Positive relationship

A

(0.01 to 1.00)

two variables tend to increase or decrease together

126
Q

negative relationship

A

(-0.01 to -1.00)

As one variable increases, the other variable tends to decrease

127
Q

No relationship

A

0.00

128
Q

Relationships

A
  • Correlation does not equal causation
  • No set criteria to evaluate the actual strength of a correlation coefficient
  • Nature of variables being studies help determine the strength of the relationship
129
Q

Relationship statistical tests

A

correlational procedures

  • person product-moment correlation (Person r)
  • -Interval/ratio data
  • Spearman rho
  • -ordinal data
130
Q

Contingency Table

A
  • means of visually displaying relationship between sets of data
  • nominal and ordinal data
131
Q

Measures and level of measurement

A

in general, these are the descriptive statistics that you will encounter in research articles.

132
Q

Type I error

A

rejection of a null hypothesis when it should not be rejected; a false-positive result
—risk of error is controlled by the level of significance (alpha) ex: a=.05 or .01

133
Q

Type II error

A

failure to reject a null hypothesis when it should be rejected; a false-negative result

  • -the risk of this error is beta (B)
  • -Power is the ability of a test to detect true relationships; power= a-B
  • -by convention, power should be at least .80
  • -Larger samples = greater power
134
Q

Parametric statistics

A
  • use involves estimation of a parameter
  • assumes variables are normally sidtributed in the population
  • measures are on interval/ratio scale
135
Q

nonparametric statistics

A
  • use does not involve estimation of a parameter
  • measurements typically on nominal or ordinal scale
  • Doesn’t assume normal distribution in the population
136
Q

Bivariate statistical tests

A
  • t-tests
  • analysis of variance (ANOVA)
  • pearson’s r
  • chi-squared test
  • correlation coefficients
137
Q

t-Test

A
  • tests the difference between two means
  • t-test for independent groups: between-subjects test
  • –ex: means for men vs. women
  • t-test for dependent (paired) groups: within-subjects test
  • –ex: means for patients before and after surgery
138
Q

Analysis of Variance (ANOVA)

A

tests the difference between more than two means

  • -one-way ANOVA (ex: 3 groups)
  • -Multifactor (ex: 2-way) ANOVA
  • -repeated measures ANOVA (RM-ANOVA): within subjects
139
Q

Chi-squared test

A
  • examines the extent of association or relationship between tow categorical variables. Researchers frequently compare two or more samples on a categorical response variable.
  • Tests the difference in proportions in the responses
140
Q

Correlation

A

Pearson’s r is both a descriptive and an inferential statistic

  • Purpose: To test the relationship between two variables
  • how to interpret:
  • –value closer to 0 –>lower or weaker correlation
  • –values closer to either +1.0 or -1.0 –>higher or stronger correlation
141
Q

Effect size

A
  • important concept in power analysis
  • summarize the magnitude of the effect (ex: how big) of the independent variable on the dependent variable
  • In comparison of two group means (ex: in a t-test situation), the effect size index is d
142
Q

Multivariate statistical analysis

A

-statistical procedures for analyzing relationships among three or more variables

143
Q

Two commonly used procedures in nursing research:

A
  • Multiple regression
  • Analysis of convariance (ANCOVA)
  • Multiariate analysis of variance (MANOVA)
144
Q

Multiple Regression

A
  • Used to predict a dependent variable based on two or more independent (predictor) variables
  • dependent variable is continuous (interval or ratio-level data)
  • predictor variables are continuous (interval or ratio) or dichotomous
145
Q

multiple regression: correlation coefficient (R)

A
  • the correlation index for a dependent variable and 2+ independent (predictor) variables: R
  • Does not have negative values: shows strength of relationships, not direction
  • R squared is an estimate of the proportion of variability in the dependent variable accounted for by all predictors
146
Q

Analysis of Covariance (ANCOVA)

A

-extends ANOVA by removing the effect of confounding variables (covariates) before testing whether mean group differences are statistically significant

147
Q

Levels of measurement of variables (ANCOVA)

A
  • dependent variable is continuous–ratio or interval level
  • independent variable is nominal (group status)
  • covariates are continuous or dichotomous
148
Q

Logistic Regression

A
  • Analyzes relationships between a nominal-level dependent variable and 2+ independent variables
  • yields an odds ratio- the risk of an outcome occuring given one condition versus the risk of it occuring given a different condition
  • The OR is calculated after first removing (statistically controlling) the effects of confounding variable
149
Q

Critique questions: Descriptive Statistics

A
  • Identify various descriptive statistics used to analyze data
  • Determine level of measurement of each variable by searching the researcher’s operational definitions
  • Check descriptive data presented in the text of the report and in tables and graphs
  • ensure that data is presented in a manner that can be understood by average practicing nurse so study can be considered for implementation in practice
150
Q

Critique questions: Inferential Statistics

A
  • are inferential statistics presented for each hypothesis (or research question) that was stated in the study?
  • is the reader provided with the calculated value of the inferential statistic, the degrees of freedom, and the level of significance that was obtained?
  • Are the results of the inferential tests clearly and thoroughly discussed?
151
Q

Results section

A

presentation of factual finding

  • -narrative form
  • -tables
152
Q

Narrative presentation

A
  • clear and concise
  • qualitative studies
  • quantitative studies
153
Q

narrative presentation QUAL

A

narrative predominates

direct quotes and discussion of patterns and themes from data

154
Q

narrative presentation QUANT

A
  • present data that support or fail to support each hypothesis
  • should include the statistical test used, test results, degrees of freedom, and probability value
155
Q

Tables

A

-easy to understand and interpret

156
Q

a footnote should include:

A
  • statistical test that was used
  • test results
  • degrees of freedom
  • probability value
157
Q

Table composition

A

columns
rows
cells

158
Q

Figures

A
  • Type of visual presentation other than a table
  • Include graphs, diagrams, line drawings, and photographs
  • Particularly useful in presenting demographic data about subjects
159
Q

Discussion of findings

A
  • researchers make interpretations of the findings
  • more subjective section than the presentation of the findings
  • discusses aspects of results that agree and those that do not agree with previous research and theoretical explanations
  • Reports study limitations
160
Q

Relating Findings to the hypothesis

A

-relation of study findings to hypotheses

161
Q

possible results of hypothesis

A
  • the null hypothesis is not rejected
  • the null hypothesis is rejected and research hypothesis is supported
  • the null hypothesis is rejected and results are opposite from the prediction of the research hypothesis
162
Q

To support or not support (the hypothesis)

A
  • research hypothesis supported

- null hypothesis not rejected

163
Q

Research hypothesis supported

A

Degree of certainty (probability level)

-Not due to chance

164
Q

Null hypothesis not rejected

A
  • Negative results may be as important as positive results

- Explanation of negative results

165
Q

Unexpected Results

A
  • Results not consistent with research hypothesis

- Recommend further research

166
Q

Results not consistent with research hypothesis

A
  • Not supportive of the study’s theoretical framework
  • May be incongruent with previous research results
  • Give tentative explanations
167
Q

Statistical

A
  • Null hypothesis was rejected
  • Difference between groups (or correlations between 2 variables) not likely due to chance
  • However differences may no be clinically important
168
Q

Clinical

A
  • Differences (or correlations) have relevance to clinical setting
  • Thus the findings can be useful in the clinical setting with patients
169
Q

When Reading Results Section…

A
  • Examine the statistical results for significant findings
  • Consider the following:
  • –Were results were developed using valid measures?
  • –Is there any evidence of bias?
  • –Are results generalizable to similar populations and settings?
  • –Is there evidence that reliable measures were used?
  • –What level of precision is presented? (ex: standard deviation, confidence intervals, effect size)
170
Q

Study Conclusions

A
  • knowledge gained
  • findings generalized
  • discussion focus
  • study problem, purpose, hypothesis, and theoretical framework
171
Q

Recommendations

A
  • propose replications
  • consider study limitations
  • suggest extension of study
172
Q

Replication of the research study

A
  • Involves carrying out a study similar to one previously done
  • minor changes are made from the previous study
173
Q

Consdieration of study limitations in future research

A
  • alteration in the sample
  • alteration in the instrument
  • control of variable
  • change in methodology
174
Q

Extensions of the research study

A
  • suggests future research based on:
  • finding of a particular study
  • finding of previous research
  • current state of the theoretical framework that was used in the study
175
Q

Research Evidence

A
  • systematic reviews

- Individual research study

176
Q

Individual research study

A
  • RCT
  • Quasi-experimental
  • Non-experimental
  • Qualitative
177
Q

Level I

A

Evidence obtained from an experimental study, randomized controlled trial (RCT), or systematic review of RCTs, with or without meta-analysis

178
Q

Level II

A

Evidence obtained from a quasi-experimental study or systematic review of a combination of RCTs and quasi-experimental studies, or quasi-experimental studies only, with or without meta-analysis

179
Q

Level III

A

Evidence obtained from a quantitative non-experimental study; systematic review of a combination of RCTs, quasi-experimental, an non-experimental studies, or non-experimental studies only with or without meta-analysis; or qualitative study or systematic review of qualitative studies, with or without a meta-analysis

180
Q

Grade A: high

A

consistent, generalizable results; sufficient sample size for study design; adequate control; definitve conclusions; consistent recommendations based on comprehensive literature review that includes thorough reference to scientific evidence

181
Q

Grade B: Good

A

Reasonably consistent results; sufficient sample size fro the study design; some control; fairly definitive conclusions; reasonably consistent recommendations based on fairly comprehensive literature review that includes some reference to scientific evidence

182
Q

Grade C: Low or Major Flaw

A

Little evidence with inconsistent results; insufficient sample size for the study design; conclusions cannot be drawn

183
Q

non-research evidence

A
  • summaries of research evidence
  • —clinical practice guidelines; consensus or position statements; literature reviews
  • organizational experience
  • —quality improvement reports; program evaluations
  • expert opinion
  • community standards
  • clinician experience
184
Q

Level IV

A

opinion of respected authorities and-or nationally recognized expert committees/consensus panels based on scientific evidence

  • Includes
  • —clinical practice guidelines
  • —consensus panels
185
Q

Level V

A

Based on experiential and non-research evidence

  • includes:
  • –literature reviews
  • –quality improvement, program or financial evaluation
  • –case reports
  • –opinion of nationally recognized experts based on experiential evidence
186
Q

Look at slides 28-32 of Appraising the level and quality of evidence

A

Look at slides

187
Q

Johns Hopkins Nursing EBP Model

A 
Phase 1
--Identify the practice question
Phase 2
--Finding Evidence
Phase 3
--Translate Evidence into practice
188
Q

Evidence-based recommendations

A

potential translation pathways

  • -strong, compelling evidence with consistent results
  • -good evidence with consistent results
  • -good evidence but conflicting results
  • -insufficient or absent evidence
189
Q

Proceeding with a practice change appropriate when:

A
  1. Search for evidence yields strong or good evidence with consistent results
  2. Current practice differs from current evidence
190
Q

Phase 3: Translate Evidence into Practice

A
  1. determine fit, feasibility, and appropriateness
  2. Create action plan and secure support and resources
  3. Implement plan, evaluate outcomes, and report to stakeholders
  4. Identify the next steps to continue and spread implementation of practice change
191
Q

Step 1: Determine Fit, Feasibility and appropriateness

A 
will the practice change
--add value?
--improve outcomes?
Is the organization ready and willing to change?
How can potential barriers be addressed?
192
Q

Step 2: Create Action Plan and Secure Support and Resources

A 
develop step-by-step plan
---uses plan-do-study-act process
obtain support for change
--colleagues
--management/leadership
secure human and material resources
193
Q

step 3: implement plan and evaluate outcomes

A 
implement small test of change (pilot)
--keeps practice change contained
--allows from controlled implementation
--facilitates evaluation
evaluate impact and progress
--use outcomes identifies in PICO questions
-Also measure whether process used was effective
share progress with stakeholders
194
Q

step 4: identify next steps and disseminate findings

A

assessment of practice change
–if successful and favorable outcome –>implement practice change on a wider scale
–if some success but outcome not achieved –>modify practice change plan and try again
communication of practice change
–depends on size/scope of change
–share within the organization
–share outside of organization

195
Q

Importance of EBP

A
  • current clinical practice rooted in research evidence
  • EBP promotes high quality patient care/outcomes
  • clinical encounters identify new practice questions
196
Q

Barriers to EBP

A
  • lack of knowledge of research findings
  • negative attitudes toward research
  • inadequate dissemination of research findings
  • lack of institutional support for research
  • findings not ready for use in practice
197
Q

Numerous EBP Models available

A
  • Johns Hopkins Nursing EBP Model
  • Iowa Model of EBP
  • Stetler Model of Research Utilization
198
Q

All models include steps to:

A
  • promote identification of clinical practice questions
  • facilitate finding evidence to address question
  • assist with translation of evidence into practice
199
Q

The Future of EBP

A
  • still a relatively new concept
  • sources of evidence continue to grow
  • -Most involve a hierarchy of evidence
  • introduction of care bundles
  • -combination of multiple evidence-based interventions (or practices) to address clinical issue
  • -Yield better outcomes than single intervention/practice
200
Q

You are the future of EBP

A
  • nursing practice should be based on evidence
  • you can promote EBP in clinical care practice
  • –adopt EBP skills
  • –identify clinical practice issues
  • –consume current research evidence
  • –sugest practice change recommendations

Scientific Basis (1 decks)

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