flashcard data - final
Evidence based practice (EBP)
Fundamental principle that the quality of care will depend on our ability to make choices that have been confirmed by sound scientific data and are based on the BEST evidence currently available
Basic experimental research
In a lab; used to further our knowledge not necessarily applicable to our everyday lives
Applied Research
Doing something to someone else
Case report
NAME?
Case series
Several case reports put together to illustrate a single point
Case Control Studies
Medical history of persons with a rare disease are compared to history of similar (matched) person without the disease
Cohort studies
One group of people
Randomized control study (RCT)
Gold standard for experimental
Systematic Review
combine many studies into one large study
Meta-analysis within systematic review
when data of the studies is complied and analyzed as one
Qualitative Research
Generally applied to descriptive or exploratory
Quantitative Research
Descriptive exploratory (correlative), experimental
Evidence based pyramid
Top:
Primary source
Authors are reporting the original report of research they have conducted (ie: applied or basic, descriptive, experimental or correlational)
Secondary source
A summary of primary work (ie: book chapters, literature review)
Experimental Research
Attempts to define a cause and effect difference through group comparisons: controlled manipulation of independent variable
Types of Experimental Research
RCT
Quasi-experimental
lacks control and or/randomization
Repeated measures
Same participant are measured under all levels of the IV
Single Subject design
Analysis of a single subject across time
Difference between case study and single subject design
Case study-description of individual response to treatment–non-experimental
Confounding variable
Variable that contaminates data
Examples of confounding variables
Assignment to group (randomized??)
What is a subject
One who has provided consent to be involved in research, not synonymous to a patient
Sampling bias
individuals selected over represent or under represent certain population attributes
Validity
The degree to which a useful (meaningful) interpretation can be inferred from a measurement
External vs Internal Validity
EV:To whom the results are applied to
Types of validity
Content (of what you want to evaluate)-accurate representation
Threats to internal validity
Subject selection
Reliability
The extent to which a measurement is free from error
Types of reliability
Inter-rater, Intra-rater, test-retest
Sources of error in reliability
Rater
Threats to reliability
Repeated errors of measurement
Independent vs Dependent variables
IV: presumed to be the cause of the dependent variable; the variable that is manipulated or controlled by the researcher
Descriptive statistics
(ie: mean median mode) formulate a description of reported data, (sample characteristics, results of the study)
Inferential statistics
Modulating numbers, Used to make comparisons, determine the effect of different factors on an outcome
Clinically relevant statistics
Evaluate the importance of outcomes for patient care
Nominal data
categories where the order does not matter (ie: colors)
Ordinal data
categories where the order matters (high, med, low)
Continuous data
Specifically ratio data (data that is measured by consecutive numbers that represent, infinity of options)
Criteria for correlation coefficient
.75 to 1.0=good to excellent
Correlation interpretation to be careful of
Correlation not that same as causal
Type I vs Type II error
Type I: rejection of the null hypothesis when it was actually true (usually because of biases)
Power
The probability that a study will detects a statistically significant difference between groups…if it exists
Parametric data vs. non-parametric data
parametric: refers to assumption about the population from which that data was obtained (bell-shaped curve)
P-value
the percent chance that the results were due to chance
Alpha value
The level that the significant difference will occur due to chance alone
Null hypothesis
any observed difference between populations means are due to chance
Alternative hypothesis
Observed differences between two populations means are not due to chance
ANOVA
comparing 3 or more independent variables (one-way, two-way, repeated measures)
one-way anova
comparing two or more independent variables on one dependent variable; hypothesis test that considers population means based on one characteristic
post-hoc testing
after rejecting the null hypothesis it tell us where the differences are….perform after the null hypothesis is rejected
two-way anova
used when two independent variables are examined in a single experiement; a hypothesis test that considders comparisons between populations based on multiple characteristics
interaction effect
one variable is dependent on the level of the second variable
MDC
minimal detectable change (represents the smallest change in score that likely reflects true change rather than measurement change alone (only shows that minimal change that is detectable by the instrument and not necessarily theamong of change that could be considered clinically meaningful to the patient)
MCID
smallest treatment effect that a clinician seems to make a change in a patient/ clients function or quality of life in response to an intervention (how much change is required for a patient to experience a meaningful change?)
NNT
number needed to treat: modest effects that occur frequently
Confidence interval
range of scores with specific boundaries that should contain the population mean
Diagnostic accuracy
ability of an exam or diagnostic test to accurately identify or rule out a condition
Diagnostic test
determines the presence of absence of a disease or abnormal condition
Reference standard
A method that will identify if a person has a condition (gold standard)
Contingency table
Top: Gold standard
sensitivity
ability to obtain a positive test when the testing condition is truly positive (true positive/(false negative+true positive))
specificity
ability to obtain a negative test when the testing condition is truly negative (true negative/(false positive+ false negative))
positive predictive value
proportion of those with a positive test that have the disease
negative predictive value
proportion of those with a negative test that do not have the disease
Positive likelihood ratio
the probability that a person with a positive diagnostic test result has the suspected problem; greater than 10 is a conclusive test
Negative likelihood ratio
the probability that a person with a negative diagnostic test result does not have the suspected problem; less than .1 is a good conclusive test
acceptable value for ICC
.8 or greater
acceptable value for likelihood ratio
positive- greater than 10
acceptable value for correlation value
greater than .75
