
What is EBP?
integration of best research evidence with our clinical expertise and our patient's unique values and circumstances

What are the sources of evidence?
 patient's unique values and circumstances
 clinical expertise
 best research evidence

Name the steps of the scientific method:
 1. observe an event
 2. develop a hypothesis that makes a prediction
 3. test the hypothesis
 4. observe the result
 5. revise the hypothesis
 6. repeat as needed

What are background questions?
ask for information about a condition

What are the two essential components of a background question?
 a question root (who, what, where, when, how, why) with a verb
 a condition or an aspect of a condition

What is a foreground question?
ask for specific information about managing patients with a condition

What are the 3 essential components of a foreground question?
 patient and/or problem
 exposure (intervention) or comparision intervention
 clinical outcomes

What are the major sources of knowledge/information?
 tradition
 authority
 trial and error
 logical reasoning
 the scientific method (research)

List the hierarchy of study types:
 evidencebased clinical guidelines
 systematic reviews and metaanalyses of randomized controlled trials
 randomized controlled trials
 nonrandomized intervention studies
 observational studies
 qualitative studies
 case series, case reports

What is the 3rd step for EBP?
critically appraise the quality of the research evidence

Critical Appraisal involes understanding:
 the question(s) being asked
 the methods used to examin the question(s)
 sampling
 data collection
 the statistical analysis of the data
 knowing basic terms and how they contribute to interpreting the data
 the conclusions made by the researchers and deciding if you think they are accurate, appropriate, and/or meaningful to you and the question(s) you have

In research studies what population sample should be used to collect data?
random sample

What are the scales/levels of data measurement?
 the mathematical precision with which the values of a variable can be expressed is the levle of measurement
 nominalqualitative, categorical
 ordinal, interval, ratioquantitative, progressively more precise mathematically

naminal measures:
 a qualitative (or categorical) level of measurement; has no mathematical interpretation
 variables whose values vary in kind or quality but not in amount
 in terms of the variable "occupation", you can say that a lawyer is not equal to a therapist, but you can't say that the lawyer is more occupational or less occupational than the therapist

Ordinal measures:
 specify only order of cases in "greater than" and "less than" distinctions
 patient/client satisfaction is an ordinal measure
 a rehabspecific example is manual muscle testing grades

Interval measures:
 specific increment but no absolute 0
 numbers represent fixed measurement units but have no absolute zero point
 ex: temperature w/ Fahrenheit scale

Ratio Measures:
 fixed measuring units with an absolute zero point. Zero means absolutely no amount of whatever the varible indicates
 ratio numbers can be added and subtracted, and because the numbers begin at an absolute zero point, they can be multiplied and divided
 ex: goniometric measures of ROM

What does reliability of measures mean?
extent to which a measure produces the same result uner different conditions

Testretest reliability:
will the measure produce the same results when given on two different occasions? Typically expressed as a correlation coefficient (r)

Interrater reliability:
the extent to which two or more individuals agree

Intrarater reliability:
the degree of agreement among multiple repetitions of a diagnostic test performed by the same individual

Reliability is a property of a:
measurement instrument...not of an experiment/study.

Validity of Measures:
extendt to which the measure indicates what it is supposed to measure

face validity:
is the measure appropriate at face value? Does the measure look like it is going to measure what it is supposed to measure?

Content validity:
does the measure cover the full range of the concept's meaning?

Criterion validity:
 can scores obtained with one measure be accurately compared to those obtained using another (more established) measure?
 two types: concurrent and predictive

Construct validity:
 a measure should fit well with other measures of similar theoretical concepts
 Ex: Scores on a marital satisfaction scale should be negatively related to spouse abuse

Internal validity:
are the methods used in the study correct and are the results accurate?

External validity:
 Are the findings applicable beyond that particular study?
 When you have a particular patient/clinical question in mind, you have to ask yourself this question when appraising the literature

KEY POINT: Questions to ask in Critical Apprasial Regarding External Validity:
 Is the study purpose relevant to your clinical question?
 Are the study's inclusion and exclusion criteria clearly defined and would the patient in your clinical question qualify for the study?
 Are the intervention and comparison/control groups receiving an intervention related to your clinical question?
 Are the outcome measures used in the study relevant to your clinical question and are they conducted in a clinically realistic manner?
 Is the study population sufficiently similar to the patient in you clinical question to justify expectation that the patient would respond similarly to the population?

Descriptive Statistics:
 statistical procedures used to summarize, organize, and simplify data
 patterns can be seen from the organized data
 they summarize data but don't test for differences or associations

All research reports include descriptive statistics, such as:
 participant characteristics
 scores of participants on outcome measure(s)
 these often are in the participants section of a research report or at the beginning of the results section.

We can describe our data by using a Frequency Distribution. This is usually presented as a table or graph and always presents:
 the set of subcategories that made up the original category
 the frequency of each score/category

What are three important characteristics of frequency distribution?
 central tendency
 spread/variability
 shape


When can mode be used?
with any type of data

When can median be used?
interval and ratio data; frequently ordinal data; never nominal data

When can mean be used?
interval and ratio data; sometimes ordinal data; never nominal data

Spread/variability:
 Describes in an exact quantitative measure, how spread out/clustered together the measures are
 Variablility is usually defined in terms of distance
 range
 interquartile range
 standard deviation

Range:
 minimummaximum
 simplest and most obvious way of describing spread/variablility
 Range= highestlowest
 the range only takes into account the two extreme scores and ignores any values in between

Interquartile range:
use with medians

Standard deviation is used with?
means

Mode:
 most frequent value
 doesn't take into account exact scores
 unaffected by extreme scores
 not useful when there are several values that occur equally often in a set

Median:
 the values that fall exactly in the midpoint of a ranked distribution
 does not take into account exact scores
 unaffected by extreme scores
 in a small set it can be unrepresentative

Mean (arithmetic average)
 takes into account all values
 easily distored by extreme values
 the mean is the preferred measure of central tendency

When is the mean not the preferred measure of central tendency?
 when there are extreme scores or skewed distribution
 non interval data
 discrete variables

Deviation:
 a more sophisticated measure of variablility is one that shows how scores cluster around the mean
 distance of a score from the mean

Standard Deviation:
a number that measures how far away each number in a set of data is from their mean

If standard deviation is large,
it means the numbers are spread out from their mean

If standard deviation is small,
it means the numbers are close to their mean

Frequency Distribution: The normal distribution:
 bellshaped
 symmetrical around the mid point, where the greatest frequency of scores occur
 in a normal distribution, the mean, median and mode are the same value

KEY POINT: The Beauty of Normal Distribution
no matter what the mena and standard deviation are for you data set, the area within one standard deviation is about 68% of your data; the area w/in 2 standard deviations is about 95%; and the area w/in 3 standard deviations is aobut 99.7%

population:
all individuals of interest to the study

sample:
the particular group of participants you are testing: Selected from population

Statistical inference about populations:
A treatment that worked for a sample of patients will work for other patients with similar characteristics

Inferential statistics let us estimate population characteristics from:
sample data

Parameters:
mathematical characteristics of populations (m)

Statistics:
mathematical characteristics of samples (x=mean)

Statistics are used to estimate:
paramenters

Samples must be:
representative of the population

Representative:
sample has same characteristics as the population

How can we ensure samples are representative?
samples drawn according to the rule of Equal Probability of Selection Method: every case in the population has the same chance of being selected for the sample

Central Limit Theorem:
for any trait or variable, even those that are not normally distributed in the population, as sample size grows larger, the sampling distribution of sample means will become normal in shape

What is a variable?
 something that varies
 represent persons or objects that can be manipulated, controlled, or merely measured for the sake of research
 independent/dependent

Independent variables:
 ones that are more or less controlled
 researchers manipulate these variables as they see fit
 they still vary, but variation is relatively known or taken into account
 often there are many in a given study

dependent variables:
 not controlled or manipulated in any way, but are simply measured
 vary in relation to independent variables, and while results can be predicted, the data is always measured
 there can be any number of dependent variables, but usually there is one of interest to isolate and study

Independent;
 intentionally manipulated
 controlled
 vary at known rate
 cause

Dependent:
 intentionally left alone
 measured
 vary at unknown rate
 effect

Graphing dependent vs. independent variables:
the dependent variable is placed on the yaxis, while the independent is on the x

What is the goal of research?
to determine if the independent variable of interest to us has a statistically significant effect on the dependent variable. That means, an effect that is unlikely to be due to chance variations or sampling error

Overview of the Hypothesis Testing Process:
 1. State the null hypothesis
 2. Look at the data and decide on an appropriate statistical test
 3. Compute the statistical test, look at pvalue
 4. if pvalue is less than alpha, reject null hypothesis; if pvalue is greater than alpha, fail to reject null hypothesis

null hypothesis:
 researchers make initial assumption that manipulation of independent variable will have NO EFFECT on the dependent variable (will be null)
 if any observed difference b/w the experimental and control groups is assumed to be due to chance unless proven otherwise
 no difference b/w groups, unless by chance

The alternative hypothesis:
the two means really are differnet, and it's not just chance

