Studied by 3 people
Causality
the relationship between cause and effect
Causes are
rarely deterministic, they only increase the likelihood that an event will occur
effect
the difference between what did happen with an exposure and what would have happened without it
research questions
drive the design of the study
experimental designs (RCT)
best possible design for showing causal relationships "gold standard"
A true RCT is characterized by
Intervention, Control, Randomization
Disadvantages to RCT
Often not feasible or ethical
Often very expensive
Quasi-experimental designs
testing an intervention but lack randomization and sometimes lack control group ex: non-equivalent control group design, one group pre-test/post-test
Non-equivalent control group design
those getting the intervention are compared with a non-randomized comparison group
One group pre-test post-test study
one group is studied before and after intervention
Advantages to quasi-experimental design
More practical: ease of implementation
More feasible: resources, subjects, time, setting
may be the only way to evaluate the effect of the independent variable of interest
disadvantages to quasi-experimental design
Difficult to make clear cause and effect relationships
Some generalizability, but more limited conclusions
Non-experimental designs
used when researchers cannot (or should not) manipulate the independent variable
correlational research
interrelationships across variables are explored with no researcher intervention
correlation is.....
not causation
types of non-experimental designs
observational cohort studies, descriptive research
observational cohort studies
researchers recruit sample of people with two distinct characteristics (ex. smokers and non smokers) and observe difference in variable (ex. lung cancer)
it is hard to draw conclusions in these types of studies as there is often confounding variables (ex. lifestyle of smokers vs non smokers)
descriptive research
researchers recruit a sample of people and observe the relationship between variables
advantages to non-experimental studies
efficient way to collect large amounts of data when intervention and/or randomization is not possible
this type of research is often needed to inform interventions
disadvantages to non-experimental studies
no persuasive evidence for causal relationships
cross-sectional studies
data are collected at a single point in time
cross sectional studies
do not provide persuasive evidence for phenomena that change over time
longitudinal designs
data collection over multiple time points
challenge to longitudinal designs
attrition- loss of participants over time
takes a lot of time and money and devoted research participants
strategies for controlling the study context
blinding, standardized communication, intervention protocols
Controlling Participant Factors
randomization, homogeneity, matching, statistical control
randomization
a process of randomly assigning subjects to different treatment groups
homogeneity
only people who are similar with respect to confounding variables are included in the study
matching
consciously forming comparable groups
statistical control
controlling of confounding variables statistically
random assignment
the most effective approach to controlling confounding variables
cross over designs
involves exposing people to one treatment first, then crossover to another treatment. Only a true experiment if people are randomly assigned to different orderings of treatment
statistical conclusion validity
The strength of the evidence that a relationship between variables exists
Statistical tests used to support inferences about relationship between IV and DV
Threats to statistical conclusion validity
poor statistical power (sample size too small)
Independent variable not powerful (poor intervention implementation) (difference between intervention and control not detectable with statistical tests)
internal validity
the strength of the evidence that the independent variable is what causes the change in the dependent variable
ALL ABOUT CONTROL (was the actual intervention the thing that affected the dependent variable)
threats to internal validity
Temporal ambiguity, selection threat, history threat, maturation threat, mortality/attrition threat
temporal ambiguity
Lack of clarity on if the IV preceded the DV (timing)
Selection Threat
bias arising from pre-existing differences between groups being compared (biggest threat to internal validity)
history threat
other events co-occurring that could influence the outcomes (observer bias, reactivity)
maturation threat
changes that occur simply as a result of time
mortality/ attrition threat
participants who are lost from the study are different from those who stay in the study
external validity
the strength of the evidence that the observed relationships are generalizable across peoples, settings and time.
Crucial for EIP (we want to generalize evidence from controlled quantitative studies to real world settings)
Very regimented and controlled studies often don't transfer to a clinical setting (not realistic)
threats to external validity
inadequate sampling, lack of replication in diverse settings
for external validity
you want samples that are representative of the general population
enhancing internal validity
can decrease external validity (generalizability)
construct validity
the likelihood that the theoretical constructs of interest are the ones being captured
Are the researchers actually manipulating and measuring what they intent to?
ex: can a pain score differentiate the facial expressions of a baby getting a vaccine vs a diaper change
threats to construct validity
if the intervention is not a good representation of the underlying construct
if awareness of the intervention led to the benefits
if the measures used are not actually measuring the construct of interest
populations
the entire group of interest. Characteristics of the population specified through eligibility criteria
eligibility criteria
inclusion and exclusion criteria
sample
a subset of the population. Goal is to have sample that is representative of the population
sampling bias
when there is a systemic over or under representation of key characteristics of a population
sampling designs in quantitative research
non-probability and probability
non-probability sampling
researchers select the participants in the study using non-random methods
less likely to produce representative samples
most common sampling approach in nursing research
Probability sampling
random selection from the population
all participants have an equal chance of being selected
types of non-probability sampling
convenience, purposive, consecutive
convenience sampling
the most convenient and available people
weakest form of sampling
highest risk of sampling bias
consecutive sampling
selecting all people from an accessible population
typically over a specific time period or to a specific sample size
often used in retrospective studies, what happened in the past
purposive sampling
using knowledge about the population to hand-pick sample members
can lead to bias
types of probability sampling
simple random, stratified random, systematic
simple random sampling
random selection of participants in a broad sampling frame
low risk of sampling bias
stratified random
sampling frame divided into strata (subgroups) and participants randomly selected within the strata
goal is to promote representative sample
systematic sampling
Selecting every nth member of the population
you can produce a similar result to simple random sampling
general sample size rule in quantitative research
larger sample size = less risk of sampling bias
large sample cannot correct for poor research design, but a large non-probability sample is better than a small one
power analysis
statistical technique used by researchers to estimate how large the sample should be in order to adequately test a hypothesis
data sources for quantitative research
questionnaires, Likert scale, visual analogue scale, category systems, rating systems, bio- physiologic
questionnaires
good for accessing large amounts of information, and information from geographically dispersed samples. They are also low cost and offer anonymity
Likert Scale
consists of declarative statements the express viewpoint, and respondents are asked to indicate how much they agree or disagree
Visual analogue scale
a straight line, the ends are labelled as extreme limits of the sensation being measured
Advantages of scales
permit researchers to efficiently quantify subtle change in the intensity of individual characteristics. Can be used for most people
Disadvantages of scales
social desirability response bias, people may give answers that are consistent with societal views
extreme response set bias: tendency to express extreme attitudes
Acquiescence response set bias: yes-sayers and nay-sayers
Observational data sources
category systems, rating systems
category systems
Formal systems for systematically recording the incidence or frequency of prespecified behaviors or events.
categories must be explicit and explained
used to construct a checklist; how observers record observations
rating systems/rating scale
observers rate phenomena along a descriptive continuum. Can be used as an extension of checklists, in which the observer records not only the occurrence of some behavior but also some qualitative aspect of it such as intensity
Bio-physiologic measures
in-vivo and in-vitro
in vivo
measurements performed directly within or on living organism, such as BP or temperature
In vitro
measures in which data is collected by extracting bio physiologic material from them and subjecting it to analysis by laboratory technicians
advantages to bio-physiologic measures
objective data, not interpretable, cannot be distorted by participants
Reliability
the extent to which scores are free from measurement error and are consistent over time for people who have not changed
test-retest reliability
often referred to as "stability" or "reproducibility"
extent to which same scores can be obtained on repeated administration when train being measured has not changed
inter-rater reliability
how reliably the measures reflect the attribute of the person being assessed (and is not related to characteristics of the reviewer)
important for observation methods
Internal consistency
when a measure has multiple components that produce similar measurement of the same trait
correlation coefficient
a statistical index of the relationship between two things (from -1 to +1)
validity
the extent to which a test measures or predicts what it is supposed to
face validity
if the measure appears to be measuring the target
not the best measure of validity
ex: measuring depression may look like measuring sleep, mood, motivation
content validity
extent to which the instruments content adequately measures the construct
often achieved through expert consultation and consideration of the literature on a construct
beyond face value, more concrete and valad
criterion validity
extent to which scores on a measure correlate the the "gold standard" measure
not possible for all measures
ex: the "gold standard" measure for testing infant pain is the PIP score, a researchers methods should be similar to this
Note
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